Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability

Abstract

Resting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 minutes per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. Despite similarities, we found individual-specific variations in transition zone properties, which were repeatable over time. Reliability of individual-specific variations increased with number of time points; however, even with only 10 minutes, individuals were nearly always distinguishable from one another. Higher-order networks exhibited greater between-individual variation for transition profiles, while sensory and motor networks exhibited greater within-individual variation. The gradients defined using alternative iFC measures revealed similar but distinct gradient profiles for each. These results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.