  Independent component analysis (ICA) of multi-subject functional magnetic
resonance imaging (fMRI) data has proven useful in providing a fully
multivariate summary that can be used for multiple purposes. ICA can identify
patterns that can discriminate between healthy controls (HC) and patients with
various mental disorders such as schizophrenia (Sz). Temporal functional
network connectivity (tFNC) obtained from ICA can effectively explain the
interactions between brain networks. On the other hand, dictionary learning
(DL) enables the discovery of hidden information in data using learnable basis
signals 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. We use multi-subject
resting-state fMRI data from $358$ subjects and form subject-specific tFNC
feature vectors from ICA results. Then, we learn sparse representations of the
tFNCs and introduce a new set of sparse features as well as new interpretable
patterns from the learned atoms. Our experimental 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 that can help understand the complexities of mental
diseases such as schizophrenia.
