Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning / 神经科学通报·英文版
Neuroscience Bulletin
; (6): 1309-1326, 2023.
Article
in En
| WPRIM
| ID: wpr-982471
Responsible library:
WPRO
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.
Key words
Full text:
1
Index:
WPRIM
Main subject:
Brain
/
Magnetic Resonance Imaging
/
Comorbidity
/
Neuroimaging
/
Machine Learning
/
Obsessive-Compulsive Disorder
Limits:
Humans
Language:
En
Journal:
Neuroscience Bulletin
Year:
2023
Type:
Article