consistency of network modules in resting-state fmri connectome data在静息状态功能磁共振成像网络模块的连接体数据的一致性.pdfVIP

Consistency of Network Modules in Resting-State fMRI Connectome Data 1 2 2 3,2 Malaak N. Moussa , Matthew R. Steen , Paul J. Laurienti , Satoru Hayasaka * 1 Neuroscience Program, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 2 Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 3 Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Abstract At rest, spontaneous brain activity measured by fMRI is summarized by a number of distinct resting state networks (RSNs) following similar temporal time courses. Such networks have been consistently identified across subjects using spatial ICA (independent component analysis). Moreover, graph theory-based network analyses have also been applied to resting-state fMRI data, identifying similar RSNs, although typically at a coarser spatial resolution. In this work, we examined resting-state fMRI networks from 194 subjects at a voxel-level resolution, and examined the consistency of RSNs across subjects using a metric called scaled inclusivity (SI), which summarizes consistency of modular partitions across networks. Our SI analyses indicated that some RSNs are robust across subjects, comparable to the corresponding RSNs identified by ICA. We also found that some commonly reported RSNs are less consistent across subjects. This is the first direct comparison of RSNs between ICAs and graph-based network analyses at a comparable resolution. Citation: Moussa MN, Steen MR, Laurienti PJ, Hayasaka S (2012) Consistency of Network Modules in Resting-State fMRI Connectome Data. PLoS ONE 7(8): e44428.