Computational psychiatry as a bridge from neuroscience to clinical applications

Huys, Quentin J M; Maia, Tiago V; Frank, Michael J

doi:10.1038/nn.4238

Cited by 1,002Mental Health Research TopicsMental Health and PsychiatryFunctional Brain Connectivity Studies

Computational psychiatry as a bridge from neuroscience to clinical applications

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Huys, Quentin J M, Maia, Tiago V, Frank, Michael J·2016·Nature Neuroscience

DOI 10.1038/nn.4238PMC PMC5443409

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Abstract

Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.

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Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry
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Self-citation
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Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder
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Frontal EEG predictors of treatment outcome in major depressive disorder
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Gradual extinction prevents the return of fear: implications for the discovery of state
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Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations
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Erratum: Computational psychiatry
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“This strategy is at the heart of an emerging discipline, “Computational Psychiatry” (Montague et al., 2012; Stephan andMathys, 2014; Friston et al., 2014b; Huys et al., 2016), and has shown promise in tackling other complex neuropsychiatric symptoms, such as delusions (Corlett et al., 2010).”
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Authors: Huys, Q. J. M., Maia, T. V., & Frank, M. J.
Affiliations: University of Zurich (CH)
University of Lisbon (PT)
Brown University (US)
Corresponding author: Huys, Quentin J M
ORCID: Huys, Quentin J M
Maia, Tiago V
Frank, Michael J
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2016-02-23
Volume: 19
Issue: 3
Pages: 404-413
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/nn.4238
PMC: PMC5443409
OpenAlex: W2276520418
Cited by: 1,002
References: 149
Citations by year: 2026: 105 · 2025: 103 · 2024: 104 · 2023: 107 · 2022: 103 · 2021: 123 · 2020: 93 · 2019: 89 · 2018: 72 · 2017: 73 · 2016: 29
FWCI: 11.59
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
Zurich Open Repository and Archive (University of Zurich)
Funders: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (153449)
National Institutes of Health (5r01mh080066-10)
U.S. Public Health Service (NIMH R01 MH080066-01)
National Institute of Mental Health (R01-MH080066)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: computational psychiatry, mental illness, data-driven machine learning, translational neuroscience, treatment outcome prediction, treatment selection, psychiatric classification, mechanistic model, clinical application
MeSH terms: Mental Disorders, Humans, Models, Neurological, Models, Psychological, Neurosciences, Translational Research, Biomedical

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Computational psychiatry as a bridge from neuroscience to clinical applications

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