Fused estimation of sparse connectivity patterns from rest fMRI

Functional magnetic resonance imaging (fMRI) is a powerful tool to analyze brain development and neuronal activity. Identifying discriminative brain regions between various groups within a population has generated great interest in recent years. In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from fMRI observations belonging to different classes. More precisely, we propose a method to analyze functional connectivity differences between children and young adults. Often, analysis is conducted on each class separately. Here, we propose to rely on a generalized fused Lasso penalty to extract both class-specific and shared co-expressed regions. In order to validate our method, experiments are performed on an fMRI dataset comprised of normally developing children from 8 to 21. The results demonstrate that the proposed method is able to properly extract meaningful sub-networks, which results in improved classification accuracy between the two classes.