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
Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter. Malamud Jolanda; Guloksuz Sinan; Winkel Ruud van; Delespaul Philippe; De Hert Marc A. F.; Derom Catherine; Thiery Evert; Jacobs Nele; Rutten Bart P. F.; Huys Quentin J. M. (2024). PLoS Computational Biology.
Mood disorders involve complex interactions between internal emotions and external factors. Dynamical systems theory suggests these interactions influence key aspects of mood disorders. The study proposes using ecological momentary assessments (EMA) with a Kalman filter framework to analyze these dynamics comprehensively.
Results show this approach outperforms standard methods, revealing that depression alters mood interactions, responses to external inputs, and mood controllability. The Kalman filter applied to EMA data offers a promising method for understanding mood dynamics in depression, potentially leading to new insights into mechanisms and treatments.
Amygdala Reactivity, Antidepressant Discontinuation, and Relapse. Erdmann Tore; Berwian Isabel M.; Stephan Klaas Enno; Seifritz Erich; Walter Henrik; Huys Quentin J. M. (2024). JAMA Psychiatry.
Antidepressant discontinuation increases depression relapse risk. The AIDA study investigated amygdala reactivity changes after discontinuation and their relation to relapse risk. This fMRI study included 80 remitted MDD patients and 53 controls. Amygdala reactivity was measured before or after discontinuation, with relapse monitored for 6 months. Increased reactivity after discontinuation was associated with depression relapse (β = 18.9, P = .04), predicted shorter time to relapse (HR = 1.05, P = .01), and relapse occurrence (67% accuracy, P = .02). These findings suggest amygdala reactivity changes may indicate relapse risk in remitted MDD patients discontinuing antidepressants.
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.