Telling functional networks apart using ...
Type de document :
Article dans une revue scientifique
URL permanente :
Titre :
Telling functional networks apart using ranked network features stability
Auteur(s) :
Zanin, Massimiliano [Auteur]
Güntekin, Bahar [Auteur]
Aktürk, Tuba [Auteur]
Yıldırım, Ebru [Auteur]
Yener, Görsev [Auteur]
Kiyi, Ilayda [Auteur]
Hünerli-Gündüz, Duygu [Auteur]
Sequeira, Henrique [Auteur]
Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193
Papo, David [Auteur]
Güntekin, Bahar [Auteur]
Aktürk, Tuba [Auteur]
Yıldırım, Ebru [Auteur]
Yener, Görsev [Auteur]
Kiyi, Ilayda [Auteur]
Hünerli-Gündüz, Duygu [Auteur]
Sequeira, Henrique [Auteur]
Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193
Papo, David [Auteur]
Titre de la revue :
Scientific Reports
Nom court de la revue :
Sci Rep
Numéro :
12
Pagination :
p.2562
Éditeur :
Springer Science and Business Media LLC
Date de publication :
2022-02-15
ISSN :
2045-2322
Mot(s)-clé(s) en anglais :
Alzheimer's disease
Complex networks
Network models
Parkinson's disease
Complex networks
Network models
Parkinson's disease
Discipline(s) HAL :
Sciences cognitives
Résumé en anglais : [en]
Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified ...
Lire la suite >Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.Lire moins >
Lire la suite >Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
CNRS
CHU Lille
CNRS
CHU Lille
Équipe(s) de recherche :
Équipe Dynamique Émotionnelle et Pathologies (DEEP)
Date de dépôt :
2024-01-17T14:32:55Z
2024-02-12T15:42:07Z
2024-02-12T15:42:07Z
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