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

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 tas...