Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

ABSTRACT: As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-ne

SUBMITTER: Jie B 

PROVIDER: S-EPMC5844189 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature