Mapping the dynamic repertoire of the resting brain

Hadriche, Abir; Pezard, Laurent; Nandrino, Jean-Louis; Ghariani, Hamadi; Kachouri, Abdennaceur; Jirsa, Viktor K.

Document type :

Article dans une revue scientifique

DOI :

10.1016/j.neuroimage.2013.04.041

Link :

https://lilloa.univ-lille.fr/handle/20.500.12210/34704

Title :

Mapping the dynamic repertoire of the resting brain

Author(s) :

Hadriche, Abir [Auteur]
Pezard, Laurent [Auteur]
Nandrino, Jean-Louis [Auteur]

Unité de Recherche en Sciences Cognitives et Affectives [URECA]
Ghariani, Hamadi [Auteur]
Kachouri, Abdennaceur [Auteur]
Jirsa, Viktor K. [Auteur]

Journal title :

Neuroimage

Abbreviated title :

NeuroImage

Pages :

p. 448-462

Publisher :

Elsevier BV

Publication date :

2013-09

ISSN :

1053-8119

English keyword(s) :

Resting state
Markov process
Discrete dynamics
Partition
State space
Graph

HAL domain(s) :

Sciences de l'Homme et Société/Psychologie

English abstract : [en]

The resting state dynamics of the brain shows robust features of spatiotemporal pattern formation but the actual nature of its time evolution remains unclear. Computational models propose specific state space organization ...
Show more >The resting state dynamics of the brain shows robust features of spatiotemporal pattern formation but the actual nature of its time evolution remains unclear. Computational models propose specific state space organization which defines the dynamic repertoire of the resting brain. Nevertheless, methods devoted to the characterization of the organization of brain state space from empirical data still lack and thus preclude comparison of the hypothetical dynamical repertoire of the brain with the actual one. We propose here an algorithm based on set oriented approach of dynamical system to extract a coarse-grained organization of brain state space on the basis of EEG signals. We use it for comparing the organization of the state space of large-scale simulation of brain dynamics with actual brain dynamics of resting activity in healthy subjects. The dynamical skeleton obtained for both simulated brain dynamics and EEG data depicts similar structures. The skeleton comprised chains of macro-states that are compatible with current interpretations of brain functioning as series of metastable states. Moreover, macro-scale dynamics depicts correlation features that differentiate them from random dynamics. We here propose a procedure for the extraction and characterization of brain dynamics at a macro-scale level. It allows for the comparison between models of brain dynamics and empirical measurements and leads to the definition of an effective coarse-grained dynamical skeleton of spatiotemporal brain dynamics.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Administrative institution(s) :

Université de Lille
CNRS
CHU Lille

Collections :

Autres travaux scientifiques

Submission date :

2020-12-29T09:06:53Z
2021-01-13T17:55:56Z

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2	20.500.12210/34704*	2021-01-13T17:53:36Z
1	20.500.12210/34704.1	2020-12-29T09:06:53Z

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