Benchmarking functional connectivity by the structure and geometry of the human brain

Author:

Liu Zhen-Qi1ORCID,Betzel Richard F.2ORCID,Misic Bratislav1ORCID

Affiliation:

1. McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada

2. Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA

Abstract

Abstract The brain’s structural connectivity supports the propagation of electrical impulses, manifesting as patterns of coactivation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through polysynaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to reweigh and reexpress functional connectivity. We find evidence of unexpectedly strong functional connectivity among distal brain regions and within the default mode network. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena—functional modules and functional hierarchies—emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.

Funder

Canada First Research Excellence Fund

Natural Sciences and Engineering Research Council of Canada

Canada Research Chairs

Publisher

MIT Press

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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