A bootstrap method to improve brain subcortical network segregation in resting-state fMRI data

Abstract : Brain functional networks are sets of distant cortical, subcortical or cerebellar regions characterized by coherent dynamics. While spatial independent component analysis (sICA) reproducibly detects the cortical components of these networks from resting-state functional magnetic resonance imaging (fMRI) data, little is known about their subcortical (basal ganglia) components. We propose a method to detect cortico-subcortical networks accross subjects. Cortical components are first detected using sICA. Subcortical components are then identified using a general linear model combined with bootstrap to ensure statistical robustness, and then compared with an atlas of the basal ganglia for validation.
Type de document :
Communication dans un congrès
COMPSTAT - 19th international symposium on computational statistics - 2010, Aug 2010, Paris, France. Springer, pp.1335-1342, 2010, Proceedings of the 19th international symposium on computational statistics
Liste complète des métadonnées

https://hal.inria.fr/inria-00538258
Contributeur : Arnaud Messé <>
Soumis le : lundi 22 novembre 2010 - 11:19:37
Dernière modification le : mercredi 21 mars 2018 - 18:57:35

Identifiants

  • HAL Id : inria-00538258, version 1

Collections

Citation

Caroline Malherbe, E. Bardinet, Arnaud Messé, Vincent Perlbarg, Guillaume Marrelec, et al.. A bootstrap method to improve brain subcortical network segregation in resting-state fMRI data. COMPSTAT - 19th international symposium on computational statistics - 2010, Aug 2010, Paris, France. Springer, pp.1335-1342, 2010, Proceedings of the 19th international symposium on computational statistics. 〈inria-00538258〉

Partager

Métriques

Consultations de la notice

324