Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

Abstract

While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state functional connectivity. Here we examine the contributions of network features to dynamic fluctuations by constructing rewired surrogate connectome in which network features of interest are selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from global geometry and topology of the connectome, as well as from localized structural connections involving visual areas. By providing structural accounts of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.

Edges in brain networks: Contributions to models of structure and function

“…In practice, time-varying connectivity resolves the transient relationships between regions, which can signal different internal states that the brain is occupying or passing through ( Fukushima et al, 2018 ). These dynamics are driven by external stimuli ( Simony et al, 2016 ) and are associated with clinical grouping or outcome ( Douw et al, 2019 ) or patterns of structural topology ( Fukushima & Sporns, 2020 ; K. Shen, Hutchison, Bezgin, Everling, & McIntosh, 2015 ; Zamora-Lopez, Chen, Deco, Kringelbach, & Zhou, 2016 ).…”

Time-resolved structure-function coupling in brain networks

“…As a result, structurefunction coupling is also likely to fluctuate over multiple timescales. Indeed, multiple studies have reported evidence of dynamic structure-function relationships over the course of single recording sessions [30,31], and over more protracted periods, including early childhood and young adult neurodevelopment [8,35].…”

Brain-environment alignment during movie watching predicts fluid intelligence and affective function in adulthood

“…This algorithm partitions a network into non-overlapping groups of nodes with the goal of maximizing an objective modularity quality function, Q ( Betzel and Bassett, 2017 ; Rubinov and Sporns, 2011 ; Sporns and Betzel, 2016 ). Following prior proposals regarding the greater biological significance of positive ROI-to-ROI connections, we implemented the Louvain algorithm by using the adapted modularity function Q*, proposed by Rubinov and Sporns (2011) , which has since been widely used (e.g., J. Chen et al., 2016 ; Fukushima and Sporns, 2020 ; Tooley et al., 2020 ), including in studies of lifespan differences in functional brain architecture ( Betzel et al., 2014 ). In this formulation, the contribution of positive weights to Q is not affected by the presence of negative weights in the network, whereas the contribution of negative weights to Q decreases with an increase in positive weights (for further details on the procedure, see “Community detection” in the Supplementary Materials).…”