Total variation regularization for fMRI-based prediction of behaviour

TL;DR

This paper introduces the novel application of total variation regularization to fMRI data for improved brain mapping and decoding, demonstrating its effectiveness in classification tasks.

Contribution

It is the first to apply TV regularization to fMRI data, enhancing interpretability and decoding performance in brain imaging analysis.

Findings

TV regularization improves brain mapping interpretability

Effective in classifying behavioural variables from fMRI data

First use of TV regularization for classification in this context

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 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 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 l_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.