Telling functional networks apart using ranked network features stability - Université de Lille Accéder directement au contenu
Article Dans Une Revue Scientific Reports Année : 2022

Telling functional networks apart using ranked network features stability

Massimiliano Zanin
  • Fonction : Auteur
Bahar Güntekin
  • Fonction : Auteur
Tuba Aktürk
  • Fonction : Auteur
Ebru Yıldırım
  • Fonction : Auteur
Görsev Yener
  • Fonction : Auteur
Ilayda Kiyi
  • Fonction : Auteur
Duygu Hünerli-Gündüz
  • Fonction : Auteur
David Papo
  • Fonction : Auteur


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.
Fichier principal
Vignette du fichier
s41598-022-06497-w.pdf (1.5 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04453540 , version 1 (12-02-2024)




Massimiliano Zanin, Bahar Güntekin, Tuba Aktürk, Ebru Yıldırım, Görsev Yener, et al.. Telling functional networks apart using ranked network features stability. Scientific Reports, 2022, Scientific Reports, 12 (1), p.2562. ⟨10.1038/s41598-022-06497-w⟩. ⟨hal-04453540⟩
4 Consultations
6 Téléchargements



Gmail Mastodon Facebook X LinkedIn More