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

Year: 2018

Type: article

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-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Source: IEEE Transactions on Image Processing

DOI: 10.1109/tip.2018.2799706

Authors: Biao Jie, Mingxia Liu, Daoqiang Zhang, Dinggang Shen

Institutions: University of North Carolina at Chapel Hill, Anhui Normal University, Nanjing University of Aeronautics and Astronautics, Korea University

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Natural Science Foundation of China, Anhui Normal University, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering

OpenAlex ID: W2787999987