HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Total variation regularization for fMRI-based prediction of behaviour.

Abstract : While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI (fMRI) data, that provide an indirect measure of taskrelated or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioural variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the '1 norm of the image gradient, a.k.a. its Total Variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification.
Complete list of metadata

Cited literature [60 references]  Display  Hide  Download

Contributor : Gaël Varoquaux Connect in order to contact the contributor
Submitted on : Saturday, February 5, 2011 - 3:16:00 PM
Last modification on : Monday, December 13, 2021 - 9:16:03 AM
Long-term archiving on: : Friday, May 6, 2011 - 2:31:13 AM


Files produced by the author(s)



Vincent Michel, Alexandre Gramfort, Gaël Varoquaux, Evelyn Eger, Bertrand Thirion. Total variation regularization for fMRI-based prediction of behaviour.. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2011, 30 (7), pp.1328 - 1340. ⟨10.1109/TMI.2011.2113378⟩. ⟨inria-00563468⟩



Record views


Files downloads