The Applied Computational Psychiatry lab focuses on developing computational tools with real potential for clinical applications.
Computational Psychiatry is a multidisciplinary field of research at the intersection of psychiatry, neuroscience, machine learning and statistics. The aim of the field is to harness advances in these fields to advance treatments for mental illnesses. For overviews over computational psychiatry, see Huys et al., 2016 Nature Neuroscience and Huys et al. 2020 Neuropsychopharmacology
We are part of the Divison of Psychiatry and the Max Planck UCL Centre for Computational Psychiatry and Ageing Research in the Institute of Neurology at University College London.
When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health.
Latest Publications
Heuristics in risky decision-making relate to preferential representation of information. Russek, Evan Russek; Moran Rani; Liu Yunzhe; Dolan Raymond J; Huys Quentin JM (2024). Nature Communications. In Press
When making choices people differ from each other, as well as from normativity, in how they weigh different types of information. One explanation for this deviance relates to selective prioritization of what information is considered during choice evaluation.
We employed a risky decision-making paradigm to examine the relationship between individual differences in neural representation of information and behavior. Individual differences in a tendency to neurally represent reward- versus probability-informative stimuli explained differences in weighting of either information type in choices.
Our overall results suggest that differences in the information individuals consider during choice shape their risk-taking tendencies.
Emotion-induced frontal α asymmetry predicts relapse after discontinuation of antidepressant medication. Berwian Isabel M; Tröndle Marius; Miquel Carlota ; Ziogas Anastasios; Stefanics Gabor; Walter Henrik; Stephan Klaas E; Huys Quentin JM (2024). Biological Psychiatry. In Press.
One in three patients relapse after antidepressant discontinuation. 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. Patients 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.
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.
Latest Preprints
Real-world fluctuations in motivation drive effort-based choices. Hewitt Samuel RC; Norbury Agnes; Huys Quentin J. M. and Hauser Tobias U (2024). PsyArxiv
Subjective experiences, like feeling motivated, fluctuate over time. However, we usually ignore these fluctuations when studying how feelings predict behaviour. Here, we examine whether naturalistic ups and downs in states influence the subjective value of choices.
Task-based willingness to exert effort for reward was specifically boosted when people felt more motivated. This naturalistic state-behaviour coupling was significantly strengthened in individuals with higher trait apathy. Computational modelling revealed that the fluctuations in state changed and preceded sensitivity to reward, thereby driving choices.
Our results show that typical, day-to-day fluctuations in feelings and cognition are tightly linked, and critical to understanding fundamental human behaviours in the real-world.
Hidden state inference requires abstract contextual representations in ventral hippocampus. Mishchanchuk Karyna; Gregoriou Gabrielle; Qü Albert; Kastler Alizée; Huys Quentin J.M.; Wilbrecht Linda and MacAskill Andrew F. (2024). bioRxiv.
The ability to form and utilize subjective, latent contextual representations to influence decision making is a crucial determinant of everyday life. Here we show that the CA1 area of the ventral hippocampus is necessary for mice to perform hidden state inference during a 2-armed bandit task.
These findings offer insight into how latent contextual information is used to optimize decision-making processes, and emphasize a key role of the hippocampus in hidden state inference.