Total variation regularization for fMRI-based prediction of behavior.

Abstract:

:While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional magnetic resonance imaging (fMRI) data, that provide an indirect measure of task-related 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 behavioral 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 l(1) norm of the image gradient, also known as 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.

journal_name

IEEE Trans Med Imaging

authors

Michel V,Gramfort A,Varoquaux G,Eger E,Thirion B

doi

10.1109/TMI.2011.2113378

subject

Has Abstract

pub_date

2011-07-01 00:00:00

pages

1328-40

issue

7

eissn

0278-0062

issn

1558-254X

journal_volume

30

pub_type

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