Neuroimaging is a branch of medical imaging that has been widely used to probe the function and anatomy of the brain noninvasively.This technique has deepened our understandings of how the brain works and has become a...Neuroimaging is a branch of medical imaging that has been widely used to probe the function and anatomy of the brain noninvasively.This technique has deepened our understandings of how the brain works and has become a valuable tool for diagnosing disease and assessing brain health.展开更多
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
文摘Neuroimaging is a branch of medical imaging that has been widely used to probe the function and anatomy of the brain noninvasively.This technique has deepened our understandings of how the brain works and has become a valuable tool for diagnosing disease and assessing brain health.
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.
