Diagnosis of Amnesic Mild Cognitive Impairment Using MGS-WBC and VGBN-LM Algorithms

• Published in: Frontiers in aging neuroscience Vol. 14; p. 893250
• Main Authors: Cai, Chunting, Cao, Jiangsheng, Yang, Chenhui, Chen, E.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 30.05.2022; Frontiers Media S.A
• Subjects: Algorithms; Alzheimer's disease; aMCI; Bayesian analysis; Biomarkers; Brain; Brain mapping; Cognitive ability; Diagnosis; Discriminant analysis; Functional magnetic resonance imaging; Machine learning; Medical diagnosis; Medical imaging; Methods; MGS-WBC; multi-scale features; Neurodegenerative diseases; Neuroimaging; Neuroscience; Support vector machines; VGBN-LM; multi-scale features; MGS-WBC; machine learning; VGBN-LM; aMCI
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2022.893250

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 not 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 a 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 multiscale 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. Finally, all multiscale 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 (SVMs), are adopted to evaluate the diagnostic performance of aMCI. This study helps to reveal discriminative neuroimaging features, and outperforms the state-of-the-art methods, providing new insights for the intelligent construction of CAD system of aMCI.