Multi-subject dictionary learning to segment an atlas of brain spontaneous activity

Gaël Varoquaux 1, 2, 3 Alexandre Gramfort 1, 2 Fabian Pedregosa 1, 2 Vincent Michel 1, 2 Bertrand Thirion 1, 2
1 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
3 UNICOG-U992 - Neuroimagerie cognitive
Abstract : Fluctuations in brain on-going activity can be used to reveal its intrinsic functional organization. To mine this information, we give a new hierarchical probabilistic model for brain activity patterns that does not require an experimental design to be specified. We estimate this model in the dictionary learning framework, learning simultaneously latent spatial maps and the corresponding brain activity time-series. Unlike previous dictionary learning frameworks, we introduce an explicit difference between subject-level spatial maps and their corresponding population-level maps, forming an atlas. We give a novel algorithm using convex optimization techniques to solve efficiently this problem with non-smooth penalties well-suited to image denoising. We show on simulated data that it can recover population-level maps as well as subject specificities. On resting-state fMRI data, we extract the first atlas of spontaneous brain activity and show how it defines a subject-specific functional parcellation of the brain in localized regions.
Keywords : sparse models atlas resting state segmentation
Document type :
Conference papers
Information Processing in Medical Imaging, Jul 2011, Kaufbeuren, Germany. Springer, 6801, pp.562-573, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-22092-0_46〉
Liste complète des métadonnées

Cited literature [27 references]  Display  Hide  Download
https://hal.inria.fr/inria-00588898
Contributor : Gaël Varoquaux <>
Submitted on : Sunday, February 19, 2012 - 3:54:18 PM
Last modification on : Tuesday, June 5, 2018 - 1:17:16 AM
Document(s) archivé(s) le : Thursday, June 14, 2012 - 4:40:26 PM

Files

paper.pdf
Files produced by the author(s)

Identifiers

Collections

CEA | INRIA | CEA-DRF

Citation

Gaël Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, Bertrand Thirion. Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Information Processing in Medical Imaging, Jul 2011, Kaufbeuren, Germany. Springer, 6801, pp.562-573, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-22092-0_46〉. 〈inria-00588898v2〉

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

2916

Files downloads

900

Contact


archive-ouverte@inria.fr