Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning
- PMID: 37093448
- PMCID: PMC10387015
- DOI: 10.1007/s12264-023-01057-2
Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning
Abstract
Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Keywords: Core region; Machine learning; Magnetic resonance imaging; Neuroimaging-based diagnosis; Obsessive-compulsive disorder; Psychiatric disorders.
© 2023. The Author(s).
Conflict of interest statement
The authors declare no conflict of interest.
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