Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest

Abstract

Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.

Keywords: ACC; BOLD; DMN; Dynamics; EDSS; EEG; Expanded Disability Status Scale; FC; Functional connectivity; HC; ICA; IFG; IPG; MFG; MTG; MTP; Multiple sclerosis; PCA; PCC; Paracen; PreC; Precen; RRMS; Resting state; RolOp; SD; SFG; SMA; SPG; SVD; SupMarg; anterior cingulate gyrus; blood-oxygen-level-dependent; default mode network; electroencephalography; fMRI; functional connectivity; functional magnetic resonance imaging; healthy control; independent component analysis; inferior frontal gyrus; inferior parietal gyrus; middle frontal gyrus; middle temporal gyrus; middle temporal pole; paracentral gyrus; posterior cingulate gyrus; precentral gyrus; precuneus; principal component analysis; relapse-remitting multiple sclerosis; rolandic operculum; singular value decomposition; standard deviation; superior frontal gyrus; superior parietal gyrus; supplementary motor area; supramarginal gyrus.