The connectome spectrum as a canonical basis for a sparse representation of fast brain activity.

Details

Serval ID
serval:BIB_9F0A729D0E38
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
The connectome spectrum as a canonical basis for a sparse representation of fast brain activity.
Journal
NeuroImage
Author(s)
Rué-Queralt J., Glomb K., Pascucci D., Tourbier S., Carboni M., Vulliémoz S., Plomp G., Hagmann P.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Publication state
Published
Issued date
01/12/2021
Peer-reviewed
Oui
Volume
244
Pages
118611
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.
Keywords
Adult, Brain/diagnostic imaging, Cognition, Connectome, Diffusion Magnetic Resonance Imaging, Electroencephalography, Female, Humans, Male, Nervous System Physiological Phenomena, Young Adult
Pubmed
Web of science
Open Access
Yes
Create date
04/10/2021 10:44
Last modification date
14/02/2022 6:35
Usage data