Frequent and discriminative subnetwork mining for mild cognitive impairment classification.
Recent studies on brain networks have suggested that many brain diseases, such as Alzheimer's disease and mild cognitive impairment (MCI), are related to a large-scale brain network, rather than individual brain regions. However, it is challenging to find such a network from the whole brain network due to the complexity of brain networks. In this article, the authors propose a novel method to mine the discriminative subnetworks for classifying MCI patients from healthy controls (HC). Specifically, the authors first extract a set of frequent subnetworks from each of the two groups (i.e., MCI and HC), respectively. Then, measure the discriminative ability of those frequent subnetworks using the graph kernel-based classification method and select the most discriminative subnetworks for subsequent classification. The results on the functional connectivity networks of 12 MCI and 25 HC show that this method can obtain competitive results compared with state-of-the-art methods on MCI classification.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics , Nanjing, China .
Fei
F
Fei
Biao
B
Jie
Daoqiang
D
Zhang
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D060825:Cognitive Dysfunction / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009415:Nerve Net / Q000503:physiopathology*; D009434:Neural Pathways / Q000503:physiopathology; D015203:Reproducibility of Results
functional connectivity network; graph kernel; mild cognitive impairment; subgraph mining