Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

• Published in: Neuroscience bulletin 2023-08, Vol.39 (8), p.1309-1326
• Main Authors: Lv, Qian, Zeljic, Kristina, Zhao, Shaoling, Zhang, Jiangtao, Zhang, Jianmin, Wang, Zheng
• Format: Article
• Language: English
• Subjects: Anatomy; Anesthesiology; Biomedical and Life Sciences; Biomedicine; Brain - diagnostic imaging; Brain - pathology; Comorbidity; Human Physiology; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Neuroimaging - methods; Neurology; Neurosciences; Obsessive-Compulsive Disorder - diagnostic imaging; Obsessive-Compulsive Disorder - epidemiology; Pain Medicine; Review
• ISSN: 1673-7067; 1995-8218
• DOI: 10.1007/s12264-023-01057-2

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.