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

• Published in: IEEE transactions on image processing Vol. 27; no. 5; pp. 2340 - 2353
• Main Authors: Jie, Biao, Liu, Mingxia, Zhang, Daoqiang, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer’s disease (AD); Brain; Brain - diagnostic imaging; Brain Mapping - methods; brain network; Classification; Cognitive Dysfunction - diagnostic imaging; Dementia; Diagnosis; Disease; Functional magnetic resonance imaging; Graph kernel; Graph theory; Graphs; Humans; Image Interpretation, Computer-Assisted - methods; Kernel; Kernels; Length measurement; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; mild cognitive impairment (MCI); Network analysis; Networks; Neurology; Similarity
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2018.2799706

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.