Gradients of connectivity as graph Fourier bases of brain activity - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Network Neuroscience Année : 2021

Gradients of connectivity as graph Fourier bases of brain activity

Résumé

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.
Fichier principal
Vignette du fichier
netn_a_00183.pdf (920.08 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03238786 , version 1 (27-05-2021)

Identifiants

Citer

Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia. Gradients of connectivity as graph Fourier bases of brain activity. Network Neuroscience, 2021, 5 (2), pp.322-336. ⟨10.1162/netn_a_00183⟩. ⟨hal-03238786⟩
25 Consultations
153 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More