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

TL;DR

This paper introduces a novel method combining ICA and dictionary learning to identify interpretable brain connectivity patterns that effectively discriminate between healthy controls and schizophrenia patients in fMRI data.

Contribution

The study presents a new approach that integrates ICA and dictionary learning to extract interpretable features and patterns for mental disorder classification.

Findings

Effective classification between HC and Sz using sparse features.

Discovery of new interpretable brain connectivity patterns.

Enhanced understanding of schizophrenia's neural basis.

Abstract

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.