Applied Computational Psychiatry

The Applied Computational Psychiatry group focuses on developing computational tools with potential for clinical applications. The lab supports research encompassing the complete translational pipeline, from computational theory, to cognitive neuroscience, imaging (EEG, MEG and fMRI), mechanistic clinical trials and also efficacy trials in pharmacotherapy and psychotherapy.

This is facilitated by being part of the Max Planck UCL Centre for Computational Psychiatry and Ageing, the Division of Psychiatry, and the Department of Imaging Neuroscience in the Queen Square Institute of Neurology.  The lab also hosts the Mood Disorders Research clinic at the North London Mental Health NHS Foundation Trust through which it supports a wide range of clinical studies.

Recent examples include:

Emotion theory:
We developed novel accounts of how progress inference shapes hedonic impact and potentially underlies anhedonia (Hall et al. 2024), how metareasoning underlies emotions (Huys & Renz 2017; Onysk et al. 2026), and how emotional report obeys the fundamental rules of perceptual inference (Serfaty & Huys 2026).

Statistical theory:
We are actively investigating the measurement properties of computational methods, particularly focusing on the issue of model comparison in interventional settings (Kim et al. 2026).

Mechanisms of psychotherapy:
We are developing cognitive-computational measures that are selectively engaged by specific psychotherapeutic interventions (Norbury et al. 2024; Dercon et al. 2025).

Mechanisms of antidepressant treatments:
RELMED is a UK-wide multisite clinical trial examining the reinforcement learning mechanisms of serotonergic and dopaminergic antidepressants. PRADAM extends the AIDA study to examine acute tryptophan depletion in the context of stopping antidepressant medication (Erdmann et al. 2024; Berwian et al. 2020; Berwian et al. 2024).

Efficacy trials:
PAX-D was a trial which showed that pramipexole is an effective antidepressant in people with treatment-resistant depression (Browning et al. 2025).

Key References

Berwian, I. M., Tröndle, M., de Miquel, C., Ziogas, A., Stefanics, G., Walter, H., Stephan, K. E., & Huys, Q. J. M. (2024). Emotion-induced frontal alpha asymmetry as a candidate predictor of relapse after discontinuation of antidepressant medication. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 9(8), 809–818. https://doi.org/10.1016/j.bpsc.2024.05.001 

Berwian, I. M., Wenzel, J. G., Collins, A. G. E., Seifritz, E., Stephan, K. E., Walter, H., & Huys, Q. J. M. (2020). Computational mechanisms of effort and reward decisions in patients with depression and their association with relapse after antidepressant discontinuation. JAMA Psychiatry, 77(5), 513–522. https://doi.org/10.1001/jamapsychiatry.2019.4971 

Browning, M., Cowen, P. J., Galal, U., Baldwin, A., Cleare, A. J., Evans, J., Huys, Q. J. M., Kessler, D., Kurkar, M., Nixon, N., Rastogi, A., Watson, S., Yu, L.-M., Mort, S., Simon, J., Laszewska, A., Lewis, A. C., Roberts, S. M., Fiske, V., Frending, L. M., Money, C., Godlewska, B. R., Ryland, H. T., Halahakoon, D. C., Wright, L. A., Salas, B., Peddada, A., Wahba, M., Taylor, K. S., Kerr-Gaffney, J., Swiffen, D., Zangani, C., Smith, K. A., Harmer, C. J., & Geddes, J. R. (2025). Pramipexole augmentation for the acute phase of treatment-resistant, unipolar depression: a placebo-controlled, double-blind, randomised trial in the UK. The Lancet Psychiatry, 12(8), 579–589. https://doi.org/10.1016/S2215-0366(25)00194-4 

Dercon, Q., Huys, Q. J. M., Rutledge, R. B., & Nord, C. L. (2025). Common psychiatric treatments alter affective dynamics. eLife. https://doi.org/10.7554/elife.107269.1 

Erdmann, T., Berwian, I. M., Stephan, K. E., Seifritz, E., Walter, H., & Huys, Q. J. M. (2024). Amygdala Reactivity, Antidepressant Discontinuation, and Relapse. JAMA Psychiatry, 81(11), 1081–1089. https://doi.org/10.1001/jamapsychiatry.2024.2136 

Hall, A. F., Browning, M., & Huys, Q. J. M. (2024). The computational structure of consummatory anhedonia. Trends in Cognitive Sciences, 28(6), 541–553. https://doi.org/10.1016/j.tics.2024.01.006 

Huys, Q. J. M., & Renz, D. (2017). A formal valuation framework for emotions and their control. Biological Psychiatry, 82(6), 413–420. https://doi.org/10.1016/j.biopsych.2017.07.003 

Kim, T., Viding, E., & Huys, Q. J. M. (2026). Reliable detection of longitudinal change in computational models. https://acplab.org/wp-content/uploads/publications/KimHuys26.pdf 

Norbury, A., Hauser, T. U., Fleming, S. M., Dolan, R. J., & Huys, Q. J. M. (2024). Different components of cognitive-behavioral therapy affect specific cognitive mechanisms. Science Advances, 10(13), Article eadk3222. https://doi.org/10.1126/sciadv.adk3222 

Onysk, J., Chen, J., & Huys, Q. J. M. (2026). Computational nature of emotions: Insights from metareasoning and transformers. PsyArXiv. https://doi.org/10.31234/osf.io/m478p_v1 

Serfaty, J., & Huys, Q. J. M. (2026). Subjective emotion judgements adhere to principles of Bayesian inference and efficient representation. PsyArXiv. https://doi.org/10.31234/osf.io/ufmqy_v1