Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

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作者
Victor Nozais
Stephanie J. Forkel
Laurent Petit
Lia Talozzi
Maurizio Corbetta
Michel Thiebaut de Schotten
Marc Joliot
机构
[1] Univ. Bordeaux,Brain Connectivity and Behaviour Laboratory
[2] CNRS,Donders Institute for Brain Cognition Behaviour
[3] CEA,Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
[4] IMN,Departments of Neurosurgery
[5] UMR 5293,Department of Neurology
[6] GIN,Department of Neuroscience, Venetian Institute of Molecular Medicine and Padova Neuroscience Center
[7] Sorbonne Universities,undefined
[8] Radboud University,undefined
[9] King’s College London,undefined
[10] Technical University of Munich School of Medicine,undefined
[11] Stanford University,undefined
[12] University of Padua,undefined
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Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain’s white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.
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