The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Computational Nosology and Precision Psychiatry
Open accessAbstract
This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present—and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.
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“From the homo- geneity assumption, the comorbidity (17) and heterogeneity (18,19) problems emerge and limit psychiatric research (20–26).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [26]
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Cites
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Self-citationNetwork analysis: An integrative approach to the structure of psychopathology
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Bayesian model selection for group studies
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Self-citationCritical slowing down as early warning for the onset and termination of depression
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Self-citationBayesian model reduction and empirical Bayes for group (DCM) studies
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“To illustrate this estimation, the upper right panel of Figure 4 reports the estimates (and 90% confidence intervals) based on an empirical Bayesian analysis (Kass & Steffey, 1989) of eight simulated patients (using spm_dcm_peb.m).”
MethodEffective connectivity: Influence, causality and biophysical modeling
Valdes-Sosa, P. A., Roebroeck, A. F., Jean Daunizeau, A., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.058 · Cited by 399
Self-citationTechnological innovations in mental healthcare: harnessing the digital revolution
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Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020) · Psychological Medicine · doi:10.1017/s0033291719003404 · Cited by 861
Automated assessment of psychiatric disorders using speech: A systematic review
Low, D. M., Bentley, K. H., & Ghosh, S. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.354 · Cited by 545
The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443
Lee, Y. L., Ragguett, R., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A. P., Brietzke, E. M., Lin, K., Pan, Z., Subramaniapillai, M., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.08.073 · Cited by 418
The future of rodent models in depression research
Gururajan, A., Reif, A., Cryan, J. F., & Slattery, D. A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0221-6 · Cited by 269
Metadata
- Authors
- Friston, K. J., Redish, A. D., & Gordon, J. A.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University of Minnesota (US)
- National Institute of Mental Health (US)
- Corresponding author
- Friston, Karl J.
- Venue
- Computational Psychiatry
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2017-10-01
- Volume
- 1
- Issue
- 0
- Pages
- 2
- ISSN
- 2379-6227 (ISSN-L)
- PMC
- PMC5774181
- OpenAlex
- W2625121567
- Cited by
- 126
- References
- 52
- Citations by year
- 2026: 14 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 18 · 2021: 20 · 2020: 14 · 2019: 15 · 2018: 14 · 2017: 3
- FWCI
- 16.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (088130/Z/09/Z)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- precision psychiatry, psychiatric diagnosis, psychopathology, pathophysiology, Research Domain Criteria, Bayesian model selection, posterior predictive distribution, treatment response prediction
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