Diagnosis of Alzheimer’s Disease Using Brain Network - Lama, Ramesh Kumar; Kwon, Goo-Rak (Frontiers in Neuroscience, 2021)
Recent studies suggest the brain functional connectivity impairment is the early event occurred in Alzheimer’s disease (AD) and its prodromal stage mild cognitive impairment (MCI). In general these impairments can be studied comprehensively by modeling brain as a graph based network. In this paper, we present a new diagnosis approach using graph based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using a support vector machine (SVM), and regularized extreme learning machine (RELM). We compare the performance of these classiﬁers for brain network data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets with different feature selection methods. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experiments on the ADNI datasets showed that SVM with the feature selection approach can signiﬁcantly improve classiﬁcation accuracy of AD from MCI and HC subjects.