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Advances in the computational understanding of mental illness

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. Green open access

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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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