Individual differences in computational psychiatry: A review of current challenges

Povilas Karvelis; Martin P Paulus; Andreea O Diaconescu

doi:10.1016/j.neubiorev.2023.105137

Individual differences in computational psychiatry: A review of current challenges

Neurosci Biobehav Rev. 2023 May:148:105137. doi: 10.1016/j.neubiorev.2023.105137. Epub 2023 Mar 20.

Authors

Povilas Karvelis¹, Martin P Paulus², Andreea O Diaconescu³

Affiliations

¹ Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada. Electronic address: povilas.karvelis@camh.ca.
² Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA.
³ Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada.

PMID: 36940888
DOI: 10.1016/j.neubiorev.2023.105137

Abstract

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Keywords: Computational modelling; Computational psychiatry; Individual differences; Reliability; Validity.

Publication types

Review
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Cross-Sectional Studies
Humans
Individuality
Mental Disorders* / therapy
Psychiatry*
Reproducibility of Results

Abstract

Publication types

MeSH terms

Grants and funding