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 Imagingjournal_title
IEEE transactions on medical imagingauthors
Michel V,Gramfort A,Varoquaux G,Eger E,Thirion Bdoi
10.1109/TMI.2011.2113378subject
Has Abstractpub_date
2011-07-01 00:00:00pages
1328-40issue
7eissn
0278-0062issn
1558-254Xjournal_volume
30pub_type
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