Title :

Telling functional networks apart using ranked network features stability

Author(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]

Journal title :

Scientific Reports

Abbreviated title :

Sci Rep

Volume number :

12

Pages :

p.2562

Publisher :

Springer Science and Business Media LLC

Publication date :

2022-02-15

ISSN :

2045-2322

English keyword(s) :

Alzheimer's disease
Complex networks
Network models
Parkinson's disease

HAL domain(s) :

Sciences cognitives

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS
CHU Lille

Collections :

• Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193

Research team(s) :

Équipe Dynamique Émotionnelle et Pathologies (DEEP)

Submission date :

2024-01-17T14:32:55Z
2024-02-12T15:42:07Z