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 discus...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.展开更多
基金supported by the Key-Area Research and Development Program of Guangdong Province(2019B030335001)the National Natural Science Foundation of China(82151303)+1 种基金the National Key R&D Program of China(2021ZD0204002)Peking-Tsinghua Centre for Life Sciences.Qian Lv was supported by a Postdoctoral Fellowship of the Peking-Tsinghua Center for Life Sciences.
文摘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.
