Three types of individual variation in brain networks revealed by single-subject functional connectivity analyses

•Brain networks derived from functional connectivity vary across individual humans.•Variability in connectional strength causes regions to exhibit variable network affiliation.•Spatial variability can displace or expand/contract comparable network nodes.•Topological variability can alter the nodes p...

Full description

Saved in:
Bibliographic Details
Published inCurrent opinion in behavioral sciences Vol. 40; pp. 79 - 86
Main Authors Gordon, Evan M, Nelson, Steven M
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2021
Online AccessGet full text

Cover

Loading…
More Information
Summary:•Brain networks derived from functional connectivity vary across individual humans.•Variability in connectional strength causes regions to exhibit variable network affiliation.•Spatial variability can displace or expand/contract comparable network nodes.•Topological variability can alter the nodes present within a network.•Each type of variability confounds measurement of the other types. The human brain is organized into large-scale networks that can be noninvasively identified using functional connectivity (FC) functional magnetic resonance imaging. FC varies across individuals, and there is significant interest in associating individual variation in FC with external behavioral measures. However, only recently has FC variation been characterized by studying brain networks within individual humans. We review these recent efforts, and we argue that individual variation in FC networks comes in three distinct forms: 1) variability in connectional strength, in which brain regions in the same location have variable FC strength across subjects; 2) variability in spatial localization, in which regions exhibit the same connections across subjects, but are expanded/contracted or spatially displaced in specific subjects; and 3) topological variability, in which networks have variable sets of constituent nodes. Unfortunately, each of these three types of variation confounds attempts to measure the others, which significantly impacts research studying brain networks.
ISSN:2352-1546
2352-1554
DOI:10.1016/j.cobeha.2021.02.014