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Main Title: Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca
Author(s): Viol, Aline
Palhano-Fontes, Fernanda
Onias, Heloisa
de Araujo, Draulio B.
Hövel, Philipp
Viswanathan, Gandhi M.
Type: Article
Language Code: en
Abstract: With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.
Issue Date: 30-Jan-2019
Date Available: 27-Aug-2019
DDC Class: 570 Biowissenschaften; Biologie
003 Systeme
Subject(s): entropy
functional brain networks
psychedelic state
complex networks
Sponsor/Funder: DFG, 163436311, SFB 910: Kontrolle selbstorganisierender nichtlinearer Systeme: Theoretische Methoden und Anwendungskonzepte
Journal Title: Entropy
Publisher: MDPI
Publisher Place: Basel
Volume: 21
Issue: 2
Article Number: 128
Publisher DOI: 10.3390/e21020128
EISSN: 1099-4300
Appears in Collections:FG Empirische Netzwerke und Neurodynamik » Publications

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