Diagnosis of Amnesic Mild Cognitive Impairment Using MGS-WBC and VGBN-LM Algorithms - Cai, Chunting; Cao, Jiangsheng; Yang, Chenhui; Chen, E. (Frontiers in Aging Neuroscience, 2022)
Computer-aided diagnosis (CAD) has undergone rapid development with the advent of advanced neuroimaging and machine learning methods. Nevertheless, how to extract discriminative features from the limited and high-dimensional data is no ideal, especially for amnesic mild cognitive impairment (aMCI) data based on resting-state functional magnetic resonance imaging (rs-fMRI). Furthermore, a robust and reliable system for aMCI detection is conducive to timely detecting and screening subjects at high risk of Alzheimer’s disease (AD). In this scenario, we first develop the mask generation strategy based on within-class and between-class criterion (MGS-WBC), which primarily aims at reducing data redundancy and excavating multi-scale features of the brain. Concurrently, vector generation for brain networks based on laplacian matrix (VGBN-LM) is presented to obtain the global features of the functional network. At last, all multi-scale features are fused to further improve the diagnostic performance of aMCI. Typical classifiers for small data learning, such as naive bayesian (NB), linear discriminant analysis (LDA), logistic regression (LR), and support vector machines (SVM) are adopted to evaluate the diagnostic performance of aMCI. Our work helps to reveal discriminative neuroimaging features, and outperforms the state-of-the-art methods, providing new insights for the intelligent construction of aMCI’s CAD system.