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Updated: Sep 6, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter
Yu Zhou1, Xiaopeng Si2,3,4, Yi-Ping Chao5,6
1School of Microelectronics, Tianjin University, Tianjin, China.
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.
Insights
This study developed a novel method to detect mild cognitive impairment (MCI) by analyzing white matter (WM) networks. The best approach uses hippocampus-related WM networks derived from mean diffusivity (MD) to achieve 89.4% accuracy in classifying MCI, aiding early Alzheimer's disease (AD) detection.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Neurology
Background:
- Mild cognitive impairment (MCI) detection is crucial for early Alzheimer's disease (AD) screening.
- Subtle changes in MCI present classification challenges for machine learning.
- Hippocampus-related white matter (WM) network damage is linked to memory decline in MCI.
Purpose of the Study:
- To propose an effective feature extraction method for whole-brain WM networks.
- To enhance the classification performance for MCI detection.
- To identify critical WM network features driven by hippocampus-related regions.
Main Methods:
- Recruited 42 MCI and 54 normal control (NC) subjects.
- Utilized diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1w imaging.
- Extracted features from whole-brain and hippocampus (HIP)-related WM networks using mean diffusivity (MD) and fractional anisotropy (FA), comparing classification performance with support vector machine (SVM).
Main Results:
- Hippocampus-related WM networks significantly improved classification performance over whole-brain networks.
- Mean diffusivity (MD) features outperformed fractional anisotropy (FA) features.
- The optimal classification achieved 89.4% accuracy and 0.954 AUC using a support vector machine (SVM) with a significant HIP-related WM network in MD.
Conclusions:
- Feature extraction from hippocampus-driven WM networks offers an effective strategy for early AD diagnosis.
- The hippocampus and thalamus are identified as crucial hubs in the WM network for MCI.
- This approach enhances the potential for early and accurate detection of neurodegenerative diseases.
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