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


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
15 02 2022
Historique:
received: 04 08 2021
revised: 10 11 2021
accepted: 29 11 2021
pubmed: 4 12 2021
medline: 8 3 2022
entrez: 3 12 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34861392
pii: S1053-8119(21)01042-9
doi: 10.1016/j.neuroimage.2021.118770
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

118770

Informations de copyright

Copyright © 2021. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None

Auteurs

Bianca Burger (B)

Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria.

Karl-Heinz Nenning (KH)

Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States.

Ernst Schwartz (E)

Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria.

Daniel S Margulies (DS)

Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, 75006 Paris, France; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom.

Alexandros Goulas (A)

Institute for Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinstr. 52, 20246 Hamburg, Germany.

Hesheng Liu (H)

Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29466, USAs.

Simon Neubauer (S)

Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany.

Justin Dauwels (J)

TU Delft Fac. EEMCS Mekelweg 4 2628 CD Delft; Nayang Technological University, 639798, Singapore.

Daniela Prayer (D)

Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuroradiology and Musculo-skeletal Radiology, Medical University of Vienna, Vienna 1090, Austria.

Georg Langs (G)

Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States. Electronic address: georg.langs@meduniwien.ac.at.

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