Preprints
Witte, Kristin; Wise, Toby; Huys, Quentin J. M.; Schulz, Eric
Exploring the Unexplored: Worry as a Catalyst for Exploratory Behavior in Anxiety and Depression Journal Article
In: psyArxiv, 2024.
@article{WitteSchulz24,
title = {Exploring the Unexplored: Worry as a Catalyst for Exploratory Behavior in Anxiety and Depression},
author = {Kristin Witte and Toby Wise and Quentin J. M. Huys and Eric Schulz},
url = {https://acplab.org/wp-content/uploads/pub/WitteSchulz24-WorryExploration.pdf},
year = {2024},
date = {2024-03-27},
journal = {psyArxiv},
abstract = {The relationship between anxious and depressive traits and exploration behavior has been examined in several studies with mixed results. While some studies suggest that anxious and depressive traits are related to avoidance and a decrease in exploratory behaviour, others find the opposite to be true. In our studies, we adopt a multi-armed bandit task in which arms that were spatially close to each other have similar rewards, allowing for generalisation from observed rewards. Furthermore, we introduced risks to simulate costs of over-exploration in the real world. In two studies, we investigate the relationship between transdiagnostic symptoms of anxiety and depression, specifically worrying, and task behaviour. While our first study uses a purely correlational design, our second study involves a psychotherapy-inspired intervention to reduce worries and investigate their causal effect on exploration behaviour. The results suggest that worrying may be a causal factor linking anxious and depressive traits to increased exploration behaviour. Specifically, using computational modelling, we show that worrying is related to an increased preference for novel options, as opposed to mere choice stochasticity. These findings enhance our understanding of the complex links between depression, anxiety and exploration behaviour, and highlight the importance of worry in driving increased exploration.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The relationship between anxious and depressive traits and exploration behavior has been examined in several studies with mixed results. While some studies suggest that anxious and depressive traits are related to avoidance and a decrease in exploratory behaviour, others find the opposite to be true. In our studies, we adopt a multi-armed bandit task in which arms that were spatially close to each other have similar rewards, allowing for generalisation from observed rewards. Furthermore, we introduced risks to simulate costs of over-exploration in the real world. In two studies, we investigate the relationship between transdiagnostic symptoms of anxiety and depression, specifically worrying, and task behaviour. While our first study uses a purely correlational design, our second study involves a psychotherapy-inspired intervention to reduce worries and investigate their causal effect on exploration behaviour. The results suggest that worrying may be a causal factor linking anxious and depressive traits to increased exploration behaviour. Specifically, using computational modelling, we show that worrying is related to an increased preference for novel options, as opposed to mere choice stochasticity. These findings enhance our understanding of the complex links between depression, anxiety and exploration behaviour, and highlight the importance of worry in driving increased exploration.
Erdmann, Tore; Berwian, Isabel M.; Stephan, Klaas Enno; Seifritz, Erich; Walter, Henrik; Huys, Quentin J. M.
Amygdala reactivity, antidepressant discontinuation and relapse: a longitudinal, observational study with a randomized component Journal Article
In: psyArxiv, 2024.
@article{ErdmannHuys24,
title = {Amygdala reactivity, antidepressant discontinuation and relapse: a longitudinal, observational study with a randomized component},
author = {Tore Erdmann and Isabel M. Berwian and Klaas Enno Stephan and Erich Seifritz and Henrik Walter and Quentin J. M. Huys},
url = {https://www.acplab.org/wp-content/uploads/pub/ErdmannHuys24-AmygdalaRelapse.pdf},
doi = {https://doi.org/10.31234/osf.io/zpf9c},
year = {2024},
date = {2024-01-26},
urldate = {2024-01-26},
journal = {psyArxiv},
abstract = {Importance: Antidepressant discontinuation substantially increases the risk of a depression relapse.
The neurobiological mechanisms through which this happens are not known. Amygdala reactivity
to negative information is a marker of negative affective processes in depression that is reduced by
antidepressant medication. However, it is unknown whether amygdala reactivity is sensitive to antide-
pressant discontinuation, and whether any change is related to the risk of relapse after antidepressant
discontinuation.
Objective: To investigate whether amygdala reactivity to negative facial emotions changes with
antidepressant discontinuation and relates to subsequent relapse.
Design: The AIDA study was a longitudinal, observational AIDA study, where patients were random-
ized to task-based fMRI measurement of amygdala reactivity either twice before, or after discontinuing
antidepressants. Relapse was monitored over a six month follow-up period. Study recruitment took
place until January 2018. Data were collected between July 1, 2015, to January 31, 2019 and statistical
analyses were conducted between June 2021 and December 2023.
Setting: University setting in Zurich, Switzerland, and Berlin, Germany.
Participants: Patients with remitted major depressive disorder (rMDD) on antidepressants. Of 123
recruited patients, 80 (mean (SD) age 35.5 (11.4) years; 60
women (75%) were included in analyses.
Of 66 recruited healthy controls matched for age, sex, and education, 53 were included in analyses
(mean (SD) age 34.9 (10.7) years); 37 women (70%)).
Exposure: Discontinuation of antidepressant medication.
Outcomes: Task-based fMRI measurement of amygdala reactivity and MDD relapse within 6 months
after discontinuation. Results: Amygdala reactivity of rMDD patients on medication did not differ from controls (left: t = 0.77},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Importance: Antidepressant discontinuation substantially increases the risk of a depression relapse.
The neurobiological mechanisms through which this happens are not known. Amygdala reactivity
to negative information is a marker of negative affective processes in depression that is reduced by
antidepressant medication. However, it is unknown whether amygdala reactivity is sensitive to antide-
pressant discontinuation, and whether any change is related to the risk of relapse after antidepressant
discontinuation.
Objective: To investigate whether amygdala reactivity to negative facial emotions changes with
antidepressant discontinuation and relates to subsequent relapse.
Design: The AIDA study was a longitudinal, observational AIDA study, where patients were random-
ized to task-based fMRI measurement of amygdala reactivity either twice before, or after discontinuing
antidepressants. Relapse was monitored over a six month follow-up period. Study recruitment took
place until January 2018. Data were collected between July 1, 2015, to January 31, 2019 and statistical
analyses were conducted between June 2021 and December 2023.
Setting: University setting in Zurich, Switzerland, and Berlin, Germany.
Participants: Patients with remitted major depressive disorder (rMDD) on antidepressants. Of 123
recruited patients, 80 (mean (SD) age 35.5 (11.4) years; 60
women (75%) were included in analyses.
Of 66 recruited healthy controls matched for age, sex, and education, 53 were included in analyses
(mean (SD) age 34.9 (10.7) years); 37 women (70%)).
Exposure: Discontinuation of antidepressant medication.
Outcomes: Task-based fMRI measurement of amygdala reactivity and MDD relapse within 6 months
after discontinuation. Results: Amygdala reactivity of rMDD patients on medication did not differ from controls (left: t = 0.77
The neurobiological mechanisms through which this happens are not known. Amygdala reactivity
to negative information is a marker of negative affective processes in depression that is reduced by
antidepressant medication. However, it is unknown whether amygdala reactivity is sensitive to antide-
pressant discontinuation, and whether any change is related to the risk of relapse after antidepressant
discontinuation.
Objective: To investigate whether amygdala reactivity to negative facial emotions changes with
antidepressant discontinuation and relates to subsequent relapse.
Design: The AIDA study was a longitudinal, observational AIDA study, where patients were random-
ized to task-based fMRI measurement of amygdala reactivity either twice before, or after discontinuing
antidepressants. Relapse was monitored over a six month follow-up period. Study recruitment took
place until January 2018. Data were collected between July 1, 2015, to January 31, 2019 and statistical
analyses were conducted between June 2021 and December 2023.
Setting: University setting in Zurich, Switzerland, and Berlin, Germany.
Participants: Patients with remitted major depressive disorder (rMDD) on antidepressants. Of 123
recruited patients, 80 (mean (SD) age 35.5 (11.4) years; 60
women (75%) were included in analyses.
Of 66 recruited healthy controls matched for age, sex, and education, 53 were included in analyses
(mean (SD) age 34.9 (10.7) years); 37 women (70%)).
Exposure: Discontinuation of antidepressant medication.
Outcomes: Task-based fMRI measurement of amygdala reactivity and MDD relapse within 6 months
after discontinuation. Results: Amygdala reactivity of rMDD patients on medication did not differ from controls (left: t = 0.77
Norbury, Agnes; Hauser, Tobias; Dolan, Raymond; Huys, Quentin J. M.
In: PsyArXiv, 2024.
@article{NorburyHuys24b,
title = {Learning training boosts causal attribution tendencies similarly to brief cognitive restructuring, depending on individual differences in learning rate},
author = {Agnes Norbury and Tobias Hauser and Raymond Dolan and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/NorburyHuys24b-LearningCBT.pdf},
doi = {10.31234/osf.io/fnez5},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {PsyArXiv},
abstract = {A core part of cognitive therapy for low mood is learning to identify and challenge negative be- liefs. However, it is currently unclear whether improved ability to recognise such beliefs, and the biased interpretations of events which may maintain them, is a mechanism of symptom change during treatment. Here, we investigated the effects of both a learning task (training to identify and select self-enhancing interpretations of events) and a brief cognitive restructuring interven- tion (how exploring alternative explanations of events may result in improved mood) on a task- based measure of causal attribution tendencies. We found that both learning training and the restructuring intervention decreased tendencies to make unhelpful attributions and increased tendencies to make self-enhancing attributions (e.g., decreased tendency to attribute negative events and increased tendency to attribute positive events to self-related causes). Across two studies, changes in attribution tendencies on the causal attribution task were associated with higher learning rates during learning training – an effect which was specific to learning about different kinds of event attribution. Contrary to expectations, there was no evidence that faster learning was associated specifically to changes in attribution tendencies following cognitive re- structuring. Since participants with higher learning rate estimates also provided explicit ratings and free-text descriptions of event causes which were closer to the ground truth, we interpret this as representing a greater benefit of learning training in individuals who were better able to understand the task state space (i.e., to recognise different attribution kinds). It is possible that this kind of training, in conjunction with feedback based on interpretable computational model output, may be a useful form of augmentation or learning-support tool during therapy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A core part of cognitive therapy for low mood is learning to identify and challenge negative be- liefs. However, it is currently unclear whether improved ability to recognise such beliefs, and the biased interpretations of events which may maintain them, is a mechanism of symptom change during treatment. Here, we investigated the effects of both a learning task (training to identify and select self-enhancing interpretations of events) and a brief cognitive restructuring interven- tion (how exploring alternative explanations of events may result in improved mood) on a task- based measure of causal attribution tendencies. We found that both learning training and the restructuring intervention decreased tendencies to make unhelpful attributions and increased tendencies to make self-enhancing attributions (e.g., decreased tendency to attribute negative events and increased tendency to attribute positive events to self-related causes). Across two studies, changes in attribution tendencies on the causal attribution task were associated with higher learning rates during learning training – an effect which was specific to learning about different kinds of event attribution. Contrary to expectations, there was no evidence that faster learning was associated specifically to changes in attribution tendencies following cognitive re- structuring. Since participants with higher learning rate estimates also provided explicit ratings and free-text descriptions of event causes which were closer to the ground truth, we interpret this as representing a greater benefit of learning training in individuals who were better able to understand the task state space (i.e., to recognise different attribution kinds). It is possible that this kind of training, in conjunction with feedback based on interpretable computational model output, may be a useful form of augmentation or learning-support tool during therapy.
Onysk, Jakub; Gregory, Nicholas; Whitefield, Mia; Jain, Maeghal; Turner, Georgia; Seymour, Ben; Mancini, Flavia
Statistical learning shapes pain perception and prediction independently of external cues Journal Article
In: MedRxiv, 2023.
@article{OnyskMancini23,
title = {Statistical learning shapes pain perception and prediction independently of external cues},
author = {Jakub Onysk and Nicholas Gregory and Mia Whitefield and Maeghal Jain and Georgia Turner and Ben Seymour and Flavia Mancini},
url = {https://www.medrxiv.org/content/10.1101/2023.03.23.23287656v2.full.pdf},
doi = { https://doi.org/10.1101/2023.03.23.23287656},
year = {2023},
date = {2023-07-04},
urldate = {2023-07-04},
journal = {MedRxiv},
abstract = {The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weight pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weight pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.
Berwian, Isabel M.; Tröndle, Marius; Miquel, Carlota; Ziogas, Anastasios; Stefanics, Gabor; Walter, Henrik; Stephan, Klaas Enno; Huys, Quentin J. M.
Emotion-induced frontal α asymmetry predicts relapse after discontinuation of antidepressant medication Journal Article
In: BioRxiv, 2023.
@article{BerwianHuys23,
title = {Emotion-induced frontal α asymmetry predicts relapse after discontinuation of antidepressant medication},
author = {Isabel M. Berwian and Marius Tröndle and Carlota Miquel and Anastasios Ziogas and Gabor Stefanics and Henrik Walter and Klaas Enno Stephan and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/BerwianHuys2023AlphaAssymetry.pdf},
doi = {10.1101/2023.07.05.547831},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
journal = {BioRxiv},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Background One in three patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of Major Depressive Disorder (MDD). However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by fMRI has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. Methods Patients (n=56) who had remitted from a depressive episode while taking antidepressants underwent EEG recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a six-months follow-up period. 35 healthy controls were also tested. Current source density of the EEG power in the α band (8-13Hz) was extracted and alpha-asymmetry was computed by comparing the power across two hemispheres at frontal electrodes (F5 and F6).
Outcomes
Sad mood induction was robust across all groups. Reactivity of α-asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in α-asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy).
Interpretation
A simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background One in three patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of Major Depressive Disorder (MDD). However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by fMRI has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. Methods Patients (n=56) who had remitted from a depressive episode while taking antidepressants underwent EEG recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a six-months follow-up period. 35 healthy controls were also tested. Current source density of the EEG power in the α band (8-13Hz) was extracted and alpha-asymmetry was computed by comparing the power across two hemispheres at frontal electrodes (F5 and F6).
Outcomes
Sad mood induction was robust across all groups. Reactivity of α-asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in α-asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy).
Interpretation
A simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation.
Outcomes
Sad mood induction was robust across all groups. Reactivity of α-asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in α-asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy).
Interpretation
A simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation.
Malamud, Jolanda; Huys, Quentin
Distancing alters the controllability of emotional states by altering both intrinsic stability and extrinsic sensitivity Journal Article
In: Europe PMC, 2023.
@article{MalamudHuys23,
title = {Distancing alters the controllability of emotional states by altering both intrinsic stability and extrinsic sensitivity},
author = {Jolanda Malamud and Quentin Huys},
url = {http://acplab.org/wp-content/uploads/pub/malamud_emotioncon_final.pdf},
doi = {10.21203/rs.3.rs-2731985/v1},
year = {2023},
date = {2023-04-06},
urldate = {2023-04-06},
journal = {Europe PMC},
abstract = {Our mental health is influenced by the emotional states we experience. Emotional states, in turn, depend on external experiences and internal processes that determine the form and persistence of emotional states. Emotion regulation strategies aim to alter emotional states and are an important element of evidence-based, effective psychotherapeutic interventions. However, the mechanisms by which emotion regulation works remain incompletely understood. Here, we combine experimental induction of momentary emotions with formal dynamical system theory to study how emotion regulation strategies influence emotion dynamics. Participants (N=109) repeatedly reported their multidimensional emotional state while watching brief validated emotional video clips. Participants were then randomized to either an emotion regulation (distancing) or control intervention before watching further video clips. Dynamical and controllability features were inferred from participants' emotion ratings using a Kalman Filter, which captures how emotions evolve, interact, and are affected by external inputs. First, we showed that the Kalman filter provided an adequate account of the data. Emotions were maintained across stimuli, interacted and were richly influenced by emotional stimuli. Second, distancing had a dual effect: It reduced the (external) controllability of emotional states both by stabilizing specific emotional dynamics and by reducing the driving force external emotional stimuli exerted. These results provide a novel, quantitative approach to characterizing how emotions are controlled and how a distancing intervention alters emotional experience. The quantitative characterization of specific psychotherapeutic interventions may help better understand and target interventions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Our mental health is influenced by the emotional states we experience. Emotional states, in turn, depend on external experiences and internal processes that determine the form and persistence of emotional states. Emotion regulation strategies aim to alter emotional states and are an important element of evidence-based, effective psychotherapeutic interventions. However, the mechanisms by which emotion regulation works remain incompletely understood. Here, we combine experimental induction of momentary emotions with formal dynamical system theory to study how emotion regulation strategies influence emotion dynamics. Participants (N=109) repeatedly reported their multidimensional emotional state while watching brief validated emotional video clips. Participants were then randomized to either an emotion regulation (distancing) or control intervention before watching further video clips. Dynamical and controllability features were inferred from participants' emotion ratings using a Kalman Filter, which captures how emotions evolve, interact, and are affected by external inputs. First, we showed that the Kalman filter provided an adequate account of the data. Emotions were maintained across stimuli, interacted and were richly influenced by emotional stimuli. Second, distancing had a dual effect: It reduced the (external) controllability of emotional states both by stabilizing specific emotional dynamics and by reducing the driving force external emotional stimuli exerted. These results provide a novel, quantitative approach to characterizing how emotions are controlled and how a distancing intervention alters emotional experience. The quantitative characterization of specific psychotherapeutic interventions may help better understand and target interventions.
Malamud, Jolanda; Lewis, Gemma; Moutoussis, Michael; Duffy, Larisa; Bone, Jessica; Lewis, Glyn; and, Quentin Huys
In: Europe PMC, 2023.
@article{MalamudHuys23b,
title = {The selective serotonin reuptake inhibitor sertraline alters learning from aversive reinforcements in patients with depression: evidence from a randomized controlled trial},
author = {Jolanda Malamud and Gemma Lewis and Michael Moutoussis and Larisa Duffy and Jessica Bone and Glyn Lewis and Quentin Huys and},
url = {http://acplab.org/wp-content/uploads/pub/MalamudHuysSSRIReinforcement2023.pdf},
doi = {https://doi.org/10.21203/rs.3.rs-2776498/v1},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {Europe PMC},
publisher = {Research Square Platform LLC},
abstract = {Selective serotonin reuptake inhibitors (SSRIs) are first-line pharmacological treatments for de- pression and anxiety. However, little is known about how pharmacological action is related to cognitive and affective processes. Here, we examine whether reinforcement learning processes mediate the treatment effects of SSRIs. Reinforcement learning provides a promising framework as both serotonin and depression have been linked to specific reinforcement learning processes such as automatic Pavlovian inhibition. The PANDA trial was a multicentre, double-blind, randomized clinical trial in UK primary care comparing the SSRI sertraline with placebo for depression and anxiety. Participants (N=655) performed an affective Go/NoGo reinforcement-learning task three times during the trial and computational models were used to infer reinforcement learning processes. There was poor task performance: only 54% of the task runs were informative, with more informative task runs in the placebo than the active group. There was no evidence for the preregistered hypothesis that Pavlovian inhibition was affected by sertraline. Exploratory analyses revealed that sertraline increased how fast participants learned from losses and faster learning from losses was associated with more severe generalised anxiety symptoms. Furthermore, in the sertraline group, early increases in Pavlovian inhibition were associated with improvements in depression after 12 weeks. In conclusion, sertraline was effective in treating anxiety, yet it increased learning from losses, and the rate of learning from losses was positively related to anxiety. Poor task performance limits the interpretability and likely generalizability of the findings, and highlights the critical importance of developing acceptable and reliable tasks for use in clinical studies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Selective serotonin reuptake inhibitors (SSRIs) are first-line pharmacological treatments for de- pression and anxiety. However, little is known about how pharmacological action is related to cognitive and affective processes. Here, we examine whether reinforcement learning processes mediate the treatment effects of SSRIs. Reinforcement learning provides a promising framework as both serotonin and depression have been linked to specific reinforcement learning processes such as automatic Pavlovian inhibition. The PANDA trial was a multicentre, double-blind, randomized clinical trial in UK primary care comparing the SSRI sertraline with placebo for depression and anxiety. Participants (N=655) performed an affective Go/NoGo reinforcement-learning task three times during the trial and computational models were used to infer reinforcement learning processes. There was poor task performance: only 54% of the task runs were informative, with more informative task runs in the placebo than the active group. There was no evidence for the preregistered hypothesis that Pavlovian inhibition was affected by sertraline. Exploratory analyses revealed that sertraline increased how fast participants learned from losses and faster learning from losses was associated with more severe generalised anxiety symptoms. Furthermore, in the sertraline group, early increases in Pavlovian inhibition were associated with improvements in depression after 12 weeks. In conclusion, sertraline was effective in treating anxiety, yet it increased learning from losses, and the rate of learning from losses was positively related to anxiety. Poor task performance limits the interpretability and likely generalizability of the findings, and highlights the critical importance of developing acceptable and reliable tasks for use in clinical studies.
Katyal, Sucharit; Huys, Quentin; Dolan, Raymond; Fleming, Stephen
How underconfidence is maintained in anxiety and depression Journal Article
In: PsyArXiv, 2023.
@article{KatyalFleming23,
title = {How underconfidence is maintained in anxiety and depression},
author = {Sucharit Katyal and Quentin Huys and Raymond Dolan and Stephen Fleming},
url = {http://acplab.org/wp-content/uploads/pub/KatyalFlemingUnderconfidence2023-2.pdf},
doi = {10.31234/osf.io/r9e4j},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {PsyArXiv},
abstract = {Individuals with anxiety and depression (AD) exhibit chronic metacognitive biases such as underconfidence. The origin of such biases is unknown. Here we quantified the impact of feedback valence on confidence in two large general population samples (N=230 and N=278). We studied metacognition both locally, as confidence in individual task instances, and globally, as self-performance estimates. Global confidence was sensitive to both local confidence and asymmetries in feedback – more frequent positive (negative) feedback increased (respectively decreased) global confidence. Feedback impacted confidence in a domain-general fashion and led to shifts in affective self-beliefs. Notably, however, global confidence was more sensitive to low (vs. high) local confidence in individuals with greater transdiagnostic anxious-depression symptomatology, despite sensitivity to feedback valence remaining intact. Together, our results reveal a mechanistic basis for chronic underconfidence in AD rooted in distorted interactions between local and global metacognition.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Individuals with anxiety and depression (AD) exhibit chronic metacognitive biases such as underconfidence. The origin of such biases is unknown. Here we quantified the impact of feedback valence on confidence in two large general population samples (N=230 and N=278). We studied metacognition both locally, as confidence in individual task instances, and globally, as self-performance estimates. Global confidence was sensitive to both local confidence and asymmetries in feedback – more frequent positive (negative) feedback increased (respectively decreased) global confidence. Feedback impacted confidence in a domain-general fashion and led to shifts in affective self-beliefs. Notably, however, global confidence was more sensitive to low (vs. high) local confidence in individuals with greater transdiagnostic anxious-depression symptomatology, despite sensitivity to feedback valence remaining intact. Together, our results reveal a mechanistic basis for chronic underconfidence in AD rooted in distorted interactions between local and global metacognition.
Volkmann, Constantin; Abulikemu, Subati; Berwian, Isabel M.; Huys, Quentin J. M.; Walter, Henrik
Do discontinuation symptoms predict depression relapse after antidepressant cessation? Journal Article
In: PsyArXiv, 2023.
@article{VolkmannWalter23,
title = {Do discontinuation symptoms predict depression relapse after antidepressant cessation?},
author = {Constantin Volkmann and Subati Abulikemu and Isabel M. Berwian and Quentin J. M. Huys and Henrik Walter},
url = {http://acplab.org/wp-content/uploads/pub/VolkmannWalter23-DiscontinuationSymptoms.pdf},
doi = {10.31234/osf.io/fxye5},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {PsyArXiv},
abstract = {Background: Discontinuing antidepressants after recovering from a depressive episode is associated with a risk for two events: discontinuation symptoms and relapse. Little is known about who can discontinue safely and whether discontinuation symptoms constitute a risk factor for relapse. This study investigated risk factors for experiencing discontinuation symptoms and whether discontinuation symptoms are associated with depression relapse. Methods: 103 patients with a currently remitted major depressive disorder were randomized to continuation or discontinuation of antidepressants. Discontinuation symptoms were assessed with the Discontinuation Emergent Signs and Symptoms (DESS) scale. The discontinuation syndrome (ADS) was defined as experiencing at least 4 DESS symptoms. We investigated the association of clinical factors with the number of discontinuation symptoms in Bayesian linear regressions. After the randomization phase, all patients discontinued their antidepressant and were followed up over 6 months. We investigated the association of discontinuation symptoms and clinical factors with relapse risk in logistic regressions and a cox proportional hazards model. Results: An ADS was experienced by 29% (95% PI [8.3%, 72%]) of patients. Women reported more discontinuation symptoms than men (factor 1.67 (95% interval [1.06, 2.56])). None of the other prespecified predictors were associated with the risk or severity of ADS. Trait anxiety (Slope = 0.42, 95% PI [-0.01, 0.90]), ADS severity (0.58, 95% PI [0.07, 1.16]) and early depressive symptoms (0.63, 95% PI [0.16, 1.17]) were associated with a higher relapse risk. Conclusion: Antidepressant discontinuation symptoms were relatively common and experienced mainly by women. Experiencing discontinuation symptoms may adversely impact relapse risk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Discontinuing antidepressants after recovering from a depressive episode is associated with a risk for two events: discontinuation symptoms and relapse. Little is known about who can discontinue safely and whether discontinuation symptoms constitute a risk factor for relapse. This study investigated risk factors for experiencing discontinuation symptoms and whether discontinuation symptoms are associated with depression relapse. Methods: 103 patients with a currently remitted major depressive disorder were randomized to continuation or discontinuation of antidepressants. Discontinuation symptoms were assessed with the Discontinuation Emergent Signs and Symptoms (DESS) scale. The discontinuation syndrome (ADS) was defined as experiencing at least 4 DESS symptoms. We investigated the association of clinical factors with the number of discontinuation symptoms in Bayesian linear regressions. After the randomization phase, all patients discontinued their antidepressant and were followed up over 6 months. We investigated the association of discontinuation symptoms and clinical factors with relapse risk in logistic regressions and a cox proportional hazards model. Results: An ADS was experienced by 29% (95% PI [8.3%, 72%]) of patients. Women reported more discontinuation symptoms than men (factor 1.67 (95% interval [1.06, 2.56])). None of the other prespecified predictors were associated with the risk or severity of ADS. Trait anxiety (Slope = 0.42, 95% PI [-0.01, 0.90]), ADS severity (0.58, 95% PI [0.07, 1.16]) and early depressive symptoms (0.63, 95% PI [0.16, 1.17]) were associated with a higher relapse risk. Conclusion: Antidepressant discontinuation symptoms were relatively common and experienced mainly by women. Experiencing discontinuation symptoms may adversely impact relapse risk.
Russek, Evan; Moran, Rani; Liu, Yunzhe; Dolan, Raymond J; Huys, Quentin JM
Selective outcome reinstatement during evaluation drives heuristics in risky choice Journal Article
In: PsyArXiv, 2021.
@article{RussekHuys21,
title = {Selective outcome reinstatement during evaluation drives heuristics in risky choice},
author = {Evan Russek and Rani Moran and Yunzhe Liu and Raymond J Dolan and Quentin JM Huys},
url = {http://acplab.org/wp-content/uploads/pub/RussekHuys21-selective-outcome-reinstatement.pdf},
doi = {10.31234/osf.io/kb6ew},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {PsyArXiv},
publisher = {Center for Open Science},
abstract = {A ubiquitous feature of human decision making under risk is that individuals differ from each other, as well as from normativity, in how they incorporate reward and probability information. One possible explanation for these deviations is a desire to reduce the number of potential outcomes considered during choice evaluation. Although multiple behavioral models can be invoked involving selective consideration of choice outcomes, whether differences in these tendencies underlie behavioral differences in sensitivity to reward and probability information is unknown. Here we consider neural evidence where we exploit magnetoencephalography (MEG) to decode the actual choice outcomes participants consider when they decide between a gamble and a safe outcome. We show that variability in tendencies of individual participants to reinstate neural outcome representations, based on either their probability or reward, explains variability in the extent to which their choices reflect consideration of probability and reward information. In keeping with this we also show that participants who are higher in behavioral impulsivity fail to preferentially reinstate outcomes with higher probability. Our results suggest that neural differences in the degree to which outcomes are considered shape risk taking strategy, both in decision making tasks, as well as in real life.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A ubiquitous feature of human decision making under risk is that individuals differ from each other, as well as from normativity, in how they incorporate reward and probability information. One possible explanation for these deviations is a desire to reduce the number of potential outcomes considered during choice evaluation. Although multiple behavioral models can be invoked involving selective consideration of choice outcomes, whether differences in these tendencies underlie behavioral differences in sensitivity to reward and probability information is unknown. Here we consider neural evidence where we exploit magnetoencephalography (MEG) to decode the actual choice outcomes participants consider when they decide between a gamble and a safe outcome. We show that variability in tendencies of individual participants to reinstate neural outcome representations, based on either their probability or reward, explains variability in the extent to which their choices reflect consideration of probability and reward information. In keeping with this we also show that participants who are higher in behavioral impulsivity fail to preferentially reinstate outcomes with higher probability. Our results suggest that neural differences in the degree to which outcomes are considered shape risk taking strategy, both in decision making tasks, as well as in real life.
Talwar, Anahita; Huys, Quentin; Cormack, Francesca; Roiser, Jonathan P
A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting Journal Article
In: BioRxiv, 2021.
@article{TalwarRoiser21,
title = {A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting},
author = {Anahita Talwar and Quentin Huys and Francesca Cormack and Jonathan P Roiser},
url = {http://acplab.org/wp-content/uploads/pub/TalwarRoiser21-IED.pdf},
doi = {10.1101/2021.10.05.463165},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {BioRxiv},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Attentional set shifting refers to the ease with which the focus of attention is directed and switched.
Cognitive tasks such as CANTAB IED reveal great variation in set shifting ability in the general
population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability, and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED and psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data, and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. This data showcase a new methodology to analyse data from the CANTAB IED task, and suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Attentional set shifting refers to the ease with which the focus of attention is directed and switched.
Cognitive tasks such as CANTAB IED reveal great variation in set shifting ability in the general
population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability, and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED and psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data, and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. This data showcase a new methodology to analyse data from the CANTAB IED task, and suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.
Cognitive tasks such as CANTAB IED reveal great variation in set shifting ability in the general
population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability, and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED and psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data, and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. This data showcase a new methodology to analyse data from the CANTAB IED task, and suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.
Peer-reviewed original publications
Hall, Anna; Browning, Michael; Huys, Q. J. M.
The computational structure of consummatory anhedonia Journal Article
In: Trends. Cog. Sci., 2024.
@article{HallHuys24,
title = {The computational structure of consummatory anhedonia},
author = {Anna Hall and Michael Browning and Q. J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/HallHuys24-ComputationalStructureAnhedonia-1.pdf},
year = {2024},
date = {2024-01-19},
urldate = {2024-01-19},
journal = {Trends. Cog. Sci.},
abstract = {Anhedonia is a reduction in enjoyment, motivation or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed ’consummatory anhedonia’, in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement- learning account of consummatory anhedonia with a reconceptualisation of subjective hedonic expe- rience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Anhedonia is a reduction in enjoyment, motivation or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed ’consummatory anhedonia’, in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement- learning account of consummatory anhedonia with a reconceptualisation of subjective hedonic expe- rience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.
Norbury, Agnes; Hauser, Tobias; Fleming, Stephen; Dolan, Raymond; Huys, Quentin J. M.
Different components of cognitive-behavioural therapy affect specific cognitive mechanisms Journal Article
In: Science Advances, 2024.
@article{NorburyHuys24,
title = {Different components of cognitive-behavioural therapy affect specific cognitive mechanisms},
author = {Agnes Norbury and Tobias Hauser and Stephen Fleming and Raymond Dolan and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/NorburyHuysComponentsCBT.pdf},
doi = {https://doi.org/10.1126/sciadv.adk3222},
year = {2024},
date = {2024-01-14},
urldate = {2024-01-14},
journal = {Science Advances},
abstract = {Psychological therapies are among the most effective treatments for a range of common mental health problems – however, we still know relatively little about how exactly they improve symp- toms. Here, we demonstrate the power of combing theory with computational methods to parse effects of different components of cognitive-behavioural therapies on to underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the ef- fects of components of behavioural and cognitive therapies on different cognitive processes, us- ing well-validated behavioural measures and associated computational models (total N=807). We found that a goal-setting intervention, based on behavioural activation therapy, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, we found that a cognitive restructuring intervention, based on cognitive therapy, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. Importantly, the effects of each intervention were specific to these respective measures. Our approach provides a basis for understanding how different elements of common psychotherapy programs work, which may enable theoretically-informed treatment targeting in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Psychological therapies are among the most effective treatments for a range of common mental health problems – however, we still know relatively little about how exactly they improve symp- toms. Here, we demonstrate the power of combing theory with computational methods to parse effects of different components of cognitive-behavioural therapies on to underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the ef- fects of components of behavioural and cognitive therapies on different cognitive processes, us- ing well-validated behavioural measures and associated computational models (total N=807). We found that a goal-setting intervention, based on behavioural activation therapy, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, we found that a cognitive restructuring intervention, based on cognitive therapy, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. Importantly, the effects of each intervention were specific to these respective measures. Our approach provides a basis for understanding how different elements of common psychotherapy programs work, which may enable theoretically-informed treatment targeting in the future.
Chen, Hao; Belanger, Matthew J.; Garbusow, Maria; Kuitunen-Paul, Sören; Huys, Quentin J. M.; Heinz, Andreas; Rapp, Michael A.; Smolka, Michael N.
In: Addiction Biology, vol. 28, iss. 2, no. e13263, 2023.
@article{ChenSmolka23,
title = {Susceptibility to interference between Pavlovian and instrumental control predisposes risky alcohol use developmental trajectory from ages 18 to 24},
author = {Hao Chen and Matthew J. Belanger and Maria Garbusow and Sören Kuitunen-Paul and Quentin J. M. Huys and Andreas Heinz and Michael A. Rapp and Michael N. Smolka},
url = {http://acplab.org/wp-content/uploads/pub/ChenSmolka23-Suceptibility-to-interference.pdf},
doi = {10.1111/adb.13263},
year = {2023},
date = {2023-01-04},
urldate = {2023-01-04},
journal = {Addiction Biology},
volume = {28},
number = {e13263},
issue = {2},
abstract = {Pavlovian cues can influence ongoing instrumental behaviour via Pavlovian-to-instrumental transfer (PIT) processes. While appetitive Pavlovian cues tend to promote instrumental approach, they are detrimental when avoidance behaviour is required, and vice versa for aversive cues. We recently reported that susceptibility to interference between Pavlovian and instrumental control assessed via a PIT task was associated with risky alcohol use at age 18. We now investigated whether such susceptibility also predicts drinking trajectories until age 24, based on AUDIT (Alcohol Use Disorders Identification Test) consumption and binge drinking (gramme alcohol/drinking occasion) scores. The interference PIT effect, assessed at ages 18 and 21 during fMRI, was characterized by increased error rates (ER) and enhanced neural responses in the ventral striatum (VS), the lateral and dorsomedial prefrontal cortices (dmPFC) during conflict, that is, when an instrumental approach was required in the presence of an aversive Pavlovian cue or vice versa. We found that a stronger VS response during conflict at age 18 was associated with a higher starting point of both drinking trajectories but predicted a decrease in binge drinking. At age 21, high ER and enhanced neural responses in the dmPFC were associated with increasing AUDIT-C scores over the next 3 years until age 24. Overall, susceptibility to interference between Pavlovian and instrumental control might be viewed as a predisposing mechanism towards hazardous alcohol use during young adulthood, and the identified high-risk group may profit from targeted interventions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pavlovian cues can influence ongoing instrumental behaviour via Pavlovian-to-instrumental transfer (PIT) processes. While appetitive Pavlovian cues tend to promote instrumental approach, they are detrimental when avoidance behaviour is required, and vice versa for aversive cues. We recently reported that susceptibility to interference between Pavlovian and instrumental control assessed via a PIT task was associated with risky alcohol use at age 18. We now investigated whether such susceptibility also predicts drinking trajectories until age 24, based on AUDIT (Alcohol Use Disorders Identification Test) consumption and binge drinking (gramme alcohol/drinking occasion) scores. The interference PIT effect, assessed at ages 18 and 21 during fMRI, was characterized by increased error rates (ER) and enhanced neural responses in the ventral striatum (VS), the lateral and dorsomedial prefrontal cortices (dmPFC) during conflict, that is, when an instrumental approach was required in the presence of an aversive Pavlovian cue or vice versa. We found that a stronger VS response during conflict at age 18 was associated with a higher starting point of both drinking trajectories but predicted a decrease in binge drinking. At age 21, high ER and enhanced neural responses in the dmPFC were associated with increasing AUDIT-C scores over the next 3 years until age 24. Overall, susceptibility to interference between Pavlovian and instrumental control might be viewed as a predisposing mechanism towards hazardous alcohol use during young adulthood, and the identified high-risk group may profit from targeted interventions.
Ebrahimi, Claudia; Garbusow, Maria; Sebold, Miriam; Chen, Ke; Smolka, Michael N.; Huys, Quentin J. M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Heinz, Andreas
In: Biological Psychiatry Global Open Science, 2023.
@article{EbrahimiHeinz23,
title = {Elevated amygdala responses during de-novo Pavlovian conditioning in alcohol-use disorder are associated with Pavlovian-to-Instrumental transfer and relapse latency},
author = {Claudia Ebrahimi and Maria Garbusow and Miriam Sebold and Ke Chen and Michael N. Smolka and Quentin J. M. Huys and Ulrich S. Zimmermann and Florian Schlagenhauf and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/EbrahimiHeinz2023.pdf},
doi = {10.1016/j.bpsgos.2023.02.003},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Biological Psychiatry Global Open Science},
abstract = {Background
Contemporary learning theories of drug addiction ascribe a key role to Pavlovian learning mechanisms in the development, maintenance and relapse of addiction. In fact, cue-reactivity research has demonstrated the power of alcohol-associated cues to activate the brain’s reward system, which has been linked to craving and subsequent relapse. However, whether de-novo Pavlovian conditioning is altered in alcohol use disorder (AUD) has been rarely investigated.
Methods To characterize de-novo Pavlovian conditioning in AUD},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Contemporary learning theories of drug addiction ascribe a key role to Pavlovian learning mechanisms in the development, maintenance and relapse of addiction. In fact, cue-reactivity research has demonstrated the power of alcohol-associated cues to activate the brain’s reward system, which has been linked to craving and subsequent relapse. However, whether de-novo Pavlovian conditioning is altered in alcohol use disorder (AUD) has been rarely investigated.
Methods To characterize de-novo Pavlovian conditioning in AUD
Contemporary learning theories of drug addiction ascribe a key role to Pavlovian learning mechanisms in the development, maintenance and relapse of addiction. In fact, cue-reactivity research has demonstrated the power of alcohol-associated cues to activate the brain’s reward system, which has been linked to craving and subsequent relapse. However, whether de-novo Pavlovian conditioning is altered in alcohol use disorder (AUD) has been rarely investigated.
Methods To characterize de-novo Pavlovian conditioning in AUD
Geurts, Quentin J M Huys Anna Katinka Louise Von Borries Dirk
In: Frontiers in Behavioral Neuroscience, 2022.
@article{HuysGeurts22b,
title = {Psychopathic tendency in violent offenders is associated with reduced aversive Pavlovian inhibition of behaviour and associated striatal BOLD signal},
author = {Quentin J M Huys Anna Katinka Louise Von Borries Dirk Geurts},
url = {http://acplab.org/wp-content/uploads/pub/GeurtsCools22b.pdf},
doi = {10.3389/fnbeh.2022.963776},
year = {2022},
date = {2022-10-14},
urldate = {2022-09-22},
journal = {Frontiers in Behavioral Neuroscience},
abstract = {Background: Violent offenders with psychopathic tendencies are characterized by instrumental, i.e., planned, callous, and unemotional (aggressive) behavior and have been shown to exhibit abnormal aversive processing. However, the consequences of abnormal aversive processing for instrumental action and associated neural mechanisms are unclear.
Materials and methods: Here we address this issue by using event-related functional magnetic resonance imaging (fMRI) in 15 violent offenders with high psychopathic tendencies and 18 matched controls during the performance of an aversive Pavlovian-to-instrumental transfer paradigm. This paradigm allowed us to assess the degree to which aversive Pavlovian cues affect instrumental action and associated neural signaling.
Results: Psychopathic tendency scores were associated with an attenuation of aversive Pavlovian inhibition of instrumental action. Moreover, exploratory analyses revealed an anomalous positive association between aversive inhibition of action and aversive inhibition of BOLD signal in the caudate nucleus of violent offenders with psychopathic tendencies. In addition, psychopathic tendency also correlated positively with amygdala reactivity during aversive versus neutral cues in Pavlovian training.
Conclusion: These findings strengthen the hypothesis that psychopathic tendencies in violent offenders are related to abnormal impact of aversive processing on instrumental behavior. The neural effects raise the possibility that this reflects deficient transfer of aversive Pavlovian inhibitory biases onto neural systems that implement instrumental action, including the caudate nucleus.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Violent offenders with psychopathic tendencies are characterized by instrumental, i.e., planned, callous, and unemotional (aggressive) behavior and have been shown to exhibit abnormal aversive processing. However, the consequences of abnormal aversive processing for instrumental action and associated neural mechanisms are unclear.
Materials and methods: Here we address this issue by using event-related functional magnetic resonance imaging (fMRI) in 15 violent offenders with high psychopathic tendencies and 18 matched controls during the performance of an aversive Pavlovian-to-instrumental transfer paradigm. This paradigm allowed us to assess the degree to which aversive Pavlovian cues affect instrumental action and associated neural signaling.
Results: Psychopathic tendency scores were associated with an attenuation of aversive Pavlovian inhibition of instrumental action. Moreover, exploratory analyses revealed an anomalous positive association between aversive inhibition of action and aversive inhibition of BOLD signal in the caudate nucleus of violent offenders with psychopathic tendencies. In addition, psychopathic tendency also correlated positively with amygdala reactivity during aversive versus neutral cues in Pavlovian training.
Conclusion: These findings strengthen the hypothesis that psychopathic tendencies in violent offenders are related to abnormal impact of aversive processing on instrumental behavior. The neural effects raise the possibility that this reflects deficient transfer of aversive Pavlovian inhibitory biases onto neural systems that implement instrumental action, including the caudate nucleus.
Materials and methods: Here we address this issue by using event-related functional magnetic resonance imaging (fMRI) in 15 violent offenders with high psychopathic tendencies and 18 matched controls during the performance of an aversive Pavlovian-to-instrumental transfer paradigm. This paradigm allowed us to assess the degree to which aversive Pavlovian cues affect instrumental action and associated neural signaling.
Results: Psychopathic tendency scores were associated with an attenuation of aversive Pavlovian inhibition of instrumental action. Moreover, exploratory analyses revealed an anomalous positive association between aversive inhibition of action and aversive inhibition of BOLD signal in the caudate nucleus of violent offenders with psychopathic tendencies. In addition, psychopathic tendency also correlated positively with amygdala reactivity during aversive versus neutral cues in Pavlovian training.
Conclusion: These findings strengthen the hypothesis that psychopathic tendencies in violent offenders are related to abnormal impact of aversive processing on instrumental behavior. The neural effects raise the possibility that this reflects deficient transfer of aversive Pavlovian inhibitory biases onto neural systems that implement instrumental action, including the caudate nucleus.
Chen, Ke; Schlagenhauf, Florian; Sebold, Miriam; Kuitunen-Paul, Sören; Chen, Hao; Huys, Quentin J. M.; Heinz, Andreas; Smolka, Michael N.; Zimmermann, Ulrich S.; Garbusow, Maria
In: Biological Psychiatry, vol. 93, iss. 6, pp. 558-565, 2022.
@article{ChenGarbusow22,
title = {The association of non-drug-related Pavlovian-to-instrumental transfer effect in nucleus accumbens with relapse in alcohol dependence: a replication},
author = {Ke Chen and Florian Schlagenhauf and Miriam Sebold and Sören Kuitunen-Paul and Hao Chen and Quentin J. M. Huys and Andreas Heinz and Michael N. Smolka and Ulrich S. Zimmermann and Maria Garbusow},
url = {http://acplab.org/wp-content/uploads/pub/ChenSchlagenhauf2022-PIT_replication.pdf},
doi = {10.1016/j.biopsych.2022.09.017},
year = {2022},
date = {2022-09-22},
urldate = {2022-09-22},
booktitle = {Biological Psychiatry},
journal = {Biological Psychiatry},
volume = {93},
issue = {6},
pages = {558-565},
abstract = {Background
The Pavlovian-to-instrumental transfer (PIT) paradigm measures the effects of Pavlovian conditioned cues on instrumental behavior in the laboratory. A previous study in our research group observed activity in the left nucleus accumbens (NAcc) elicited by a non-drug-related PIT task across alcohol-dependent (AD) patients and healthy controls, and the left NAcc PIT effect differentiated patients who subsequently relapsed from who remained abstinent. In the present study, we aimed to examine whether such effects were present in a larger subsequently collected sample.
Methods
A total of 129 recently detoxified AD patients (21 females) and 74 healthy, age- and sex-matched controls (12 females) performing a PIT task during functional magnetic resonance imaging (fMRI) were examined. After task assessments, patients were followed up for 6 months. Forty-seven patients relapsed and 37 remained abstinent.
Results
We found a significant behavioral non-drug-related PIT effect and PIT-related activity in the NAcc across all participants. Moreover, subsequent relapsers showed stronger behavioral and left NAcc PIT effects compared to abstainers. These findings are consistent with the previous findings.
Conclusions
Behavioral non-drug-related PIT and neural PIT correlates are associated with prospective relapse risk in alcohol dependence. This study replicated previous findings and provide evidence for the clinical relevance of PIT mechanisms with the treatment outcome in alcohol dependence. The observed difference between prospective relapsers and abstainers in NAcc PIT effect in our study is overall small. Future studies are needed to further elucidate the mechanisms and the possible modulators of neural PIT in relapse in alcohol dependence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
The Pavlovian-to-instrumental transfer (PIT) paradigm measures the effects of Pavlovian conditioned cues on instrumental behavior in the laboratory. A previous study in our research group observed activity in the left nucleus accumbens (NAcc) elicited by a non-drug-related PIT task across alcohol-dependent (AD) patients and healthy controls, and the left NAcc PIT effect differentiated patients who subsequently relapsed from who remained abstinent. In the present study, we aimed to examine whether such effects were present in a larger subsequently collected sample.
Methods
A total of 129 recently detoxified AD patients (21 females) and 74 healthy, age- and sex-matched controls (12 females) performing a PIT task during functional magnetic resonance imaging (fMRI) were examined. After task assessments, patients were followed up for 6 months. Forty-seven patients relapsed and 37 remained abstinent.
Results
We found a significant behavioral non-drug-related PIT effect and PIT-related activity in the NAcc across all participants. Moreover, subsequent relapsers showed stronger behavioral and left NAcc PIT effects compared to abstainers. These findings are consistent with the previous findings.
Conclusions
Behavioral non-drug-related PIT and neural PIT correlates are associated with prospective relapse risk in alcohol dependence. This study replicated previous findings and provide evidence for the clinical relevance of PIT mechanisms with the treatment outcome in alcohol dependence. The observed difference between prospective relapsers and abstainers in NAcc PIT effect in our study is overall small. Future studies are needed to further elucidate the mechanisms and the possible modulators of neural PIT in relapse in alcohol dependence.
The Pavlovian-to-instrumental transfer (PIT) paradigm measures the effects of Pavlovian conditioned cues on instrumental behavior in the laboratory. A previous study in our research group observed activity in the left nucleus accumbens (NAcc) elicited by a non-drug-related PIT task across alcohol-dependent (AD) patients and healthy controls, and the left NAcc PIT effect differentiated patients who subsequently relapsed from who remained abstinent. In the present study, we aimed to examine whether such effects were present in a larger subsequently collected sample.
Methods
A total of 129 recently detoxified AD patients (21 females) and 74 healthy, age- and sex-matched controls (12 females) performing a PIT task during functional magnetic resonance imaging (fMRI) were examined. After task assessments, patients were followed up for 6 months. Forty-seven patients relapsed and 37 remained abstinent.
Results
We found a significant behavioral non-drug-related PIT effect and PIT-related activity in the NAcc across all participants. Moreover, subsequent relapsers showed stronger behavioral and left NAcc PIT effects compared to abstainers. These findings are consistent with the previous findings.
Conclusions
Behavioral non-drug-related PIT and neural PIT correlates are associated with prospective relapse risk in alcohol dependence. This study replicated previous findings and provide evidence for the clinical relevance of PIT mechanisms with the treatment outcome in alcohol dependence. The observed difference between prospective relapsers and abstainers in NAcc PIT effect in our study is overall small. Future studies are needed to further elucidate the mechanisms and the possible modulators of neural PIT in relapse in alcohol dependence.
Geurts, Dirk E. M.; Heuvel, Thom J. Van; Huys, Quentin J. M.; Verkes, Robbert J.; Cools, Roshan
Amygdala response predicts clinical symptom reduction in patients with borderline personality disorder: A pilot fMRI study Journal Article
In: Frontiers in Behavioral Neuroscience, pp. 36, 2022.
@article{GeurtsCools22,
title = {Amygdala response predicts clinical symptom reduction in patients with borderline personality disorder: A pilot fMRI study},
author = {Dirk E. M. Geurts and Thom J. Van Heuvel and Quentin J. M. Huys and Robbert J. Verkes and Roshan Cools},
url = {http://acplab.org/wp-content/uploads/pub/GeurtsCools22-AmygdalaBorderlinePIT.pdf},
doi = {10.3389/fnbeh.2022.938403},
year = {2022},
date = {2022-08-30},
urldate = {2022-08-30},
journal = {Frontiers in Behavioral Neuroscience},
pages = {36},
abstract = {Borderline personality disorder (BPD) is a prevalent, devastating, and heterogeneous psychiatric disorder. Treatment success is highly variable within this patient group. A cognitive neuroscientific approach to BPD might contribute to precision psychiatry by identifying neurocognitive factors that predict who will benefit from a specific treatment. Here, we build on observations that BPD is accompanied by the enhanced impact of the aversive effect on behavior and abnormal neural signaling in the amygdala. We assessed whether BPD is accompanied by abnormal aversive regulation of instrumental behavior and associated neural signaling, in a manner that is predictive of symptom reduction after therapy. We tested a clinical sample of 15 female patients with BPD, awaiting dialectical behavior therapy (DBT), and 16 matched healthy controls using fMRI and an aversive Pavlovian-to-instrumental transfer (PIT) task that assesses how instrumental behaviors are influenced by aversive Pavlovian stimuli. Patients were assessed 1 year after the start of DBT to quantify changes in BPD symptom severity. At baseline, behavioral aversive PIT and associated neural signaling did not differ between groups. However, the BOLD signal in the amygdala measured during aversive PIT was associated with symptom reduction at 1-year follow-up: higher PIT-related aversive amygdala signaling before treatment was associated with reduced clinical improvement at follow-up. Thus, within the evaluated group of BPD patients, the BOLD signal in the amygdala before treatment was related to clinical symptom reduction 1 year after the start of treatment. The results suggest that less PIT-related responsiveness of the amygdala increases the chances of treatment success. We note that the relatively small sample size is a limitation of this study and that replication is warranted.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Borderline personality disorder (BPD) is a prevalent, devastating, and heterogeneous psychiatric disorder. Treatment success is highly variable within this patient group. A cognitive neuroscientific approach to BPD might contribute to precision psychiatry by identifying neurocognitive factors that predict who will benefit from a specific treatment. Here, we build on observations that BPD is accompanied by the enhanced impact of the aversive effect on behavior and abnormal neural signaling in the amygdala. We assessed whether BPD is accompanied by abnormal aversive regulation of instrumental behavior and associated neural signaling, in a manner that is predictive of symptom reduction after therapy. We tested a clinical sample of 15 female patients with BPD, awaiting dialectical behavior therapy (DBT), and 16 matched healthy controls using fMRI and an aversive Pavlovian-to-instrumental transfer (PIT) task that assesses how instrumental behaviors are influenced by aversive Pavlovian stimuli. Patients were assessed 1 year after the start of DBT to quantify changes in BPD symptom severity. At baseline, behavioral aversive PIT and associated neural signaling did not differ between groups. However, the BOLD signal in the amygdala measured during aversive PIT was associated with symptom reduction at 1-year follow-up: higher PIT-related aversive amygdala signaling before treatment was associated with reduced clinical improvement at follow-up. Thus, within the evaluated group of BPD patients, the BOLD signal in the amygdala before treatment was related to clinical symptom reduction 1 year after the start of treatment. The results suggest that less PIT-related responsiveness of the amygdala increases the chances of treatment success. We note that the relatively small sample size is a limitation of this study and that replication is warranted.
Berwian, Isabel M.; G.Wenzel, Julia; Kuehn, Leonie; Schnuerer, Inga; Seifritz, Erich; Stephan, Klaas E.; Walter, Henrik; Huys, Quentin J. M.
Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting Journal Article
In: Scientific Reports, vol. 12, iss. 1, pp. 11171, 2022.
@article{BerwianHuys22,
title = {Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting},
author = {Isabel M. Berwian and Julia G.Wenzel and Leonie Kuehn and Inga Schnuerer and Erich Seifritz and Klaas E. Stephan and Henrik Walter and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/Berwian22_low-predictive-power.pdf},
doi = {10.1038/s41598-022-13893-9},
year = {2022},
date = {2022-07-01},
urldate = {2022-07-01},
journal = {Scientific Reports},
volume = {12},
issue = {1},
pages = {11171},
abstract = {The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during 6 months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during 6 months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.
Huys, Quentin J M; Russek, Evan M; Abitante, George; Kahnt, Thorsten; Gollan, Jacqueline K
Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study Journal Article
In: Computational Psychiatry, vol. 6, iss. 1, 2022.
@article{HuysGollan22,
title = {Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study},
author = {Quentin J M Huys and Evan M Russek and George Abitante and Thorsten Kahnt and Jacqueline K Gollan},
url = {http://acplab.org/wp-content/uploads/pub/HuysGollan-BA-components.pdf},
doi = {10.5334/cpsy.81},
year = {2022},
date = {2022-05-11},
urldate = {2022-05-11},
journal = {Computational Psychiatry},
volume = {6},
issue = {1},
abstract = {Background: Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms.
Objective: To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation.
Method: The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decisionmaking tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models.
Results: Treatment effects during different phases of the therapy were captured by specific decision making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia.
Conclusions: In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms.
Objective: To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation.
Method: The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decisionmaking tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models.
Results: Treatment effects during different phases of the therapy were captured by specific decision making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia.
Conclusions: In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response.
Objective: To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation.
Method: The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decisionmaking tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models.
Results: Treatment effects during different phases of the therapy were captured by specific decision making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia.
Conclusions: In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response.
Kuylen, Margot; Han, Shihui; Harris, Lasana; Huys, Quentin; Monsó, Susana; Pitman, Alexandra; Fleming, Stephen M.; David, Anthony S.
Mortality Awareness: New Directions Journal Article
In: OMEGA - Journal of Death and Dying, 2022.
@article{KuylenDavid22,
title = {Mortality Awareness: New Directions},
author = {Margot Kuylen and Shihui Han and Lasana Harris and Quentin Huys and Susana Monsó and Alexandra Pitman and Stephen M. Fleming and Anthony S. David},
url = {http://acplab.org/wp-content/uploads/pub/Kuylen_2022-Mortality_awareness.pdf},
doi = {10.1177/00302228221100640},
year = {2022},
date = {2022-05-01},
urldate = {2022-05-01},
journal = {OMEGA - Journal of Death and Dying},
publisher = {SAGE Publications},
abstract = {Thinking about our own death and its salience in relation to decision making has become a fruitful area of multidisciplinary research across the breadth of psychological science. By bringing together experts from philosophy, cognitive and affective neuroscience, clinical and computational psychiatry we have attempted to set out the current state of the art and point to areas of further enquiry. One stimulus for doing this is the need to engage with policy makers who are now having to consider guidelines on suicide and assisted suicide so that they may be aware of their own as well as the wider populations’ cognitive processes when confronted with the ultimate truth of mortality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Thinking about our own death and its salience in relation to decision making has become a fruitful area of multidisciplinary research across the breadth of psychological science. By bringing together experts from philosophy, cognitive and affective neuroscience, clinical and computational psychiatry we have attempted to set out the current state of the art and point to areas of further enquiry. One stimulus for doing this is the need to engage with policy makers who are now having to consider guidelines on suicide and assisted suicide so that they may be aware of their own as well as the wider populations’ cognitive processes when confronted with the ultimate truth of mortality.
Chen, Ke; Garbusowa, Maria; Sebold, Miriam; Kuitunen-Paul, Sören; N.Smolka, Michael; J.M.Huys, Quentin; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Heinz, Andreas
Alcohol Approach Bias Is Associated With Both Behavioral and Neural Pavlovian-to-Instrumental Transfer Effects in Alcohol-Dependent Patients Journal Article
In: Biological Psychiatry Global Open Science, 2022.
@article{ChenHeinz22,
title = {Alcohol Approach Bias Is Associated With Both Behavioral and Neural Pavlovian-to-Instrumental Transfer Effects in Alcohol-Dependent Patients},
author = {Ke Chen and Maria Garbusowa and Miriam Sebold and Sören Kuitunen-Paul and Michael N.Smolka and Quentin J.M.Huys and Ulrich S. Zimmermann and Florian Schlagenhauf and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/Chen_2022-1.pdf
http://acplab.org/wp-content/uploads/2022/07/Revised_supplement.docx},
doi = {10.1016/j.bpsgos.2022.03.014},
year = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
journal = {Biological Psychiatry Global Open Science},
abstract = {BACKGROUND: Even after qualified detoxification, alcohol-dependent (AD) patients may relapse to drinking alcohol despite their decision to abstain. Two mechanisms may play important roles. First, the impact of environmental cues on instrumental behavior (i.e., Pavlovian-to-instrumental transfer [PIT] effect), which was found to be stronger in prospectively relapsing AD patients than in abstaining patients. Second, an automatic approach bias toward alcohol stimuli was observed in AD patients, and interventions targeting this bias reduced the relapse risk in some studies. Previous findings suggest a potential behavioral and neurobiological overlap between these two
mechanisms.
METHODS: In this study, we examined the association between alcohol approach bias and both behavioral and neural non–drug-related PIT effects in AD patients after detoxification. A total of 100 AD patients (17 females) performed a PIT task and an alcohol approach/avoidance task. Patients were followed for 6 months.
RESULTS: A stronger alcohol approach bias was associated with both a more pronounced behavioral PIT effect and stronger PIT-related neural activity in the right nucleus accumbens. Moreover, the association between alcohol approach bias and behavioral PIT increased with the severity of alcohol dependence and trait impulsivity and was stronger in patients who relapsed during follow-up in the exploratory analysis.
CONCLUSIONS: These findings indicate partial behavioral and neurobiological overlap between alcohol approach bias and the PIT effect assessed with our tasks. The association was stronger in patients with more severe alcohol dependence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND: Even after qualified detoxification, alcohol-dependent (AD) patients may relapse to drinking alcohol despite their decision to abstain. Two mechanisms may play important roles. First, the impact of environmental cues on instrumental behavior (i.e., Pavlovian-to-instrumental transfer [PIT] effect), which was found to be stronger in prospectively relapsing AD patients than in abstaining patients. Second, an automatic approach bias toward alcohol stimuli was observed in AD patients, and interventions targeting this bias reduced the relapse risk in some studies. Previous findings suggest a potential behavioral and neurobiological overlap between these two
mechanisms.
METHODS: In this study, we examined the association between alcohol approach bias and both behavioral and neural non–drug-related PIT effects in AD patients after detoxification. A total of 100 AD patients (17 females) performed a PIT task and an alcohol approach/avoidance task. Patients were followed for 6 months.
RESULTS: A stronger alcohol approach bias was associated with both a more pronounced behavioral PIT effect and stronger PIT-related neural activity in the right nucleus accumbens. Moreover, the association between alcohol approach bias and behavioral PIT increased with the severity of alcohol dependence and trait impulsivity and was stronger in patients who relapsed during follow-up in the exploratory analysis.
CONCLUSIONS: These findings indicate partial behavioral and neurobiological overlap between alcohol approach bias and the PIT effect assessed with our tasks. The association was stronger in patients with more severe alcohol dependence.
mechanisms.
METHODS: In this study, we examined the association between alcohol approach bias and both behavioral and neural non–drug-related PIT effects in AD patients after detoxification. A total of 100 AD patients (17 females) performed a PIT task and an alcohol approach/avoidance task. Patients were followed for 6 months.
RESULTS: A stronger alcohol approach bias was associated with both a more pronounced behavioral PIT effect and stronger PIT-related neural activity in the right nucleus accumbens. Moreover, the association between alcohol approach bias and behavioral PIT increased with the severity of alcohol dependence and trait impulsivity and was stronger in patients who relapsed during follow-up in the exploratory analysis.
CONCLUSIONS: These findings indicate partial behavioral and neurobiological overlap between alcohol approach bias and the PIT effect assessed with our tasks. The association was stronger in patients with more severe alcohol dependence.
Sharp, Paul B.; Russek, Evan; Huys, Quentin JM; Dolan, Raymond J; Eldar, Eran
Humans perseverate on punishment avoidance goals in multigoal reinforcement learning Journal Article
In: eLife, vol. 11, no. e74402, 2022.
@article{SharpEldar22,
title = {Humans perseverate on punishment avoidance goals in multigoal reinforcement learning},
author = {Paul B. Sharp and Evan Russek and Quentin JM Huys and Raymond J Dolan and Eran Eldar},
url = {http://acplab.org/wp-content/uploads/pub/Sharp_2022-Human-perseverate.pdf},
doi = {10.7554/eLife.74402},
year = {2022},
date = {2022-02-24},
urldate = {2022-02-24},
journal = {eLife},
volume = {11},
number = {e74402},
publisher = {Center for Open Science},
abstract = {Managing multiple goals is essential to adaptation, yet we are only beginning to understand computations by which we navigate the resource-demands entailed in so doing. Here, we sought to elucidate how humans balance reward seeking and punishment avoidance goals, and relate this to variation in its expression within anxious individuals. To do so, we developed a novel multigoal pursuit task that includes trial-specific instructed goals to either pursue reward (without risk of punishment) or avoid punishment (without the opportunity for reward). We constructed a computational model of multigoal pursuit to quantify the degree to which participants could disengage from the pursuit goals when instructed to, as well as devote less model-based resources towards goals that were less abundant. In general, participants (n=192) were less flexible in avoiding punishment than in pursuing reward. Thus, when instructed to pursue reward, participants often persisted in avoiding features that had previously been associated with punishment, even though at decision time these features were unambiguously benign. In a similar vein, participants showed no significant downregulation of avoidance when punishment avoidance goals were less abundant in the task. Importantly, individuals with chronic worry had particular difficulty disengaging from punishment avoidance under an instructed reward seeking goal. Taken together, the findings demonstrate that people avoid punishment less flexibly than they pursue reward, a difference that is more pronounced in individuals with chronic worry.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Managing multiple goals is essential to adaptation, yet we are only beginning to understand computations by which we navigate the resource-demands entailed in so doing. Here, we sought to elucidate how humans balance reward seeking and punishment avoidance goals, and relate this to variation in its expression within anxious individuals. To do so, we developed a novel multigoal pursuit task that includes trial-specific instructed goals to either pursue reward (without risk of punishment) or avoid punishment (without the opportunity for reward). We constructed a computational model of multigoal pursuit to quantify the degree to which participants could disengage from the pursuit goals when instructed to, as well as devote less model-based resources towards goals that were less abundant. In general, participants (n=192) were less flexible in avoiding punishment than in pursuing reward. Thus, when instructed to pursue reward, participants often persisted in avoiding features that had previously been associated with punishment, even though at decision time these features were unambiguously benign. In a similar vein, participants showed no significant downregulation of avoidance when punishment avoidance goals were less abundant in the task. Importantly, individuals with chronic worry had particular difficulty disengaging from punishment avoidance under an instructed reward seeking goal. Taken together, the findings demonstrate that people avoid punishment less flexibly than they pursue reward, a difference that is more pronounced in individuals with chronic worry.
Sebold, Miriam; Chen, Hao; Önal, Aleyna; Kuitunen-Paul, Sören; Mojtahedzadeh, Negin; Garbusow, Maria; Nebe, Stephan; Wittchen, Hans-Ulrich; Huys, Quentin J. M.; Schlagenhauf, Florian; Rapp, Michael A.; Smolka, Michael N.; Heinz, Andreas
Stronger Prejudices Are Associated With Decreased Model-Based Control Journal Article
In: Frontiers in Psychology, pp. 6042, 2022.
@article{SeboldHeinz22,
title = {Stronger Prejudices Are Associated With Decreased Model-Based Control},
author = {Miriam Sebold and Hao Chen and Aleyna Önal and Sören Kuitunen-Paul and Negin Mojtahedzadeh and Maria Garbusow and Stephan Nebe and Hans-Ulrich Wittchen and Quentin J. M. Huys and Florian Schlagenhauf and Michael A. Rapp and Michael N. Smolka and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/Sebold_2022-stronger-prejudices.pdf},
doi = {10.3389/fpsyg.2021.767022},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Frontiers in Psychology},
pages = {6042},
publisher = {Frontiers Media SA},
abstract = {Background: Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or “automatic” reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making.
Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale.
Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices.
Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or “automatic” reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making.
Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale.
Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices.
Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.
Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale.
Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices.
Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.
Atiya, Nadim A. A.; Huys, Quentin J. M.; Dolan, Raymond J.; Fleming, Stephen M.
Explaining distortions in metacognition with an attractor network model of decision uncertainty Journal Article
In: PLOS Computational Biology, vol. 17, iss. 7, no. e1009201, 2021.
@article{AtiyaFleming21,
title = {Explaining distortions in metacognition with an attractor network model of decision uncertainty},
author = {Nadim A. A. Atiya and Quentin J. M. Huys and Raymond J. Dolan and Stephen M. Fleming},
editor = {Alireza Soltani},
url = {http://acplab.org/wp-content/uploads/pub/Atiya_2021-Explaining-Distortions-in-Metacognition-with-an-Attractor-Network-Model-of-Decision-Uncertainty.pdf},
doi = {10.1371/journal.pcbi.1009201},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
journal = {PLOS Computational Biology},
volume = {17},
number = {e1009201},
issue = {7},
publisher = {Public Library of Science (PLoS)},
abstract = {Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.
Charpentier, Caroline J; Faulkner, Paul; Pool, Eva R; Ly, Verena; Tollenaar, Marieke S; Kluen, Lisa M; Fransen, Aniek; Yamamori, Yumeya; Lally, Níall; Mkrtchian, Anahit; Valton, Vincent; Huys, Quentin J M; Sarigiannidis, Ioannis; Morrow, Kelly A; Krenz, Valentina; Kalbe, Felix; Cremer, Anna; Zerbes, Gundula; Kausche, Franziska M; Wanke, Nadine; Giarrizzo, Alessio; Pulcu, Erdem; Murphy, Susannah; Kaltenboeck, Alexander; Browning, Michael; Paul, Lynn K; Cools, Roshan; Roelofs, Karin; Pessoa, Luiz; Harmer, Catherine J; Chase, Henry W; Grillon, Christian; Schwabe, Lars; Roiser, Jonathan P; Robinson, Oliver J; O'Doherty, John P
In: Social Cognitive and Affective Neuroscience, vol. 16, iss. 10, pp. 1057-1070, 2021.
@article{CharpentierODoherty21,
title = {How representative are neuroimaging samples? Large-scale evidence for trait anxiety differences between fMRI and behaviour-only research participants},
author = {Caroline J Charpentier and Paul Faulkner and Eva R Pool and Verena Ly and Marieke S Tollenaar and Lisa M Kluen and Aniek Fransen and Yumeya Yamamori and Níall Lally and Anahit Mkrtchian and Vincent Valton and Quentin J M Huys and Ioannis Sarigiannidis and Kelly A Morrow and Valentina Krenz and Felix Kalbe and Anna Cremer and Gundula Zerbes and Franziska M Kausche and Nadine Wanke and Alessio Giarrizzo and Erdem Pulcu and Susannah Murphy and Alexander Kaltenboeck and Michael Browning and Lynn K Paul and Roshan Cools and Karin Roelofs and Luiz Pessoa and Catherine J Harmer and Henry W Chase and Christian Grillon and Lars Schwabe and Jonathan P Roiser and Oliver J Robinson and John P O'Doherty},
url = {http://acplab.org/wp-content/uploads/pub/CharpentierODoherty21-How-Representative-Are-Neuroimaging-Samples_-Large-Scale-Evidence-for-Trait-Anxiety-Differences-between-FMRI-and-Behaviour-Only-Research-Participants.pdf},
doi = {10.1093/scan/nsab057},
year = {2021},
date = {2021-05-01},
urldate = {2021-05-01},
journal = {Social Cognitive and Affective Neuroscience},
volume = {16},
issue = {10},
pages = {1057-1070},
publisher = {Oxford University Press (OUP)},
abstract = {Over the past three decades, functional magnetic resonance imaging (fMRI) has become crucial to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into fMRI studies differ from participants recruited into other study contexts has received little to no attention. This is particularly pertinent when effects fail to generalize across study contexts: for example, a behavioural effect discovered in a non-imaging context not replicating in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (N = 272), that fMRI participants differ from behaviour-only participants on one fundamental individual difference variable: trait anxiety. Analysing trait anxiety scores and possible confounding variables from healthy volunteers across multiple institutions (N = 3317), we found robust support for lower trait anxiety in fMRI study participants, consistent with a sampling or self-selection bias. The bias was larger in studies that relied on phone screening (compared with full in-person psychiatric screening), recruited at least partly from convenience samples (compared with community samples), and in pharmacology studies. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures or sampling strategies to mitigate this bias.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past three decades, functional magnetic resonance imaging (fMRI) has become crucial to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into fMRI studies differ from participants recruited into other study contexts has received little to no attention. This is particularly pertinent when effects fail to generalize across study contexts: for example, a behavioural effect discovered in a non-imaging context not replicating in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (N = 272), that fMRI participants differ from behaviour-only participants on one fundamental individual difference variable: trait anxiety. Analysing trait anxiety scores and possible confounding variables from healthy volunteers across multiple institutions (N = 3317), we found robust support for lower trait anxiety in fMRI study participants, consistent with a sampling or self-selection bias. The bias was larger in studies that relied on phone screening (compared with full in-person psychiatric screening), recruited at least partly from convenience samples (compared with community samples), and in pharmacology studies. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures or sampling strategies to mitigate this bias.
Faulkner, Paul; Huys, Quentin J M; Renz, Daniel; Eshel, Neir; Pilling, Stephen; Dayan, Peter; Roiser, Jonathan P
A comparison of 'pruning' during multi-step planning in depressed and healthy individuals Journal Article
In: Psychological Medicine, vol. 52, iss. 16, pp. 3948-3956, 2021.
@article{FaulknerRoiser21c,
title = {A comparison of 'pruning' during multi-step planning in depressed and healthy individuals},
author = {Paul Faulkner and Quentin J M Huys and Daniel Renz and Neir Eshel and Stephen Pilling and Peter Dayan and Jonathan P Roiser},
url = {http://acplab.org/wp-content/uploads/pub/FaulknerRoiser21-A-Comparison-of-pruning-during-Multi-Step-Planning-in-Depressed-and-Healthy-Individuals-1.pdf},
doi = {10.1017/S0033291721000799},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
journal = {Psychological Medicine},
volume = {52},
issue = {16},
pages = {3948-3956},
abstract = {Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people 'prune' (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals. Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms. Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety. We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people 'prune' (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals. Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms. Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety. We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.
Sebold, Miriam; Garbusow, Maria; Cerci, Deniz; Chen, Ke; Sommer, Christian; Huys, Quentin JM; Nebe, Stephan; Rapp, Michael; Veer, Ilya M; Zimmermann, Ulrich S; Smolka, Michael N; Walter, Henrik; Heinz, Andreas; Friedel, Eva
Association of the OPRM1 A118G polymorphism and Pavlovian-to-instrumental transfer: Clinical relevance for alcohol dependence Journal Article
In: Journal of Psychopharmacology, vol. 35, iss. 5, pp. 566-578, 2021.
@article{SeboldFriedel21b,
title = {Association of the OPRM1 A118G polymorphism and Pavlovian-to-instrumental transfer: Clinical relevance for alcohol dependence},
author = {Miriam Sebold and Maria Garbusow and Deniz Cerci and Ke Chen and Christian Sommer and Quentin JM Huys and Stephan Nebe and Michael Rapp and Ilya M Veer and Ulrich S Zimmermann and Michael N Smolka and Henrik Walter and Andreas Heinz and Eva Friedel},
url = {http://acplab.org/wp-content/uploads/pub/Sebold_2021-Association-of-the-OPRM1-A118G-Polymorphism-and-Pavlovian-to-Instrumental-Transfer_-Clinical-Relevance-for-Alcohol-Dependence.pdf},
doi = {10.1177/0269881121991992},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
journal = {Journal of Psychopharmacology},
volume = {35},
issue = {5},
pages = {566-578},
publisher = {SAGE Publications},
abstract = {Background: Pavlovian-to-instrumental transfer (PIT) quantifies the extent to which a stimulus that has been associated with reward or punishment alters operant behaviour. In alcohol dependence (AD), the PIT effect serves as a paradigmatic model of cue-induced relapse. Preclinical studies have suggested a critical role of the opioid system in modulating Pavlovian–instrumental interactions. The A118G polymorphism of the OPRM1 gene affects opioid receptor availability and function. Furthermore, this polymorphism interacts with cue-induced approach behaviour and is a potential biomarker for pharmacological treatment response in AD. In this study, we tested whether the OPRM1 polymorphism is associated with the PIT effect and relapse in AD. Methods: Using a PIT task, we examined three independent samples: young healthy subjects (N = 161), detoxified alcohol-dependent patients (N = 186) and age-matched healthy controls (N = 105). We used data from a larger study designed to assess the role of learning mechanisms in the development and maintenance of AD. Subjects were genotyped for the A118G (rs1799971) polymorphism of the OPRM1 gene. Relapse was assessed after three months.
Results:
In all three samples, participants with the minor OPRM1 G-Allele (G+ carriers) showed increased expression of the PIT effect in the absence of learning differences. Relapse was not associated with the OPRM1 polymorphism. Instead, G+ carriers displaying increased PIT effects were particularly prone to relapse.
Conclusion:
These results support a role for the opioid system in incentive salience motivation. Furthermore, they inform a mechanistic model of aberrant salience processing and are in line with the pharmacological potential of opioid receptor targets in the treatment of AD.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Pavlovian-to-instrumental transfer (PIT) quantifies the extent to which a stimulus that has been associated with reward or punishment alters operant behaviour. In alcohol dependence (AD), the PIT effect serves as a paradigmatic model of cue-induced relapse. Preclinical studies have suggested a critical role of the opioid system in modulating Pavlovian–instrumental interactions. The A118G polymorphism of the OPRM1 gene affects opioid receptor availability and function. Furthermore, this polymorphism interacts with cue-induced approach behaviour and is a potential biomarker for pharmacological treatment response in AD. In this study, we tested whether the OPRM1 polymorphism is associated with the PIT effect and relapse in AD. Methods: Using a PIT task, we examined three independent samples: young healthy subjects (N = 161), detoxified alcohol-dependent patients (N = 186) and age-matched healthy controls (N = 105). We used data from a larger study designed to assess the role of learning mechanisms in the development and maintenance of AD. Subjects were genotyped for the A118G (rs1799971) polymorphism of the OPRM1 gene. Relapse was assessed after three months.
Results:
In all three samples, participants with the minor OPRM1 G-Allele (G+ carriers) showed increased expression of the PIT effect in the absence of learning differences. Relapse was not associated with the OPRM1 polymorphism. Instead, G+ carriers displaying increased PIT effects were particularly prone to relapse.
Conclusion:
These results support a role for the opioid system in incentive salience motivation. Furthermore, they inform a mechanistic model of aberrant salience processing and are in line with the pharmacological potential of opioid receptor targets in the treatment of AD.
Results:
In all three samples, participants with the minor OPRM1 G-Allele (G+ carriers) showed increased expression of the PIT effect in the absence of learning differences. Relapse was not associated with the OPRM1 polymorphism. Instead, G+ carriers displaying increased PIT effects were particularly prone to relapse.
Conclusion:
These results support a role for the opioid system in incentive salience motivation. Furthermore, they inform a mechanistic model of aberrant salience processing and are in line with the pharmacological potential of opioid receptor targets in the treatment of AD.
Chen, Hao; Mojtahedzadeh, Negin; Belanger, Matthew J.; Nebe, Stephan; Kuitunen-Paul, Sören; Sebold, Miriam; Garbusow, Maria; Huys, Quentin J. M.; Heinz, Andreas; Rapp, Michael A.; Smolka, Michael N.
Model-Based and Model-Free Control Predicts Alcohol Consumption Developmental Trajectory in Young Adults: A 3-Year Prospective Study Journal Article
In: Biological Psychiatry, vol. 89, iss. 10, pp. 980-989, 2021.
@article{ChenSmolka21c,
title = {Model-Based and Model-Free Control Predicts Alcohol Consumption Developmental Trajectory in Young Adults: A 3-Year Prospective Study},
author = {Hao Chen and Negin Mojtahedzadeh and Matthew J. Belanger and Stephan Nebe and Sören Kuitunen-Paul and Miriam Sebold and Maria Garbusow and Quentin J. M. Huys and Andreas Heinz and Michael A. Rapp and Michael N. Smolka},
url = {http://acplab.org/wp-content/uploads/pub/ChenSmolka21-MBMFDevelopmentalTrajectory.pdf},
doi = {10.1016/j.biopsych.2021.01.009},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Biological Psychiatry},
volume = {89},
issue = {10},
pages = {980-989},
abstract = {A shift from goal-directed toward habitual control has been associated with alcohol dependence. Whether such a shift predisposes to risky drinking is not yet clear. We investigated how goal-directed and habitual control at age 18 predict alcohol use trajectories over the course of 3 years. Goal-directed and habitual control, as informed by model-based (MB) and model-free (MF) learning, were assessed with a two-step sequential decision-making task during functional magnetic resonance imaging in 146 healthy 18-year-old men. Three-year alcohol use developmental trajectories were based on either a consumption score from the self-reported Alcohol Use Disorders Identification Test (assessed every 6 months) or an interview-based binge drinking score (grams of alcohol/occasion; assessed every year). We applied a latent growth curve model to examine how MB and MF control predicted the drinking trajectory. Drinking behavior was best characterized by a linear trajectory. MB behavioral control was negatively associated with the development of the binge drinking score; MF reward prediction error blood oxygen level-dependent signals in the ventromedial prefrontal cortex and the ventral striatum predicted a higher starting point and steeper increase of the Alcohol Use Disorders Identification Test consumption score over time, respectively. We found that MB behavioral control was associated with the binge drinking trajectory, while the MF reward prediction error signal was closely linked to the consumption score development. These findings support the idea that unbalanced MB and MF control might be an important individual vulnerability in predisposing to risky drinking behavior.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A shift from goal-directed toward habitual control has been associated with alcohol dependence. Whether such a shift predisposes to risky drinking is not yet clear. We investigated how goal-directed and habitual control at age 18 predict alcohol use trajectories over the course of 3 years. Goal-directed and habitual control, as informed by model-based (MB) and model-free (MF) learning, were assessed with a two-step sequential decision-making task during functional magnetic resonance imaging in 146 healthy 18-year-old men. Three-year alcohol use developmental trajectories were based on either a consumption score from the self-reported Alcohol Use Disorders Identification Test (assessed every 6 months) or an interview-based binge drinking score (grams of alcohol/occasion; assessed every year). We applied a latent growth curve model to examine how MB and MF control predicted the drinking trajectory. Drinking behavior was best characterized by a linear trajectory. MB behavioral control was negatively associated with the development of the binge drinking score; MF reward prediction error blood oxygen level-dependent signals in the ventromedial prefrontal cortex and the ventral striatum predicted a higher starting point and steeper increase of the Alcohol Use Disorders Identification Test consumption score over time, respectively. We found that MB behavioral control was associated with the binge drinking trajectory, while the MF reward prediction error signal was closely linked to the consumption score development. These findings support the idea that unbalanced MB and MF control might be an important individual vulnerability in predisposing to risky drinking behavior.
Rupprechter, S.; Stankevicius, A.; Huys, Q. J. M.; Series, P.; Steele, J. D.
Abnormal reward valuation and event-related connectivity in unmedicated major depressive disorder Journal Article
In: Psychological Medicine, vol. 51, iss. 5, pp. 795-803, 2021.
@article{RupprechterSteele21,
title = {Abnormal reward valuation and event-related connectivity in unmedicated major depressive disorder},
author = {S. Rupprechter and A. Stankevicius and Q. J. M. Huys and P. Series and J. D. Steele},
url = {http://acplab.org/wp-content/uploads/pub/RupprechterEa20-Abnormal-Reward-Valuation-and-Event-Related-Connectivity-in-Unmedicated-Major-Depressive-Disorder.pdf},
doi = {10.1017/s0033291719003799},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Psychological Medicine},
volume = {51},
issue = {5},
pages = {795-803},
publisher = {Cambridge University Press (CUP)},
abstract = {Background
Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.
Methods
Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.
Results
Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.
Conclusions
Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.
Methods
Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.
Results
Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.
Conclusions
Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.
Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.
Methods
Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.
Results
Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.
Conclusions
Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.
Berwian, Isabel M.; Wenzel, Julia G.; Kuehn, Leonie; Schnuerer, Inga; Kasper, Lars; Veer, Ilya M.; Seifritz, Erich; Stephan, Klaas E.; Walter, Henrik; Huys, Quentin J. M.
The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse Journal Article
In: Scientific reports, vol. 10, iss. 1, pp. 1-10, 2020.
@article{BerwianHuys20bc,
title = {The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse},
author = {Isabel M. Berwian and Julia G. Wenzel and Leonie Kuehn and Inga Schnuerer and Lars Kasper and Ilya M. Veer and Erich Seifritz and Klaas E. Stephan and Henrik Walter and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/BerwianHuys20b-The-Relationship-between-Resting-State-Functional-Connectivity-Antidepressant-Discontinuation-and-Depression-Relapse-2.pdf},
doi = {10.1038/s41598-020-79170-9},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
journal = {Scientific reports},
volume = {10},
issue = {1},
pages = {1-10},
abstract = {The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.
Chen, Hao; Nebe, Stephan; Mojtahedzadeh, Negin; Kuitunen-Paul, Sören; Garbusow, Maria; Schad, Daniel J.; Rapp, Michael A.; Huys, Quentin J. M.; Heinz, Andreas; Smolka, Michael N.
Susceptibility to interference between Pavlovian and instrumental control is associated with early hazardous alcohol use Journal Article
In: Addiction Biology, vol. 26, iss. 4, no. e12983, 2020.
@article{ChenSmolka20b,
title = {Susceptibility to interference between Pavlovian and instrumental control is associated with early hazardous alcohol use},
author = {Hao Chen and Stephan Nebe and Negin Mojtahedzadeh and Sören Kuitunen-Paul and Maria Garbusow and Daniel J. Schad and Michael A. Rapp and Quentin J. M. Huys and Andreas Heinz and Michael N. Smolka},
url = {http://acplab.org/wp-content/uploads/pub/ChenSmolka20-PITAlcRisk.pdf},
doi = {10.1111/adb.12983},
year = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
journal = {Addiction Biology},
volume = {26},
number = {e12983},
issue = {4},
publisher = {Wiley},
abstract = {Pavlovian-to-instrumental transfer (PIT) tasks examine the influence of Pavlovian stimuli on ongoing instrumental behaviour. Previous studies reported associations between a strong PIT effect, high-risk drinking and alcohol use disorder. This study investigated whether susceptibility to interference between Pavlovian and instrumental control is linked to risky alcohol use in a community sample of 18-year-old male adults. Participants (N = 191) were instructed to ‘collect good shells’ and ‘leave bad shells’ during the presentation of appetitive (monetary reward), aversive (monetary loss) or neutral Pavlovian stimuli. We compared instrumental error rates (ER) and functional magnetic resonance imaging (fMRI) brain responses between the congruent and incongruent conditions, as well as among high-risk and low-risk drinking groups. On average, individuals showed a substantial PIT effect, that is, increased ER when Pavlovian cues and instrumental stimuli were in conflict compared with congruent trials. Neural PIT correlates were found in the ventral striatum and the dorsomedial and lateral prefrontal cortices (lPFC). Importantly, high-risk drinking was associated with a stronger behavioural PIT effect, a decreased lPFC response and an increased neural response in the ventral striatum on the trend level. Moreover, high-risk drinkers showed weaker connectivity from the ventral striatum to the lPFC during incongruent trials. Our study links interference during PIT to drinking behaviour in healthy, young adults. High-risk drinkers showed higher susceptibility to Pavlovian cues, especially when they conflicted with instrumental behaviour, indicating lower interference control abilities. Increased activity in the ventral striatum (bottom-up), decreased lPFC response (top-down), and their altered interplay may contribute to poor interference control in the high-risk drinkers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pavlovian-to-instrumental transfer (PIT) tasks examine the influence of Pavlovian stimuli on ongoing instrumental behaviour. Previous studies reported associations between a strong PIT effect, high-risk drinking and alcohol use disorder. This study investigated whether susceptibility to interference between Pavlovian and instrumental control is linked to risky alcohol use in a community sample of 18-year-old male adults. Participants (N = 191) were instructed to ‘collect good shells’ and ‘leave bad shells’ during the presentation of appetitive (monetary reward), aversive (monetary loss) or neutral Pavlovian stimuli. We compared instrumental error rates (ER) and functional magnetic resonance imaging (fMRI) brain responses between the congruent and incongruent conditions, as well as among high-risk and low-risk drinking groups. On average, individuals showed a substantial PIT effect, that is, increased ER when Pavlovian cues and instrumental stimuli were in conflict compared with congruent trials. Neural PIT correlates were found in the ventral striatum and the dorsomedial and lateral prefrontal cortices (lPFC). Importantly, high-risk drinking was associated with a stronger behavioural PIT effect, a decreased lPFC response and an increased neural response in the ventral striatum on the trend level. Moreover, high-risk drinkers showed weaker connectivity from the ventral striatum to the lPFC during incongruent trials. Our study links interference during PIT to drinking behaviour in healthy, young adults. High-risk drinkers showed higher susceptibility to Pavlovian cues, especially when they conflicted with instrumental behaviour, indicating lower interference control abilities. Increased activity in the ventral striatum (bottom-up), decreased lPFC response (top-down), and their altered interplay may contribute to poor interference control in the high-risk drinkers.
Kühnel, Anne; Teckentrup, Vanessa; Neuser, Monja P.; Huys, Quentin J. M.; Burrasch, Caroline; Walter, Martin; Kroemer, Nils B.
Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task Journal Article
In: European Neuropsychopharmacology, vol. 35, pp. 17-29, 2020.
@article{KuehnelKroemer20b,
title = {Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task},
author = {Anne Kühnel and Vanessa Teckentrup and Monja P. Neuser and Quentin J. M. Huys and Caroline Burrasch and Martin Walter and Nils B. Kroemer},
url = {http://acplab.org/wp-content/uploads/pub/KuehnelKroemer20-tVNSGoNogo.pdf},
doi = {10.1016/j.euroneuro.2020.03.023},
year = {2020},
date = {2020-06-01},
urldate = {2020-06-01},
journal = {European Neuropsychopharmacology},
volume = {35},
pages = {17-29},
publisher = {Elsevier BV},
abstract = {When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well documented. However, whether and how vagal feedback signals from the gut influence instrumental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm in 39 healthy human participants (23 female) after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that taVNS acutely impaired decision-making},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well documented. However, whether and how vagal feedback signals from the gut influence instrumental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm in 39 healthy human participants (23 female) after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that taVNS acutely impaired decision-making
Berwian, Isabel M.; Wenzel, Julia G.; Collins, Anne G. E.; Seifritz, Erich; Stephan, Klaas E.; Walter, Henrik; Huys, Quentin J. M.
In: JAMA Psychiatry, vol. 77, iss. 5, pp. 513-522, 2020.
@article{BerwianHuys20d,
title = {Computational Mechanisms of Effort and Reward Decisions in Patients With Depression and Their Association With Relapse After Antidepressant Discontinuation},
author = {Isabel M. Berwian and Julia G. Wenzel and Anne G. E. Collins and Erich Seifritz and Klaas E. Stephan and Henrik Walter and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/BerwianHuys20-Effort.pdf},
doi = {10.1001/jamapsychiatry.2019.4971},
year = {2020},
date = {2020-02-01},
urldate = {2020-02-01},
journal = {JAMA Psychiatry},
volume = {77},
issue = {5},
pages = {513-522},
publisher = {American Medical Association (AMA)},
abstract = {Importance Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario.
Objectives To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation.
Design, Setting, and Participants This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018.
Exposure Discontinuation of antidepressants. Main Outcomes and Measures Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model. Results A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Importance Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario.
Objectives To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation.
Design, Setting, and Participants This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018.
Exposure Discontinuation of antidepressants. Main Outcomes and Measures Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model. Results A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1
Objectives To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation.
Design, Setting, and Participants This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018.
Exposure Discontinuation of antidepressants. Main Outcomes and Measures Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model. Results A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1
Schad, Daniel J; Rapp, Michael A; Garbusow, Maria; Nebe, Stephan; Sebold, Miriam; Obst, Elisabeth; Sommer, Christian; Deserno, Lorenz; Rabovsky, Milena; Friedel, Eva; Romanczuk-Seiferth, Nina; Wittchen, Hans-Ulrich; Zimmermann, Ulrich S; Walter, Henrik; Sterzer, Philipp; Smolka, Michael N; Schlagenhauf, Florian; Heinz, Andreas; Dayan, Peter; Huys, Quentin J M
Dissociating neural learning signals in human sign- and goal-trackers Journal Article
In: Nature Human Behaviour, vol. 4, iss. 2, pp. 201-214, 2020.
@article{SchadHuys20b,
title = {Dissociating neural learning signals in human sign- and goal-trackers},
author = {Daniel J Schad and Michael A Rapp and Maria Garbusow and Stephan Nebe and Miriam Sebold and Elisabeth Obst and Christian Sommer and Lorenz Deserno and Milena Rabovsky and Eva Friedel and Nina Romanczuk-Seiferth and Hans-Ulrich Wittchen and Ulrich S Zimmermann and Henrik Walter and Philipp Sterzer and Michael N Smolka and Florian Schlagenhauf and Andreas Heinz and Peter Dayan and Quentin J M Huys},
url = {http://acplab.org/wp-content/uploads/pub/SchadHuys20-dissociating-neural-learning.pdf},
doi = {10.1038/s41562-019-0765-5},
year = {2020},
date = {2020-02-01},
urldate = {2020-02-01},
journal = {Nature Human Behaviour},
volume = {4},
issue = {2},
pages = {201-214},
abstract = {Individuals differ in how they learn from experience. In Pavlovian conditioning models, where cues predict reinforcer delivery at a different goal location, some animals-called sign-trackers-come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimuli 'wanted'. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 129 male humans using eye-tracking, pupillometry and functional magnetic resonance imaging informed by computational models of sign- and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Individuals differ in how they learn from experience. In Pavlovian conditioning models, where cues predict reinforcer delivery at a different goal location, some animals-called sign-trackers-come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimuli 'wanted'. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 129 male humans using eye-tracking, pupillometry and functional magnetic resonance imaging informed by computational models of sign- and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers.
Rupprechter, S.; Stankevicius, A.; Huys, Q. J. M.; Series, P.; Steele, J. D.
Abnormal reward valuation and event-related connectivity in unmedicated major depressive disorder Journal Article
In: Psychological Medicine, vol. 51, iss. 5, pp. 795-803, 2020.
@article{RupprechterSteele20,
title = {Abnormal reward valuation and event-related connectivity in unmedicated major depressive disorder},
author = {S. Rupprechter and A. Stankevicius and Q. J. M. Huys and P. Series and J. D. Steele},
url = {http://acplab.org/wp-content/uploads/2022/02/RupprechterEa20-Abnormal-Reward-Valuation-and-Event-Related-Connectivity-in-Unmedicated-Major-Depressive-Disorder.pdf},
doi = {10.1017/s0033291719003799},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Psychological Medicine},
volume = {51},
issue = {5},
pages = {795-803},
publisher = {Cambridge University Press (CUP)},
abstract = {Background
Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.
Methods
Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.
Results
Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.
Conclusions
Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.
Methods
Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.
Results
Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.
Conclusions
Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.
Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.
Methods
Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.
Results
Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.
Conclusions
Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.
Grosskurth, Elmar D.; Bach, Dominik R.; Economides, Marcos; Huys, Quentin J. M.; Holper, Lisa
No substantial change in the balance between model-free and model-based control via training on the two-step task Journal Article
In: PLOS Computational Biology, vol. 15, no. 11, pp. e1007443, 2019.
@article{GrosskurthHolper19,
title = {No substantial change in the balance between model-free and model-based control via training on the two-step task},
author = {Elmar D. Grosskurth and Dominik R. Bach and Marcos Economides and Quentin J. M. Huys and Lisa Holper},
editor = {Alireza Soltani},
url = {http://acplab.org/wp-content/uploads/pub/Grosskurth_2019-No-Substantial-Change-in-the-Balance-between-Model-Free-and-Model-Based-Control-Via-Training-on-the-Two-Step-Task.pdf},
doi = {10.1371/journal.pcbi.1007443},
year = {2019},
date = {2019-11-01},
urldate = {2019-11-01},
journal = {PLOS Computational Biology},
volume = {15},
number = {11},
pages = {e1007443},
publisher = {Public Library of Science (PLoS)},
abstract = {Human decisions can be habitual or goal-directed, also known as model-free (MF) or model-based (MB) control. Previous work suggests that the balance between the two decision systems is impaired in psychiatric disorders such as compulsion and addiction, via overreliance on MF control. However, little is known whether the balance can be altered through task training. Here, 20 healthy participants performed a well-established two-step task that differentiates MB from MF control, across five training sessions. We used computational modelling and functional near-infrared spectroscopy to assess changes in decision-making and brain hemodynamic over time. Mixed-effects modelling revealed overall no substantial changes in MF and MB behavior across training. Although our behavioral and brain findings show task-induced changes in learning rates, these parameters have no direct relation to either MF or MB control or the balance between the two systems, and thus do not support the assumption of training effects on MF or MB strategies. Our findings indicate that training on the two-step paradigm in its current form does not support a shift in the balance between MF and MB control. We discuss these results with respect to implications for restoring the balance between MF and MB control in psychiatric conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human decisions can be habitual or goal-directed, also known as model-free (MF) or model-based (MB) control. Previous work suggests that the balance between the two decision systems is impaired in psychiatric disorders such as compulsion and addiction, via overreliance on MF control. However, little is known whether the balance can be altered through task training. Here, 20 healthy participants performed a well-established two-step task that differentiates MB from MF control, across five training sessions. We used computational modelling and functional near-infrared spectroscopy to assess changes in decision-making and brain hemodynamic over time. Mixed-effects modelling revealed overall no substantial changes in MF and MB behavior across training. Although our behavioral and brain findings show task-induced changes in learning rates, these parameters have no direct relation to either MF or MB control or the balance between the two systems, and thus do not support the assumption of training effects on MF or MB strategies. Our findings indicate that training on the two-step paradigm in its current form does not support a shift in the balance between MF and MB control. We discuss these results with respect to implications for restoring the balance between MF and MB control in psychiatric conditions.
Poldrack, Russell A; Feingold, Franklin; Frank, Michael J; Gleeson, Padraig; Hollander, Gilles; Huys, Quentin J. M.; Love, Bradley C.; Markiewicz, Christopher J.; Moran, Rosalyn; Ritter, Petra; Rogers, Timothy T.; Turner, Brandon M.; Yarkoni, Tal; Zhan, Ming; Cohen, Jonathan D.
The Importance of Standards for Sharing of Computational Models and Data Journal Article
In: Computational Brain and Behavior, vol. 2, no. 3-4, pp. 229-232, 2019.
@article{PoldrackCohen19,
title = {The Importance of Standards for Sharing of Computational Models and Data},
author = {Russell A Poldrack and Franklin Feingold and Michael J Frank and Padraig Gleeson and Gilles Hollander and Quentin J. M. Huys and Bradley C. Love and Christopher J. Markiewicz and Rosalyn Moran and Petra Ritter and Timothy T. Rogers and Brandon M. Turner and Tal Yarkoni and Ming Zhan and Jonathan D. Cohen},
url = {http://acplab.org/wp-content/uploads/pub/PoldrackEa19-TheImportanceOfStandardsForSha.pdf},
doi = {10.1007/s42113-019-00062-x},
year = {2019},
date = {2019-10-01},
urldate = {2019-10-01},
journal = {Computational Brain and Behavior},
volume = {2},
number = {3-4},
pages = {229-232},
publisher = {Springer Science and Business Media LLC},
abstract = {The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.
Garbusow, Maria; Nebe, Stephan; Sommer, Christian; Kuitunen-Paul, Sören; Sebold, Miriam; Schad, Daniel J; Friedel, Eva; Veer, Ilya M; Wittchen, Hans-Ulrich; Rapp, Michael A; Ripke, Stephan; Walter, Henrik; Huys, Quentin J M; Schlagenhauf, Florian; Smolka, Michael N; Heinz, Andreas
Pavlovian-To-Instrumental Transfer and Alcohol Consumption in Young Male Social Drinkers: Behavioral, Neural and Polygenic Correlates Journal Article
In: J Clin Med, vol. 8, 2019.
@article{GarbusowHeinz19,
title = {Pavlovian-To-Instrumental Transfer and Alcohol Consumption in Young Male Social Drinkers: Behavioral, Neural and Polygenic Correlates},
author = {Maria Garbusow and Stephan Nebe and Christian Sommer and Sören Kuitunen-Paul and Miriam Sebold and Daniel J Schad and Eva Friedel and Ilya M Veer and Hans-Ulrich Wittchen and Michael A Rapp and Stephan Ripke and Henrik Walter and Quentin J M Huys and Florian Schlagenhauf and Michael N Smolka and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/GarbusowHeinz19-Pavlovian-to-Instrumental.pdf},
doi = {10.3390/jcm8081188},
year = {2019},
date = {2019-08-01},
urldate = {2019-08-01},
journal = {J Clin Med},
volume = {8},
abstract = {In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men ( = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers ( = 0.09, = 0.03, = 2.7, < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala ( = 3.25, = 0.04, = 26, = -6, = -12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption ( = 0.17, = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men ( = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers ( = 0.09, = 0.03, = 2.7, < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala ( = 3.25, = 0.04, = 26, = -6, = -12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption ( = 0.17, = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.
Webb, Christian A; Trivedi, Madhukar H; Cohen, Zachary D; Dillon, Daniel G; Fournier, Jay C; Goer, Franziska; Fava, Maurizio; McGrath, Patrick J; Weissman, Myrna; Parsey, Ramin; Adams, Phil; Trombello, Joseph M; Cooper, Crystal; Deldin, Patricia; Oquendo, Maria A; McInnis, Melvin G; Huys, Quentin; Bruder, Gerard; Kurian, Benji T; Jha, Manish; DeRubeis, Robert J; Pizzagalli, Diego A
Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study Journal Article
In: Psychol Med, vol. 49, pp. 1118–1127, 2019.
@article{WebbPizzagalli19,
title = {Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study},
author = {Christian A Webb and Madhukar H Trivedi and Zachary D Cohen and Daniel G Dillon and Jay C Fournier and Franziska Goer and Maurizio Fava and Patrick J McGrath and Myrna Weissman and Ramin Parsey and Phil Adams and Joseph M Trombello and Crystal Cooper and Patricia Deldin and Maria A Oquendo and Melvin G McInnis and Quentin Huys and Gerard Bruder and Benji T Kurian and Manish Jha and Robert J DeRubeis and Diego A Pizzagalli},
url = {http://acplab.org/wp-content/uploads/pub/personalized-prediction-of-antidepressant-v-placebo-response-evidence-from-the-embarc-study.pdf},
doi = {10.1017/S0033291718001708},
year = {2019},
date = {2019-05-01},
urldate = {2019-05-01},
journal = {Psychol Med},
volume = {49},
pages = {1118–1127},
abstract = {Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits. Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics. Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58). A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits. Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics. Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58). A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.
Schad, Daniel J; Garbusow, Maria; Friedel, Eva; Sommer, Christian; Sebold, Miriam; Hägele, Claudia; Bernhardt, Nadine; Nebe, Stephan; Kuitunen-Paul, Sören; Liu, Shuyan; Eichmann, Uta; Beck, Anne; Wittchen, Hans-Ulrich; Walter, Henrik; Sterzer, Philipp; Zimmermann, Ulrich S; Smolka, Michael N; Schlagenhauf, Florian; Huys, Quentin J M; Heinz, Andreas; Rapp, Michael A
Neural correlates of instrumental responding in the context of alcohol-related cues index disorder severity and relapse risk Journal Article
In: European Archives of Psychiatry and Clinical Neuroscience, 2019.
@article{SchadRapp19,
title = {Neural correlates of instrumental responding in the context of alcohol-related cues index disorder severity and relapse risk},
author = {Daniel J Schad and Maria Garbusow and Eva Friedel and Christian Sommer and Miriam Sebold and Claudia Hägele and Nadine Bernhardt and Stephan Nebe and Sören Kuitunen-Paul and Shuyan Liu and Uta Eichmann and Anne Beck and Hans-Ulrich Wittchen and Henrik Walter and Philipp Sterzer and Ulrich S Zimmermann and Michael N Smolka and Florian Schlagenhauf and Quentin J M Huys and Andreas Heinz and Michael A Rapp},
url = {https://www.tnu.ethz.ch/fileadmin/user_upload/documents/Publications/2019/2019_Schad2019_Article_NeuralCorrelatesOfInstrumental.pdf},
doi = {10.1007/s00406-017-0860-4},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {European Archives of Psychiatry and Clinical Neuroscience},
abstract = {The influence of Pavlovian conditioned stimuli on ongoing behavior may contribute to explaining how alcohol cues stimulate drug seeking and intake. Using a Pavlovian-instrumental transfer task, we investigated the effects of alcohol-related cues on approach behavior (i.e., instrumental response behavior) and its neural correlates, and related both to the relapse after detoxification in alcohol-dependent patients. Thirty-one recently detoxified alcohol-dependent patients and 24 healthy controls underwent instrumental training, where approach or non-approach towards initially neutral stimuli was reinforced by monetary incentives. Approach behavior was tested during extinction with either alcohol-related or neutral stimuli (as Pavlovian cues) presented in the background during functional magnetic resonance imaging (fMRI). Patients were subsequently followed up for 6 months. We observed that alcohol-related background stimuli inhibited the approach behavior in detoxified alcohol-dependent patients (t = - 3.86, p < .001), but not in healthy controls (t = - 0.92},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The influence of Pavlovian conditioned stimuli on ongoing behavior may contribute to explaining how alcohol cues stimulate drug seeking and intake. Using a Pavlovian-instrumental transfer task, we investigated the effects of alcohol-related cues on approach behavior (i.e., instrumental response behavior) and its neural correlates, and related both to the relapse after detoxification in alcohol-dependent patients. Thirty-one recently detoxified alcohol-dependent patients and 24 healthy controls underwent instrumental training, where approach or non-approach towards initially neutral stimuli was reinforced by monetary incentives. Approach behavior was tested during extinction with either alcohol-related or neutral stimuli (as Pavlovian cues) presented in the background during functional magnetic resonance imaging (fMRI). Patients were subsequently followed up for 6 months. We observed that alcohol-related background stimuli inhibited the approach behavior in detoxified alcohol-dependent patients (t = - 3.86, p < .001), but not in healthy controls (t = - 0.92
Sommer, Christian; Birkenstock, Julian; Garbusow, Maria; Obst, Elisabeth; Schad, Daniel J; Bernhardt, Nadine; Huys, Quentin M; Wurst, Friedrich M; Weinmann, Wolfgang; Heinz, Andreas; Smolka, Michael N; Zimmermann, Ulrich S
Dysfunctional approach behavior triggered by alcohol-unrelated Pavlovian cues predicts long-term relapse in alcohol dependence Journal Article
In: Addiction biology, 2018.
@article{SommerZimmermann18,
title = {Dysfunctional approach behavior triggered by alcohol-unrelated Pavlovian cues predicts long-term relapse in alcohol dependence},
author = {Christian Sommer and Julian Birkenstock and Maria Garbusow and Elisabeth Obst and Daniel J Schad and Nadine Bernhardt and Quentin M Huys and Friedrich M Wurst and Wolfgang Weinmann and Andreas Heinz and Michael N Smolka and Ulrich S Zimmermann},
url = {http://acplab.org/wp-content/uploads/pub/SommerZimmermann18-dysfunctional-approach-behavior.pdf},
doi = {10.1111/adb.12703},
year = {2018},
date = {2018-12-01},
urldate = {2018-12-01},
journal = {Addiction biology},
abstract = {We demonstrated that alcohol-dependent patients who relapsed within 1 year after detoxification showed stronger PIT effects compared with abstainers and controls. Relapsers particularly failed to correctly perform in trials where an instrumental stimulus required inhibition while a Pavlovian background cue indicated a monetary gain. Under that condition, relapsers approached the instrumental stimulus, independent of the expected punishment. The failure of inhibiting an aversive stimulus in favor of approaching an appetitive context cue reflects dysfunctional altered learning mechanisms in relapsers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We demonstrated that alcohol-dependent patients who relapsed within 1 year after detoxification showed stronger PIT effects compared with abstainers and controls. Relapsers particularly failed to correctly perform in trials where an instrumental stimulus required inhibition while a Pavlovian background cue indicated a monetary gain. Under that condition, relapsers approached the instrumental stimulus, independent of the expected punishment. The failure of inhibiting an aversive stimulus in favor of approaching an appetitive context cue reflects dysfunctional altered learning mechanisms in relapsers.
Kirschner, Matthias; Sladky, Ronald; Haugg, Amelie; Stämpfli, Philipp; Jehli, Elisabeth; Hodel, Martina; Engeli, Etna; Hösli, Sarah; Baumgartner, Markus R; Sulzer, James; Huys, Quentin J M; Seifritz, Erich; Quednow, Boris B; Scharnowski, Frank; Herdener, Marcus
Self-regulation of the dopaminergic reward circuit in cocaine users with mental imagery and neurofeedback Journal Article
In: EBioMedicine, vol. 37, pp. 489–498, 2018.
@article{KirschnerHerdener18a,
title = {Self-regulation of the dopaminergic reward circuit in cocaine users with mental imagery and neurofeedback},
author = {Matthias Kirschner and Ronald Sladky and Amelie Haugg and Philipp Stämpfli and Elisabeth Jehli and Martina Hodel and Etna Engeli and Sarah Hösli and Markus R Baumgartner and James Sulzer and Quentin J M Huys and Erich Seifritz and Boris B Quednow and Frank Scharnowski and Marcus Herdener},
url = {http://acplab.org/wp-content/uploads/pub/KirschnerEa18a-Self-Regulation-of-the-Dopaminergic-Reward-Circuit-in-Cocaine-Users-with-Mental-Imagery-and-Neurofeedback-2.pdf},
doi = {10.1016/j.ebiom.2018.10.052},
year = {2018},
date = {2018-11-01},
urldate = {2018-11-01},
journal = {EBioMedicine},
volume = {37},
pages = {489–498},
abstract = {Enhanced drug-related reward sensitivity accompanied by impaired sensitivity to non-drug related rewards in the mesolimbic dopamine system are thought to underlie the broad motivational deficits and dysfunctional decision-making frequently observed in cocaine use disorder (CUD). Effective approaches to modify this imbalance and reinstate non-drug reward responsiveness are urgently needed. Here, we examined whether cocaine users (CU) can use mental imagery of non-drug rewards to self-regulate the ventral tegmental area and substantia nigra (VTA/SN). We expected that obsessive and compulsive thoughts about cocaine consumption would hamper the ability to self-regulate the VTA/SN activity and tested if real-time fMRI (rtfMRI) neurofeedback (NFB) can improve self-regulation of the VTA/SN. Twenty-two CU and 28 healthy controls (HC) were asked to voluntarily up-regulate VTA/SN activity with non-drug reward imagery alone, or combined with rtfMRI NFB. On a group level, HC and CU were able to activate the dopaminergic midbrain and other reward regions with reward imagery. In CU, the individual ability to self-regulate the VTA/SN was reduced in those with more severe obsessive-compulsive drug use. NFB enhanced the effect of reward imagery but did not result in transfer effects at the end of the session. CU can voluntary activate their reward system with non-drug reward imagery and improve this ability with rtfMRI NFB. Combining mental imagery and rtFMRI NFB has great potential for modifying the maladapted reward sensitivity and reinstating non-drug reward responsiveness. This motivates further work to examine the use of rtfMRI NFB in the treatment of CUD.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Enhanced drug-related reward sensitivity accompanied by impaired sensitivity to non-drug related rewards in the mesolimbic dopamine system are thought to underlie the broad motivational deficits and dysfunctional decision-making frequently observed in cocaine use disorder (CUD). Effective approaches to modify this imbalance and reinstate non-drug reward responsiveness are urgently needed. Here, we examined whether cocaine users (CU) can use mental imagery of non-drug rewards to self-regulate the ventral tegmental area and substantia nigra (VTA/SN). We expected that obsessive and compulsive thoughts about cocaine consumption would hamper the ability to self-regulate the VTA/SN activity and tested if real-time fMRI (rtfMRI) neurofeedback (NFB) can improve self-regulation of the VTA/SN. Twenty-two CU and 28 healthy controls (HC) were asked to voluntarily up-regulate VTA/SN activity with non-drug reward imagery alone, or combined with rtfMRI NFB. On a group level, HC and CU were able to activate the dopaminergic midbrain and other reward regions with reward imagery. In CU, the individual ability to self-regulate the VTA/SN was reduced in those with more severe obsessive-compulsive drug use. NFB enhanced the effect of reward imagery but did not result in transfer effects at the end of the session. CU can voluntary activate their reward system with non-drug reward imagery and improve this ability with rtfMRI NFB. Combining mental imagery and rtFMRI NFB has great potential for modifying the maladapted reward sensitivity and reinstating non-drug reward responsiveness. This motivates further work to examine the use of rtfMRI NFB in the treatment of CUD.
Schulz, Eric; Wu, Charley M; Huys, Quentin J M; Krause, Andreas; Speekenbrink, Maarten
Generalization and Search in Risky Environments Journal Article
In: Cognitive science, vol. 42, pp. 2592–2620, 2018.
@article{SchulzSpeekenbrink18,
title = {Generalization and Search in Risky Environments},
author = {Eric Schulz and Charley M Wu and Quentin J M Huys and Andreas Krause and Maarten Speekenbrink},
url = {http://acplab.org/wp-content/uploads/pub/SchulzEa18-GeneralizationSearchRisk.pdf},
doi = {10.1111/cogs.12695},
year = {2018},
date = {2018-11-01},
urldate = {2018-11-01},
journal = {Cognitive science},
volume = {42},
pages = {2592–2620},
abstract = {How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants' behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants' behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.
Rupprechter, Samuel; Stankevicius, Aistis; Huys, Quentin J M; Steele, J Douglas; Seriès, Peggy
Major Depression Impairs the Use of Reward Values for Decision-Making Journal Article
In: Scientific reports, vol. 8, pp. 13798, 2018.
@article{RupprechterSeriès18,
title = {Major Depression Impairs the Use of Reward Values for Decision-Making},
author = {Samuel Rupprechter and Aistis Stankevicius and Quentin J M Huys and J Douglas Steele and Peggy Seriès},
url = {http://acplab.org/wp-content/uploads/pub/RupprechterEa18-Major-Depression-Impairs-the-Use-of-Reward-Values-for-Decision-Making-1.pdf},
doi = {10.1038/s41598-018-31730-w},
year = {2018},
date = {2018-09-01},
urldate = {2018-09-01},
journal = {Scientific reports},
volume = {8},
pages = {13798},
abstract = {Depression is a debilitating condition with a high prevalence. Depressed patients have been shown to be diminished in their ability to integrate their reinforcement history to adjust future behaviour during instrumental reward learning tasks. Here, we tested whether such impairments could also be observed in a Pavlovian conditioning task. We recruited and analysed 32 subjects, 15 with depression and 17 healthy controls, to study behavioural group differences in learning and decision-making. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few passive observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants' behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Depressed subjects showed lower memory of observed rewards and had an impaired ability to use internal value estimations to guide decision-making in our task.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Depression is a debilitating condition with a high prevalence. Depressed patients have been shown to be diminished in their ability to integrate their reinforcement history to adjust future behaviour during instrumental reward learning tasks. Here, we tested whether such impairments could also be observed in a Pavlovian conditioning task. We recruited and analysed 32 subjects, 15 with depression and 17 healthy controls, to study behavioural group differences in learning and decision-making. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few passive observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants' behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Depressed subjects showed lower memory of observed rewards and had an impaired ability to use internal value estimations to guide decision-making in our task.
Kirschner, Matthias; Haugg, Amelie; Manoliu, Andrei; Simon, Joe J; Huys, Quentin J M; Seifritz, Erich; Tobler, Philippe N; Kaiser, Stefan
Deficits in context-dependent adaptive coding in early psychosis and healthy individuals with schizotypal personality traits Journal Article
In: Brain : a journal of neurology, vol. 141, pp. 2806–2819, 2018.
@article{KirschnerKaiser18,
title = {Deficits in context-dependent adaptive coding in early psychosis and healthy individuals with schizotypal personality traits},
author = {Matthias Kirschner and Amelie Haugg and Andrei Manoliu and Joe J Simon and Quentin J M Huys and Erich Seifritz and Philippe N Tobler and Stefan Kaiser},
url = {http://acplab.org/wp-content/uploads/pub/KirschnerEa18-Deficits-in-Context-Dependent-Adaptive-Coding-in-Early-Psychosis-and-Healthy-Individuals-with-Schizotypal-Personality-Traits-2.pdf},
doi = {10.1093/brain/awy203},
year = {2018},
date = {2018-09-01},
urldate = {2018-09-01},
journal = {Brain : a journal of neurology},
volume = {141},
pages = {2806–2819},
abstract = {Adaptive coding of information is a fundamental principle of brain functioning. It allows for efficient representation over a large range of inputs and thereby alleviates the limited coding range of neurons. In the present study, we investigated for the first time potential alterations in context-dependent reward adaptation and its association with symptom dimensions in the schizophrenia spectrum. We studied 27 patients with first-episode psychosis, 26 individuals with schizotypal personality traits and 25 healthy controls. We used functional MRI in combination with a variant of the monetary incentive delay task and assessed adaptive reward coding in two reward conditions with different reward ranges. Compared to healthy controls, patients with first-episode psychosis and healthy individuals with schizotypal personality traits showed a deficit in increasing the blood oxygen level-dependent response slope in the right caudate for the low reward range compared to the high reward range. In other words, the two groups showed inefficient neural adaptation to the current reward context. In addition, we found impaired adaptive coding of reward in the caudate nucleus and putamen to be associated with total symptom severity across the schizophrenia spectrum. Symptom severity was more strongly associated with neural deficits in adaptive coding than with the neural coding of absolute reward outcomes. Deficits in adaptive coding were prominent across the schizophrenia spectrum and even detectable in unmedicated (healthy) individuals with schizotypal personality traits. Furthermore, the association between total symptom severity and impaired adaptive coding in the right caudate and putamen suggests a dimensional mechanism underlying imprecise neural adaptation. Our findings support the idea that impaired adaptive coding may be a general information-processing deficit explaining disturbances within the schizophrenia spectrum over and above a simple model of blunted absolute reward signals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Adaptive coding of information is a fundamental principle of brain functioning. It allows for efficient representation over a large range of inputs and thereby alleviates the limited coding range of neurons. In the present study, we investigated for the first time potential alterations in context-dependent reward adaptation and its association with symptom dimensions in the schizophrenia spectrum. We studied 27 patients with first-episode psychosis, 26 individuals with schizotypal personality traits and 25 healthy controls. We used functional MRI in combination with a variant of the monetary incentive delay task and assessed adaptive reward coding in two reward conditions with different reward ranges. Compared to healthy controls, patients with first-episode psychosis and healthy individuals with schizotypal personality traits showed a deficit in increasing the blood oxygen level-dependent response slope in the right caudate for the low reward range compared to the high reward range. In other words, the two groups showed inefficient neural adaptation to the current reward context. In addition, we found impaired adaptive coding of reward in the caudate nucleus and putamen to be associated with total symptom severity across the schizophrenia spectrum. Symptom severity was more strongly associated with neural deficits in adaptive coding than with the neural coding of absolute reward outcomes. Deficits in adaptive coding were prominent across the schizophrenia spectrum and even detectable in unmedicated (healthy) individuals with schizotypal personality traits. Furthermore, the association between total symptom severity and impaired adaptive coding in the right caudate and putamen suggests a dimensional mechanism underlying imprecise neural adaptation. Our findings support the idea that impaired adaptive coding may be a general information-processing deficit explaining disturbances within the schizophrenia spectrum over and above a simple model of blunted absolute reward signals.
Ousdal, Olga Therese; Milde, Anne Marita; Craven, Alexander R; Ersland, Lars; Endestad, Tor; Melinder, Annika; Huys, Quentin J; Hugdahl, Kenneth
Prefrontal glutamate levels predict altered amygdala-prefrontal connectivity in traumatized youths Journal Article
In: Psychological medicine, pp. 1–9, 2018.
@article{OusdalHugdahl18b,
title = {Prefrontal glutamate levels predict altered amygdala-prefrontal connectivity in traumatized youths},
author = {Olga Therese Ousdal and Anne Marita Milde and Alexander R Craven and Lars Ersland and Tor Endestad and Annika Melinder and Quentin J Huys and Kenneth Hugdahl},
url = {http://acplab.org/wp-content/uploads/pub/OusdalEa18b-Prefrontal-Glutamate-Levels-Predict-Altered-Amygdala-Prefrontal-Connectivity-in-Traumatized-Youths-1.pdf},
doi = {10.1017/S0033291718002519},
year = {2018},
date = {2018-09-01},
urldate = {2018-09-01},
journal = {Psychological medicine},
pages = {1–9},
abstract = {Neurobiological models of stress and stress-related mental illness, including post-traumatic stress disorder, converge on the amygdala and the prefrontal cortex (PFC). While a surge of research has reported altered structural and functional connectivity between amygdala and the medial PFC following severe stress, few have addressed the underlying neurochemistry. We combined resting-state functional magnetic resonance imaging measures of amygdala connectivity with in vivo MR-spectroscopy (1H-MRS) measurements of glutamate in 26 survivors from the 2011 Norwegian terror attack and 34 control subjects. Traumatized youths showed altered amygdala-anterior midcingulate cortex (aMCC) and amygdala-ventromedial prefrontal cortex (vmPFC) connectivity. Moreover, the trauma survivors exhibited reduced levels of glutamate in the vmPFC which fits with the previous findings of reduced levels of Glx (glutamate + glutamine) in the aMCC (Ousdal et al., 2017) and together suggest long-term impact of a traumatic experience on glutamatergic pathways. Importantly, local glutamatergic metabolite levels predicted the individual amygdala-aMCC and amygdala-vmPFC functional connectivity, and also mediated the observed group difference in amygdala-aMCC connectivity. Our findings suggest that traumatic stress may influence amygdala-prefrontal neuronal connectivity through an effect on prefrontal glutamate and its compounds. Understanding the neurochemical underpinning of altered amygdala connectivity after trauma may ultimately lead to the discovery of new pharmacological agents which can prevent or treat stress-related mental illness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neurobiological models of stress and stress-related mental illness, including post-traumatic stress disorder, converge on the amygdala and the prefrontal cortex (PFC). While a surge of research has reported altered structural and functional connectivity between amygdala and the medial PFC following severe stress, few have addressed the underlying neurochemistry. We combined resting-state functional magnetic resonance imaging measures of amygdala connectivity with in vivo MR-spectroscopy (1H-MRS) measurements of glutamate in 26 survivors from the 2011 Norwegian terror attack and 34 control subjects. Traumatized youths showed altered amygdala-anterior midcingulate cortex (aMCC) and amygdala-ventromedial prefrontal cortex (vmPFC) connectivity. Moreover, the trauma survivors exhibited reduced levels of glutamate in the vmPFC which fits with the previous findings of reduced levels of Glx (glutamate + glutamine) in the aMCC (Ousdal et al., 2017) and together suggest long-term impact of a traumatic experience on glutamatergic pathways. Importantly, local glutamatergic metabolite levels predicted the individual amygdala-aMCC and amygdala-vmPFC functional connectivity, and also mediated the observed group difference in amygdala-aMCC connectivity. Our findings suggest that traumatic stress may influence amygdala-prefrontal neuronal connectivity through an effect on prefrontal glutamate and its compounds. Understanding the neurochemical underpinning of altered amygdala connectivity after trauma may ultimately lead to the discovery of new pharmacological agents which can prevent or treat stress-related mental illness.
Nord, C L; Lawson, R P; Huys, Q J M; Pilling, S; Roiser, J P
Depression is associated with enhanced aversive Pavlovian control over instrumental behaviour Journal Article
In: Scientific reports, vol. 8, pp. 12582, 2018.
@article{NordRoiser18,
title = {Depression is associated with enhanced aversive Pavlovian control over instrumental behaviour},
author = {C L Nord and R P Lawson and Q J M Huys and S Pilling and J P Roiser},
url = {http://acplab.org/wp-content/uploads/pub/NordEa18-Depression-Is-Associated-with-Enhanced-Aversive-Pavlovian-Control-Over-Instrumental-Behaviour-1.pdf},
doi = {10.1038/s41598-018-30828-5},
year = {2018},
date = {2018-08-01},
urldate = {2018-08-01},
journal = {Scientific reports},
volume = {8},
pages = {12582},
abstract = {The dynamic modulation of instrumental behaviour by conditioned Pavlovian cues is an important process in decision-making. Patients with major depressive disorder (MDD) are known to exhibit mood-congruent biases in information processing, which may occur due to Pavlovian influences, but this hypothesis has never been tested directly in an unmedicated sample. To address this we tested unmedicated MDD patients and healthy volunteers on a computerized Pavlovian-Instrumental Transfer (PIT) task designed to separately examine instrumental approach and withdrawal actions in the context of Pavlovian appetitive and aversive cues. This design allowed us to directly measure the degree to which Pavlovian cues influence instrumental responding. Depressed patients were profoundly influenced by aversive Pavlovian stimuli, to a significantly greater degree than healthy volunteers. This was the case for instrumental behaviour both in the approach condition (in which aversive Pavlovian cues inhibited 'go' responses), and in the withdrawal condition (in which aversive Pavlovian cues facilitated 'go' responses). Exaggerated aversive PIT provides a potential cognitive mechanism for biased emotion processing in major depression. This finding also has wider significance for the understanding of disrupted motivational processing in neuropsychiatric disorders.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The dynamic modulation of instrumental behaviour by conditioned Pavlovian cues is an important process in decision-making. Patients with major depressive disorder (MDD) are known to exhibit mood-congruent biases in information processing, which may occur due to Pavlovian influences, but this hypothesis has never been tested directly in an unmedicated sample. To address this we tested unmedicated MDD patients and healthy volunteers on a computerized Pavlovian-Instrumental Transfer (PIT) task designed to separately examine instrumental approach and withdrawal actions in the context of Pavlovian appetitive and aversive cues. This design allowed us to directly measure the degree to which Pavlovian cues influence instrumental responding. Depressed patients were profoundly influenced by aversive Pavlovian stimuli, to a significantly greater degree than healthy volunteers. This was the case for instrumental behaviour both in the approach condition (in which aversive Pavlovian cues inhibited 'go' responses), and in the withdrawal condition (in which aversive Pavlovian cues facilitated 'go' responses). Exaggerated aversive PIT provides a potential cognitive mechanism for biased emotion processing in major depression. This finding also has wider significance for the understanding of disrupted motivational processing in neuropsychiatric disorders.
Obst, Elisabeth; Schad, Daniel J; Huys, Quentin Jm; Sebold, Miriam; Nebe, Stephan; Sommer, Christian; Smolka, Michael N; Zimmermann, Ulrich S
Drunk decisions: Alcohol shifts choice from habitual towards goal-directed control in adolescent intermediate-risk drinkers Journal Article
In: Journal of psychopharmacology (Oxford, England), vol. 32, pp. 855–866, 2018.
@article{ObstZimmermann18,
title = {Drunk decisions: Alcohol shifts choice from habitual towards goal-directed control in adolescent intermediate-risk drinkers},
author = {Elisabeth Obst and Daniel J Schad and Quentin Jm Huys and Miriam Sebold and Stephan Nebe and Christian Sommer and Michael N Smolka and Ulrich S Zimmermann},
url = {http://acplab.org/wp-content/uploads/pub/ObstEa18-Drunk-Decisions_-Alcohol-Shifts-Choice-from-Habitual-Towards-Goal-Directed-Control-in-Adolescent-Intermediate-Risk-Drinkers-1.pdf},
doi = {10.1177/0269881118772454},
year = {2018},
date = {2018-08-01},
urldate = {2018-08-01},
journal = {Journal of psychopharmacology (Oxford, England)},
volume = {32},
pages = {855–866},
abstract = {Studies in humans and animals suggest a shift from goal-directed to habitual decision-making in addiction. We therefore tested whether acute alcohol administration reduces goal-directed and promotes habitual decision-making, and whether these effects are moderated by self-reported drinking problems. Fifty-three socially drinking males completed the two-step task in a randomised crossover design while receiving an intravenous infusion of ethanol (blood alcohol level=80 mg%), or placebo. To minimise potential bias by long-standing heavy drinking and subsequent neuropsychological impairment, we tested 18- to 19-year-old adolescents. Alcohol administration consistently reduced habitual, model-free decisions, while its effects on goal-directed, model-based behaviour varied as a function of drinking problems measured with the Alcohol Use Disorders Identification Test. While adolescents with low risk for drinking problems (scoring <8) exhibited an alcohol-induced numerical reduction in goal-directed choices, intermediate-risk drinkers showed a shift away from habitual towards goal-directed decision-making, such that alcohol possibly even improved their performance. We assume that alcohol disrupted basic cognitive functions underlying habitual and goal-directed decisions in low-risk drinkers, thereby enhancing hasty choices. Further, we speculate that intermediate-risk drinkers benefited from alcohol as a negative reinforcer that reduced unpleasant emotional states, possibly displaying a novel risk factor for drinking in adolescence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Studies in humans and animals suggest a shift from goal-directed to habitual decision-making in addiction. We therefore tested whether acute alcohol administration reduces goal-directed and promotes habitual decision-making, and whether these effects are moderated by self-reported drinking problems. Fifty-three socially drinking males completed the two-step task in a randomised crossover design while receiving an intravenous infusion of ethanol (blood alcohol level=80 mg%), or placebo. To minimise potential bias by long-standing heavy drinking and subsequent neuropsychological impairment, we tested 18- to 19-year-old adolescents. Alcohol administration consistently reduced habitual, model-free decisions, while its effects on goal-directed, model-based behaviour varied as a function of drinking problems measured with the Alcohol Use Disorders Identification Test. While adolescents with low risk for drinking problems (scoring <8) exhibited an alcohol-induced numerical reduction in goal-directed choices, intermediate-risk drinkers showed a shift away from habitual towards goal-directed decision-making, such that alcohol possibly even improved their performance. We assume that alcohol disrupted basic cognitive functions underlying habitual and goal-directed decisions in low-risk drinkers, thereby enhancing hasty choices. Further, we speculate that intermediate-risk drinkers benefited from alcohol as a negative reinforcer that reduced unpleasant emotional states, possibly displaying a novel risk factor for drinking in adolescence.
Pooseh, Shakoor; Bernhardt, Nadine; Guevara, Alvaro; Huys, Quentin J M; Smolka, Michael N
Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior Journal Article
In: Behav Res Meth, vol. 50, pp. 236–249, 2018.
@article{PoosehSmolka18,
title = {Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior},
author = {Shakoor Pooseh and Nadine Bernhardt and Alvaro Guevara and Quentin J M Huys and Michael N Smolka},
url = {http://acplab.org/wp-content/uploads/pub/PoosehEa18-Value-Based-Decision-Making-Battery_-a-Bayesian-Adaptive-Approach-to-Assess-Impulsive-and-Risky-Behavior-1.pdf},
doi = {10.3758/s13428-017-0866-x},
year = {2018},
date = {2018-02-01},
urldate = {2018-02-01},
journal = {Behav Res Meth},
volume = {50},
pages = {236–249},
abstract = {Using simple mathematical models of choice behavior, we present a Bayesian adaptive algorithm to assess measures of impulsive and risky decision making. Practically, these measures are characterized by discounting rates and are used to classify individuals or population groups, to distinguish unhealthy behavior, and to predict developmental courses. However, a constant demand for improved tools to assess these constructs remains unanswered. The algorithm is based on trial-by-trial observations. At each step, a choice is made between immediate (certain) and delayed (risky) options. Then the current parameter estimates are updated by the likelihood of observing the choice, and the next offers are provided from the indifference point, so that they will acquire the most informative data based on the current parameter estimates. The procedure continues for a certain number of trials in order to reach a stable estimation. The algorithm is discussed in detail for the delay discounting case, and results from decision making under risk for gains, losses, and mixed prospects are also provided. Simulated experiments using prescribed parameter values were performed to justify the algorithm in terms of the reproducibility of its parameters for individual assessments, and to test the reliability of the estimation procedure in a group-level analysis. The algorithm was implemented as an experimental battery to measure temporal and probability discounting rates together with loss aversion, and was tested on a healthy participant sample.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Using simple mathematical models of choice behavior, we present a Bayesian adaptive algorithm to assess measures of impulsive and risky decision making. Practically, these measures are characterized by discounting rates and are used to classify individuals or population groups, to distinguish unhealthy behavior, and to predict developmental courses. However, a constant demand for improved tools to assess these constructs remains unanswered. The algorithm is based on trial-by-trial observations. At each step, a choice is made between immediate (certain) and delayed (risky) options. Then the current parameter estimates are updated by the likelihood of observing the choice, and the next offers are provided from the indifference point, so that they will acquire the most informative data based on the current parameter estimates. The procedure continues for a certain number of trials in order to reach a stable estimation. The algorithm is discussed in detail for the delay discounting case, and results from decision making under risk for gains, losses, and mixed prospects are also provided. Simulated experiments using prescribed parameter values were performed to justify the algorithm in terms of the reproducibility of its parameters for individual assessments, and to test the reliability of the estimation procedure in a group-level analysis. The algorithm was implemented as an experimental battery to measure temporal and probability discounting rates together with loss aversion, and was tested on a healthy participant sample.
Nebe, Stephan; Kroemer, Nils B; Schad, Daniel J; Bernhardt, Nadine; Sebold, Miriam; Müller, Dirk K; Scholl, Lucie; Kuitunen-Paul, Sören; Heinz, Andreas; Rapp, Michael A; Huys, Quentin J M; Smolka, Michael N
No association of goal-directed and habitual control with alcohol consumption in young adults Journal Article
In: Addiction Biology, vol. 23, pp. 379–393, 2018.
@article{NebeSmolka18,
title = {No association of goal-directed and habitual control with alcohol consumption in young adults},
author = {Stephan Nebe and Nils B Kroemer and Daniel J Schad and Nadine Bernhardt and Miriam Sebold and Dirk K Müller and Lucie Scholl and Sören Kuitunen-Paul and Andreas Heinz and Michael A Rapp and Quentin J M Huys and Michael N Smolka},
url = {http://acplab.org/wp-content/uploads/pub/NebeEa18-No-association-of-goal‐directed-and-habitual-control-with-alcohol-consumption-in-young.pdf},
doi = {10.1111/adb.12490},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Addiction Biology},
volume = {23},
pages = {379–393},
abstract = {Alcohol dependence is a mental disorder that has been associated with an imbalance in behavioral control favoring model-free habitual over model-based goal-directed strategies. It is as yet unknown, however, whether such an imbalance reflects a predisposing vulnerability or results as a consequence of repeated and/or excessive alcohol exposure. We, therefore, examined the association of alcohol consumption with model-based goal-directed and model-free habitual control in 188 18-year-old social drinkers in a two-step sequential decision-making task while undergoing functional magnetic resonance imaging before prolonged alcohol misuse could have led to severe neurobiological adaptations. Behaviorally, participants showed a mixture of model-free and model-based decision-making as observed previously. Measures of impulsivity were positively related to alcohol consumption. In contrast, neither model-free nor model-based decision weights nor the trade-off between them were associated with alcohol consumption. There were also no significant associations between alcohol consumption and neural correlates of model-free or model-based decision quantities in either ventral striatum or ventromedial prefrontal cortex. Exploratory whole-brain functional magnetic resonance imaging analyses with a lenient threshold revealed early onset of drinking to be associated with an enhanced representation of model-free reward prediction errors in the posterior putamen. These results suggest that an imbalance between model-based goal-directed and model-free habitual control might rather not be a trait marker of alcohol intake per se.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Alcohol dependence is a mental disorder that has been associated with an imbalance in behavioral control favoring model-free habitual over model-based goal-directed strategies. It is as yet unknown, however, whether such an imbalance reflects a predisposing vulnerability or results as a consequence of repeated and/or excessive alcohol exposure. We, therefore, examined the association of alcohol consumption with model-based goal-directed and model-free habitual control in 188 18-year-old social drinkers in a two-step sequential decision-making task while undergoing functional magnetic resonance imaging before prolonged alcohol misuse could have led to severe neurobiological adaptations. Behaviorally, participants showed a mixture of model-free and model-based decision-making as observed previously. Measures of impulsivity were positively related to alcohol consumption. In contrast, neither model-free nor model-based decision weights nor the trade-off between them were associated with alcohol consumption. There were also no significant associations between alcohol consumption and neural correlates of model-free or model-based decision quantities in either ventral striatum or ventromedial prefrontal cortex. Exploratory whole-brain functional magnetic resonance imaging analyses with a lenient threshold revealed early onset of drinking to be associated with an enhanced representation of model-free reward prediction errors in the posterior putamen. These results suggest that an imbalance between model-based goal-directed and model-free habitual control might rather not be a trait marker of alcohol intake per se.
Ousdal, O T; Huys, Q J; Milde, A M; Craven, A R; Ersland, L; Endestad, T; Melinder, A; Hugdahl, K; Dolan, R J
The impact of traumatic stress on Pavlovian biases Journal Article
In: Psychological medicine, vol. 48, pp. 327–336, 2018.
@article{OusdalDolan18,
title = {The impact of traumatic stress on Pavlovian biases},
author = {O T Ousdal and Q J Huys and A M Milde and A R Craven and L Ersland and T Endestad and A Melinder and K Hugdahl and R J Dolan},
url = {http://acplab.org/wp-content/uploads/pub/OusdalEa18-The-Impact-of-Traumatic-Stress-on-Pavlovian-Biases-1.pdf},
doi = {10.1017/S003329171700174X},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Psychological medicine},
volume = {48},
pages = {327–336},
abstract = {Disturbances in Pavlovian valuation systems are reported to follow traumatic stress exposure. However, motivated decisions are also guided by instrumental mechanisms, but to date the effect of traumatic stress on these instrumental systems remain poorly investigated. Here, we examine whether a single episode of severe traumatic stress influences flexible instrumental decisions through an impact on a Pavlovian system. Twenty-six survivors of the 2011 Norwegian terror attack and 30 matched control subjects performed an instrumental learning task in which Pavlovian and instrumental associations promoted congruent or conflicting responses. We used reinforcement learning models to infer how traumatic stress affected learning and decision-making. Based on the importance of dorsal anterior cingulate cortex (dACC) for cognitive control, we also investigated if individual concentrations of Glx (=glutamate + glutamine) in dACC predicted the Pavlovian bias of choice. Survivors of traumatic stress expressed a greater Pavlovian interference with instrumental action selection and had significantly lower levels of Glx in the dACC. Across subjects, the degree of Pavlovian interference was negatively associated with dACC Glx concentrations. Experiencing traumatic stress appears to render instrumental decisions less flexible by increasing the susceptibility to Pavlovian influences. An observed association between prefrontal glutamatergic levels and this Pavlovian bias provides novel insight into the neurochemical basis of decision-making, and suggests a mechanism by which traumatic stress can impair flexible instrumental behaviours.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Disturbances in Pavlovian valuation systems are reported to follow traumatic stress exposure. However, motivated decisions are also guided by instrumental mechanisms, but to date the effect of traumatic stress on these instrumental systems remain poorly investigated. Here, we examine whether a single episode of severe traumatic stress influences flexible instrumental decisions through an impact on a Pavlovian system. Twenty-six survivors of the 2011 Norwegian terror attack and 30 matched control subjects performed an instrumental learning task in which Pavlovian and instrumental associations promoted congruent or conflicting responses. We used reinforcement learning models to infer how traumatic stress affected learning and decision-making. Based on the importance of dorsal anterior cingulate cortex (dACC) for cognitive control, we also investigated if individual concentrations of Glx (=glutamate + glutamine) in dACC predicted the Pavlovian bias of choice. Survivors of traumatic stress expressed a greater Pavlovian interference with instrumental action selection and had significantly lower levels of Glx in the dACC. Across subjects, the degree of Pavlovian interference was negatively associated with dACC Glx concentrations. Experiencing traumatic stress appears to render instrumental decisions less flexible by increasing the susceptibility to Pavlovian influences. An observed association between prefrontal glutamatergic levels and this Pavlovian bias provides novel insight into the neurochemical basis of decision-making, and suggests a mechanism by which traumatic stress can impair flexible instrumental behaviours.
Lieder, F; Griffiths, TL; Huys, QJM; Goodman, ND
Empirical evidence for resource-rational anchoring and adjustment Journal Article
In: Psychonomic Bulletin & Review, 2018.
@article{LiederGoodman18,
title = {Empirical evidence for resource-rational anchoring and adjustment},
author = {F Lieder and TL Griffiths and QJM Huys and ND Goodman},
url = {http://acplab.org/wp-content/uploads/pub/LiederEa17b-Empirical-Evidence-for-Resource-Rational-Anchoring-and-Adjustment.pdf},
doi = {10.3758/s13423-017-1288-6},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Psychonomic Bulletin & Review},
abstract = {People’s estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people’s rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people’s knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
People’s estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people’s rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people’s knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.
Sebold, Miriam; Nebe, Stephan; Garbusow, Maria; Guggenmos, Matthias; Schad, Daniel J; Beck, Anne; Kuitunen-Paul, Soeren; Sommer, Christian; Frank, Robin; Neu, Peter; Zimmermann, Ulrich S; Rapp, Michael A; Smolka, Michael N; Huys, Quentin J M; Schlagenhauf, Florian; Heinz, Andreas
When Habits Are Dangerous: Alcohol Expectancies and Habitual Decision Making Predict Relapse in Alcohol Dependence Journal Article
In: Biol. Psychiatry, vol. 82, pp. 847–856, 2017.
@article{SeboldHeinz17,
title = {When Habits Are Dangerous: Alcohol Expectancies and Habitual Decision Making Predict Relapse in Alcohol Dependence},
author = {Miriam Sebold and Stephan Nebe and Maria Garbusow and Matthias Guggenmos and Daniel J Schad and Anne Beck and Soeren Kuitunen-Paul and Christian Sommer and Robin Frank and Peter Neu and Ulrich S Zimmermann and Michael A Rapp and Michael N Smolka and Quentin J M Huys and Florian Schlagenhauf and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/SeboldEa17-When-Habits-Are-Dangerous_-Alcohol-Expectancies-and-Habitual-Decision-Making-Predict-Relapse-in-Alcohol-Dependence-1.pdf},
doi = {10.1016/j.biopsych.2017.04.019},
year = {2017},
date = {2017-12-01},
urldate = {2017-12-01},
journal = {Biol. Psychiatry},
volume = {82},
pages = {847–856},
abstract = {Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. Ninety detoxified, medication-free, alcohol-dependent patients and 96 age- and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. Ninety detoxified, medication-free, alcohol-dependent patients and 96 age- and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
Lally, Níall; Huys, Quentin J M; Eshel, Neir; Faulkner, Paul; Dayan, Peter; Roiser, Jonathan P
The Neural Basis of Aversive Pavlovian Guidance during Planning Journal Article
In: The Journal of Neuroscience, vol. 37, pp. 10215–10229, 2017.
@article{LallyRoiser17,
title = {The Neural Basis of Aversive Pavlovian Guidance during Planning},
author = {Níall Lally and Quentin J M Huys and Neir Eshel and Paul Faulkner and Peter Dayan and Jonathan P Roiser},
url = {http://acplab.org/wp-content/uploads/pub/LallyEa17-The-Neural-Basis-of-Aversive-Pavlovian-Guidance-during-Planning-1.pdf},
doi = {10.1523/JNEUROSCI.0085-17.2017},
year = {2017},
date = {2017-10-01},
urldate = {2017-10-01},
journal = {The Journal of Neuroscience},
volume = {37},
pages = {10215–10229},
abstract = {Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes.SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences is often impractical; thus, cognitive shortcuts are often essential. One pervasive and powerful heuristic is aversive pruning, in which potential decision-making avenues are curtailed at immediate negative outcomes. We used neuroimaging to examine how humans implement such pruning. We found it to be associated with activity in the subgenual cingulate cortex, with neural signatures that were distinguishable from those covarying with planning and valuation. Individual variations in aversive pruning levels related to subclinical anxiety levels and insular cortex activation. These findings reveal the neural mechanisms by which basic negative Pavlovian influences guide decision-making during planning, with implications for disrupted decision-making in psychiatric disorders.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes.SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences is often impractical; thus, cognitive shortcuts are often essential. One pervasive and powerful heuristic is aversive pruning, in which potential decision-making avenues are curtailed at immediate negative outcomes. We used neuroimaging to examine how humans implement such pruning. We found it to be associated with activity in the subgenual cingulate cortex, with neural signatures that were distinguishable from those covarying with planning and valuation. Individual variations in aversive pruning levels related to subclinical anxiety levels and insular cortex activation. These findings reveal the neural mechanisms by which basic negative Pavlovian influences guide decision-making during planning, with implications for disrupted decision-making in psychiatric disorders.
Sommer, C; Garbusow, M; Jünger, E; Pooseh, S; Bernhardt, N; Birkenstock, J; Schad, D J; Jabs, B; Glöckler, T; Huys, Q M; Heinz, A; Smolka, M N; Zimmermann, U S
Strong seduction: impulsivity and the impact of contextual cues on instrumental behavior in alcohol dependence Journal Article
In: Translational psychiatry, vol. 7, pp. e1183, 2017.
@article{SommerZimmermann17,
title = {Strong seduction: impulsivity and the impact of contextual cues on instrumental behavior in alcohol dependence},
author = {C Sommer and M Garbusow and E Jünger and S Pooseh and N Bernhardt and J Birkenstock and D J Schad and B Jabs and T Glöckler and Q M Huys and A Heinz and M N Smolka and U S Zimmermann},
url = {http://acplab.org/wp-content/uploads/pub/SommerEa17-Strong-Seduction_-Impulsivity-and-the-Impact-of-Contextual-Cues-on-Instrumental-Behavior-in-Alcohol-Dependence-1.pdf},
doi = {10.1038/tp.2017.158},
year = {2017},
date = {2017-08-01},
urldate = {2017-08-01},
journal = {Translational psychiatry},
volume = {7},
pages = {e1183},
abstract = {Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early-abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcohol-related stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their long-term goals. Such behaviors may include, but are not limited to, approaching alcohol.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early-abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcohol-related stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their long-term goals. Such behaviors may include, but are not limited to, approaching alcohol.
Sebold, Miriam; Schad, Daniel J; Nebe, Stephan; Garbusow, Maria; Jünger, Elisabeth; Kroemer, Nils B; Kathmann, Norbert; Zimmermann, Ulrich S; Smolka, Michael N; Rapp, Michael A; Heinz, Andreas; Huys, Quentin J M
In: Journal of Cognitive Neuroscience, vol. 28, pp. 985–995, 2016.
@article{SeboldHuys16,
title = {Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning},
author = {Miriam Sebold and Daniel J Schad and Stephan Nebe and Maria Garbusow and Elisabeth Jünger and Nils B Kroemer and Norbert Kathmann and Ulrich S Zimmermann and Michael N Smolka and Michael A Rapp and Andreas Heinz and Quentin J M Huys},
url = {http://acplab.org/wp-content/uploads/pub/SeboldEa16-Dont-Think-Just-Feel-the-Music_-Individuals-with-Strong-Pavlovian-to-Instrumental-Transfer-Effects-Rely-Less-on-Model-Based-Reinforcement-Learning-1.pdf},
doi = {10.1162/jocn_a_00945},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Journal of Cognitive Neuroscience},
volume = {28},
pages = {985–995},
abstract = {Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian-instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian-instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction.
Garbusow, Maria; Schad, Daniel J.; Sebold, Miriam; Friedel, Eva; Bernhardt, Nadine; Koch, Stefan P.; Steinacher, Bruno; Kathmann, Norbert; Geurts, Dirk E M.; Sommer, Christian; Müller, Dirk K.; Nebe, Stephan; Paul, Sören; Wittchen, Hans-Ulrich; Zimmermann, Ulrich S.; Walter, Henrik; Smolka, Michael N.; Sterzer, Philipp; Rapp, Michael A.; Huys, Quentin J M.; Schlagenhauf, Florian; Heinz, Andreas
Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence Journal Article
In: Addict Biol, vol. 21, no. 3, pp. 719–731, 2016.
@article{GarbusowHeinz16,
title = {Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence},
author = {Maria Garbusow and Daniel J. Schad and Miriam Sebold and Eva Friedel and Nadine Bernhardt and Stefan P. Koch and Bruno Steinacher and Norbert Kathmann and Dirk E M. Geurts and Christian Sommer and Dirk K. Müller and Stephan Nebe and Sören Paul and Hans-Ulrich Wittchen and Ulrich S. Zimmermann and Henrik Walter and Michael N. Smolka and Philipp Sterzer and Michael A. Rapp and Quentin J M. Huys and Florian Schlagenhauf and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/GarbusowEa16-Pavlovian‐to‐instrumental-transfer-effects-in-the-nucleus-accumbens-relate-to-relapse.pdf},
doi = {10.1111/adb.12243},
year = {2016},
date = {2016-05-01},
urldate = {2016-05-01},
journal = {Addict Biol},
volume = {21},
number = {3},
pages = {719–731},
school = {Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Campus Mitte, Germany.},
abstract = {In detoxified alcohol-dependent patients, alcohol-related stimuli can promote relapse. However, to date, the mechanisms by which contextual stimuli promote relapse have not been elucidated in detail. One hypothesis is that such contextual stimuli directly stimulate the motivation to drink via associated brain regions like the ventral striatum and thus promote alcohol seeking, intake and relapse. Pavlovian-to-Instrumental-Transfer (PIT) may be one of those behavioral phenomena contributing to relapse, capturing how Pavlovian conditioned (contextual) cues determine instrumental behavior (e.g. alcohol seeking and intake). We used a PIT paradigm during functional magnetic resonance imaging to examine the effects of classically conditioned Pavlovian stimuli on instrumental choices in n = 31 detoxified patients diagnosed with alcohol dependence and n = 24 healthy controls matched for age and gender. Patients were followed up over a period of 3 months. We observed that (1) there was a significant behavioral PIT effect for all participants, which was significantly more pronounced in alcohol-dependent patients; (2) PIT was significantly associated with blood oxygen level-dependent (BOLD) signals in the nucleus accumbens (NAcc) in subsequent relapsers only; and (3) PIT-related NAcc activation was associated with, and predictive of, critical outcomes (amount of alcohol intake and relapse during a 3 months follow-up period) in alcohol-dependent patients. These observations show for the first time that PIT-related BOLD signals, as a measure of the influence of Pavlovian cues on instrumental behavior, predict alcohol intake and relapse in alcohol dependence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In detoxified alcohol-dependent patients, alcohol-related stimuli can promote relapse. However, to date, the mechanisms by which contextual stimuli promote relapse have not been elucidated in detail. One hypothesis is that such contextual stimuli directly stimulate the motivation to drink via associated brain regions like the ventral striatum and thus promote alcohol seeking, intake and relapse. Pavlovian-to-Instrumental-Transfer (PIT) may be one of those behavioral phenomena contributing to relapse, capturing how Pavlovian conditioned (contextual) cues determine instrumental behavior (e.g. alcohol seeking and intake). We used a PIT paradigm during functional magnetic resonance imaging to examine the effects of classically conditioned Pavlovian stimuli on instrumental choices in n = 31 detoxified patients diagnosed with alcohol dependence and n = 24 healthy controls matched for age and gender. Patients were followed up over a period of 3 months. We observed that (1) there was a significant behavioral PIT effect for all participants, which was significantly more pronounced in alcohol-dependent patients; (2) PIT was significantly associated with blood oxygen level-dependent (BOLD) signals in the nucleus accumbens (NAcc) in subsequent relapsers only; and (3) PIT-related NAcc activation was associated with, and predictive of, critical outcomes (amount of alcohol intake and relapse during a 3 months follow-up period) in alcohol-dependent patients. These observations show for the first time that PIT-related BOLD signals, as a measure of the influence of Pavlovian cues on instrumental behavior, predict alcohol intake and relapse in alcohol dependence.
Huys, Q J M.; Gölzer, M.; Friedel, E.; Heinz, A.; Cools, R.; Dayan, P.; Dolan, R. J.
The specificity of Pavlovian regulation is associated with recovery from depression Journal Article
In: Psychol Med, vol. 46, no. 5, pp. 1027–1035, 2016.
@article{HuysDolan16b,
title = {The specificity of Pavlovian regulation is associated with recovery from depression},
author = {Q J M. Huys and M. Gölzer and E. Friedel and A. Heinz and R. Cools and P. Dayan and R. J. Dolan},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa16b-the-specificity-of-pavlovian-regulation-is-associated-with-recovery-from-depression.pdf},
doi = {10.1017/S0033291715002597},
year = {2016},
date = {2016-04-01},
urldate = {2016-04-01},
journal = {Psychol Med},
volume = {46},
number = {5},
pages = {1027–1035},
school = {Wellcome Trust Centre for Neuroimaging,University College London,London,UK.},
abstract = {Changes in reflexive emotional responses are hallmarks of depression, but how emotional reflexes make an impact on adaptive decision-making in depression has not been examined formally. Using a Pavlovian-instrumental transfer (PIT) task, we compared the influence of affectively valenced stimuli on decision-making in depression and generalized anxiety disorder compared with healthy controls; and related this to the longitudinal course of the illness.A total of 40 subjects with a current DSM-IV-TR diagnosis of major depressive disorder, dysthymia, generalized anxiety disorder, or a combination thereof, and 40 matched healthy controls performed a PIT task that assesses how instrumental approach and withdrawal behaviours are influenced by appetitive and aversive Pavlovian conditioned stimuli (CSs). Patients were followed up after 4-6 months. Analyses focused on patients with depression alone (n = 25).In healthy controls, Pavlovian CSs exerted action-specific effects, with appetitive CSs boosting active approach and aversive CSs active withdrawal. This action-specificity was absent in currently depressed subjects. Greater action-specificity in patients was associated with better recovery over the follow-up period.Depression is associated with an abnormal influence of emotional reactions on decision-making in a way that may predict recovery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Changes in reflexive emotional responses are hallmarks of depression, but how emotional reflexes make an impact on adaptive decision-making in depression has not been examined formally. Using a Pavlovian-instrumental transfer (PIT) task, we compared the influence of affectively valenced stimuli on decision-making in depression and generalized anxiety disorder compared with healthy controls; and related this to the longitudinal course of the illness.A total of 40 subjects with a current DSM-IV-TR diagnosis of major depressive disorder, dysthymia, generalized anxiety disorder, or a combination thereof, and 40 matched healthy controls performed a PIT task that assesses how instrumental approach and withdrawal behaviours are influenced by appetitive and aversive Pavlovian conditioned stimuli (CSs). Patients were followed up after 4-6 months. Analyses focused on patients with depression alone (n = 25).In healthy controls, Pavlovian CSs exerted action-specific effects, with appetitive CSs boosting active approach and aversive CSs active withdrawal. This action-specificity was absent in currently depressed subjects. Greater action-specificity in patients was associated with better recovery over the follow-up period.Depression is associated with an abnormal influence of emotional reactions on decision-making in a way that may predict recovery.
Huys, Quentin J M; Renz, Daniel; Petzschner, Frederike; Berwian, Isabel; Stoppel, Christian; Haker, Helene
German Translation and Validation of the Cognitive Style Questionnaire Short Form (CSQ-SF-D) Journal Article
In: PLoS One, vol. 11, pp. e0149530, 2016.
@article{HuysHaker16a,
title = {German Translation and Validation of the Cognitive Style Questionnaire Short Form (CSQ-SF-D)},
author = {Quentin J M Huys and Daniel Renz and Frederike Petzschner and Isabel Berwian and Christian Stoppel and Helene Haker},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa16a-German-Translation-and-Validation-of-the-Cognitive-Style-Questionnaire-Short-Form-CSQ-SF-D-1.pdf},
doi = {10.1371/journal.pone.0149530},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {PLoS One},
volume = {11},
pages = {e0149530},
abstract = {The Cognitive Style Questionnaire is a valuable tool for the assessment of hopeless cognitive styles in depression research, with predictive power in longitudinal studies. However, it is very burdensome to administer. Even the short form is still long, and neither this nor the original version exist in validated German translations. The questionnaire was translated from English to German, back-translated and commented on by clinicians. The reliability, factor structure and external validity of an online form of the questionnaire were examined on 214 participants. External validity was measured on a subset of 90 subjects. The resulting CSQ-SF-D had good to excellent reliability, both across items and subscales, and similar external validity to the original English version. The internality subscale appeared less robust than other subscales. A detailed analysis of individual item performance suggests that stable results could be achieved with a very short form (CSQ-VSF-D) including only 27 of the 72 items. The CSQ-SF-D is a validated and freely distributed translation of the CSQ-SF into German. This should make efficient assessment of cognitive style in German samples more accessible to researchers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Cognitive Style Questionnaire is a valuable tool for the assessment of hopeless cognitive styles in depression research, with predictive power in longitudinal studies. However, it is very burdensome to administer. Even the short form is still long, and neither this nor the original version exist in validated German translations. The questionnaire was translated from English to German, back-translated and commented on by clinicians. The reliability, factor structure and external validity of an online form of the questionnaire were examined on 214 participants. External validity was measured on a subset of 90 subjects. The resulting CSQ-SF-D had good to excellent reliability, both across items and subscales, and similar external validity to the original English version. The internality subscale appeared less robust than other subscales. A detailed analysis of individual item performance suggests that stable results could be achieved with a very short form (CSQ-VSF-D) including only 27 of the 72 items. The CSQ-SF-D is a validated and freely distributed translation of the CSQ-SF into German. This should make efficient assessment of cognitive style in German samples more accessible to researchers.
Webb, Christian A; Dillon, Daniel G; Pechtel, Pia; Goer, Franziska K; Murray, Laura; Huys, Quentin J M; Fava, Maurizio; McGrath, Patrick J; Weissman, Myrna; Parsey, Ramin; Kurian, Benji T; Adams, Phillip; Weyandt, Sarah; Trombello, Joseph M; Grannemann, Bruce; Cooper, Crystal M; Deldin, Patricia; Tenke, Craig; Trivedi, Madhukar; Bruder, Gerard; Pizzagalli, Diego A
Neural Correlates of Three Promising Endophenotypes of Depression: Evidence from the EMBARC Study Journal Article
In: Neuropsychopharmacology, vol. 41, pp. 454–463, 2016.
@article{WebbPizzagalli16,
title = {Neural Correlates of Three Promising Endophenotypes of Depression: Evidence from the EMBARC Study},
author = {Christian A Webb and Daniel G Dillon and Pia Pechtel and Franziska K Goer and Laura Murray and Quentin J M Huys and Maurizio Fava and Patrick J McGrath and Myrna Weissman and Ramin Parsey and Benji T Kurian and Phillip Adams and Sarah Weyandt and Joseph M Trombello and Bruce Grannemann and Crystal M Cooper and Patricia Deldin and Craig Tenke and Madhukar Trivedi and Gerard Bruder and Diego A Pizzagalli},
url = {http://acplab.org/wp-content/uploads/pub/WebbEa16-Neural-Correlates-of-Three-Promising-Endophenotypes-of-Depression_-Evidence-from-the-EMBARC-Study-1.pdf},
doi = {10.1038/npp.2015.165},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Neuropsychopharmacology},
volume = {41},
pages = {454–463},
abstract = {Major depressive disorder (MDD) is clinically, and likely pathophysiologically, heterogeneous. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes. Guided by the NIMH Research Domain Criteria initiative, we used source localization of scalp-recorded EEG resting data to examine the neural correlates of three emerging endophenotypes of depression: neuroticism, blunted reward learning, and cognitive control deficits. Data were drawn from the ongoing multi-site EMBARC study. We estimated intracranial current density for standard EEG frequency bands in 82 unmedicated adults with MDD, using Low-Resolution Brain Electromagnetic Tomography. Region-of-interest and whole-brain analyses tested associations between resting state EEG current density and endophenotypes of interest. Neuroticism was associated with increased resting gamma (36.5-44 Hz) current density in the ventral (subgenual) anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). In contrast, reduced cognitive control correlated with decreased gamma activity in the left dorsolateral prefrontal cortex (dlPFC), decreased theta (6.5-8 Hz) and alpha2 (10.5-12 Hz) activity in the dorsal ACC, and increased alpha2 activity in the right dlPFC. Finally, blunted reward learning correlated with lower OFC and left dlPFC gamma activity. Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC gamma activity was linked to reduced reward sensitivity. Three putative endophenotypes of depression were found to have partially dissociable resting intracranial EEG correlates, reflecting different underlying neural dysfunctions. Overall, these findings highlight the need to parse the heterogeneity of MDD by focusing on promising endophenotypes linked to specific pathophysiological abnormalities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Major depressive disorder (MDD) is clinically, and likely pathophysiologically, heterogeneous. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes. Guided by the NIMH Research Domain Criteria initiative, we used source localization of scalp-recorded EEG resting data to examine the neural correlates of three emerging endophenotypes of depression: neuroticism, blunted reward learning, and cognitive control deficits. Data were drawn from the ongoing multi-site EMBARC study. We estimated intracranial current density for standard EEG frequency bands in 82 unmedicated adults with MDD, using Low-Resolution Brain Electromagnetic Tomography. Region-of-interest and whole-brain analyses tested associations between resting state EEG current density and endophenotypes of interest. Neuroticism was associated with increased resting gamma (36.5-44 Hz) current density in the ventral (subgenual) anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). In contrast, reduced cognitive control correlated with decreased gamma activity in the left dorsolateral prefrontal cortex (dlPFC), decreased theta (6.5-8 Hz) and alpha2 (10.5-12 Hz) activity in the dorsal ACC, and increased alpha2 activity in the right dlPFC. Finally, blunted reward learning correlated with lower OFC and left dlPFC gamma activity. Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC gamma activity was linked to reduced reward sensitivity. Three putative endophenotypes of depression were found to have partially dissociable resting intracranial EEG correlates, reflecting different underlying neural dysfunctions. Overall, these findings highlight the need to parse the heterogeneity of MDD by focusing on promising endophenotypes linked to specific pathophysiological abnormalities.
Schulz, E; Huys, Quentin J M; Bach, Dominik R; Speekenbrink, M; Krause, A
Better safe than sorry: Risky function exploitation through safe optimization Journal Article
In: 2016.
@article{SchulzKrause16,
title = {Better safe than sorry: Risky function exploitation through safe optimization},
author = {E Schulz and Quentin J M Huys and Dominik R Bach and M Speekenbrink and A Krause},
url = {https://www.zora.uzh.ch/id/eprint/134707/1/SchulzEa16-GPexploration.pdf},
doi = {10.5167/uzh-134707},
year = {2016},
date = {2016-01-01},
publisher = {s.n.},
abstract = {Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are
poisonous, harmful, or otherwise dangerous. We test participants’ behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we find that Safe-Optimization, a Gaussian Process-based exploration-exploitation algorithm, describes participants’ behavior well and
that participants seem to care firstly whether a point is safe and then try to pick the optimal point from all such safe points. This means that their trade-off between exploration and exploitation indicates intelligent, approximate, and homeostasis-driven behavior.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are
poisonous, harmful, or otherwise dangerous. We test participants’ behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we find that Safe-Optimization, a Gaussian Process-based exploration-exploitation algorithm, describes participants’ behavior well and
that participants seem to care firstly whether a point is safe and then try to pick the optimal point from all such safe points. This means that their trade-off between exploration and exploitation indicates intelligent, approximate, and homeostasis-driven behavior.
poisonous, harmful, or otherwise dangerous. We test participants’ behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we find that Safe-Optimization, a Gaussian Process-based exploration-exploitation algorithm, describes participants’ behavior well and
that participants seem to care firstly whether a point is safe and then try to pick the optimal point from all such safe points. This means that their trade-off between exploration and exploitation indicates intelligent, approximate, and homeostasis-driven behavior.
Friedel, Eva; Schlagenhauf, Florian; Beck, Anne; Dolan, Raymond J.; Huys, Quentin J M.; Rapp, Michael A.; Heinz, Andreas
The effects of life stress and neural learning signals on fluid intelligence Journal Article
In: Eur Arch Psychiatry Clin Neurosci, 2015.
@article{FriedelHeinz15,
title = {The effects of life stress and neural learning signals on fluid intelligence},
author = {Eva Friedel and Florian Schlagenhauf and Anne Beck and Raymond J. Dolan and Quentin J M. Huys and Michael A. Rapp and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/FriedelEa14-The-Effects-of-Life-Stress-and-Neural-Learning-Signals-on-Fluid-Intelligence-1.pdf},
doi = {10.1007/s00406-014-0519-3},
year = {2015},
date = {2015-08-01},
urldate = {2015-08-01},
journal = {Eur Arch Psychiatry Clin Neurosci},
school = {Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Charitéplatz 1, 10117, Berlin, Germany.},
abstract = {Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account.
Huys, Quentin J M.; Lally, Níall; Faulkner, Paul; Eshel, Neir; Seifritz, Erich; Gershman, Samuel J.; Dayan, Peter; Roiser, Jonathan P.
Interplay of approximate planning strategies Journal Article
In: Proc Natl Acad Sci, vol. 112, no. 10, pp. 3098–3103, 2015.
@article{HuysRoiser15c,
title = {Interplay of approximate planning strategies},
author = {Quentin J M. Huys and Níall Lally and Paul Faulkner and Neir Eshel and Erich Seifritz and Samuel J. Gershman and Peter Dayan and Jonathan P. Roiser},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa15c-Interplay-of-Approximate-Planning-Strategies-1.pdf},
doi = {10.1073/pnas.1414219112},
year = {2015},
date = {2015-03-01},
urldate = {2015-03-01},
journal = {Proc Natl Acad Sci},
volume = {112},
number = {10},
pages = {3098–3103},
school = {Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom;},
abstract = {Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or "options."},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or "options."
Deserno, Lorenz; Huys, Quentin J M; Boehme, Rebecca; Buchert, Ralph; Heinze, Hans-Jochen; Grace, Anthony A; Dolan, Raymond J; Heinz, Andreas; Schlagenhauf, Florian
Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making Journal Article
In: Proc Natl Acad Sci U S A, vol. 112, no. 5, pp. 1595–1600, 2015.
@article{DesernoSchlagenhauf15,
title = {Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making},
author = {Lorenz Deserno and Quentin J M Huys and Rebecca Boehme and Ralph Buchert and Hans-Jochen Heinze and Anthony A Grace and Raymond J Dolan and Andreas Heinz and Florian Schlagenhauf},
url = {http://acplab.org/wp-content/uploads/pub/DesernoEa15-Ventral-Striatal-Dopamine-Reflects-Behavioral-and-Neural-Signatures-of-Model-Based-Control-during-Sequential-Decision-Making-1.pdf},
doi = {10.1073/pnas.1417219112},
year = {2015},
date = {2015-02-01},
urldate = {2015-02-01},
journal = {Proc Natl Acad Sci U S A},
volume = {112},
number = {5},
pages = {1595–1600},
abstract = {Dual system theories suggest that behavioral control is parsed between a deliberative "model-based" and a more reflexive "model-free" system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [(18)F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dual system theories suggest that behavioral control is parsed between a deliberative "model-based" and a more reflexive "model-free" system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [(18)F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.
Deserno, Lorenz; Beck, Anne; Huys, Quentin J M; Lorenz, Robert C; Buchert, Ralph; Buchholz, Hans-Georg; Plotkin, Michail; Kumakara, Yoshitaka; Cumming, Paul; Heinze, Hans-Jochen; Grace, Anthony A; Rapp, Michael A; Schlagenhauf, Florian; Heinz, Andreas
Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum Journal Article
In: Eur J Neurosci, vol. 41, no. 4, pp. 477–486, 2015.
@article{DesernoHeinz15a,
title = {Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum},
author = {Lorenz Deserno and Anne Beck and Quentin J M Huys and Robert C Lorenz and Ralph Buchert and Hans-Georg Buchholz and Michail Plotkin and Yoshitaka Kumakara and Paul Cumming and Hans-Jochen Heinze and Anthony A Grace and Michael A Rapp and Florian Schlagenhauf and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/DesernoEa15a-chronic-alcohol-intake.pdf},
doi = {10.1111/ejn.12802},
year = {2015},
date = {2015-02-01},
urldate = {2015-02-01},
journal = {Eur J Neurosci},
volume = {41},
number = {4},
pages = {477–486},
abstract = {Drugs of abuse elicit dopamine release in the ventral striatum, possibly biasing dopamine-driven reinforcement learning towards drug-related reward at the expense of non-drug-related reward. Indeed, in alcohol-dependent patients, reactivity in dopaminergic target areas is shifted from non-drug-related stimuli towards drug-related stimuli. Such 'hijacked' dopamine signals may impair flexible learning from non-drug-related rewards, and thus promote craving for the drug of abuse. Here, we used functional magnetic resonance imaging to measure ventral striatal activation by reward prediction errors (RPEs) during a probabilistic reversal learning task in recently detoxified alcohol-dependent patients and healthy controls (N = 27). All participants also underwent 6-[(18) F]fluoro-DOPA positron emission tomography to assess ventral striatal dopamine synthesis capacity. Neither ventral striatal activation by RPEs nor striatal dopamine synthesis capacity differed between groups. However, ventral striatal coding of RPEs correlated inversely with craving in patients. Furthermore, we found a negative correlation between ventral striatal coding of RPEs and dopamine synthesis capacity in healthy controls, but not in alcohol-dependent patients. Moderator analyses showed that the magnitude of the association between dopamine synthesis capacity and RPE coding depended on the amount of chronic, habitual alcohol intake. Despite the relatively small sample size, a power analysis supports the reported results. Using a multimodal imaging approach, this study suggests that dopaminergic modulation of neural learning signals is disrupted in alcohol dependence in proportion to long-term alcohol intake of patients. Alcohol intake may perpetuate itself by interfering with dopaminergic modulation of neural learning signals in the ventral striatum, thus increasing craving for habitual drug intake.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Drugs of abuse elicit dopamine release in the ventral striatum, possibly biasing dopamine-driven reinforcement learning towards drug-related reward at the expense of non-drug-related reward. Indeed, in alcohol-dependent patients, reactivity in dopaminergic target areas is shifted from non-drug-related stimuli towards drug-related stimuli. Such 'hijacked' dopamine signals may impair flexible learning from non-drug-related rewards, and thus promote craving for the drug of abuse. Here, we used functional magnetic resonance imaging to measure ventral striatal activation by reward prediction errors (RPEs) during a probabilistic reversal learning task in recently detoxified alcohol-dependent patients and healthy controls (N = 27). All participants also underwent 6-[(18) F]fluoro-DOPA positron emission tomography to assess ventral striatal dopamine synthesis capacity. Neither ventral striatal activation by RPEs nor striatal dopamine synthesis capacity differed between groups. However, ventral striatal coding of RPEs correlated inversely with craving in patients. Furthermore, we found a negative correlation between ventral striatal coding of RPEs and dopamine synthesis capacity in healthy controls, but not in alcohol-dependent patients. Moderator analyses showed that the magnitude of the association between dopamine synthesis capacity and RPE coding depended on the amount of chronic, habitual alcohol intake. Despite the relatively small sample size, a power analysis supports the reported results. Using a multimodal imaging approach, this study suggests that dopaminergic modulation of neural learning signals is disrupted in alcohol dependence in proportion to long-term alcohol intake of patients. Alcohol intake may perpetuate itself by interfering with dopaminergic modulation of neural learning signals in the ventral striatum, thus increasing craving for habitual drug intake.
Stankevicius, Aistis; Huys, Quentin J M.; Kalra, Aditi; Seriès, Peggy
Optimism as a prior belief about the probability of future reward Journal Article
In: PLoS Comput Biol, vol. 10, no. 5, pp. e1003605, 2014.
@article{StankeviciusSeriès14,
title = {Optimism as a prior belief about the probability of future reward},
author = {Aistis Stankevicius and Quentin J M. Huys and Aditi Kalra and Peggy Seriès},
url = {http://acplab.org/wp-content/uploads/pub/StankeviciusEa14-Optimism-As-a-Prior-Belief-about-the-Probability-of-Future-Reward-1.pdf},
doi = {10.1371/journal.pcbi.1003605},
year = {2014},
date = {2014-05-01},
urldate = {2014-05-01},
journal = {PLoS Comput Biol},
volume = {10},
number = {5},
pages = {e1003605},
school = {Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom.},
abstract = {Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.
Schlagenhauf, Florian; Huys, Quentin J M.; Deserno, Lorenz; Rapp, Michael A.; Beck, Anne; Heinze, Hans-Joachim; Dolan, Ray; Heinz, Andreas
Striatal dysfunction during reversal learning in unmedicated schizophrenia patients Journal Article
In: Neuroimage, vol. 89, pp. 171–180, 2014.
@article{SchlagenhaufHeinz14,
title = {Striatal dysfunction during reversal learning in unmedicated schizophrenia patients},
author = {Florian Schlagenhauf and Quentin J M. Huys and Lorenz Deserno and Michael A. Rapp and Anne Beck and Hans-Joachim Heinze and Ray Dolan and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/SchlagenhaufEa14-striatal-dysfunction-during-reversal-learning.pdf},
doi = {10.1016/j.neuroimage.2013.11.034},
year = {2014},
date = {2014-04-01},
urldate = {2014-04-01},
journal = {Neuroimage},
volume = {89},
pages = {171–180},
school = {Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Germany; Cluster of Excellence NeuroCure, Charité-Universitätsmedizin Berlin, Berlin, Germany.},
abstract = {Subjects with schizophrenia are impaired at reinforcement-driven reversal learning from as early as their first episode. The neurobiological basis of this deficit is unknown. We obtained behavioral and fMRI data in 24 unmedicated, primarily first episode, schizophrenia patients and 24 age-, IQ- and gender-matched healthy controls during a reversal learning task. We supplemented our fMRI analysis, focusing on learning from prediction errors, with detailed computational modeling to probe task solving strategy including an ability to deploy an internal goal directed model of the task. Patients displayed reduced functional activation in the ventral striatum (VS) elicited by prediction errors. However, modeling task performance revealed that a subgroup did not adjust their behavior according to an accurate internal model of the task structure, and these were also the more severely psychotic patients. In patients who could adapt their behavior, as well as in controls, task solving was best described by cognitive strategies according to a Hidden Markov Model. When we compared patients and controls who acted according to this strategy, patients still displayed a significant reduction in VS activation elicited by informative errors that precede salient changes of behavior (reversals). Thus, our study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies. This result highlights VS dysfunction is tightly linked to a reward-related reversal learning deficit in early, unmedicated schizophrenia patients.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Subjects with schizophrenia are impaired at reinforcement-driven reversal learning from as early as their first episode. The neurobiological basis of this deficit is unknown. We obtained behavioral and fMRI data in 24 unmedicated, primarily first episode, schizophrenia patients and 24 age-, IQ- and gender-matched healthy controls during a reversal learning task. We supplemented our fMRI analysis, focusing on learning from prediction errors, with detailed computational modeling to probe task solving strategy including an ability to deploy an internal goal directed model of the task. Patients displayed reduced functional activation in the ventral striatum (VS) elicited by prediction errors. However, modeling task performance revealed that a subgroup did not adjust their behavior according to an accurate internal model of the task structure, and these were also the more severely psychotic patients. In patients who could adapt their behavior, as well as in controls, task solving was best described by cognitive strategies according to a Hidden Markov Model. When we compared patients and controls who acted according to this strategy, patients still displayed a significant reduction in VS activation elicited by informative errors that precede salient changes of behavior (reversals). Thus, our study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies. This result highlights VS dysfunction is tightly linked to a reward-related reversal learning deficit in early, unmedicated schizophrenia patients.
Guitart-Masip, Marc; Economides, Marcos; Huys, Quentin J M.; Frank, Michael J.; Chowdhury, Rumana; Duzel, Emrah; Dayan, Peter; Dolan, Raymond J.
Differential, but not opponent, effects of L-DOPA and citalopram on action learning with reward and punishment Journal Article
In: Psychopharmacology (Berl), vol. 231, no. 5, pp. 955–966, 2014.
@article{GuitartDolan14,
title = {Differential, but not opponent, effects of L-DOPA and citalopram on action learning with reward and punishment},
author = {Marc Guitart-Masip and Marcos Economides and Quentin J M. Huys and Michael J. Frank and Rumana Chowdhury and Emrah Duzel and Peter Dayan and Raymond J. Dolan},
url = {http://acplab.org/wp-content/uploads/pub/GuitartEa14-Differential-but-Not-Opponent-Effects-of-L-DOPA-and-Citalopram-on-Action-Learning-with-Reward-and-Punishment-1.pdf},
doi = {10.1007/s00213-013-3313-4},
year = {2014},
date = {2014-03-01},
urldate = {2014-03-01},
journal = {Psychopharmacology (Berl)},
volume = {231},
number = {5},
pages = {955–966},
abstract = {Decision-making involves two fundamental axes of control namely valence, spanning reward and punishment, and action, spanning invigoration and inhibition. We recently exploited a go/no-go task whose contingencies explicitly decouple valence and action to show that these axes are inextricably coupled during learning. This results in a disadvantage in learning to go to avoid punishment and in learning to no-go to obtain a reward. The neuromodulators dopamine and serotonin are likely to play a role in these asymmetries: Dopamine signals anticipation of future rewards and is also involved in an invigoration of motor responses leading to reward, but it also arbitrates between different forms of control. Conversely, serotonin is implicated in motor inhibition and punishment processing.To investigate the role of dopamine and serotonin in the interaction between action and valence during learning.Methods We combined computational modeling with pharmacological manipulation in 90 healthy human volunteers, using levodopa and citalopram to affect dopamine and serotonin, respectively.We found that, after administration of levodopa,action learning was less affected by outcome valence when compared with the placebo and citalopram groups. This highlights in this context a predominant effect of levodopa in controlling the balance between different forms of control.Citalopram had distinct effects, increasing participants'tendency to perform active responses independent of outcome valence, consistent with a role in decreasing motor inhibition.Our findings highlight the rich complexities of the roles played by dopamine and serotonin during instrumental learning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Decision-making involves two fundamental axes of control namely valence, spanning reward and punishment, and action, spanning invigoration and inhibition. We recently exploited a go/no-go task whose contingencies explicitly decouple valence and action to show that these axes are inextricably coupled during learning. This results in a disadvantage in learning to go to avoid punishment and in learning to no-go to obtain a reward. The neuromodulators dopamine and serotonin are likely to play a role in these asymmetries: Dopamine signals anticipation of future rewards and is also involved in an invigoration of motor responses leading to reward, but it also arbitrates between different forms of control. Conversely, serotonin is implicated in motor inhibition and punishment processing.To investigate the role of dopamine and serotonin in the interaction between action and valence during learning.Methods We combined computational modeling with pharmacological manipulation in 90 healthy human volunteers, using levodopa and citalopram to affect dopamine and serotonin, respectively.We found that, after administration of levodopa,action learning was less affected by outcome valence when compared with the placebo and citalopram groups. This highlights in this context a predominant effect of levodopa in controlling the balance between different forms of control.Citalopram had distinct effects, increasing participants'tendency to perform active responses independent of outcome valence, consistent with a role in decreasing motor inhibition.Our findings highlight the rich complexities of the roles played by dopamine and serotonin during instrumental learning.
Garbusow, Maria; Schad, Daniel J.; Sommer, Christian; Jünger, Elisabeth; Sebold, Miriam; Friedel, Eva; Wendt, Jean; Kathmann, Norbert; Schlagenhauf, Florian; Zimmermann, Ulrich S.; Heinz, Andreas; Huys, Quentin J M.; Rapp, Michael A.
Pavlovian-to-instrumental transfer in alcohol dependence: a pilot study Journal Article
In: Neuropsychobiology, vol. 70, no. 2, pp. 111–121, 2014.
@article{GarbusowRapp14,
title = {Pavlovian-to-instrumental transfer in alcohol dependence: a pilot study},
author = {Maria Garbusow and Daniel J. Schad and Christian Sommer and Elisabeth Jünger and Miriam Sebold and Eva Friedel and Jean Wendt and Norbert Kathmann and Florian Schlagenhauf and Ulrich S. Zimmermann and Andreas Heinz and Quentin J M. Huys and Michael A. Rapp},
url = {http://acplab.org/wp-content/uploads/pub/GarbusowEa14-Pavlovian-to-Instrumental-Transfer-in-Alcohol-Dependence_-a-Pilot-Study-1.pdf},
doi = {10.1159/000363507},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Neuropsychobiology},
volume = {70},
number = {2},
pages = {111–121},
school = {Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité Universitätsmedizin Berlin, Berlin, Berlin.},
abstract = {Pavlovian processes are thought to play an important role in the development, maintenance and relapse of alcohol dependence, possibly by influencing and usurping ongoing thought and behavior. The influence of pavlovian stimuli on ongoing behavior is paradigmatically measured by pavlovian-to-instrumental transfer (PIT) tasks. These involve multiple stages and are complex. Whether increased PIT is involved in human alcohol dependence is uncertain. We therefore aimed to establish and validate a modified PIT paradigm that would be robust, consistent and tolerated by healthy controls as well as by patients suffering from alcohol dependence, and to explore whether alcohol dependence is associated with enhanced PIT.Thirty-two recently detoxified alcohol-dependent patients and 32 age- and gender-matched healthy controls performed a PIT task with instrumental go/no-go approach behaviors. The task involved both pavlovian stimuli associated with monetary rewards and losses, and images of drinks.Both patients and healthy controls showed a robust and temporally stable PIT effect. Strengths of PIT effects to drug-related and monetary conditioned stimuli were highly correlated. Patients more frequently showed a PIT effect, and the effect was stronger in response to aversively conditioned CSs (conditioned suppression), but there was no group difference in response to appetitive CSs.The implementation of PIT has favorably robust properties in chronic alcohol-dependent patients and in healthy controls. It shows internal consistency between monetary and drug-related cues. The findings support an association of alcohol dependence with an increased propensity towards PIT.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pavlovian processes are thought to play an important role in the development, maintenance and relapse of alcohol dependence, possibly by influencing and usurping ongoing thought and behavior. The influence of pavlovian stimuli on ongoing behavior is paradigmatically measured by pavlovian-to-instrumental transfer (PIT) tasks. These involve multiple stages and are complex. Whether increased PIT is involved in human alcohol dependence is uncertain. We therefore aimed to establish and validate a modified PIT paradigm that would be robust, consistent and tolerated by healthy controls as well as by patients suffering from alcohol dependence, and to explore whether alcohol dependence is associated with enhanced PIT.Thirty-two recently detoxified alcohol-dependent patients and 32 age- and gender-matched healthy controls performed a PIT task with instrumental go/no-go approach behaviors. The task involved both pavlovian stimuli associated with monetary rewards and losses, and images of drinks.Both patients and healthy controls showed a robust and temporally stable PIT effect. Strengths of PIT effects to drug-related and monetary conditioned stimuli were highly correlated. Patients more frequently showed a PIT effect, and the effect was stronger in response to aversively conditioned CSs (conditioned suppression), but there was no group difference in response to appetitive CSs.The implementation of PIT has favorably robust properties in chronic alcohol-dependent patients and in healthy controls. It shows internal consistency between monetary and drug-related cues. The findings support an association of alcohol dependence with an increased propensity towards PIT.
Ly, Verena; Huys, Quentin J M.; Stins, John F.; Roelofs, Karin; Cools, Roshan
Individual differences in bodily freezing predict emotional biases in decision making Journal Article
In: Front Behav Neurosci, vol. 8, pp. 237, 2014.
@article{LyCools14,
title = {Individual differences in bodily freezing predict emotional biases in decision making},
author = {Verena Ly and Quentin J M. Huys and John F. Stins and Karin Roelofs and Roshan Cools},
url = {http://acplab.org/wp-content/uploads/pub/LyEa14-SocialPIT.pdf},
doi = {10.3389/fnbeh.2014.00237},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Front Behav Neurosci},
volume = {8},
pages = {237},
school = {ychiatry, Radboud University Nijmegen Medical Centre Nijmegen, Netherlands.},
abstract = {Instrumental decision making has long been argued to be vulnerable to emotional responses. Literature on multiple decision making systems suggests that this emotional biasing might reflect effects of a system that regulates innately specified, evolutionarily preprogrammed responses. To test this hypothesis directly, we investigated whether effects of emotional faces on instrumental action can be predicted by effects of emotional faces on bodily freezing, an innately specified response to aversive relative to appetitive cues. We tested 43 women using a novel emotional decision making task combined with posturography, which involves a force platform to detect small oscillations of the body to accurately quantify postural control in upright stance. On the platform, participants learned whole body approach-avoidance actions based on monetary feedback, while being primed by emotional faces (angry/happy). Our data evidence an emotional biasing of instrumental action. Thus, angry relative to happy faces slowed instrumental approach relative to avoidance responses. Critically, individual differences in this emotional biasing effect were predicted by individual differences in bodily freezing. This result suggests that emotional biasing of instrumental action involves interaction with a system that controls innately specified responses. Furthermore, our findings help bridge (animal and human) decision making and emotion research to advance our mechanistic understanding of decision making anomalies in daily encounters as well as in a wide range of psychopathology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Instrumental decision making has long been argued to be vulnerable to emotional responses. Literature on multiple decision making systems suggests that this emotional biasing might reflect effects of a system that regulates innately specified, evolutionarily preprogrammed responses. To test this hypothesis directly, we investigated whether effects of emotional faces on instrumental action can be predicted by effects of emotional faces on bodily freezing, an innately specified response to aversive relative to appetitive cues. We tested 43 women using a novel emotional decision making task combined with posturography, which involves a force platform to detect small oscillations of the body to accurately quantify postural control in upright stance. On the platform, participants learned whole body approach-avoidance actions based on monetary feedback, while being primed by emotional faces (angry/happy). Our data evidence an emotional biasing of instrumental action. Thus, angry relative to happy faces slowed instrumental approach relative to avoidance responses. Critically, individual differences in this emotional biasing effect were predicted by individual differences in bodily freezing. This result suggests that emotional biasing of instrumental action involves interaction with a system that controls innately specified responses. Furthermore, our findings help bridge (animal and human) decision making and emotion research to advance our mechanistic understanding of decision making anomalies in daily encounters as well as in a wide range of psychopathology.
Schad, Daniel J.; Jünger, Elisabeth; Sebold, Miriam; Garbusow, Maria; Bernhardt, Nadine; Javadi, Amir Homayoun; Zimmermann, Ulrich S.; Smolka, Michael N.; Heinz, Andreas; Rapp, Michael A.; Huys, Quentin J. M.
Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning Journal Article
In: Front Psychol, vol. 5, pp. 1450, 2014.
@article{SchadHuys14,
title = {Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning},
author = {Daniel J. Schad and Elisabeth Jünger and Miriam Sebold and Maria Garbusow and Nadine Bernhardt and Amir Homayoun Javadi and Ulrich S. Zimmermann and Michael N. Smolka and Andreas Heinz and Michael A. Rapp and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/SchadEa14-processing-speed.pdf},
doi = {10.3389/fpsyg.2014.01450},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Front Psychol},
volume = {5},
pages = {1450},
abstract = {Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation.
Sebold, Miriam; Deserno, Lorenz; Nebe, Stefan; Schad, Daniel J.; Garbusow, Maria; Hägele, Claudia; Keller, Jürgen; Jünger, Elisabeth; Kathmann, Norbert; Smolka, Michael; Rapp, Michael A.; Schlagenhauf, Florian; Heinz, Andreas; Huys, Quentin J M.
Model-based and model-free decisions in alcohol dependence Journal Article
In: Neuropsychobiology, vol. 70, no. 2, pp. 122–131, 2014.
@article{SeboldHuys14,
title = {Model-based and model-free decisions in alcohol dependence},
author = {Miriam Sebold and Lorenz Deserno and Stefan Nebe and Daniel J. Schad and Maria Garbusow and Claudia Hägele and Jürgen Keller and Elisabeth Jünger and Norbert Kathmann and Michael Smolka and Michael A. Rapp and Florian Schlagenhauf and Andreas Heinz and Quentin J M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/SeboldEa14-Model-Based-and-Model-Free-Decisions-in-Alcohol-Dependence-1.pdf},
doi = {10.1159/000362840},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Neuropsychobiology},
volume = {70},
number = {2},
pages = {122–131},
school = {Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Berlin, Germany.},
abstract = {Human and animal work suggests a shift from goal-directed to habitual decision-making in addiction. However, the evidence for this in human alcohol dependence is as yet inconclusive.Twenty-six healthy controls and 26 recently detoxified alcohol-dependent patients underwent behavioral testing with a 2-step task designed to disentangle goal-directed and habitual response patterns.Alcohol-dependent patients showed less evidence of goal-directed choices than healthy controls, particularly after losses. There was no difference in the strength of the habitual component. The group differences did not survive controlling for performance on the Digit Symbol Substitution Task.Chronic alcohol use appears to selectively impair goal-directed function, rather than promoting habitual responding. It appears to do so particularly after nonrewards, and this may be mediated by the effects of alcohol on more general cognitive functions subserved by the prefrontal cortex. © 2014 S. Karger AG, Basel.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human and animal work suggests a shift from goal-directed to habitual decision-making in addiction. However, the evidence for this in human alcohol dependence is as yet inconclusive.Twenty-six healthy controls and 26 recently detoxified alcohol-dependent patients underwent behavioral testing with a 2-step task designed to disentangle goal-directed and habitual response patterns.Alcohol-dependent patients showed less evidence of goal-directed choices than healthy controls, particularly after losses. There was no difference in the strength of the habitual component. The group differences did not survive controlling for performance on the Digit Symbol Substitution Task.Chronic alcohol use appears to selectively impair goal-directed function, rather than promoting habitual responding. It appears to do so particularly after nonrewards, and this may be mediated by the effects of alcohol on more general cognitive functions subserved by the prefrontal cortex. © 2014 S. Karger AG, Basel.
Geurts, Dirk E M; Huys, Quentin J M; Ouden, Hanneke E M; Cools, Roshan
Serotonin and aversive Pavlovian control of instrumental behavior in humans Journal Article
In: J Neurosci, vol. 33, no. 48, pp. 18932–18939, 2013.
@article{GeurtsCools13a,
title = {Serotonin and aversive Pavlovian control of instrumental behavior in humans},
author = {Dirk E M Geurts and Quentin J M Huys and Hanneke E M Ouden and Roshan Cools},
url = {http://acplab.org/wp-content/uploads/pub/GeurtsEa13a-Serotonin-and-Aversive-Pavlovian-Control-of-Instrumental-Behavior-in-Humans-1.pdf},
doi = {10.1523/JNEUROSCI.2749-13.2013},
year = {2013},
date = {2013-11-01},
urldate = {2013-11-01},
journal = {J Neurosci},
volume = {33},
number = {48},
pages = {18932–18939},
abstract = {Adaptive decision-making involves interaction between systems regulating Pavlovian and instrumental control of behavior. Here we investigate in humans the role of serotonin in such Pavlovian-instrumental transfer in both the aversive and the appetitive domain using acute tryptophan depletion, known to lower central serotonin levels. Acute tryptophan depletion attenuated the inhibiting effect of aversive Pavlovian cues on instrumental behavior, while leaving unaltered the activating effect of appetitive Pavlovian cues. These data suggest that serotonin is selectively involved in Pavlovian inhibition due to aversive expectations and have implications for our understanding of the mechanisms underlying a range of affective, impulsive, and aggressive neuropsychiatric disorders.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Adaptive decision-making involves interaction between systems regulating Pavlovian and instrumental control of behavior. Here we investigate in humans the role of serotonin in such Pavlovian-instrumental transfer in both the aversive and the appetitive domain using acute tryptophan depletion, known to lower central serotonin levels. Acute tryptophan depletion attenuated the inhibiting effect of aversive Pavlovian cues on instrumental behavior, while leaving unaltered the activating effect of appetitive Pavlovian cues. These data suggest that serotonin is selectively involved in Pavlovian inhibition due to aversive expectations and have implications for our understanding of the mechanisms underlying a range of affective, impulsive, and aggressive neuropsychiatric disorders.
Geurts, Dirk E M.; Huys, Quentin J M.; Ouden, Hanneke E M.; Cools, Roshan
Aversive Pavlovian control of instrumental behavior in humans Journal Article
In: J Cogn Neurosci, vol. 25, no. 9, pp. 1428–1441, 2013.
@article{GeurtsCools13,
title = {Aversive Pavlovian control of instrumental behavior in humans},
author = {Dirk E M. Geurts and Quentin J M. Huys and Hanneke E M. Ouden and Roshan Cools},
url = {http://acplab.org/wp-content/uploads/pub/GeurtsEa13-Aversive-Pavlovian-Control-of-Instrumental-Behavior-in-Humans-1.pdf},
doi = {10.1162/jocn_a_00425},
year = {2013},
date = {2013-09-01},
urldate = {2013-09-01},
journal = {J Cogn Neurosci},
volume = {25},
number = {9},
pages = {1428–1441},
school = {Radboud University Nijmegen Medical Centre, The Netherlands. d.geurts@donders.ru.nl},
abstract = {Adaptive behavior involves interactions between systems regulating Pavlovian and instrumental control of actions. Here, we present the first investigation of the neural mechanisms underlying aversive Pavlovian-instrumental transfer using fMRI in humans. Recent evidence indicates that these Pavlovian influences on instrumental actions are action-specific: Instrumental approach is invigorated by appetitive Pavlovian cues but inhibited by aversive Pavlovian cues. Conversely, instrumental withdrawal is inhibited by appetitive Pavlovian cues but invigorated by aversive Pavlovian cues. We show that BOLD responses in the amygdala and the nucleus accumbens were associated with behavioral inhibition by aversive Pavlovian cues, irrespective of action context. Furthermore, BOLD responses in the ventromedial prefrontal cortex differed between approach and withdrawal actions. Aversive Pavlovian conditioned stimuli modulated connectivity between the ventromedial prefrontal cortex and the caudate nucleus. These results show that action-specific aversive control of instrumental behavior involves the modulation of fronto-striatal interactions by Pavlovian conditioned stimuli.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Adaptive behavior involves interactions between systems regulating Pavlovian and instrumental control of actions. Here, we present the first investigation of the neural mechanisms underlying aversive Pavlovian-instrumental transfer using fMRI in humans. Recent evidence indicates that these Pavlovian influences on instrumental actions are action-specific: Instrumental approach is invigorated by appetitive Pavlovian cues but inhibited by aversive Pavlovian cues. Conversely, instrumental withdrawal is inhibited by appetitive Pavlovian cues but invigorated by aversive Pavlovian cues. We show that BOLD responses in the amygdala and the nucleus accumbens were associated with behavioral inhibition by aversive Pavlovian cues, irrespective of action context. Furthermore, BOLD responses in the ventromedial prefrontal cortex differed between approach and withdrawal actions. Aversive Pavlovian conditioned stimuli modulated connectivity between the ventromedial prefrontal cortex and the caudate nucleus. These results show that action-specific aversive control of instrumental behavior involves the modulation of fronto-striatal interactions by Pavlovian conditioned stimuli.
Huys, Quentin J. M.; Pizzagalli, Diego A.; Bogdan, Ryan; Dayan, Peter
Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis Journal Article
In: Biol Mood Anx Dis, vol. 3, no. 1, pp. 12, 2013.
@article{HuysDayan13,
title = {Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis},
author = {Quentin J. M. Huys and Diego A. Pizzagalli and Ryan Bogdan and Peter Dayan},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa13-Mapping-Anhedonia-onto-Reinforcement-Learning_-a-Behavioural-Meta-Analysis-1.pdf},
doi = {10.1186/2045-5380-3-12},
year = {2013},
date = {2013-06-01},
urldate = {2013-06-01},
journal = {Biol Mood Anx Dis},
volume = {3},
number = {1},
pages = {12},
abstract = {BACKGROUND: Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge. METHODS: Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate. RESULTS: MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate. CONCLUSION: Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND: Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge. METHODS: Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate. RESULTS: MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate. CONCLUSION: Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior.
Schlagenhauf, Florian; Rapp, Michael A.; Huys, Quentin J M.; Beck, Anne; Wüstenberg, Torsten; Deserno, Lorenz; Buchholz, Hans-Georg; Kalbitzer, Jan; Buchert, Ralph; Bauer, Michael; Kienast, Thorsten; Cumming, Paul; Plotkin, Michail; Kumakura, Yoshitaka; Grace, Anthony A.; Dolan, Raymond J.; Heinz, Andreas
Ventral striatal prediction error signaling is associated with dopamine synthesis capacity and fluid intelligence Journal Article
In: Hum Brain Mapp, vol. 34, no. 6, pp. 1490–1499, 2013.
@article{SchlagenhaufHeinz13,
title = {Ventral striatal prediction error signaling is associated with dopamine synthesis capacity and fluid intelligence},
author = {Florian Schlagenhauf and Michael A. Rapp and Quentin J M. Huys and Anne Beck and Torsten Wüstenberg and Lorenz Deserno and Hans-Georg Buchholz and Jan Kalbitzer and Ralph Buchert and Michael Bauer and Thorsten Kienast and Paul Cumming and Michail Plotkin and Yoshitaka Kumakura and Anthony A. Grace and Raymond J. Dolan and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/SchlagenhaufEa13-Ventral-striatal-prediction-error-signaling-is-associated-with-dopamine.pdf},
doi = {10.1002/hbm.22000},
year = {2013},
date = {2013-06-01},
urldate = {2013-06-01},
journal = {Hum Brain Mapp},
volume = {34},
number = {6},
pages = {1490–1499},
school = {Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Germany.},
abstract = {Fluid intelligence represents the capacity for flexible problem solving and rapid behavioral adaptation. Rewards drive flexible behavioral adaptation, in part via a teaching signal expressed as reward prediction errors in the ventral striatum, which has been associated with phasic dopamine release in animal studies. We examined a sample of 28 healthy male adults using multimodal imaging and biological parametric mapping with (1) functional magnetic resonance imaging during a reversal learning task and (2) in a subsample of 17 subjects also with positron emission tomography using 6-[(18) F]fluoro-L-DOPA to assess dopamine synthesis capacity. Fluid intelligence was measured using a battery of nine standard neuropsychological tests. Ventral striatal BOLD correlates of reward prediction errors were positively correlated with fluid intelligence and, in the right ventral striatum, also inversely correlated with dopamine synthesis capacity (FDOPA K inapp). When exploring aspects of fluid intelligence, we observed that prediction error signaling correlates with complex attention and reasoning. These findings indicate that individual differences in the capacity for flexible problem solving relate to ventral striatal activation during reward-related learning, which in turn proved to be inversely associated with ventral striatal dopamine synthesis capacity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fluid intelligence represents the capacity for flexible problem solving and rapid behavioral adaptation. Rewards drive flexible behavioral adaptation, in part via a teaching signal expressed as reward prediction errors in the ventral striatum, which has been associated with phasic dopamine release in animal studies. We examined a sample of 28 healthy male adults using multimodal imaging and biological parametric mapping with (1) functional magnetic resonance imaging during a reversal learning task and (2) in a subsample of 17 subjects also with positron emission tomography using 6-[(18) F]fluoro-L-DOPA to assess dopamine synthesis capacity. Fluid intelligence was measured using a battery of nine standard neuropsychological tests. Ventral striatal BOLD correlates of reward prediction errors were positively correlated with fluid intelligence and, in the right ventral striatum, also inversely correlated with dopamine synthesis capacity (FDOPA K inapp). When exploring aspects of fluid intelligence, we observed that prediction error signaling correlates with complex attention and reasoning. These findings indicate that individual differences in the capacity for flexible problem solving relate to ventral striatal activation during reward-related learning, which in turn proved to be inversely associated with ventral striatal dopamine synthesis capacity.
Cavanagh, James F.; Eisenberg, Ian; Guitart-Masip, Marc; Huys, Quentin; Frank, Michael J.
Frontal theta overrides pavlovian learning biases Journal Article
In: J Neurosci, vol. 33, no. 19, pp. 8541–8548, 2013.
@article{CavanaghFrank13,
title = {Frontal theta overrides pavlovian learning biases},
author = {James F. Cavanagh and Ian Eisenberg and Marc Guitart-Masip and Quentin Huys and Michael J. Frank},
url = {http://acplab.org/wp-content/uploads/pub/CavanaghEa13-Frontal-Theta-Overrides-Pavlovian-Learning-Biases-1.pdf},
doi = {10.1523/JNEUROSCI.5754-12.2013},
year = {2013},
date = {2013-05-01},
urldate = {2013-05-01},
journal = {J Neurosci},
volume = {33},
number = {19},
pages = {8541–8548},
school = {Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02915, USA. jim.f.cav@gmail.com},
abstract = {Pavlovian biases influence learning and decision making by intricately coupling reward seeking with action invigoration and punishment avoidance with action suppression. This bias is not always adaptive-it can often interfere with instrumental requirements. The prefrontal cortex is thought to help resolve such conflict between motivational systems, but the nature of this control process remains unknown. EEG recordings of midfrontal theta band power are sensitive to conflict and predictive of adaptive control over behavior, but it is not clear whether this signal reflects control over conflict between motivational systems. Here we used a task that orthogonalized action requirements and outcome valence while recording concurrent EEG in human participants. By applying a computational model of task performance, we derived parameters reflective of the latent influence of Pavlovian bias and how it was modulated by midfrontal theta power during motivational conflict. Between subjects, those who performed better under Pavlovian conflict exhibited higher midfrontal theta power. Within subjects, trial-to-trial variance in theta power was predictive of ability to overcome the influence of the Pavlovian bias, and this effect was most pronounced in subjects with higher midfrontal theta to conflict. These findings demonstrate that midfrontal theta is not only a sensitive index of prefrontal control, but it can also reflect the application of top-down control over instrumental processes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pavlovian biases influence learning and decision making by intricately coupling reward seeking with action invigoration and punishment avoidance with action suppression. This bias is not always adaptive-it can often interfere with instrumental requirements. The prefrontal cortex is thought to help resolve such conflict between motivational systems, but the nature of this control process remains unknown. EEG recordings of midfrontal theta band power are sensitive to conflict and predictive of adaptive control over behavior, but it is not clear whether this signal reflects control over conflict between motivational systems. Here we used a task that orthogonalized action requirements and outcome valence while recording concurrent EEG in human participants. By applying a computational model of task performance, we derived parameters reflective of the latent influence of Pavlovian bias and how it was modulated by midfrontal theta power during motivational conflict. Between subjects, those who performed better under Pavlovian conflict exhibited higher midfrontal theta power. Within subjects, trial-to-trial variance in theta power was predictive of ability to overcome the influence of the Pavlovian bias, and this effect was most pronounced in subjects with higher midfrontal theta to conflict. These findings demonstrate that midfrontal theta is not only a sensitive index of prefrontal control, but it can also reflect the application of top-down control over instrumental processes.
Chowdhury, Rumana; Guitart-Masip, Marc; Lambert, Christian; Dayan, Peter; Huys, Quentin; Düzel, Emrah; Dolan, Raymond J.
Dopamine restores reward prediction errors in old age Journal Article
In: Nat Neurosci, vol. 16, no. 5, pp. 648–653, 2013.
@article{ChowdhuryDolan13,
title = {Dopamine restores reward prediction errors in old age},
author = {Rumana Chowdhury and Marc Guitart-Masip and Christian Lambert and Peter Dayan and Quentin Huys and Emrah Düzel and Raymond J. Dolan},
url = {http://acplab.org/wp-content/uploads/pub/ChowdhuryEa13-Dopamine-Restores-Reward-Prediction-Errors-in-Old-Age-1.pdf},
doi = {10.1038/nn.3364},
year = {2013},
date = {2013-05-01},
urldate = {2013-05-01},
journal = {Nat Neurosci},
volume = {16},
number = {5},
pages = {648–653},
school = {Institute of Cognitive Neuroscience, University College London, London, UK. rumana.neuro@gmail.com},
abstract = {Senescence affects the ability to utilize information about the likelihood of rewards for optimal decision-making. Using functional magnetic resonance imaging in humans, we found that healthy older adults had an abnormal signature of expected value, resulting in an incomplete reward prediction error (RPE) signal in the nucleus accumbens, a brain region that receives rich input projections from substantia nigra/ventral tegmental area (SN/VTA) dopaminergic neurons. Structural connectivity between SN/VTA and striatum, measured by diffusion tensor imaging, was tightly coupled to inter-individual differences in the expression of this expected reward value signal. The dopamine precursor levodopa (L-DOPA) increased the task-based learning rate and task performance in some older adults to the level of young adults. This drug effect was linked to restoration of a canonical neural RPE. Our results identify a neurochemical signature underlying abnormal reward processing in older adults and indicate that this can be modulated by L-DOPA.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Senescence affects the ability to utilize information about the likelihood of rewards for optimal decision-making. Using functional magnetic resonance imaging in humans, we found that healthy older adults had an abnormal signature of expected value, resulting in an incomplete reward prediction error (RPE) signal in the nucleus accumbens, a brain region that receives rich input projections from substantia nigra/ventral tegmental area (SN/VTA) dopaminergic neurons. Structural connectivity between SN/VTA and striatum, measured by diffusion tensor imaging, was tightly coupled to inter-individual differences in the expression of this expected reward value signal. The dopamine precursor levodopa (L-DOPA) increased the task-based learning rate and task performance in some older adults to the level of young adults. This drug effect was linked to restoration of a canonical neural RPE. Our results identify a neurochemical signature underlying abnormal reward processing in older adults and indicate that this can be modulated by L-DOPA.
Lieder, Falk; Goodman, Noah; Huys, Quentin J M
Learned Helplessness and Generalization Proceedings Article
In: Cognitive Science Conference, 2013.
@inproceedings{LiederHuys13,
title = {Learned Helplessness and Generalization},
author = {Falk Lieder and Noah Goodman and Quentin J M Huys},
url = {https://web.stanford.edu/~ngoodman/papers/LiederGoodmanHuys2013.pdf},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {Cognitive Science Conference},
abstract = {In learned helplessness experiments, subjects first expe-rience a lack of control in one situation, and then showlearning deficits when performing or learning anothertask in another situation. Generalization, thus, is at thecore of the learned helplessness phenomenon. Substan-tial experimental and theoretical effort has been investedinto establishing that a state- and task-independent be-lief about controllability is necessary. However, to whatextent generalization is also sufficient to explain thetransfer has not been examined. Here, we show qual-itatively and quantitatively that Bayesian learning ofaction-outcome contingencies at three levels of abstrac-tion is sufficient to account for the key features of learnedhelplessness, including escape deficits and impairment ofappetitive learning after inescapable shocks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In learned helplessness experiments, subjects first expe-rience a lack of control in one situation, and then showlearning deficits when performing or learning anothertask in another situation. Generalization, thus, is at thecore of the learned helplessness phenomenon. Substan-tial experimental and theoretical effort has been investedinto establishing that a state- and task-independent be-lief about controllability is necessary. However, to whatextent generalization is also sufficient to explain thetransfer has not been examined. Here, we show qual-itatively and quantitatively that Bayesian learning ofaction-outcome contingencies at three levels of abstrac-tion is sufficient to account for the key features of learnedhelplessness, including escape deficits and impairment ofappetitive learning after inescapable shocks.
Guitart-Masip, Marc; Huys, Quentin J M.; Fuentemilla, Lluis; Dayan, Peter; Duzel, Emrah; Dolan, Raymond J.
Go and no-go learning in reward and punishment: interactions between affect and effect Journal Article
In: Neuroimage, vol. 62, no. 1, pp. 154–166, 2012.
@article{GuitartDolan12,
title = {Go and no-go learning in reward and punishment: interactions between affect and effect},
author = {Marc Guitart-Masip and Quentin J M. Huys and Lluis Fuentemilla and Peter Dayan and Emrah Duzel and Raymond J. Dolan},
url = {http://acplab.org/wp-content/uploads/pub/GuitartEa12-Go-and-No-Go-Learning-in-Reward-and-Punishment_-Interactions-between-Affect-and-Effect-1.pdf},
doi = {10.1016/j.neuroimage.2012.04.024},
year = {2012},
date = {2012-08-01},
urldate = {2012-08-01},
journal = {Neuroimage},
volume = {62},
number = {1},
pages = {154–166},
school = {Institute of Cognitive Neuroscience, University College London, London, W1CN 4AR, UK. m.guitart@ucl.ac.uk},
abstract = {Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.
Huys, Quentin J M.; Eshel, Neir; O'Nions, Elizabeth; Sheridan, Luke; Dayan, Peter; Roiser, Jonathan P.
Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees Journal Article
In: PLoS Comput Biol, vol. 8, no. 3, pp. e1002410, 2012.
@article{HuysRoiser12,
title = {Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees},
author = {Quentin J M. Huys and Neir Eshel and Elizabeth O'Nions and Luke Sheridan and Peter Dayan and Jonathan P. Roiser},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa12-Bonsai-Trees-in-Your-Head_-How-the-Pavlovian-System-Sculpts-Goal-Directed-Choices-by-Pruning-Decision-Trees-1.pdf},
doi = {10.1371/journal.pcbi.1002410},
year = {2012},
date = {2012-03-01},
urldate = {2012-03-01},
journal = {PLoS Comput Biol},
volume = {8},
number = {3},
pages = {e1002410},
school = {Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.},
abstract = {When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders.
Guitart-Masip, Marc; Fuentemilla, Lluis; Bach, Dominik R; Huys, Quentin J M; Dayan, Peter; Dolan, Raymond J; Duzel, Emrah
Action dominates valence in anticipatory representations in the human striatum and dopaminergic midbrain Journal Article
In: J Neurosci, vol. 31, no. 21, pp. 7867–7875, 2011.
@article{GuitartDuzel11,
title = {Action dominates valence in anticipatory representations in the human striatum and dopaminergic midbrain},
author = {Marc Guitart-Masip and Lluis Fuentemilla and Dominik R Bach and Quentin J M Huys and Peter Dayan and Raymond J Dolan and Emrah Duzel},
url = {http://acplab.org/wp-content/uploads/pub/GuitartEa11-Action-Dominates-Valence-in-Anticipatory-Representations-in-the-Human-Striatum-and-Dopaminergic-Midbrain-1.pdf},
doi = {10.1523/JNEUROSCI.6376-10.2011},
year = {2011},
date = {2011-05-01},
urldate = {2011-05-01},
journal = {J Neurosci},
volume = {31},
number = {21},
pages = {7867–7875},
abstract = {The acquisition of reward and the avoidance of punishment could logically be contingent on either emitting or withholding particular actions. However, the separate pathways in the striatum for go and no-go appear to violate this independence, instead coupling affect and effect. Respect for this interdependence has biased many studies of reward and punishment, so potential action-outcome valence interactions during anticipatory phases remain unexplored. In a functional magnetic resonance imaging study with healthy human volunteers, we manipulated subjects' requirement to emit or withhold an action independent from subsequent receipt of reward or avoidance of punishment. During anticipation, in the striatum and a lateral region within the substantia nigra/ventral tegmental area (SN/VTA), action representations dominated over valence representations. Moreover, we did not observe any representation associated with different state values through accumulation of outcomes, challenging a conventional and dominant association between these areas and state value representations. In contrast, a more medial sector of the SN/VTA responded preferentially to valence, with opposite signs depending on whether action was anticipated to be emitted or withheld. This dominant influence of action requires an enriched notion of opponency between reward and punishment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The acquisition of reward and the avoidance of punishment could logically be contingent on either emitting or withholding particular actions. However, the separate pathways in the striatum for go and no-go appear to violate this independence, instead coupling affect and effect. Respect for this interdependence has biased many studies of reward and punishment, so potential action-outcome valence interactions during anticipatory phases remain unexplored. In a functional magnetic resonance imaging study with healthy human volunteers, we manipulated subjects' requirement to emit or withhold an action independent from subsequent receipt of reward or avoidance of punishment. During anticipation, in the striatum and a lateral region within the substantia nigra/ventral tegmental area (SN/VTA), action representations dominated over valence representations. Moreover, we did not observe any representation associated with different state values through accumulation of outcomes, challenging a conventional and dominant association between these areas and state value representations. In contrast, a more medial sector of the SN/VTA responded preferentially to valence, with opposite signs depending on whether action was anticipated to be emitted or withheld. This dominant influence of action requires an enriched notion of opponency between reward and punishment.
Huys, Quentin J M.; Cools, Roshan; Gölzer, Martin; Friedel, Eva; Heinz, Andreas; Dolan, Raymond J.; Dayan, Peter
Disentangling the roles of approach, activation and valence in instrumental and Pavlovian responding Journal Article
In: PLoS Comput Biol, vol. 7, no. 4, pp. e1002028, 2011.
@article{HuysDayan11,
title = {Disentangling the roles of approach, activation and valence in instrumental and Pavlovian responding},
author = {Quentin J M. Huys and Roshan Cools and Martin Gölzer and Eva Friedel and Andreas Heinz and Raymond J. Dolan and Peter Dayan},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa11-Disentangling-the-Roles-of-Approach-Activation-and-Valence-in-Instrumental-and-Pavlovian-Responding-1.pdf},
doi = {10.1371/journal.pcbi.1002028},
year = {2011},
date = {2011-04-01},
urldate = {2011-04-01},
journal = {PLoS Comput Biol},
volume = {7},
number = {4},
pages = {e1002028},
school = {Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom. qhuys@cantab.net},
abstract = {Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.
Huys, Quentin J M; Dayan, Peter
A Bayesian formulation of behavioral control Journal Article
In: Cognition, vol. 113, no. 3, pp. 314–328, 2009.
@article{HuysDayan09,
title = {A Bayesian formulation of behavioral control},
author = {Quentin J M Huys and Peter Dayan},
url = {http://acplab.org/wp-content/uploads/pub/HuysDayan09-A-Bayesian-formulation-of-behavioral-control.pdf},
doi = {10.1016/j.cognition.2009.01.008},
year = {2009},
date = {2009-12-01},
urldate = {2009-12-01},
journal = {Cognition},
volume = {113},
number = {3},
pages = {314–328},
school = {Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N3AR, UK. qhuys@cantab.net},
abstract = {Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or psychiatry, and the formal treatments in engineering appear to capture only limited aspects of the intuitive concepts. Here, we formalize controllability in terms of characteristics of prior distributions over affectively charged environments. We explore the relevance of this notion of control to reinforcement learning methods of optimising behavior in such environments and consider how apparently maladaptive beliefs can result from normative inference processes. These results are discussed with reference to depression and animal models thereof.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or psychiatry, and the formal treatments in engineering appear to capture only limited aspects of the intuitive concepts. Here, we formalize controllability in terms of characteristics of prior distributions over affectively charged environments. We explore the relevance of this notion of control to reinforcement learning methods of optimising behavior in such environments and consider how apparently maladaptive beliefs can result from normative inference processes. These results are discussed with reference to depression and animal models thereof.
Huys, Quentin J M.; Paninski, Liam
Smoothing of, and parameter estimation from, noisy biophysical recordings Journal Article
In: PLoS Comput Biol, vol. 5, no. 5, pp. e1000379, 2009.
@article{HuysPaninski09,
title = {Smoothing of, and parameter estimation from, noisy biophysical recordings},
author = {Quentin J M. Huys and Liam Paninski},
url = {http://acplab.org/wp-content/uploads/pub/HuysPaninski09-Smoothing-Of-and-Parameter-Estimation-From-Noisy-Biophysical-Recordings-1.pdf},
doi = {10.1371/journal.pcbi.1000379},
year = {2009},
date = {2009-05-01},
urldate = {2009-05-01},
journal = {PLoS Comput Biol},
volume = {5},
number = {5},
pages = {e1000379},
school = {Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom. qhuys@cantab.net},
abstract = {Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering") methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering") methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.
Natarajan, Rama; Huys, Quentin J M; Dayan, Peter; Zemel, Richard S
Encoding and decoding spikes for dynamic stimuli Journal Article
In: Neural Comput, vol. 20, no. 9, pp. 2325–2360, 2008.
@article{NatarajanZemel08,
title = {Encoding and decoding spikes for dynamic stimuli},
author = {Rama Natarajan and Quentin J M Huys and Peter Dayan and Richard S Zemel},
url = {http://acplab.org/wp-content/uploads/pub/NatarajanEa08-Encoding-and-Decoding-Spikes-for-Dynamic-Stimuli-1.pdf},
doi = {10.1162/neco.2008.01-07-436},
year = {2008},
date = {2008-09-01},
urldate = {2008-09-01},
journal = {Neural Comput},
volume = {20},
number = {9},
pages = {2325–2360},
school = {Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. rama@cs.toronto.edu},
abstract = {Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.
Dayan, P; Huys, Q. J. M.
Serotonin, Inhibition, and Negative Mood Journal Article
In: PLoS Comput Biol, vol. 4, no. 2, pp. e4, 2008.
@article{DayanHuys08,
title = {Serotonin, Inhibition, and Negative Mood},
author = {P Dayan and Q. J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/DayanHuys08-Serotonin-Inhibition-and-Negative-Mood-1.pdf},
doi = {10.1371/journal.pcbi.0040004},
year = {2008},
date = {2008-02-01},
urldate = {2008-02-01},
journal = {PLoS Comput Biol},
volume = {4},
number = {2},
pages = {e4},
abstract = {Pavlovian predictions of future aversive outcomes lead to behavioral inhibition, suppression, and withdrawal. There is considerable evidence for the involvement of serotonin in both the learning of these predictions and the inhibitory consequences that ensue, although less for a causal relationship between the two. In the context of a highly simplified model of chains of affectively charged thoughts, we interpret the combined effects of serotonin in terms of pruning a tree of possible decisions, (i.e., eliminating those choices that have low or negative expected outcomes). We show how a drop in behavioral inhibition, putatively resulting from an experimentally or psychiatrically influenced drop in serotonin, could result in unexpectedly large negative prediction errors and a significant aversive shift in reinforcement statistics. We suggest an interpretation of this finding that helps dissolve the apparent contradiction between the fact that inhibition of serotonin reuptake is the first-line treatment of depression, although serotonin itself is most strongly linked with aversive rather than appetitive outcomes and predictions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pavlovian predictions of future aversive outcomes lead to behavioral inhibition, suppression, and withdrawal. There is considerable evidence for the involvement of serotonin in both the learning of these predictions and the inhibitory consequences that ensue, although less for a causal relationship between the two. In the context of a highly simplified model of chains of affectively charged thoughts, we interpret the combined effects of serotonin in terms of pruning a tree of possible decisions, (i.e., eliminating those choices that have low or negative expected outcomes). We show how a drop in behavioral inhibition, putatively resulting from an experimentally or psychiatrically influenced drop in serotonin, could result in unexpectedly large negative prediction errors and a significant aversive shift in reinforcement statistics. We suggest an interpretation of this finding that helps dissolve the apparent contradiction between the fact that inhibition of serotonin reuptake is the first-line treatment of depression, although serotonin itself is most strongly linked with aversive rather than appetitive outcomes and predictions.
Huys, Quentin J M; Vogelstein, Joshua; Dayan, Peter
Psychiatry: Insights into depression through normative decision-making models Book Section
In: Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L. (Ed.): Advances in Neural Information Processing Systems 21, pp. 729–736, MIT Press, 2008.
@incollection{HuysDayan08,
title = {Psychiatry: Insights into depression through normative decision-making models},
author = {Quentin J M Huys and Joshua Vogelstein and Peter Dayan},
editor = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
url = {http://acplab.org/wp-content/uploads/pub/NIPS-2008-psychiatry-insights-into-depression-through-normative-decision-making-models-Paper.pdf},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Advances in Neural Information Processing Systems 21},
pages = {729–736},
publisher = {MIT Press},
abstract = {Decision making lies at the very heart of many psychiatric diseases. It is also a
central theoretical concern in a wide variety of fields and has undergone detailed,
in-depth, analyses. We take as an example Major Depressive Disorder (MDD),
applying insights from a Bayesian reinforcement learning framework. We focus
on anhedonia and helplessness. Helplessness—a core element in the conceptualizations of MDD that has lead to major advances in its treatment, pharmacological and neurobiological understanding—is formalized as a simple prior over the
outcome entropy of actions in uncertain environments. Anhedonia, which is an
equally fundamental aspect of the disease, is related to the effective reward size.
These formulations allow for the design of specific tasks to measure anhedonia
and helplessness behaviorally. We show that these behavioral measures capture
explicit, questionnaire-based cognitions. We also provide evidence that these tasks
may allow classification of subjects into healthy and MDD groups based purely
on a behavioural measure and avoiding any verbal reports.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Decision making lies at the very heart of many psychiatric diseases. It is also a
central theoretical concern in a wide variety of fields and has undergone detailed,
in-depth, analyses. We take as an example Major Depressive Disorder (MDD),
applying insights from a Bayesian reinforcement learning framework. We focus
on anhedonia and helplessness. Helplessness—a core element in the conceptualizations of MDD that has lead to major advances in its treatment, pharmacological and neurobiological understanding—is formalized as a simple prior over the
outcome entropy of actions in uncertain environments. Anhedonia, which is an
equally fundamental aspect of the disease, is related to the effective reward size.
These formulations allow for the design of specific tasks to measure anhedonia
and helplessness behaviorally. We show that these behavioral measures capture
explicit, questionnaire-based cognitions. We also provide evidence that these tasks
may allow classification of subjects into healthy and MDD groups based purely
on a behavioural measure and avoiding any verbal reports.
central theoretical concern in a wide variety of fields and has undergone detailed,
in-depth, analyses. We take as an example Major Depressive Disorder (MDD),
applying insights from a Bayesian reinforcement learning framework. We focus
on anhedonia and helplessness. Helplessness—a core element in the conceptualizations of MDD that has lead to major advances in its treatment, pharmacological and neurobiological understanding—is formalized as a simple prior over the
outcome entropy of actions in uncertain environments. Anhedonia, which is an
equally fundamental aspect of the disease, is related to the effective reward size.
These formulations allow for the design of specific tasks to measure anhedonia
and helplessness behaviorally. We show that these behavioral measures capture
explicit, questionnaire-based cognitions. We also provide evidence that these tasks
may allow classification of subjects into healthy and MDD groups based purely
on a behavioural measure and avoiding any verbal reports.
Nouraei, S. A R; Huys, Q. J M; Chatrath, P.; Powles, J.; Harcourt, J. P.
Screening patients with sensorineural hearing loss for vestibular schwannoma using a Bayesian classifier Journal Article
In: Clin Otolaryngol, vol. 32, no. 4, pp. 248–254, 2007.
@article{NouraeiHarcourt07,
title = {Screening patients with sensorineural hearing loss for vestibular schwannoma using a Bayesian classifier},
author = {S. A R Nouraei and Q. J M Huys and P. Chatrath and J. Powles and J. P. Harcourt},
url = {http://acplab.org/wp-content/uploads/pub/NouraeiEa07-Screening-patients-with-sensorineural-hearing-loss-for-vestibular-schwannoma.pdf},
doi = {10.1111/j.1365-2273.2007.01460.x},
year = {2007},
date = {2007-08-01},
urldate = {2007-08-01},
journal = {Clin Otolaryngol},
volume = {32},
number = {4},
pages = {248–254},
school = {Department of Otolaryngology, Charing Cross Hospital, London, UK. RN@cantab.net},
abstract = {OBJECTIVES: Selecting patients with asymmetrical sensorineural hearing loss for further investigation continues to pose clinical and medicolegal challenges, given the disparity between the number of symptomatic patients, and the low incidence of vestibular schwannoma as the underlying cause. We developed and validated a diagnostic model using a generalisation of neural networks, for detecting vestibular schwannomas from clinical and audiological data, and compared its performance with six previously published clinical and audiological decision-support screening protocols. DESIGN: Probabilistic complex data classification using a neural network generalization. SETTINGS: Tertiary referral lateral skull base and a computational neuroscience unit. PARTICIPANTS: Clinical and audiometric details of 129 patients with, and as many age and sex-matched patients without vestibular schwannomas, as determined with magnetic resonance imaging. MAIN OUTCOME MEASURES: The ability to diagnose a patient as having or not having vestibular schwannoma. RESULTS: A Gaussian Process Ordinal Regression Classifier was trained and cross-validated to classify cases as 'with' or 'without' vestibular schwannoma, and its diagnostic performance was assessed using receiver operator characteristic plots. It proved possible to pre-select sensitivity and specificity, with an area under the curve of 0.8025. At 95% sensitivity, the trained system had a specificity of 56%, 30% better than audiological protocols with closest sensitivities. The sensitivities of previously-published audiological protocols ranged between 82-97%, and their specificities ranged between 15-61%. DISCUSSION: The Gaussian Process ORdinal Regression Classifier increased the flexibility and specificity of the screening process for vestibular schwannoma when applied to a sample of matched patients with and without this condition. If applied prospectively, it could reduce the number of 'normal' magnetic resonance (MR) scans by as much as 30% without reducing detection sensitivity. Performance can be further improed through incorporating additional data domains. Current findings need to be reproduced using a larger dataset.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
OBJECTIVES: Selecting patients with asymmetrical sensorineural hearing loss for further investigation continues to pose clinical and medicolegal challenges, given the disparity between the number of symptomatic patients, and the low incidence of vestibular schwannoma as the underlying cause. We developed and validated a diagnostic model using a generalisation of neural networks, for detecting vestibular schwannomas from clinical and audiological data, and compared its performance with six previously published clinical and audiological decision-support screening protocols. DESIGN: Probabilistic complex data classification using a neural network generalization. SETTINGS: Tertiary referral lateral skull base and a computational neuroscience unit. PARTICIPANTS: Clinical and audiometric details of 129 patients with, and as many age and sex-matched patients without vestibular schwannomas, as determined with magnetic resonance imaging. MAIN OUTCOME MEASURES: The ability to diagnose a patient as having or not having vestibular schwannoma. RESULTS: A Gaussian Process Ordinal Regression Classifier was trained and cross-validated to classify cases as 'with' or 'without' vestibular schwannoma, and its diagnostic performance was assessed using receiver operator characteristic plots. It proved possible to pre-select sensitivity and specificity, with an area under the curve of 0.8025. At 95% sensitivity, the trained system had a specificity of 56%, 30% better than audiological protocols with closest sensitivities. The sensitivities of previously-published audiological protocols ranged between 82-97%, and their specificities ranged between 15-61%. DISCUSSION: The Gaussian Process ORdinal Regression Classifier increased the flexibility and specificity of the screening process for vestibular schwannoma when applied to a sample of matched patients with and without this condition. If applied prospectively, it could reduce the number of 'normal' magnetic resonance (MR) scans by as much as 30% without reducing detection sensitivity. Performance can be further improed through incorporating additional data domains. Current findings need to be reproduced using a larger dataset.
Huys, Quentin J M; Zemel, Rich; Natarajan, Rama; Dayan, Peter
Fast population coding Journal Article
In: Neural Computation., vol. 19, no. 2, pp. 460-97, 2007.
@article{HuysDayan07,
title = {Fast population coding},
author = {Quentin J M Huys and Rich Zemel and Rama Natarajan and Peter Dayan},
url = {http://www.gatsby.ucl.ac.uk/~dayan/papers/nc07.pdf},
doi = {10.1162/neco.2007.19.2.404},
year = {2007},
date = {2007-01-01},
journal = {Neural Computation.},
volume = {19},
number = {2},
pages = {460-97},
abstract = {Uncertainty coming from the noise in its neurons and the ill-posed nature
of many tasks plagues neural computations. Maybe surprisingly, many
studies show that the brain manipulates these forms of uncertainty in
a probabilistically consistent and normative manner, and there is now a
rich theoretical literature on the capabilities of populations of neurons to
implement computations in the face of uncertainty. However, one major
facet of uncertainty has received comparatively little attention: time. In
a dynamic, rapidly changing world, data are only temporarily relevant.
Here, we analyze the computational consequences of encoding stimulus
trajectories in populations of neurons. For the most obvious, simple, instantaneous encoder, the correlations induced by natural, smooth stimuli
engender a decoder that requires access to information that is nonlocal
both in time and across neurons. This formally amounts to a ruinous
representation. We show that there is an alternative encoder that is computationally and representationally powerful in which each spike contributes independent information; it is independently decodable, in other
words. We suggest this as an appropriate foundation for understanding
time-varying population codes. Furthermore, we show how adaptation to temporal stimulus statistics emerges directly from the demands of simple
decoding.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Uncertainty coming from the noise in its neurons and the ill-posed nature
of many tasks plagues neural computations. Maybe surprisingly, many
studies show that the brain manipulates these forms of uncertainty in
a probabilistically consistent and normative manner, and there is now a
rich theoretical literature on the capabilities of populations of neurons to
implement computations in the face of uncertainty. However, one major
facet of uncertainty has received comparatively little attention: time. In
a dynamic, rapidly changing world, data are only temporarily relevant.
Here, we analyze the computational consequences of encoding stimulus
trajectories in populations of neurons. For the most obvious, simple, instantaneous encoder, the correlations induced by natural, smooth stimuli
engender a decoder that requires access to information that is nonlocal
both in time and across neurons. This formally amounts to a ruinous
representation. We show that there is an alternative encoder that is computationally and representationally powerful in which each spike contributes independent information; it is independently decodable, in other
words. We suggest this as an appropriate foundation for understanding
time-varying population codes. Furthermore, we show how adaptation to temporal stimulus statistics emerges directly from the demands of simple
decoding.
of many tasks plagues neural computations. Maybe surprisingly, many
studies show that the brain manipulates these forms of uncertainty in
a probabilistically consistent and normative manner, and there is now a
rich theoretical literature on the capabilities of populations of neurons to
implement computations in the face of uncertainty. However, one major
facet of uncertainty has received comparatively little attention: time. In
a dynamic, rapidly changing world, data are only temporarily relevant.
Here, we analyze the computational consequences of encoding stimulus
trajectories in populations of neurons. For the most obvious, simple, instantaneous encoder, the correlations induced by natural, smooth stimuli
engender a decoder that requires access to information that is nonlocal
both in time and across neurons. This formally amounts to a ruinous
representation. We show that there is an alternative encoder that is computationally and representationally powerful in which each spike contributes independent information; it is independently decodable, in other
words. We suggest this as an appropriate foundation for understanding
time-varying population codes. Furthermore, we show how adaptation to temporal stimulus statistics emerges directly from the demands of simple
decoding.
Reviews
Pezzoli, Patrizia; Parsons, Sam; Kievit, Rogier A.; Astle, Duncan E.; Huys, Quentin J. M.; Steinbeis, Nikolaus; Viding, Essi
Challenges and Solutions to the Measurement of Neurocognitive Mechanisms in Developmental Settings Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2023.
@article{PezzoliViding23,
title = {Challenges and Solutions to the Measurement of Neurocognitive Mechanisms in Developmental Settings},
author = {Patrizia Pezzoli and Sam Parsons and Rogier A. Kievit and Duncan E. Astle and Quentin J. M. Huys and Nikolaus Steinbeis and Essi Viding},
url = {http://acplab.org/wp-content/uploads/pub/PezzolliVidingNeurocognitiveDevelopment2023.pdf},
doi = {10.1016/j.bpsc.2023.03.011},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
publisher = {Elsevier BV},
abstract = {Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful early interventions. Currently, however, we have limited understanding of the neurocognitive mechanisms involved in shaping mental health trajectories from childhood through young adulthood, and this constrains our ability to develop effective clinical interventions. In particular, there is an urgent need to develop more sensitive, reliable, and scalable measures of individual differences for use in developmental settings.
In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach—which we refer to as “cognitive microscopy”—that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful early interventions. Currently, however, we have limited understanding of the neurocognitive mechanisms involved in shaping mental health trajectories from childhood through young adulthood, and this constrains our ability to develop effective clinical interventions. In particular, there is an urgent need to develop more sensitive, reliable, and scalable measures of individual differences for use in developmental settings.
In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach—which we refer to as “cognitive microscopy”—that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework.
In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach—which we refer to as “cognitive microscopy”—that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework.
Yip, Sarah W.; Barch, Deanna M.; Chase, Henry W.; Flagel, Shelly; Huys, Quentin J. M.; Konova, Anna B.; Montague, Read; Paulus, Martin
From computation to clinic Journal Article
In: Biological Psychiatry Global Open Science, 2022.
@article{YipPaulus22,
title = {From computation to clinic},
author = {Sarah W. Yip and Deanna M. Barch and Henry W. Chase and Shelly Flagel and Quentin J. M. Huys and Anna B. Konova and Read Montague and Martin Paulus},
url = {http://acplab.org/wp-content/uploads/pub/Yip2022.pdf},
doi = {https://doi.org/10.1016/j.bpsgos.2022.03.011},
year = {2022},
date = {2022-04-02},
urldate = {2022-04-02},
journal = {Biological Psychiatry Global Open Science},
publisher = {Elsevier BV},
abstract = {Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from ‘clinic-to-computation’, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from ‘computation-to-clinic’, remains rare. In addition, consensus regarding specific barriers to forward translation—and on the best strategies to overcome these barriers—is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to: (i) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect; and (ii) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost effectiveness analyses).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from ‘clinic-to-computation’, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from ‘computation-to-clinic’, remains rare. In addition, consensus regarding specific barriers to forward translation—and on the best strategies to overcome these barriers—is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to: (i) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect; and (ii) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost effectiveness analyses).
Reiter, Andrea MF; Atiya, Nadim AA; Berwian, Isabel M; Huys, Quentin JM
Neuro-cognitive processes as mediators of psychological treatment effects Journal Article
In: Current Opinion in Behavioral Sciences, 2021.
@article{ReiterHuys21c,
title = {Neuro-cognitive processes as mediators of psychological treatment effects},
author = {Andrea MF Reiter and Nadim AA Atiya and Isabel M Berwian and Quentin JM Huys},
url = {http://acplab.org/wp-content/uploads/pub/ReiterHuys21-Neuro-Cognitivve-Process.pdf},
doi = {10.1016/j.cobeha.2021.02.007},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
journal = {Current Opinion in Behavioral Sciences},
publisher = {Elsevier BV},
abstract = {Psychological interventions are first-line treatments of depression. Despite a rich theoretical background, the mediators of treatment effects remain only partially understood: it has been difficult to precisely delineate the targets psychological interventions engage, and even more difficult to differentiate amongst the targets engaged by different psychological interventions. Here, we outline these issues and discuss a surprisingly understudied approach, namely the study of cognitive and computational tasks to measure psychological treatment targets. Such tasks benefit from substantial advances in cognitive neuroscience over the past two decades, and have excellent face validity. We discuss two candidate tasks for back-translation and conclude with a critical evaluation of potential problems associated with this neuro-cognitive approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Psychological interventions are first-line treatments of depression. Despite a rich theoretical background, the mediators of treatment effects remain only partially understood: it has been difficult to precisely delineate the targets psychological interventions engage, and even more difficult to differentiate amongst the targets engaged by different psychological interventions. Here, we outline these issues and discuss a surprisingly understudied approach, namely the study of cognitive and computational tasks to measure psychological treatment targets. Such tasks benefit from substantial advances in cognitive neuroscience over the past two decades, and have excellent face validity. We discuss two candidate tasks for back-translation and conclude with a critical evaluation of potential problems associated with this neuro-cognitive approach.
Durstewitz, Daniel; Huys, Quentin J M; Koppe, Georgia
Psychiatric Illnesses as Disorders of Network Dynamics Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2021.
@article{DurstewitzKoppe21b,
title = {Psychiatric Illnesses as Disorders of Network Dynamics},
author = {Daniel Durstewitz and Quentin J M Huys and Georgia Koppe},
url = {http://acplab.org/wp-content/uploads/pub/DurstewitzEa18-Psychiatric-Illnesses-As-Disorders-of-Network-Dynamics-1.pdf},
doi = {10.1016/j.bpsc.2020.01.001},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
abstract = {This review provides a dynamical systems perspective on mental illness. After a brief introduction to the theory of dynamical systems, we focus on the common assumption in theoretical and computational neuroscience that phenomena at subcellular, cellular, network, cognitive, and even societal levels could be described and explained in terms of dynamical systems theory. As such, dynamical systems theory may also provide a framework for understanding mental illnesses. The review examines a number of core dynamical systems phenomena and relates each of these to aspects of mental illnesses. This provides an outline of how a broad set of phenomena in serious and common mental illnesses and neurological conditions can be understood in dynamical systems terms. It suggests that the dynamical systems level may provide a central, hublike level of convergence that unifies and links multiple biophysical and behavioral phenomena in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. We also briefly outline current methodological approaches for inferring dynamical systems from data such as electroencephalography, functional magnetic resonance imaging, or self-reports, and we discuss the implications of a dynamical view for the diagnosis, prognosis, and treatment of psychiatric conditions. We argue that a consideration of dynamics could play a potentially transformative role in the choice and target of interventions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This review provides a dynamical systems perspective on mental illness. After a brief introduction to the theory of dynamical systems, we focus on the common assumption in theoretical and computational neuroscience that phenomena at subcellular, cellular, network, cognitive, and even societal levels could be described and explained in terms of dynamical systems theory. As such, dynamical systems theory may also provide a framework for understanding mental illnesses. The review examines a number of core dynamical systems phenomena and relates each of these to aspects of mental illnesses. This provides an outline of how a broad set of phenomena in serious and common mental illnesses and neurological conditions can be understood in dynamical systems terms. It suggests that the dynamical systems level may provide a central, hublike level of convergence that unifies and links multiple biophysical and behavioral phenomena in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. We also briefly outline current methodological approaches for inferring dynamical systems from data such as electroencephalography, functional magnetic resonance imaging, or self-reports, and we discuss the implications of a dynamical view for the diagnosis, prognosis, and treatment of psychiatric conditions. We argue that a consideration of dynamics could play a potentially transformative role in the choice and target of interventions.
Huys, Quentin J. M.; Browning, Michael; Paulus, Martin P.; Frank, Michael J.
Advances in the computational understanding of mental illness. Journal Article
In: Neuropsychopharmacology, 2021.
@article{HuysFrank21b,
title = {Advances in the computational understanding of mental illness.},
author = {Quentin J. M. Huys and Michael Browning and Martin P. Paulus and Michael J. Frank},
url = {http://acplab.org/wp-content/uploads/pub/HuysNPP20-Advances-in-the-Computational-Understanding-of-Mental-Illness-1.pdf},
doi = {10.1038/s41386-020-0746-4},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Neuropsychopharmacology},
abstract = {Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.
Rutledge, Robb B; Chekroud, Adam M; Huys, Quentin Jm
Machine learning and big data in psychiatry: toward clinical applications Journal Article
In: Current Opinion Neurobiol, vol. 55, pp. 152–159, 2019.
@article{RutledgeHuys19,
title = {Machine learning and big data in psychiatry: toward clinical applications},
author = {Robb B Rutledge and Adam M Chekroud and Quentin Jm Huys},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0959438818300898?via=ihubhttp://acplab.org/wp-content/uploads/2022/02/RutledgeEa19-Machine-Learning-and-Big-Data-in-Psychiatry_-toward-Clinical-Applications-1.pdf},
doi = {10.1016/j.conb.2019.02.006},
year = {2019},
date = {2019-04-01},
urldate = {2019-04-01},
journal = {Current Opinion Neurobiol},
volume = {55},
pages = {152–159},
abstract = {Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.
Huys, Quentin J M; Renz, Daniel
A Formal Valuation Framework for Emotions and Their Control Journal Article
In: Biological Psychiatry, vol. 82, iss. 6, pp. 413–420, 2017.
@article{HuysRenz17,
title = {A Formal Valuation Framework for Emotions and Their Control},
author = {Quentin J M Huys and Daniel Renz},
url = {http://acplab.org/wp-content/uploads/pub/A-Formal-Valuation-Framework-for-Emotions-and-Their-Control.pdf},
doi = {10.1016/j.biopsych.2017.07.003},
year = {2017},
date = {2017-09-01},
urldate = {2017-09-01},
journal = {Biological Psychiatry},
volume = {82},
issue = {6},
pages = {413–420},
abstract = {Computational psychiatry aims to apply mathematical and computational techniques to help improve psychiatric care. To achieve this, the phenomena under scrutiny should be within the scope of formal methods. As emotions play an important role across many psychiatric disorders, such computational methods must encompass emotions. Here, we consider formal valuation accounts of emotions. We focus on the fact that the flexibility of emotional responses and the nature of appraisals suggest the need for a model-based valuation framework for emotions. However, resource limitations make plain model-based valuation impossible and require metareasoning strategies to apportion cognitive resources adaptively. We argue that emotions may implement such metareasoning approximations by restricting the range of behaviors and states considered. We consider the processes that guide the deployment of the approximations, discerning between innate, model-free, heuristic, and model-based controllers. A formal valuation and metareasoning framework may thus provide a principled approach to examining emotions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational psychiatry aims to apply mathematical and computational techniques to help improve psychiatric care. To achieve this, the phenomena under scrutiny should be within the scope of formal methods. As emotions play an important role across many psychiatric disorders, such computational methods must encompass emotions. Here, we consider formal valuation accounts of emotions. We focus on the fact that the flexibility of emotional responses and the nature of appraisals suggest the need for a model-based valuation framework for emotions. However, resource limitations make plain model-based valuation impossible and require metareasoning strategies to apportion cognitive resources adaptively. We argue that emotions may implement such metareasoning approximations by restricting the range of behaviors and states considered. We consider the processes that guide the deployment of the approximations, discerning between innate, model-free, heuristic, and model-based controllers. A formal valuation and metareasoning framework may thus provide a principled approach to examining emotions.
Berwian, I M; Walter, H; Seifritz, E; Huys, Q J M
Predicting relapse after antidepressant withdrawal - a systematic review Journal Article
In: Psychol Med, vol. 47, iss. 3, pp. 426–437, 2017.
@article{BerwianHuys17,
title = {Predicting relapse after antidepressant withdrawal - a systematic review},
author = {I M Berwian and H Walter and E Seifritz and Q J M Huys},
url = {http://acplab.org/wp-content/uploads/pub/BerwianEa17-Predicting-Relapse-After-Antidepressant-Withdrawal-a-Systematic-Review-1.pdf},
doi = {10.1017/S0033291716002580},
year = {2017},
date = {2017-02-01},
urldate = {2017-02-01},
journal = {Psychol Med},
volume = {47},
issue = {3},
pages = {426–437},
abstract = {A substantial proportion of the burden of depression arises from its recurrent nature. The risk of relapse after antidepressant medication (ADM) discontinuation is high but not uniform. Predictors of individual relapse risk after antidepressant discontinuation could help to guide treatment and mitigate the long-term course of depression. We conducted a systematic literature search in PubMed to identify relapse predictors using the search terms '(depress* OR MDD*) AND (relapse* OR recurren*) AND (predict* OR risk) AND (discontinu* OR withdraw* OR maintenance OR maintain or continu*) AND (antidepress* OR medication OR drug)' for published studies until November 2014. Studies investigating predictors of relapse in patients aged between 18 and 65 years with a main diagnosis of major depressive disorder (MDD), who remitted from a depressive episode while treated with ADM and were followed up for at least 6 months to assess relapse after part of the sample discontinued their ADM, were included in the review. Although relevant information is present in many studies, only 13 studies based on nine separate samples investigated predictors for relapse after ADM discontinuation. There are multiple promising predictors, including markers of true treatment response and the number of prior episodes. However, the existing evidence is weak and there are no established, validated markers of individual relapse risk after antidepressant cessation. There is little evidence to guide discontinuation decisions in an individualized manner beyond overall recurrence risk. Thus, there is a pressing need to investigate neurobiological markers of individual relapse risk, focusing on treatment discontinuation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A substantial proportion of the burden of depression arises from its recurrent nature. The risk of relapse after antidepressant medication (ADM) discontinuation is high but not uniform. Predictors of individual relapse risk after antidepressant discontinuation could help to guide treatment and mitigate the long-term course of depression. We conducted a systematic literature search in PubMed to identify relapse predictors using the search terms '(depress* OR MDD*) AND (relapse* OR recurren*) AND (predict* OR risk) AND (discontinu* OR withdraw* OR maintenance OR maintain or continu*) AND (antidepress* OR medication OR drug)' for published studies until November 2014. Studies investigating predictors of relapse in patients aged between 18 and 65 years with a main diagnosis of major depressive disorder (MDD), who remitted from a depressive episode while treated with ADM and were followed up for at least 6 months to assess relapse after part of the sample discontinued their ADM, were included in the review. Although relevant information is present in many studies, only 13 studies based on nine separate samples investigated predictors for relapse after ADM discontinuation. There are multiple promising predictors, including markers of true treatment response and the number of prior episodes. However, the existing evidence is weak and there are no established, validated markers of individual relapse risk after antidepressant cessation. There is little evidence to guide discontinuation decisions in an individualized manner beyond overall recurrence risk. Thus, there is a pressing need to investigate neurobiological markers of individual relapse risk, focusing on treatment discontinuation.
Stephan, K E; Schlagenhauf, F; Huys, Q J M; Raman, S; Aponte, E A; Brodersen, K H; Rigoux, L; Moran, R J; Daunizeau, J; Dolan, R J; Friston, K J; Heinz, A
Computational neuroimaging strategies for single patient predictions Journal Article
In: NeuroImage, vol. 145, iss. Pt B, pp. 180–199, 2017.
@article{StephanHeinz17,
title = {Computational neuroimaging strategies for single patient predictions},
author = {K E Stephan and F Schlagenhauf and Q J M Huys and S Raman and E A Aponte and K H Brodersen and L Rigoux and R J Moran and J Daunizeau and R J Dolan and K J Friston and A Heinz},
url = {http://acplab.org/wp-content/uploads/pub/StephanEa17-Computational-neuroimaging-strategies-for-single-patient-predictions.pdf},
doi = {10.1016/j.neuroimage.2016.06.038},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {NeuroImage},
volume = {145},
issue = {Pt B},
pages = {180–199},
abstract = {Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.
Paulus, Martin P; Huys, Quentin J M; Maia, Tiago V
A Roadmap for the Development of Applied Computational Psychiatry Journal Article
In: Biological psychiatry: CNNI, vol. 1, iss. 5, pp. 386–392, 2016.
@article{PaulusMaia16,
title = {A Roadmap for the Development of Applied Computational Psychiatry},
author = {Martin P Paulus and Quentin J M Huys and Tiago V Maia},
url = {http://acplab.org/wp-content/uploads/pub/PaulusEa16-A-Roadmap-for-the-Development-of-Applied-Computational-Psychiatry-1.pdf},
doi = {10.1016/j.bpsc.2016.05.001},
year = {2016},
date = {2016-09-01},
urldate = {2016-09-01},
journal = {Biological psychiatry: CNNI},
volume = {1},
issue = {5},
pages = {386–392},
abstract = {Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational-psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational-psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.
Huys, Quentin J M; Maia, Tiago V; Frank, Michael J
Computational psychiatry as a bridge from neuroscience to clinical applications Journal Article
In: Nature Neuroscience, vol. 19, no. 3, pp. 404–413, 2016.
@article{HuysFrank16,
title = {Computational psychiatry as a bridge from neuroscience to clinical applications},
author = {Quentin J M Huys and Tiago V Maia and Michael J Frank},
url = {http://acplab.org/wp-content/uploads/pub/Huys_2016-Computational-Psychiatry-As-a-Bridge-from-Neuroscience-to-Clinical-Applications.pdf},
doi = {10.1038/nn.4238},
year = {2016},
date = {2016-02-01},
urldate = {2016-02-01},
journal = {Nature Neuroscience},
volume = {19},
number = {3},
pages = {404–413},
publisher = {Springer Science and Business Media LLC},
abstract = {Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.
Huys, Quentin J. M.; Deserno, Lorenz; Obermayer, Klaus; Schlagenhauf, Florian; Heinz, Andreas
Model-Free Temporal-Difference Learning and Dopamine in Alcohol Dependence: Examining Concepts From Theory and Animals in Human Imaging Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 1, no. 5, pp. 401 - 410, 2016.
@article{HuysHeinz16c,
title = {Model-Free Temporal-Difference Learning and Dopamine in Alcohol Dependence: Examining Concepts From Theory and Animals in Human Imaging},
author = {Quentin J. M. Huys and Lorenz Deserno and Klaus Obermayer and Florian Schlagenhauf and Andreas Heinz},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa16c.pdf},
doi = {http://dx.doi.org/10.1016/j.bpsc.2016.06.005},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
volume = {1},
number = {5},
pages = {401 - 410},
abstract = {Abstract Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model-free temporal-difference reinforcement learning. Theories of addiction have long suggested that drugs of abuse may usurp dopamineâs role in learning. Here, we briefly review the preclinical literature to motivate specific hypotheses about model-free temporal-difference learning and then review the imaging evidence in the drug of abuse with the most substantial societal consequences: alcohol. Despite the breadth of the literature, only a few studies have examined the predictions directly, and these provide at best inconclusive evidence for the involvement of temporal-difference learning alterations in alcohol dependence. We discuss the difficulties of testing the theory in humans, make specific suggestions, and close with a focus on the interaction with other learning mechanisms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model-free temporal-difference reinforcement learning. Theories of addiction have long suggested that drugs of abuse may usurp dopamineâs role in learning. Here, we briefly review the preclinical literature to motivate specific hypotheses about model-free temporal-difference learning and then review the imaging evidence in the drug of abuse with the most substantial societal consequences: alcohol. Despite the breadth of the literature, only a few studies have examined the predictions directly, and these provide at best inconclusive evidence for the involvement of temporal-difference learning alterations in alcohol dependence. We discuss the difficulties of testing the theory in humans, make specific suggestions, and close with a focus on the interaction with other learning mechanisms.
Stephan, Klaas E; Bach, Dominik R; Fletcher, Paul C; Flint, Jonathan; Frank, Michael J; Friston, Karl J; Heinz, Andreas; Huys, Quentin J M; Owen, Michael J; Binder, Elisabeth B; Dayan, Peter; Johnstone, Eve C; Meyer-Lindenberg, Andreas; Montague, P Read; Schnyder, Ulrich; Wang, Xiao-Jing; Breakspear, Michael
Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis Journal Article
In: Lancet Psychiatry, vol. 3, iss. 1, pp. 77–83, 2016.
@article{StephanBreakspear16b,
title = {Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis},
author = {Klaas E Stephan and Dominik R Bach and Paul C Fletcher and Jonathan Flint and Michael J Frank and Karl J Friston and Andreas Heinz and Quentin J M Huys and Michael J Owen and Elisabeth B Binder and Peter Dayan and Eve C Johnstone and Andreas Meyer-Lindenberg and P Read Montague and Ulrich Schnyder and Xiao-Jing Wang and Michael Breakspear},
url = {http://acplab.org/wp-content/uploads/pub/StephanEa16-Hilbert1-1.pdf},
doi = {10.1016/S2215-0366(15)00361-2},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Lancet Psychiatry},
volume = {3},
issue = {1},
pages = {77–83},
abstract = {Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science.
Huys, Quentin J M; Daw, Nathaniel D; Dayan, Peter
Depression: a decision-theoretic analysis Journal Article
In: Annual review of neuroscience, vol. 38, pp. 1–23, 2015.
@article{HuysDayan15,
title = {Depression: a decision-theoretic analysis},
author = {Quentin J M Huys and Nathaniel D Daw and Peter Dayan},
url = {http://acplab.org/wp-content/uploads/pub/Huys2015-Depression_a-decision-theoretic-analysis-1.pdf},
doi = {10.1146/annurev-neuro-071714-033928},
year = {2015},
date = {2015-07-01},
urldate = {2015-07-01},
journal = {Annual review of neuroscience},
volume = {38},
pages = {1–23},
abstract = {The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.
Adams, Rick A; Huys, Quentin J M; Roiser, Jonathan P
Computational Psychiatry: towards a mathematically informed understanding of mental illness Journal Article
In: Journal of neurology, neurosurgery, and psychiatry, vol. 87, iss. 1, pp. 53–63, 2015.
@article{AdamsRoiser15,
title = {Computational Psychiatry: towards a mathematically informed understanding of mental illness},
author = {Rick A Adams and Quentin J M Huys and Jonathan P Roiser},
url = {http://acplab.org/wp-content/uploads/pub/AdamsEa16-Computational-Psychiatry_-Towards-a-Mathematically-Informed-Understanding-of-Mental-Illness-1.pdf},
doi = {10.1136/jnnp-2015-310737},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Journal of neurology, neurosurgery, and psychiatry},
volume = {87},
issue = {1},
pages = {53–63},
abstract = {Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.
Huys, Q J M; Guitart-Masip, M; Dolan, R J; Dayan, P
Decision-Theoretic Psychiatry Journal Article
In: Clinical Psychological Science, vol. 3, no. 3, pp. 400–421, 2015.
@article{HuysDayan15d,
title = {Decision-Theoretic Psychiatry},
author = {Q J M Huys and M Guitart-Masip and R J Dolan and P Dayan},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa15d-Decision-Theoretic-Psychiatry.pdf},
doi = {10.1177/2167702614562040},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Clinical Psychological Science},
volume = {3},
number = {3},
pages = {400–421},
abstract = {Psychiatric disorders profoundly impair many aspects of decision making. Poor choices have negative consequences in the moment and make it very hard to navigate complex social environments. Computational neuroscience provides normative, neurobiologically informed descriptions of the components of decision making that serve as a platform for a principled exploration of dysfunctions. Here, we identify and discuss three classes of failure modes arising in these formalisms. They stem from abnormalities in the framing of problems or tasks, from the mechanisms of cognition used to solve the tasks, or from the historical data available from the environment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Psychiatric disorders profoundly impair many aspects of decision making. Poor choices have negative consequences in the moment and make it very hard to navigate complex social environments. Computational neuroscience provides normative, neurobiologically informed descriptions of the components of decision making that serve as a platform for a principled exploration of dysfunctions. Here, we identify and discuss three classes of failure modes arising in these formalisms. They stem from abnormalities in the framing of problems or tasks, from the mechanisms of cognition used to solve the tasks, or from the historical data available from the environment.
Huys, Quentin J M.; Tobler, Philippe N.; Hasler, Gregor; Flagel, Shelly B.
The role of learning-related dopamine signals in addiction vulnerability Journal Article
In: Prog Brain Res, vol. 211, pp. 31–77, 2014.
@article{HuysFlagel14c,
title = {The role of learning-related dopamine signals in addiction vulnerability},
author = {Quentin J M. Huys and Philippe N. Tobler and Gregor Hasler and Shelly B. Flagel},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa14c-The-Role-of-Learning-Related-Dopamine-Signals-in-Addiction-Vulnerability-1.pdf},
doi = {10.1016/B978-0-444-63425-2.00003-9},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Prog Brain Res},
volume = {211},
pages = {31–77},
school = {Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.},
abstract = {Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction.
Huys, Quentin J. M.; Moutoussis, Michael; Williams, Jonathan
Are computational models of any use to psychiatry? Journal Article
In: Neural Networks, vol. 24, no. 6, pp. 544–551, 2011.
@article{HuysWilliams11,
title = {Are computational models of any use to psychiatry?},
author = {Quentin J. M. Huys and Michael Moutoussis and Jonathan Williams},
url = {http://acplab.org/wp-content/uploads/pub/Huys_2011-Are-Computational-Models-of-Any-Use-to-Psychiatry_.pdf},
doi = {10.1016/j.neunet.2011.03.001},
year = {2011},
date = {2011-08-01},
urldate = {2011-08-01},
journal = {Neural Networks},
volume = {24},
number = {6},
pages = {544–551},
publisher = {Elsevier BV},
abstract = {Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases.},
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Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases.
Dayan, Peter; Huys, Quentin J M
Serotonin in affective control Journal Article
In: Annu Rev Neurosci, vol. 32, pp. 95–126, 2009.
@article{DayanHuys09,
title = {Serotonin in affective control},
author = {Peter Dayan and Quentin J M Huys},
url = {http://acplab.org/wp-content/uploads/pub/DayanHuys09-serotonin-in-affective.pdf},
doi = {10.1146/annurev.neuro.051508.135607},
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abstract = {Serotonin is a neuromodulator that is extensively entangled in fundamental aspects of brain function and behavior. We present a computational view of its involvement in the control of appetitively and aversively motivated actions. We first describe a range of its effects in invertebrates, endowing specific structurally fixed networks with plasticity at multiple spatial and temporal scales. We then consider its rather widespread distribution in the mammalian brain. We argue that this is associated with a more unified representational and functional role in aversive processing that is amenable to computational analyses with the kinds of reinforcement learning techniques that have helped elucidate dopamine's role in appetitive behavior. Finally, we suggest that it is only a partial reflection of dopamine because of essential asymmetries between the natural statistics of rewards and punishments.},
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Serotonin is a neuromodulator that is extensively entangled in fundamental aspects of brain function and behavior. We present a computational view of its involvement in the control of appetitively and aversively motivated actions. We first describe a range of its effects in invertebrates, endowing specific structurally fixed networks with plasticity at multiple spatial and temporal scales. We then consider its rather widespread distribution in the mammalian brain. We argue that this is associated with a more unified representational and functional role in aversive processing that is amenable to computational analyses with the kinds of reinforcement learning techniques that have helped elucidate dopamine's role in appetitive behavior. Finally, we suggest that it is only a partial reflection of dopamine because of essential asymmetries between the natural statistics of rewards and punishments.
Book Chapters
Huys, Quentin J. M.; Browning, Michael
A Computational View on the Nature of Reward and Value in Anhedonia Journal Article
In: pp. 421-441, 2022.
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title = {A Computational View on the Nature of Reward and Value in Anhedonia},
author = {Quentin J. M. Huys and Michael Browning},
url = {http://acplab.org/wp-content/uploads/pub/HuysBrowning21-Anhedonia.pdf},
doi = {10.1007/7854_2021_290},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
booktitle = {Current Topics in Behavioural Neurosciences},
pages = {421-441},
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abstract = {Anhedonia—a common feature of depression—encompasses a reduction in the subjective experience and anticipation of rewarding events, and a reduction in the motivation to seek out such events. The presence of anhedonia often predicts or accompanies treatment resistance, and as such better interventions and treatments are important. Yet the mechanisms giving rise to anhedonia are not well-understood. In this chapter, we briefly review existing computational conceptualisations of anhedonia. We argue that they are mostly descriptive and fail to provide an
explanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multidimensional drive reduction settings through a trade-off between different rewards or needs. We then generalize this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. In summary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment.},
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Anhedonia—a common feature of depression—encompasses a reduction in the subjective experience and anticipation of rewarding events, and a reduction in the motivation to seek out such events. The presence of anhedonia often predicts or accompanies treatment resistance, and as such better interventions and treatments are important. Yet the mechanisms giving rise to anhedonia are not well-understood. In this chapter, we briefly review existing computational conceptualisations of anhedonia. We argue that they are mostly descriptive and fail to provide an
explanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multidimensional drive reduction settings through a trade-off between different rewards or needs. We then generalize this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. In summary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment.
explanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multidimensional drive reduction settings through a trade-off between different rewards or needs. We then generalize this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. In summary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment.
Huys, Quentin J. M.; Series, Peggy
Reward-Based Learning, Model-Based and Model-Free Book Chapter
In: Jager, D.; Jung, R. (Ed.): Encyclopaedia of Computational Neuroscience, pp. 3042–3050, Springer, New York, NY, 2022.
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Huys, Q. J. M.
Computational cognitive methods for precision psychiatry (with a preprint link) Book Chapter
In: Precision Psychiatry. Using Neuroscience Insights toInform Personally Tailored, Measurement-Based Care, pp. 179, 2021.
@inbook{Huys21c,
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booktitle = {Precision Psychiatry. Using Neuroscience Insights toInform Personally Tailored, Measurement-Based Care},
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abstract = {When an organ is unable to meet the demands placed on it, illness can arise. As the main func-tions of the brain are to compute and learn, an understanding of mental illnesses will benefitfrom an understanding of the computational and learning functions the brain performs, and howthese are affected in states of ill-health. Tasks allow highly specific computational and learningprocesses to be probed. After a brief introduction into computational psychiatry more broadly,this chapter explores how tasks can be used for precision psychiatry. It is argued that identifi-cation of disease mechanisms via tasks can facilitate the development of targeted interventionsand the targeted administration of therapies. However, the clinical use of tasks currently faceissues around reliability and robustness. This chapter describes and discusses four reasons forthis: time, strategy, noise and research setting, and describes how these are at least partiallyamenable to computational techniques.},
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When an organ is unable to meet the demands placed on it, illness can arise. As the main func-tions of the brain are to compute and learn, an understanding of mental illnesses will benefitfrom an understanding of the computational and learning functions the brain performs, and howthese are affected in states of ill-health. Tasks allow highly specific computational and learningprocesses to be probed. After a brief introduction into computational psychiatry more broadly,this chapter explores how tasks can be used for precision psychiatry. It is argued that identifi-cation of disease mechanisms via tasks can facilitate the development of targeted interventionsand the targeted administration of therapies. However, the clinical use of tasks currently faceissues around reliability and robustness. This chapter describes and discusses four reasons forthis: time, strategy, noise and research setting, and describes how these are at least partiallyamenable to computational techniques.
Visser, R. M.; Anderson, M. C.; Aron, A.; Banich, M. T.; Brady, K. T.; Huys, Q. J. M.; Monfils, M. -H.; Schiller, D.; Schlagenhauf, F.; Schooler, J.; Robbins, T. W.
Neuropsychological Mechanisms of Intrusive Thinking Book Chapter
In: Intrusive Thinking, pp. 124-184, The MIT Press, 2020.
@inbook{VisserRobbins20b,
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url = {https://labs.psych.ucsb.edu/schooler/jonathan/sites/labs.psych.ucsb.edu.schooler.jonathan/files/pubs/223._neuropsychological_mechanisms_of_intrusive_thinking.pdf},
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booktitle = {Intrusive Thinking},
pages = {124-184},
publisher = {The MIT Press},
abstract = {A classic definition of intrusive thinking is “any distinct, identifiable cognitive event that is unwanted, unintended, and recurrent. It interrupts the flow of thought, inter- feres in task performance, is associated with negative affect, and is difficult to control” (Clark 2005:4). While easy to understand and applicable to many cases, this definition does not seem to encompass the entire spectrum of intrusions. For example, intrusive thoughts may not always be experienced as unpleasant or unwanted, and may in some situations even be adaptive. This chapter revisits the definition of intrusive thinking, by systematically considering all the circumstances in which intrusions might occur, their manifestations across health and disorders, and develops an alternative, more inclusive definition of intrusions as being “interruptive, salient, experienced mental events.” It proposes that clinical intrusive thinking differs from its nonclinical form with regard to frequency, intensity, and maladaptive reappraisal. Further, it discusses the neurocogni- tive processes underlying intrusive thinking and its control, including memory pro- cesses involved in action control, working memory and long-term memory encoding, retrieval, and suppression. As part of this, current methodologies used to study intrusive thinking are evaluated and areas are highlighted where more research and/or technical innovation is needed. It concludes with a discussion of the theoretical, therapeutic, and sociocultural implications of intrusive thinking and its control.},
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A classic definition of intrusive thinking is “any distinct, identifiable cognitive event that is unwanted, unintended, and recurrent. It interrupts the flow of thought, inter- feres in task performance, is associated with negative affect, and is difficult to control” (Clark 2005:4). While easy to understand and applicable to many cases, this definition does not seem to encompass the entire spectrum of intrusions. For example, intrusive thoughts may not always be experienced as unpleasant or unwanted, and may in some situations even be adaptive. This chapter revisits the definition of intrusive thinking, by systematically considering all the circumstances in which intrusions might occur, their manifestations across health and disorders, and develops an alternative, more inclusive definition of intrusions as being “interruptive, salient, experienced mental events.” It proposes that clinical intrusive thinking differs from its nonclinical form with regard to frequency, intensity, and maladaptive reappraisal. Further, it discusses the neurocogni- tive processes underlying intrusive thinking and its control, including memory pro- cesses involved in action control, working memory and long-term memory encoding, retrieval, and suppression. As part of this, current methodologies used to study intrusive thinking are evaluated and areas are highlighted where more research and/or technical innovation is needed. It concludes with a discussion of the theoretical, therapeutic, and sociocultural implications of intrusive thinking and its control.
Huys, Quentin J. M.; Seriès, Peggy
Reward-Based Learning, Model-Based and Model-Free Book Chapter
In: Encyclopedia of Computational Neuroscience, pp. 1–9, Springer New York, 2019.
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Huys, Q J M
Bayesian approaches to learning and decision-making Book Chapter
In: Anticevic, A; Murray, J (Ed.): Computational Psychiatry: Mathematical Modelling of Mental Illness, vol. 75, pp. 225–226, Elsevier, 2018.
@inbook{Huys18,
title = {Bayesian approaches to learning and decision-making},
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doi = {10.1016/B978-0-12-809825-7.00010-9},
year = {2018},
date = {2018-01-01},
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booktitle = {Computational Psychiatry: Mathematical Modelling of Mental Illness},
volume = {75},
issue = {3},
pages = {225–226},
publisher = {Elsevier},
abstract = {Behavioral phenomena are central to psychiatric disorders. Computational modeling allows the learning and decision-making processes underlying behavior to be modeled in great detail. By doing so, specific and possibly highly complex hypotheses about the underlying processes can be directly tested on the data. The first part of this chapter introduces Markov Decision Problems (MDPs) as a formal framework for decision-making. It then describes several solutions to MDPs, including reinforcement learning and dynamic programming, and briefly introduces some of their key characteristics. The second part of the chapter provides a tutorial overview over how to use MDPs in a generative modeling framework to test hypotheses about learning and decision-making. The final part of the chapter discusses the methods using a few worked examples from the literature.},
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Behavioral phenomena are central to psychiatric disorders. Computational modeling allows the learning and decision-making processes underlying behavior to be modeled in great detail. By doing so, specific and possibly highly complex hypotheses about the underlying processes can be directly tested on the data. The first part of this chapter introduces Markov Decision Problems (MDPs) as a formal framework for decision-making. It then describes several solutions to MDPs, including reinforcement learning and dynamic programming, and briefly introduces some of their key characteristics. The second part of the chapter provides a tutorial overview over how to use MDPs in a generative modeling framework to test hypotheses about learning and decision-making. The final part of the chapter discusses the methods using a few worked examples from the literature.
Huys, Quentin J. M.
A valuation framework for emotions applied to depression and recurrence Book Chapter
In: Reddish, A. David; Gordon, Joshua A. (Ed.): Computational Psychiatry: New Perspectives on Mental Illness, vol. 20, pp. 275–292, MIT Press, 2016.
@inbook{Huys16b,
title = {A valuation framework for emotions applied to depression and recurrence},
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abstract = {The burden of depression is substantially aggravated by relapses and recurrences, and these become more inevitable with every episode of depression. This chapter describes how computational psychiatry can provide a normative framework for emotions and an integrative approach to core cognitive components of depression and relapse. Central to this is the notion that emotions effectively imply a valuation; thus they are amenable to description and dissection by reinforcement-learning methods. It is argued that cognitive accounts of emotion can be viewed in terms of model-based valuation, and that automatic emotional responses relate to model-free valuation and the innate recruitment of fixed behavioral patterns. This model-based view captures phenomena such as helplessness, hopelessness, attributions, and stress sensitization. Considering it in more atomic algorithmic detail opens up the possibility of viewing rumination and emotion regulation in this same normative framework. The problem of treatment selection for relapse and recurrence prevention is outlined and suggestions made on how the computational framework of emotions might help improve this. The chapter closes with a brief overview of what we can hope to gain from computational psychiatry.},
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The burden of depression is substantially aggravated by relapses and recurrences, and these become more inevitable with every episode of depression. This chapter describes how computational psychiatry can provide a normative framework for emotions and an integrative approach to core cognitive components of depression and relapse. Central to this is the notion that emotions effectively imply a valuation; thus they are amenable to description and dissection by reinforcement-learning methods. It is argued that cognitive accounts of emotion can be viewed in terms of model-based valuation, and that automatic emotional responses relate to model-free valuation and the innate recruitment of fixed behavioral patterns. This model-based view captures phenomena such as helplessness, hopelessness, attributions, and stress sensitization. Considering it in more atomic algorithmic detail opens up the possibility of viewing rumination and emotion regulation in this same normative framework. The problem of treatment selection for relapse and recurrence prevention is outlined and suggestions made on how the computational framework of emotions might help improve this. The chapter closes with a brief overview of what we can hope to gain from computational psychiatry.
Totah, Nelson; Akil, Huda; Huys, Quentin J. M.; Krystal, John H.; III, Tiago V. Maia Angus W. Maconald; Malenka, Robert C.; Pauli, Wolfgang M.
Complexity and Heterogeneity in Psychiatric Disorders Book Chapter
In: Reddish, A. David; Gordon, Joshua A. (Ed.): Computational Psychiatry: New Perspectives on Mental Illness, vol. 20, pp. 33–59, MIT Press, 2016.
@inbook{TotahPauli16,
title = {Complexity and Heterogeneity in Psychiatric Disorders},
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editor = {A. David Reddish and Joshua A. Gordon},
url = {https://www.quentinhuys.com/pub/TotahEa16-Complexity.pdf},
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booktitle = {Computational Psychiatry: New Perspectives on Mental Illness},
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abstract = {Psychiatry faces a number of challenges, among them are the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. Achieving these goals will require an increase in the biological, quantitative, and theoretical grounding of psychiatry. To address these challenges, psychiatry must confront the complexity and heterogeneity intrinsic to the nature of brain disorders. This chapter seeks to identify the sources of complexity and heterogeneity as a means of confronting the challenges facing the field. These sources include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Moreover, these interactions are expressed dynamically over the course of development and continue to play out during the disease process and treatment.
We propose that computational approaches provide a framework for addressing the complexity and heterogeneity that underlie the challenges facing psychiatry. Central to our argument is the idea that these characteristics are not noise to be eliminated from diagnosis and treatment of disorders. Instead, such complexity and heterogeneity arises from intrinsic features of brain function and, therefore, represent opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. The challenges to be addressed by a computational framework include the following. First, it must improve the search for risk factors and biomarkers, which can be used toward primary prevention of disease. Second, it must help to represent the biological ground truth of psychiatric disorders, which will im- prove the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine. Third, to be useful for secondary prevention, it must represent how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.},
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Psychiatry faces a number of challenges, among them are the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. Achieving these goals will require an increase in the biological, quantitative, and theoretical grounding of psychiatry. To address these challenges, psychiatry must confront the complexity and heterogeneity intrinsic to the nature of brain disorders. This chapter seeks to identify the sources of complexity and heterogeneity as a means of confronting the challenges facing the field. These sources include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Moreover, these interactions are expressed dynamically over the course of development and continue to play out during the disease process and treatment.
We propose that computational approaches provide a framework for addressing the complexity and heterogeneity that underlie the challenges facing psychiatry. Central to our argument is the idea that these characteristics are not noise to be eliminated from diagnosis and treatment of disorders. Instead, such complexity and heterogeneity arises from intrinsic features of brain function and, therefore, represent opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. The challenges to be addressed by a computational framework include the following. First, it must improve the search for risk factors and biomarkers, which can be used toward primary prevention of disease. Second, it must help to represent the biological ground truth of psychiatric disorders, which will im- prove the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine. Third, to be useful for secondary prevention, it must represent how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.
We propose that computational approaches provide a framework for addressing the complexity and heterogeneity that underlie the challenges facing psychiatry. Central to our argument is the idea that these characteristics are not noise to be eliminated from diagnosis and treatment of disorders. Instead, such complexity and heterogeneity arises from intrinsic features of brain function and, therefore, represent opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. The challenges to be addressed by a computational framework include the following. First, it must improve the search for risk factors and biomarkers, which can be used toward primary prevention of disease. Second, it must help to represent the biological ground truth of psychiatric disorders, which will im- prove the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine. Third, to be useful for secondary prevention, it must represent how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.
Cools,; Ouden,; Huys,
How do emotion and cognition interact? Book Chapter
In: 2016.
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Huys, Quentin J. M.; Cruickshank, Anthony; Seriès, Peggy
Model-based and model-free decision-making Book Chapter
In: Encyclopedia of Computational Neuroscience, pp. 1–10, Springer New York, 2014.
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Huys, Quentin
Computational Psychiatry Book Chapter
In: Encyclopedia of Computational Neuroscience, pp. 1–10, Springer New York, 2014.
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Huys, Q J M; Beck, A; Dayan, P; Heinz, A
Neurobiological structure and computational understanding of addictive behaviour Book Section
In: Phenomenological neuropsychiatry: bridging the clinic and clinical neuroscience, New York: Springer, 2013.
@incollection{HuysHeinz13,
title = {Neurobiological structure and computational understanding of addictive behaviour},
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abstract = {An increasing wealth of experimental detail is becoming available about the development and nature of addiction. Critical issues such as the varying vulnerabilities of individuals who develop addiction are being illuminated across levels of phenomenological, psychological and neurobiological detail. Furthermore, a rich theoretical understanding is emerging in the field of neural reinforcement learning, with glimmers as to how this might be related to the subjective experience of those individuals affected. In this chapter, we consider some particuarly pressing current issues in the interface between experiment and theory, notably the so-called “compulsive” phase of drug taking.},
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An increasing wealth of experimental detail is becoming available about the development and nature of addiction. Critical issues such as the varying vulnerabilities of individuals who develop addiction are being illuminated across levels of phenomenological, psychological and neurobiological detail. Furthermore, a rich theoretical understanding is emerging in the field of neural reinforcement learning, with glimmers as to how this might be related to the subjective experience of those individuals affected. In this chapter, we consider some particuarly pressing current issues in the interface between experiment and theory, notably the so-called “compulsive” phase of drug taking.
Lieder, F.; Goodman, N.; Huys, Q J M
Controllability and resource-rational planning Proceedings Article
In: Comp. Sys. Neurosci., 2013.
@inproceedings{LiederHuys13b,
title = {Controllability and resource-rational planning},
author = {F. Lieder and N. Goodman and Q J M Huys},
doi = {10.13140/RG.2.2.12979.35361},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {Comp. Sys. Neurosci.},
abstract = {Learned helplessness experiments involving controllable vs. uncontrollable stressors have shown that the perceived ability to control events has profound consequences for decision making. Normative models of decision making, however, do not naturally incorporate knowledge about controllability, and previous approaches to incorporating it have led to solutions with biologically implausible computational demands [1,2]. Intuitively, controllability bounds the differential rewards for choosing one strategy over another, and therefore believing that the environment is uncontrollable should reduce one’s willingness
to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Learned helplessness experiments involving controllable vs. uncontrollable stressors have shown that the perceived ability to control events has profound consequences for decision making. Normative models of decision making, however, do not naturally incorporate knowledge about controllability, and previous approaches to incorporating it have led to solutions with biologically implausible computational demands [1,2]. Intuitively, controllability bounds the differential rewards for choosing one strategy over another, and therefore believing that the environment is uncontrollable should reduce one’s willingness
to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.
to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.
Commentaries, Editorials & Misc.
Nour, Matthew M.; Huys, Quentin J. M.
Natural Language Processing in Psychiatry: A Field at an Inflection Point Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 8, no. 10, pp. 979–981, 2023.
@article{NourHuys23,
title = {Natural Language Processing in Psychiatry: A Field at an Inflection Point},
author = {Matthew M. Nour and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/NourHuys2023NLP.pdf},
doi = {10.1016/j.bpsc.2023.08.001},
year = {2023},
date = {2023-10-01},
urldate = {2023-10-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
volume = {8},
number = {10},
pages = {979–981},
publisher = {Elsevier BV},
keywords = {},
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Huys, Quentin; Paulus, Martin
Special Issue on Reliable Mechanisms for Translational Applications Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 8, no. 8, pp. 778-779, 2023.
@article{HuysPaulus23,
title = {Special Issue on Reliable Mechanisms for Translational Applications},
author = {Quentin Huys and Martin Paulus},
url = {http://acplab.org/wp-content/uploads/pub/Special-Issue-on-Reliable-Mechanisms-for-Translational-Applications.pdf},
doi = {https://doi.org/10.1016/j.bpsc.2023.06.004},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
volume = {8},
number = {8},
pages = {778-779},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Browning, Michael; Paulus, Martin; Huys, Quentin J. M.
What is Computational Psychiatry Good For? Journal Article
In: Biological Psychiatry, 2022.
@article{BrowningHuys22,
title = {What is Computational Psychiatry Good For?},
author = {Michael Browning and Martin Paulus and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/BrowningHuys22-CompPsychEditorial.pdf},
doi = {10.1016/j.biopsych.2022.08.030},
year = {2022},
date = {2022-09-01},
urldate = {2022-09-01},
journal = {Biological Psychiatry},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Huys, Quentin J. M.
Is there mathematics to madness? Journal Article
In: Brain, pp. 944–952, 2022.
@article{Huys22,
title = {Is there mathematics to madness?},
author = {Quentin J. M. Huys},
editor = {Jung D. Jaeger},
url = {http://acplab.org/wp-content/uploads/pub/Huys22-review.pdf},
doi = {10.1093/brain/awac047},
year = {2022},
date = {2022-02-09},
urldate = {2022-02-09},
booktitle = {Encyclopaedia of Computational Neuroscience},
journal = {Brain},
pages = {944–952},
publisher = {Springer, New York, NY},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Huys, Quentin J. M.
Computational Psychiatry Series Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2020.
@article{Huys20,
title = {Computational Psychiatry Series},
author = {Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/Huys_2020-Computational-Psychiatry-Series.pdf},
doi = {10.1016/j.bpsc.2019.11.009},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mihalik, Agoston; Adams, Rick A.; Huys, Quentin
Canonical Correlation Analysis for Identifying Biotypes of Depression Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2020.
@article{MihalikHuys20b,
title = {Canonical Correlation Analysis for Identifying Biotypes of Depression},
author = {Agoston Mihalik and Rick A. Adams and Quentin Huys},
url = {http://acplab.org/wp-content/uploads/pub/Mihalik_2020-Canonical-correlation.pdf},
doi = {10.1016/j.bpsc.2020.02.002},
year = {2020},
date = {2020-05-01},
urldate = {2020-05-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Browning, Michael; Carter, Cameron S.; Chatham, Christopher; Ouden, Hanneke Den; Gillan, Claire M; Baker, Justin T.; Chekroud, Adam M.; Cools, Roshan; Dayan, Peter; Gold, James; Goldstein, Rita Z.; Hartley, Catherine A.; Kepecs, Adam; Lawson, Rebecca P.; Miranda, Janaina Mourao-; Phillips, Mary L.; Pizzagalli, Diego A.; Powers, Albert; Rindskopf, David; Roiser, Jonathan P; Schmack, Katharina; Schiller, Daniela; Sebold, Miriam; Stephan, Klaas Enno; Frank, Michael J; Huys, Quentin J. M.; Paulus, Martin
Realizing the Clinical Potential of Computational Psychiatry: Report from the Banbury Center Meeting, February 2019 Journal Article
In: Biological Psychiatry, 2020.
@article{BrowningPaulus20,
title = {Realizing the Clinical Potential of Computational Psychiatry: Report from the Banbury Center Meeting, February 2019},
author = {Michael Browning and Cameron S. Carter and Christopher Chatham and Hanneke Den Ouden and Claire M Gillan and Justin T. Baker and Adam M. Chekroud and Roshan Cools and Peter Dayan and James Gold and Rita Z. Goldstein and Catherine A. Hartley and Adam Kepecs and Rebecca P. Lawson and Janaina Mourao- Miranda and Mary L. Phillips and Diego A. Pizzagalli and Albert Powers and David Rindskopf and Jonathan P Roiser and Katharina Schmack and Daniela Schiller and Miriam Sebold and Klaas Enno Stephan and Michael J Frank and Quentin J. M. Huys and Martin Paulus},
url = {http://acplab.org/wp-content/uploads/pub/BrowningEa19-Banbury.pdf},
doi = {10.1016/j.biopsych.2019.12.026},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Biological Psychiatry},
abstract = {In February 2019 a workshop was convened at the Banbury Centre at Cold Spring Harbor, NY. The purpose of the meeting was to identify key developments required in the practice and infrastructure of computational psychiatry research to accelerate its ability to address real world clinical problems in the near future. This report provides a summary of the conclusions of the meeting. At its core are suggestions to improve the measurement properties of computational assays through a rapid, iterative process that leverages coordinated waves of online and clinical testing, followed by deployment of the assays in innovative study designs to address clinically relevant questions. We particularly focus on theory-driven tasks but, where possible, the potential of data-driven approaches is also highlighted. Finally, the report suggests that for the promise of computational psychiatry to be realized, the research environment must be developed to encourage large-scale, collaborative, interdisciplinary consortia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In February 2019 a workshop was convened at the Banbury Centre at Cold Spring Harbor, NY. The purpose of the meeting was to identify key developments required in the practice and infrastructure of computational psychiatry research to accelerate its ability to address real world clinical problems in the near future. This report provides a summary of the conclusions of the meeting. At its core are suggestions to improve the measurement properties of computational assays through a rapid, iterative process that leverages coordinated waves of online and clinical testing, followed by deployment of the assays in innovative study designs to address clinically relevant questions. We particularly focus on theory-driven tasks but, where possible, the potential of data-driven approaches is also highlighted. Finally, the report suggests that for the promise of computational psychiatry to be realized, the research environment must be developed to encourage large-scale, collaborative, interdisciplinary consortia.
Russek, Evan M.; Moran, Rani; McNamee, Daniel; Reiter, Andrea; Liu, Yunzhe; Dolan, Raymond J.; Huys, Quentin J. M.
Opportunities for emotion and mental health research in the resource-rationality framework Journal Article
In: Behavioral and Brain Sciences, vol. 43, 2020.
@article{RussekHuys20,
title = {Opportunities for emotion and mental health research in the resource-rationality framework},
author = {Evan M. Russek and Rani Moran and Daniel McNamee and Andrea Reiter and Yunzhe Liu and Raymond J. Dolan and Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/Russek_2020-Opportunities-for-emotion-and-mental-health-research-in-the-resource.pdf},
doi = {10.1017/S0140525X19001663},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Behavioral and Brain Sciences},
volume = {43},
publisher = {Cambridge University Press (CUP)},
abstract = {We discuss opportunities in applying the resource-rationality framework toward answering questions in emotion and mental health research. These opportunities rely on characterization of individual differences in cognitive strategies; an endeavor that may be at odds with the normative approach outlined in the target article. We consider ways individual differences might enter the framework and the translational opportunities offered by each.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We discuss opportunities in applying the resource-rationality framework toward answering questions in emotion and mental health research. These opportunities rely on characterization of individual differences in cognitive strategies; an endeavor that may be at odds with the normative approach outlined in the target article. We consider ways individual differences might enter the framework and the translational opportunities offered by each.
Maia, Tiago V; Huys, Quentin J M; Frank, Michael J
Theory-Based Computational Psychiatry Journal Article
In: Biological psychiatry, vol. 82, iss. 6, pp. 382–384, 2017.
@article{MaiaFrank17,
title = {Theory-Based Computational Psychiatry},
author = {Tiago V Maia and Quentin J M Huys and Michael J Frank},
url = {http://acplab.org/wp-content/uploads/pub/MaiaEa17-Theory-Based-Computational-Psychiatry-1.pdf},
doi = {10.1016/j.biopsych.2017.07.016},
year = {2017},
date = {2017-09-01},
urldate = {2017-09-01},
journal = {Biological psychiatry},
volume = {82},
issue = {6},
pages = {382–384},
abstract = {Theory development is an intrinsic part of science. Radical empiricism is a logical impossibility: the number of phenomena that can be measured and manipulated is infinite, so the very selection of phenomena to investigate must be driven by a priori considerations. Loose facts, moreover, point to nothing but themselves; only theories, even if incipient, have explanatory and predictive power extending beyond prior observations. Yet, theory is sometimes seen with suspicion. Cajal, a giant in neuroscience history, wrote, “the theorist is a lazy person masquerading as a diligent one…a scholar’s positive contribution is measured by the sum of the original data that he contributes…. Theories desert us, while data defend us” ( 1 ). Cajal wrote this text more than 2 centuries after the scientific revolution emphasized mathematical theories (consider Galileo’s epitaph). Why? Later in the same paragraph, Cajal writes, “So many apparently conclusive theories…have collapsed in the last few decades! On the other hand, the well-established facts of anatomy and physiology…and the laws and equations of astronomy and physics remain” [( 1 ), italics added]. Cajal was therefore not arguing against mathematical formulations of general principles—i.e., mathematical theories. Instead, he was arguing against vague verbal descriptions that constituted the theories in neuroscience at the time and that still characterize most theories in psychiatry, neuroscience, and related fields.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Theory development is an intrinsic part of science. Radical empiricism is a logical impossibility: the number of phenomena that can be measured and manipulated is infinite, so the very selection of phenomena to investigate must be driven by a priori considerations. Loose facts, moreover, point to nothing but themselves; only theories, even if incipient, have explanatory and predictive power extending beyond prior observations. Yet, theory is sometimes seen with suspicion. Cajal, a giant in neuroscience history, wrote, “the theorist is a lazy person masquerading as a diligent one…a scholar’s positive contribution is measured by the sum of the original data that he contributes…. Theories desert us, while data defend us” ( 1 ). Cajal wrote this text more than 2 centuries after the scientific revolution emphasized mathematical theories (consider Galileo’s epitaph). Why? Later in the same paragraph, Cajal writes, “So many apparently conclusive theories…have collapsed in the last few decades! On the other hand, the well-established facts of anatomy and physiology…and the laws and equations of astronomy and physics remain” [( 1 ), italics added]. Cajal was therefore not arguing against mathematical formulations of general principles—i.e., mathematical theories. Instead, he was arguing against vague verbal descriptions that constituted the theories in neuroscience at the time and that still characterize most theories in psychiatry, neuroscience, and related fields.
Huys, Quentin J M; Maia, Tiago V; Paulus, Martin P
Computational Psychiatry: From Mechanistic Insights to the Development of New Treatments Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 1, iss. 5, pp. 382–385, 2016.
@article{HuysPaulus16d,
title = {Computational Psychiatry: From Mechanistic Insights to the Development of New Treatments},
author = {Quentin J M Huys and Tiago V Maia and Martin P Paulus},
url = {http://acplab.org/wp-content/uploads/pub/HuysEa16d-Computational-Psychiatry.pdf},
doi = {10.1016/j.bpsc.2016.08.001},
year = {2016},
date = {2016-09-01},
urldate = {2016-09-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
volume = {1},
issue = {5},
pages = {382–385},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Huys, Quentin J. M.
Rückfallvorhersage bei Depressionen: welche Rolle spielen Schuldgefühle? Journal Article
In: Info Neurologie und Psychiatrie, vol. 65, pp. 21–26, 2016.
@article{Huys16,
title = {Rückfallvorhersage bei Depressionen: welche Rolle spielen Schuldgefühle?},
author = {Quentin J. M. Huys},
url = {http://acplab.org/wp-content/uploads/pub/Huys16-ComputationalPsychiatry.pdf},
doi = {10.1024/1661-4747/a000297},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Info Neurologie und Psychiatrie},
volume = {65},
pages = {21–26},
abstract = {Computational psychiatry is a young research field which attempts to bring advances from theoretical and experimental neurosciences to bear on clinical issues in psychiatry. The motivation for the use of computational techniques arises from the complexity of psychiatric phenomena. Computational techniques facilitate the measurement of intrapsychic processes that are not otherwise directly observable (e.g. learning processes) and allow phenomena arising at different levels of description to be related, for instance the impact of ion channel disturbances on short-term memory. Methods from machine learning can be combined with such models and facilitate the analysis of larger, complex datasets. Although there are promising leads, the effort is in its initial stages and it may be appropriate to adopt procedures from the development of pharmaceutical to speed up the validation and translation of computational efforts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational psychiatry is a young research field which attempts to bring advances from theoretical and experimental neurosciences to bear on clinical issues in psychiatry. The motivation for the use of computational techniques arises from the complexity of psychiatric phenomena. Computational techniques facilitate the measurement of intrapsychic processes that are not otherwise directly observable (e.g. learning processes) and allow phenomena arising at different levels of description to be related, for instance the impact of ion channel disturbances on short-term memory. Methods from machine learning can be combined with such models and facilitate the analysis of larger, complex datasets. Although there are promising leads, the effort is in its initial stages and it may be appropriate to adopt procedures from the development of pharmaceutical to speed up the validation and translation of computational efforts.
Huys, Quentin J M; Petzschner, Frederike H
Failure modes of the will: from goals to habits to compulsions? Journal Article
In: The American journal of psychiatry, vol. 172, iss. 3, pp. 216–218, 2015.
@article{HuysPetzschner15,
title = {Failure modes of the will: from goals to habits to compulsions?},
author = {Quentin J M Huys and Frederike H Petzschner},
url = {http://acplab.org/wp-content/uploads/pub/HuysPetzschner15-Failure-Modes-of-the-Will_-from-Goals-to-Habits-to-Compulsions_.pdf},
doi = {10.1176/appi.ajp.2014.14121502},
year = {2015},
date = {2015-03-01},
urldate = {2015-03-01},
journal = {The American journal of psychiatry},
volume = {172},
issue = {3},
pages = {216–218},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dayan, Peter; Huys, Quentin
Serotonin's many meanings elude simple theories Journal Article
In: Elife, vol. 4, 2015.
@article{DayanHuys15,
title = {Serotonin's many meanings elude simple theories},
author = {Peter Dayan and Quentin Huys},
url = {http://acplab.org/wp-content/uploads/pub/DayanHuys15-Serotonins-Many-Meanings-Elude-Simple-Theories-1.pdf},
doi = {10.7554/eLife.07390},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Elife},
volume = {4},
school = {ETH Zurich, Zurich, Switzerland and Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland qhuys@cantab.net.},
abstract = {Neurons that produce serotonin respond in a number of different and complex ways in anticipation and receipt of rewards or punishments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neurons that produce serotonin respond in a number of different and complex ways in anticipation and receipt of rewards or punishments.
Conradi, Lisa Holper Quentin Huys Von Jan
Dualdiagnose – Schizophrenie und Sucht Journal Article
In: Pro Mente Sana aktuell, pp. 28-29, 2014.
@article{HolperConradi14,
title = {Dualdiagnose – Schizophrenie und Sucht},
author = {Lisa Holper Quentin Huys Von Jan Conradi},
url = {https://quentinhuys.com/pub/ConradiEa14-SchizophrenieSucht.pdf},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Pro Mente Sana aktuell},
pages = {28-29},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Genauck, Alexander; Huys, Quentin J M; Heinz, Andreas; Rapp, Michael A
Pawlowsch-Instrumentelle Transfereffekte bei Alkoholabhängigkeit Journal Article
In: SUCHT-Zeitschrift für Wissenschaft und Praxis/Journal of Addiction Research and Practice, vol. 59, no. 4, pp. 215–223, 2013.
@article{GenauckRapp13,
title = {Pawlowsch-Instrumentelle Transfereffekte bei Alkoholabhängigkeit},
author = {Alexander Genauck and Quentin J M Huys and Andreas Heinz and Michael A Rapp},
url = {http://acplab.org/wp-content/uploads/pub/GenauckEa13-SuchtPITReview.pdf},
doi = {10.1024/0939-5911.a000256},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {SUCHT-Zeitschrift für Wissenschaft und Praxis/Journal of Addiction Research and Practice},
volume = {59},
number = {4},
pages = {215–223},
publisher = {Hogrefe & Huber},
abstract = {Background: Disadvantageous decisions with respect to alcohol consumption play a central role in alcohol dependency (AD). This decision making pattern seems to be in part a result of Pavlovian to Instrumental Transfer effects (PIT effects). The aim of this review is to summarize important findings on PIT within the scope of addiction disorders. Building on this, open questions in the field of human AD are discussed. Methods: This review is not based on a systematic and standardized literature research. Instead the review was based on the literature search conducted in group 1617 of the German Research Foundation (DFG; Learning and Habitization in Alcohol Dependence, LeAD). Selection of research articles was based on expert opinion. Results: PIT effects in AD might possibly lead to a vicious cycle consisting of enhanced PIT effects through alcohol consumption and enhanced alcohol consumption through enhanced PIT effects. Discussion: PIT effects in alcohol addiction are mainly known from animal studies. In human AD research the PIT paradigm may be able to reveal how particular cues disproportionally motivate AD patients to drink alcohol. PIT experiments thus have potential uses in the prediction of relapse and the measurement of addiction severity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Disadvantageous decisions with respect to alcohol consumption play a central role in alcohol dependency (AD). This decision making pattern seems to be in part a result of Pavlovian to Instrumental Transfer effects (PIT effects). The aim of this review is to summarize important findings on PIT within the scope of addiction disorders. Building on this, open questions in the field of human AD are discussed. Methods: This review is not based on a systematic and standardized literature research. Instead the review was based on the literature search conducted in group 1617 of the German Research Foundation (DFG; Learning and Habitization in Alcohol Dependence, LeAD). Selection of research articles was based on expert opinion. Results: PIT effects in AD might possibly lead to a vicious cycle consisting of enhanced PIT effects through alcohol consumption and enhanced alcohol consumption through enhanced PIT effects. Discussion: PIT effects in alcohol addiction are mainly known from animal studies. In human AD research the PIT paradigm may be able to reveal how particular cues disproportionally motivate AD patients to drink alcohol. PIT experiments thus have potential uses in the prediction of relapse and the measurement of addiction severity.
Huys,; Dayan,
Computational unhappiness: Modelling depression Journal Article
In: Frontiers in Computational Neuroscience, 2009.
@article{HuysDayan09b,
title = {Computational unhappiness: Modelling depression},
author = {Huys and Dayan},
url = {http://acplab.org/wp-content/uploads/pub/Huys2009.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
journal = {Frontiers in Computational Neuroscience},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Natarajan, Rama; Huys, Quentin JM; Dayan, Peter; Zemel, Richard S
Online learning and inference in spiking populations Online
2007, visited: 01.01.2007.
@online{NatarajanZemel06,
title = {Online learning and inference in spiking populations},
author = {Rama Natarajan and Quentin JM Huys and Peter Dayan and Richard S Zemel},
url = {http://acplab.org/wp-content/uploads/pub/NatarajanEa06-Online_learning_and_inference_in_spiking.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
abstract = {We propose a reinforcement based framework for learning in recurrentlyconnected populations of spiking neurons. Learning makes use of a re-wardsignal, whichconveysinformationaboutthe qualityofprobabilisticinferencebased on the populationspikes, and yet requires predominantlylocal informationto specify synaptic plasticity. We apply this framework to the canonical example of probabilistic inference, namely the Bayesiancombination of prior and likelihood about an input, but in the richestcase of rapidly changing stimuli sparsely sampled by input spikes andre-represented in a plastic spiking population. We develop the ideal ob-server, which here involves inference in a Gaussian process, in a formthat bears directly on the spiking network and compare their relative re-sponses.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
We propose a reinforcement based framework for learning in recurrentlyconnected populations of spiking neurons. Learning makes use of a re-wardsignal, whichconveysinformationaboutthe qualityofprobabilisticinferencebased on the populationspikes, and yet requires predominantlylocal informationto specify synaptic plasticity. We apply this framework to the canonical example of probabilistic inference, namely the Bayesiancombination of prior and likelihood about an input, but in the richestcase of rapidly changing stimuli sparsely sampled by input spikes andre-represented in a plastic spiking population. We develop the ideal ob-server, which here involves inference in a Gaussian process, in a formthat bears directly on the spiking network and compare their relative re-sponses.
Huys, Quentin J M; Paninski, Liam
Model-based optimal interpolation and filtering for noisy, intermittent biophysical recordings Journal Article
In: Fifteenth Annual Computational Neuroscience Meeting, vol. 96, no. 2, pp. 872–890, 2006.
@article{HuysPaninski06,
title = {Model-based optimal interpolation and filtering for noisy, intermittent biophysical recordings},
author = {Quentin J M Huys and Liam Paninski},
url = {https://www.quentinhuys.com/pub/hp06-cnsabstract.pdf},
doi = {10.1152/jn.00079.2006},
year = {2006},
date = {2006-01-01},
urldate = {2006-01-01},
journal = {Fifteenth Annual Computational Neuroscience Meeting},
volume = {96},
number = {2},
pages = {872–890},
abstract = {Biophysically accurate multicompartmental models of individual neurons have significantly advanced our understanding of the input-output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1-3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10(4) parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Biophysically accurate multicompartmental models of individual neurons have significantly advanced our understanding of the input-output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1-3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10(4) parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.
Renz, Daniel
2018.
@phdthesis{Renz18,
title = {Advancing Markov Decision Processes and Multivariate Gaussian Processes as Tools for Computational Psychiatry},
author = {Daniel Renz},
url = {http://acplab.org/wp-content/uploads/pub/Renz18-Advancing-Markov-Decision-Processes.pdf},
doi = {10.3929/ethz-b-000265595},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
publisher = {ETH Zurich},
abstract = {In the relatively recent field of computational psychiatry, computational methods can advance the current state of the art in various ways. Progress is possible by advancing current methods of statistical analyses from a model first perspective, for example to increase sensitivity or to regularize complex models. We propose a simple extension to multiple sequential linear regression - the Gaussian Process Trajectory Prediction. The method predicts individual non-linear time courses of a longitudinal marker based on cross-sectional baseline data. It implicitly assumes that each baseline feature contributes a temporal kernel (i.e., feature weight trajectory) to the evolution of the marker. The Gaussian Process acts as a prior on these feature weight trajectories, making an otherwise overly complex regression problem solvable in many practical settings. The time course of the clinical marker then arises as a weighted linear combination of these kernels. The method is tested on a large trial of depression treatments, where it provides prediction of treatment success sufficiently better than previous approaches as well as the first prediction of chance of relapse.
While methods that do not rest on a model of the cognitive or neurobiological processes may provide improved predictions, they do not help to gain better insights into how psychiatric diseases work mechanistically. Thus, we investigate in the second part models of complex, sequential decision-making. This is relevant because in order to solve the sequential decision-making problem, the problem of how and in which order to evaluate the possible options before committing to an actual decision (the meta-decision problem) has to be considered. Emotions are thought to result in fast approximations to this meta problem, and since depression is an emotional disorder, meta-decision making might be dysfunctional in depression. We develop several detailed models describing both the decision and meta-decision processes, and fit them to data that was collected from an experiment specifically designed for this purpose. We focus on the development of inference methods, as the problem of how exactly to fit the models turns out to be hard. Thus, we develop an approximative Expectation-Maximization sampling scheme across trials, as well as a trial-by-trial inference based on advanced Markov Chain Monte Carlo methods.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
In the relatively recent field of computational psychiatry, computational methods can advance the current state of the art in various ways. Progress is possible by advancing current methods of statistical analyses from a model first perspective, for example to increase sensitivity or to regularize complex models. We propose a simple extension to multiple sequential linear regression - the Gaussian Process Trajectory Prediction. The method predicts individual non-linear time courses of a longitudinal marker based on cross-sectional baseline data. It implicitly assumes that each baseline feature contributes a temporal kernel (i.e., feature weight trajectory) to the evolution of the marker. The Gaussian Process acts as a prior on these feature weight trajectories, making an otherwise overly complex regression problem solvable in many practical settings. The time course of the clinical marker then arises as a weighted linear combination of these kernels. The method is tested on a large trial of depression treatments, where it provides prediction of treatment success sufficiently better than previous approaches as well as the first prediction of chance of relapse.
While methods that do not rest on a model of the cognitive or neurobiological processes may provide improved predictions, they do not help to gain better insights into how psychiatric diseases work mechanistically. Thus, we investigate in the second part models of complex, sequential decision-making. This is relevant because in order to solve the sequential decision-making problem, the problem of how and in which order to evaluate the possible options before committing to an actual decision (the meta-decision problem) has to be considered. Emotions are thought to result in fast approximations to this meta problem, and since depression is an emotional disorder, meta-decision making might be dysfunctional in depression. We develop several detailed models describing both the decision and meta-decision processes, and fit them to data that was collected from an experiment specifically designed for this purpose. We focus on the development of inference methods, as the problem of how exactly to fit the models turns out to be hard. Thus, we develop an approximative Expectation-Maximization sampling scheme across trials, as well as a trial-by-trial inference based on advanced Markov Chain Monte Carlo methods.
While methods that do not rest on a model of the cognitive or neurobiological processes may provide improved predictions, they do not help to gain better insights into how psychiatric diseases work mechanistically. Thus, we investigate in the second part models of complex, sequential decision-making. This is relevant because in order to solve the sequential decision-making problem, the problem of how and in which order to evaluate the possible options before committing to an actual decision (the meta-decision problem) has to be considered. Emotions are thought to result in fast approximations to this meta problem, and since depression is an emotional disorder, meta-decision making might be dysfunctional in depression. We develop several detailed models describing both the decision and meta-decision processes, and fit them to data that was collected from an experiment specifically designed for this purpose. We focus on the development of inference methods, as the problem of how exactly to fit the models turns out to be hard. Thus, we develop an approximative Expectation-Maximization sampling scheme across trials, as well as a trial-by-trial inference based on advanced Markov Chain Monte Carlo methods.
Huys, Quentin Jan Marie
Reinforcers and control. Towards a computational ætiology of depression PhD Thesis
Gatsby Computational Neuroscience Unit, UCL, University of London, 2007.
@phdthesis{Huys07,
title = {Reinforcers and control. Towards a computational ætiology of depression},
author = {Quentin Jan Marie Huys},
url = {https://discovery.ucl.ac.uk/id/eprint/174043/1/Huys_thesis.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
school = {Gatsby Computational Neuroscience Unit, UCL, University of London},
abstract = {Depression, like many psychiatric disorders, is a disorder of affect. Over the past decades, a large number of affective issues in depression have been characterised, both in human experiments and animal models of the dis order. Over the same period, experimental neuroscience, helped by com putational theories such as reinforcement learning, has provided detailed descriptions of the psychology and neurobiology of affective decision mak ing. Here, we attempt to harvest the advances in the understanding of the brain's normal dealings with rewards and punishments to dissect out and define more clearly the components that make up depression. We start by exploring changes to primary reinforcer sensitivity in the learned helpless ness animal models of depression. Then, a detailed formalisation of control in a goal-directed decision making framework is presented and related to animal and human data. Finally, we show how serotonin's joint involve ment in reporting negative values and inhibiting actions may explain some aspects of its involvement in depression. Throughout, aspects of depres sion are seen as emerging from normal affective function and reinforcement learning, and we thus conclude that computational descriptions of normal affective function provide one possible avenue by which to define an aetiol ogy of depression.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Depression, like many psychiatric disorders, is a disorder of affect. Over the past decades, a large number of affective issues in depression have been characterised, both in human experiments and animal models of the dis order. Over the same period, experimental neuroscience, helped by com putational theories such as reinforcement learning, has provided detailed descriptions of the psychology and neurobiology of affective decision mak ing. Here, we attempt to harvest the advances in the understanding of the brain's normal dealings with rewards and punishments to dissect out and define more clearly the components that make up depression. We start by exploring changes to primary reinforcer sensitivity in the learned helpless ness animal models of depression. Then, a detailed formalisation of control in a goal-directed decision making framework is presented and related to animal and human data. Finally, we show how serotonin's joint involve ment in reporting negative values and inhibiting actions may explain some aspects of its involvement in depression. Throughout, aspects of depres sion are seen as emerging from normal affective function and reinforcement learning, and we thus conclude that computational descriptions of normal affective function provide one possible avenue by which to define an aetiol ogy of depression.