Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics

• Published in: Frontiers in aging neuroscience Vol. 15; p. 1212275
• Main Authors: Long, Zhuqing, Li, Jie, Fan, Jianghua, Li, Bo, Du, Yukeng, Qiu, Shuang, Miao, Jichang, Chen, Jian, Yin, Juanwu, Jing, Bin
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 31.08.2023; Frontiers Media S.A
• Subjects: Activities of daily living; Aging Neuroscience; Algorithms; Alzheimer's disease; artificial neural network; brain atlas; Brain mapping; Classification; Cognitive ability; Dementia; Fractals; Frontal gyrus; Functional magnetic resonance imaging; Hospitals; Hurst exponent; Image processing; Machine learning; Magnetic resonance imaging; Medical imaging; Memory; multi-modal imaging; Neural networks; Neurodegenerative diseases; Neuroimaging; Substantia grisea; support vector machine; Time series
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2023.1212275

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. The results of the SVM and ANN based methods indicated the best accuracies of 80.36% and 74.40% respectively by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55%, 78.79% and 82.76% respectively in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82%, 80.30% and 75.86% respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.