Skip to Main content Skip to Navigation
Journal articles

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.
Complete list of metadata

https://hal-amu.archives-ouvertes.fr/hal-03107402
Contributor : Veronique Paban Connect in order to contact the contributor
Submitted on : Tuesday, January 12, 2021 - 3:27:48 PM
Last modification on : Tuesday, October 19, 2021 - 10:59:50 PM
Long-term archiving on: : Tuesday, April 13, 2021 - 6:43:58 PM

File

Kabbara_NeuroImage_21.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

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⟩

Share

Metrics

Record views

162

Files downloads

333