Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network

Yu Zhou; Xiaopeng Si; Yi-Ping Chao; Yuanyuan Chen; Ching-Po Lin; Sicheng Li; Xingjian Zhang; Yulin Sun; Dong Ming; Qiang Li

doi:10.3389/fnagi.2022.866230

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network

Front Aging Neurosci. 2022 Jun 14:14:866230. doi: 10.3389/fnagi.2022.866230. eCollection 2022.

Authors

Yu Zhou¹, Xiaopeng Si^{2

3

4}, Yi-Ping Chao^{5

6}, Yuanyuan Chen^{2

3}, Ching-Po Lin⁷, Sicheng Li^{2

3}, Xingjian Zhang^{2

3}, Yulin Sun^{2

3}, Dong Ming^{2

3}, Qiang Li¹

Affiliations

¹ School of Microelectronics, Tianjin University, Tianjin, China.
² Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
³ Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.
⁴ Institute of Applied Psychology, Tianjin University, Tianjin, China.
⁵ Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan.
⁶ Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
⁷ Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Abstract

Background: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer's disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.

Methods: Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.

Results: (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.

Conclusion: Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Keywords: Alzheimer’s disease; early diagnosis; feature extraction; machine learning; mild cognitive impairment; white matter connectivity.