Spectral graph theory of brain oscillations.
Adolescent
Adult
Brain Waves
/ physiology
Cerebral Cortex
/ anatomy & histology
Child
Connectome
/ methods
Diffusion Magnetic Resonance Imaging
/ methods
Female
Humans
Magnetic Resonance Imaging
/ methods
Magnetoencephalography
/ methods
Male
Middle Aged
Models, Theoretical
Nerve Net
/ anatomy & histology
Young Adult
alpha rhythm
brain activity
connectomes
magnetoencephalography
spectral graph theory
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
01 08 2020
01 08 2020
Historique:
received:
26
07
2019
revised:
13
02
2020
accepted:
16
02
2020
pubmed:
24
3
2020
medline:
11
11
2021
entrez:
24
3
2020
Statut:
ppublish
Résumé
The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .
Identifiants
pubmed: 32202027
doi: 10.1002/hbm.24991
pmc: PMC7336150
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2980-2998Subventions
Organisme : NIDCD NIH HHS
ID : R56 DC019282
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB022717
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS076171
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG062196
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS100440
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC013979
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC017091
Pays : United States
Informations de copyright
© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
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