Huys, QJM;
Browning, M;
Paulus, M;
Frank, MJ;
(2021)
Advances in the computational understanding of mental illness.
Neuropsychopharmacology
, 46
pp. 3-19.
10.1038/s41386-020-0746-4.
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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.
| Type: | Article |
|---|---|
| Title: | Advances in the computational understanding of mental illness |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41386-020-0746-4 |
| Publisher version: | https://doi.org/10.1038/s41386-020-0746-4 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Depression, Predictive markers |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10106140 |
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