Computational approaches to psychiatry
Abstract
A major reason for disappointing progress of psychiatric diagnostics and nosology is the lack of tests which enable mechanistic inference on disease processes within individual patients. The resulting inability to pursue formal differential diagnosis has forced the field to stick to symptom-based diagnostic schemes with limited predictive validity concerning treatment response and clinical outcome. A promising new approach is the use of computational modeling for inferring mechanisms which generate observed behavior and brain activity in psychiatric patients. However, while this computational approach to psychiatry is rapidly gaining attention, much work remains to be done to finesse existing computational models, making them 'fit for practice' in a clinical setting and proving their validity in longitudinal studies. This review outlines recent methodological advances and strategies in this regard, focusing on generative models which infer mechanistically interpretable parameters (of computational or physiological processes) from measured behavior and brain activity.
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Because of the mapping between predictive coding and cortical wiring, proposed changes in predictive coding represent testable hypotheses of pathophysiology with biologically meaningful parameters.
“Crucially, because of the mapping between predictive coding and cortical wiring, proposed changes in predictive coding represent testable hypotheses of pathophysiology with biologically meaningful parameters (12, 33).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [33]
Emerging computational methods based on generative models of measurable behavior or brain activity offer a potential alternative to classical neuroimaging.
“A potential alternative to classical neuroimaging is offered by emerging computational methods based on generative models of measurable behavior or brain activity (59).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [59]
The majority of current computational concepts of psychiatric disorders regard aberrant learning and inference as core components of maladaptive cognition.
“Since the majority of current computational concepts of psychiatric disorders regard aberrant learning and inference as core components of maladaptive cognition (59), the three elements in Eq. 1 – prediction, prediction error, and precision – offer an interesting perspective for clinicians.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [59]
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References 73 · Cited by 260
- Authors
- Stephan, K. E., & Mathys, C.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2014-04
- Volume
- 25
- Pages
- 85-92
- ISSN
- 0959-4388 (ISSN-L)
- OpenAlex
- W2030602986
- Cited by
- 260
- References
- 73
- Citations by year
- 2026: 21 · 2025: 14 · 2024: 20 · 2023: 15 · 2022: 13 · 2021: 24 · 2020: 21 · 2019: 21 · 2018: 26 · 2017: 22 · 2016: 29 · 2015: 23 · 2014: 11
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- SDL (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Wellcome Trust (091593)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Keywords
- computational psychiatry, computational modeling, psychiatric diagnosis, differential diagnosis, mechanistic inference, generative models, parameter inference, treatment response prediction, clinical validity, longitudinal validation
- MeSH terms
- Mental Disorders, Humans, Models, Biological, Psychiatry, Computational Biology
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