Group independent component analysis reveals consistent resting-state networks across multiple sessions

Sharon Chen, Thomas J. Ross, Wang Zhana, Carol S. Myers, Keh-Shih Chuang, Stephen J. Heishman, Elliot A. Stein, Yihong Yang,

Group independent component analysis (gICA) was performed on resting-state data from 14 healthy subjects scanned on 5 fMRI scan sessions across 16 days. The data were reduced and aggregated in 3 steps using Principal Components Analysis (PCA, within scan, within session and across session) and subjected to gICAprocedures. The amount of reductionwas estimated by an improved method that utilizes a first-order autoregressive fitting technique to the PCA spectrum. Analyses were performed using all sessions in order to maximize sensitivity and alleviate the problem of component identification across session. Across-session consistency was examined by three methods, all using back-reconstruction of the single-session or singlesubject/
session maps from the grand (5-session) maps. The gICA analysis produced 55 spatially independent maps. Obvious artifactual maps were eliminated and the remainder were grouped baseduponphysiological recognizability. Biologically relevantcomponentmaps were found, including sensory,motor and a ‘default-mode’map. All analysismethods showed that components were remarkably consistent across session. Critically, the components with themost obvious physiological relevance were the most consistent. The consistency of these maps suggests that, at least over a period of several weeks, these networks would be useful to follow longitudinal treatment-related manipulations.