Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162752
Title: Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment
Authors: Burger, Bianca
Nenning, Karl-Heinz
Schwartz, Ernst
Margulies, Daniel S.
Goulas, Alexandros
Liu, Hesheng
Neubauer, Simon
Dauwels, Justin
Prayer, Daniela
Langs, Georg
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Burger, B., Nenning, K., Schwartz, E., Margulies, D. S., Goulas, A., Liu, H., Neubauer, S., Dauwels, J., Prayer, D. & Langs, G. (2022). Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment. NeuroImage, 247, 118770-. https://dx.doi.org/10.1016/j.neuroimage.2021.118770
Journal: NeuroImage 
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.
URI: https://hdl.handle.net/10356/162752
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2021.118770
Rights: © 2021 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles

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