Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning.
10.1007/s12264-023-01057-2
- Author:
Qian LV
1
;
Kristina ZELJIC
2
;
Shaoling ZHAO
3
;
Jiangtao ZHANG
4
;
Jianmin ZHANG
4
;
Zheng WANG
5
Author Information
1. School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China. lvqian@pku.edu.cn.
2. School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK.
3. Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
4. Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China.
5. School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China. zheng.wang@pku.edu.cn.
- Publication Type:Review
- Keywords:
Core region;
Machine learning;
Magnetic resonance imaging;
Neuroimaging-based diagnosis;
Obsessive-compulsive disorder;
Psychiatric disorders
- MeSH:
Humans;
Obsessive-Compulsive Disorder/epidemiology*;
Brain/pathology*;
Neuroimaging/methods*;
Machine Learning;
Comorbidity;
Magnetic Resonance Imaging/methods*
- From:
Neuroscience Bulletin
2023;39(8):1309-1326
- CountryChina
- Language:English
-
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.
