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The dynamic modular fingerprints of the human brain at rest

Abstract : The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets (N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network. Taken together, our findings show that large-scale electrophysiological networks have modularity-dependent dynamic fingerprints at rest.
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Contributor : Veronique Paban Connect in order to contact the contributor
Submitted on : Tuesday, January 12, 2021 - 3:27:48 PM
Last modification on : Friday, February 4, 2022 - 3:25:45 AM
Long-term archiving on: : Tuesday, April 13, 2021 - 6:43:58 PM


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Aya Kabbara, Veronique Paban, Mahmoud Hassan. The dynamic modular fingerprints of the human brain at rest. NeuroImage, Elsevier, 2021, 227, pp.117674. ⟨10.1016/j.neuroimage.2020.117674⟩. ⟨hal-03107402⟩



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