Total variation regularization for fMRI-based prediction of behaviour

—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 ℓ1 norm of the...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Vincent Michel, Alexandre Gramfort, Gaël Varoquaux, Evelyn Eger, Bertrand Thirion
• Keywords: CORR 2011, Education, Spontaneous Neuronal Activity, Standard Regularization Methods, TV Regularization