Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment

Abstract

The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

“…Indeed, these regions tend to exhibit lower structural and functional heritability 55,56 and undergo the greatest surface area expansion during development. 48 Moreover, the extended window of plasticity for these regions compared with other parts of the cortex 57 renders them more likely to be shaped by an individual’s environments and experiences, 56 potentially further contributing to their unique spatial patterning across individuals. Encouragingly, this extended window in which association networks remain plastic may also indicate that interventions targeting these systems could be effective in supporting the development of healthy cognition.…”

Dissociable Multi-scale Patterns of Development in Personalized Brain Networks

“…In studies of adults, transmodal association networks tend to have the greatest variability in functional topography (Kong et al, 2019;Li et al, 2019;Gordon et al, 2017;Xu et al, 2016); recent work has shown that this is also true in children and adolescents (Cui et al, 2020). Accounting for such individual variation in functional topography may be critical for understanding the development of coupling between networks, as prior work has shown that differences in topography can be aliased into estimates of connectivity (Bijsterbosch et al, 2018;Burger et al, 2021). Furthermore, individual-specific-or "personalized"-networks may be particularly relevant when evaluating development at multiple scales, as individual variation in topography might depend in part on network resolution (Braga & Buckner, 2017;Steinmetz & Seitz, 1991).…”

Machine learning in neuroimaging: from research to clinical practice

“…It has also enabled the comparison of neural architecture across species, since despite cross-anatomical differences there is sufficient similarity in the functional connectivity structure to match and compare across human and macaque cortex [72]. Decouplinganatomyand function has further enabled the study of the dif-ferent impact of genes and environment on the cortical topography of functional units and their interconnectedness [105].…”