MART(Splitting-Merging Assisted Reliable)Independent Component Analysis for Extracting Accurate Brain Functional Networks  

作　　者：Xingyu He Vince D.Calhoun Yuhui Du 

机构地区：[1]School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China [2]Tri-Institutional Center for Translational Research in Neuroimaging and Data Science,Georgia State University,Georgia Institute of Technology,Emory University,Atlanta 30303,USA

出　　处：《Neuroscience Bulletin》2024年第7期905-920,共16页神经科学通报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62076157 and 61703253);the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20210033);the National Institutes of Health(R01MH123610 and R01EB006841).

摘　　要：Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.

关 键 词：Independent component analysis Functional magnetic resonance imaging-Brain functional networks Clustering Multi-model-order 

分 类 号：R741[医药卫生—神经病学与精神病学]