Literature DB >> 34861392

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

Bianca Burger1, Karl-Heinz Nenning2, Ernst Schwartz1, Daniel S Margulies3, Alexandros Goulas4, Hesheng Liu5, Simon Neubauer6, Justin Dauwels7, Daniela Prayer8, Georg Langs9.   

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.
Copyright © 2021. Published by Elsevier Inc.

Entities:  

Keywords:  functional connectivity; functional magnetic resonance imaging; heritability; inter-subject variability; topography

Mesh:

Year:  2021        PMID: 34861392     DOI: 10.1016/j.neuroimage.2021.118770

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  3 in total

1.  Functional cortical associations and their intraclass correlations and heritability as revealed by the fMRI Human Connectome Project.

Authors:  Peka Christova; Jasmine Joseph; Apostolos P Georgopoulos
Journal:  Exp Brain Res       Date:  2022-03-15       Impact factor: 1.972

Review 2.  Machine learning in neuroimaging: from research to clinical practice.

Authors:  Karl-Heinz Nenning; Georg Langs
Journal:  Radiologie (Heidelb)       Date:  2022-08-31

3.  Dissociable multi-scale patterns of development in personalized brain networks.

Authors:  Adam R Pines; Bart Larsen; Zaixu Cui; Valerie J Sydnor; Maxwell A Bertolero; Azeez Adebimpe; Aaron F Alexander-Bloch; Christos Davatzikos; Damien A Fair; Ruben C Gur; Raquel E Gur; Hongming Li; Michael P Milham; Tyler M Moore; Kristin Murtha; Linden Parkes; Sharon L Thompson-Schill; Sheila Shanmugan; Russell T Shinohara; Sarah M Weinstein; Danielle S Bassett; Yong Fan; Theodore D Satterthwaite
Journal:  Nat Commun       Date:  2022-05-12       Impact factor: 17.694

  3 in total

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