title: Consistency of network modules in resting-state FMRI connectome data.
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.
authors.affiliation: Neuroscience Program, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
authors.forename: Malaak N
authors.initials: MN
authors.lastname: Moussa
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Steen
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Laurienti
authors.forename: Satoru
authors.initials: S
authors.lastname: Hayasaka
mesh_terms: D000328:Adult; D063132:Connectome; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D012146:Rest / Q000502:physiology*; D013223:Statistics as Topic; D055815:Young Adult