Ordinal Pattern Kernel for Brain Connectivity Network Classification

TL;DR

This paper introduces an ordinal pattern kernel that effectively measures similarities between weighted brain connectivity networks, improving classification accuracy for brain disease detection over existing unweighted graph kernels.

Contribution

The paper proposes a novel ordinal pattern kernel for weighted brain networks, capturing edge weight information for improved classification performance.

Findings

The ordinal pattern kernel outperforms state-of-the-art graph kernels in brain disease classification.

The depth-first-based ordinal pattern kernel enhances classification accuracy.

Experimental results on ADNI dataset validate the effectiveness of the proposed method.

Abstract

Brain connectivity networks, which characterize the functional or structural interaction of brain regions, has been widely used for brain disease classification. Kernel-based method, such as graph kernel (i.e., kernel defined on graphs), has been proposed for measuring the similarity of brain networks, and yields the promising classification performance. However, most of graph kernels are built on unweighted graph (i.e., network) with edge present or not, and neglecting the valuable weight information of edges in brain connectivity network, with edge weights conveying the strengths of temporal correlation or fiber connection between brain regions. Accordingly, in this paper, we present an ordinal pattern kernel for brain connectivity network classification. Different with existing graph kernels that measures the topological similarity of unweighted graphs, the proposed ordinal pattern kernels calculate the similarity of weighted networks by comparing ordinal patterns from weighted networks. To evaluate the effectiveness of the proposed ordinal kernel, we further develop a depth-first-based ordinal pattern kernel, and perform extensive experiments in a real dataset of brain disease from ADNI database. The results demonstrate that our proposed ordinal pattern kernel can achieve better classification performance compared with state-of-the-art graph kernels.