  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.
