Using centrality measures to extract core pattern of brain dynamics during the resting state. (October 2019)

• Record Type: Journal Article
• Authors: Hadriche, Abir; Jmail, Nawel; Blanc, Jean-Luc; Pezard, Laurent
• Is Part Of: Computer methods and programs in biomedicine. Volume 179(2019)
• Publication Date: 2019-10
• Subjects: Brain dynamics -- Coarse-graining -- Discrete dynamics -- Macroscopic transition graph -- Centrality measures -- Entropy rate -- Resting state -- Multiple sclerosis -- kNN classifier; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.104985
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Depict brain dynamics patterns in resting state for healthy and pathologic subjects. Extract core dynamical pattern on a macroscopic scale for multiple scleroses (MS). Evaluate the robustness of centrality measurements in describing MS dynamics. Recognize MS patterns with a reliable classification rate to define MS dynamics. Abstract: The patterns of brain dynamics were studied during resting state on a macroscopic scale for control subjects and multiple sclerosis patients. Macroscopic brain dynamics is defined after successive coarse-grainings and selection of significant patterns and transitions based on Markov representation of brain activity. The resulting networks show that control dynamics is merely organized according to a single principal pattern whereas patients dynamics depict more variable patterns. Centrality measures are used to extract core dynamical pattern in brain dynamics and classification technique allow to define MS dynamics with relevant error rate.