Subject identification using edge-centric functional connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 238; p. 118204
• Main Authors: Jo, Youngheun, Faskowitz, Joshua, Esfahlani, Farnaz Zamani, Sporns, Olaf, Betzel, Richard F.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.09.2021; Elsevier Limited; Elsevier
• Subjects: Brain research; Communication; Datasets; Fingerprinting; Identification; Neural networks; Sensorimotor system; Time series
• DOI: 10.1016/j.neuroimage.2021.118204
• ISSN: 1053-8119; 1095-9572

•We propose an edge-centric approach to improve subject idiosyncrasies in rsfMRI.•We obtain edge-centric FC and subject similarity matrices in two datasets.•We investigate identifiability at various spatial and organizational scales.•With edge-centric FC, idiosyncrasies are driven by heteromodal brain regions.•Subject idiosyncrasies improve with edge-centric analyses and principal components. Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed ‘fingerprinting’ analyses on functional connectivity to identify subjects’ idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects’ identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.