New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning

Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data can be used for multiple purposes, including the identification of patterns that can discriminate between healthy controls and patients with various mental disorders. Dictionary learning (DL) enables the discovery of hidden information in data using learnable basis signals called atoms through the use of sparsity. In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups. Our results show that the new representation not only leads to effective classification between HC and Sz groups using sparse features but can also identify new interpretable patterns from the learned atoms.