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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal
Tatsuya Jitsuishi1, Atsushi Yamaguchi2
1Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan.
Scientific Reports
|March 12, 2022
Summary
Machine learning models can distinguish early from late mild cognitive impairment (MCI) using MRI data. An AdaBoost model with diffusion parameters showed 70% accuracy, identifying key brain regions for early Alzheimer's disease detection.
Area of Science:
- Neuroimaging
- Machine Learning
- Neurology
Background:
- Early intervention for mild cognitive impairment (MCI) may prevent Alzheimer's disease (AD).
- Distinguishing between early MCI (EMCI) and late MCI (LMCI) is crucial for targeted interventions.
- Multimodal MRI data offers potential for identifying subtle differences between MCI subtypes.
Purpose of the Study:
- To identify the optimal machine learning (ML) model for classifying EMCI and LMCI subtypes.
- To utilize multimodal MRI data, including diffusion parameters and functional connectivity, for classification.
- To pinpoint specific brain regions and features indicative of LMCI.
Main Methods:
- Tract-based spatial statistics (TBSS) identified white matter changes in the Corpus Callosum associated with LMCI.
- ROI-based tractography mapped connected cortical areas affected by callosal fiber alterations.
- Feature subsets were created using resting-state functional connectivity (TBSS-RSFC), graph theory metrics (TBSS-Graph), and diffusion parameters.
- Multiple ML models were trained and tested using cross-validation for EMCI/LMCI classification.
Main Results:
- The ensemble AdaBoost ML model, using diffusion parameters, achieved the highest performance with a mean accuracy of 70%.
- Key brain regions for classification included the frontal lobe, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus.
- White matter changes in the Corpus Callosum were significant in LMCI patients.
Conclusions:
- The optimal ML model utilizing diffusion parameters can effectively differentiate LMCI from EMCI subjects.
- This approach holds promise for identifying individuals at the prodromal stage of Alzheimer's disease.
- Identifying specific brain regions and features aids in understanding the progression of MCI.
