CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series

Abstract : Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and canonical correlation analysis, our method is auto-calibrated and identifies the group-reproducible data subspace before performing ICA. We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible.
Type de document :
Communication dans un congrès
Medical Image Computing and Computer Aided Intervention, Sep 2009, London, United Kingdom. pp.1, 2009
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00435262
Contributeur : Gaël Varoquaux <>
Soumis le : mardi 24 novembre 2009 - 09:29:43
Dernière modification le : jeudi 7 février 2019 - 16:50:59
Document(s) archivé(s) le : jeudi 17 juin 2010 - 21:37:23

Fichiers

paper.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00435262, version 1
  • ARXIV : 0911.4650

Collections

Citation

Gaël Varoquaux, Sepideh Sadaghiani, Jean Baptiste Poline, Bertrand Thirion. CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series. Medical Image Computing and Computer Aided Intervention, Sep 2009, London, United Kingdom. pp.1, 2009. 〈hal-00435262〉

Partager

Métriques

Consultations de la notice

587

Téléchargements de fichiers

310