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

Frontiers in Aging Neuroscience

Aging Neuroscience

Jichang Miao¹  Jianghua Fan²  Bo Li³  Jie Li⁴  Shuang Qiu⁴  Juanwu Yin⁴  Yukeng Du⁴  Zhuqing Long⁵  Bin Jing⁶  Jian Chen⁷ 

[1] Department of Medical Devices, Nanfang Hospital, Guangzhou, China;Department of Pediatric Emergency Center, Hunan Children’s Hospital, Changsha, Hunan Province, China;Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China;Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China;Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, China;School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, China;Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, China;

关键词: multi-modal imaging;    brain atlas;    Hurst exponent;    support vector machine;    artificial neural network;   

DOI  :  10.3389/fnagi.2023.1212275

 received in 2023-04-27, accepted in 2023-08-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionMulti-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.MethodsIn 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.ResultsThe 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.ConclusionTaken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

【 授权许可】

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Copyright © 2023 Long, Li, Fan, Li, Du, Qiu, Miao, Chen, Yin and Jing.