Structure-Preserving Graph Kernel for Brain Network Classification

TL;DR

This paper introduces a structure-preserving graph kernel for brain network classification that leverages the inherent graph structure to improve interpretability and performance in connectome analysis tasks.

Contribution

It proposes a novel graph kernel that encodes structural features directly from connectome data, enhancing interpretability and classification accuracy.

Findings

Superior performance on HIV disease classification and emotion recognition tasks.

Relevant EEG-connectome information is primarily encoded in the alpha band.

The method is clinically interpretable.

Abstract

This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the graph representation to encode prior knowledge in the kernel. We first proposed a matrix factorization to directly extract structural features from natural symmetric graph representations of connectome data. We then used them to derive a structure-persevering graph kernel to be fed into the support vector machine. The proposed approach has the advantage of being clinically interpretable. Quantitative evaluations on challenging HIV disease classification (DTI- and fMRI-derived connectome data) and emotion recognition (EEG-derived connectome data) tasks demonstrate the superior performance of our proposed methods against the state-of-the-art. Results showed that relevant EEG-connectome information is primarily encoded in the alpha band during the emotion regulation task.