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Inverse inference, or "brain reading", is a recent paradigm for analyzingfunctional magnetic resonance imaging (fMRI) data, based on pattern recognitionand statistical learning. By predicting some cognitive variables related tobrain activation maps, this approach aims at decoding brain activity. Inverseinference takes into account the multivariate information between voxels and iscurrently the only way to assess how precisely some cognitive information isencoded by the activity of neural populations within the whole brain. However,it relies on a prediction function that is plagued by the curse ofdimensionality, since there are far more features than samples, i.e., morevoxels than fMRI volumes. To address this problem, different methods have beenproposed, such as, among others, univariate feature selection, featureagglomeration and regularization techniques. In this paper, we consider asparse hierarchical structured regularization. Specifically, the penalizationwe use is constructed from a tree that is obtained by spatially-constrainedagglomerative clustering. This approach encodes the spatial structure of thedata at different scales into the regularization, which makes the overallprediction procedure more robust to inter-subject variability. Theregularization used induces the selection of spatially coherent predictivebrain regions simultaneously at different scales. We test our algorithm on realdata acquired to study the mental representation of objects, and we show thatthe proposed algorithm not only delineates meaningful brain regions but yieldsas well better prediction accuracy than reference methods.

Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity