Eigenconnectivities of dynamic functional networks: Consistency across subjects

Functional connectivity (FC) measured using fMRI has provided significant insights into brain function. However, increasing evidence points towards continuously fluctuating FC across the duration of a scan. Using unsupervised learning techniques, reproducible patterns of dynamic FC (dFC) have been revealed. In particular, based on principal component analysis, it has recently been proposed to represent dFC as a linear combination of multiple “eigenconnectivities”. These group-level results were obtained by concatenating all subjects´ timecourses of dFC. Here we investigate the consistency of these results by introducing a subject-level and group-level PCA and comparing the results with those obtained by concatenation.