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Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics
Zhe Ma1,2, Bin Jing1,2, Yuxia Li3
1School of Biomedical Engineering, Capital Medical University, Beijing, China.
Abstract:
Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer's disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.
Insights
Early diagnosis of mild cognitive impairment (MCI) is crucial for Alzheimer's disease (AD) prevention. A random forest model combining multiple brain metrics achieved 80% accuracy in identifying MCI, outperforming single metrics.
Area of Science:
- Neuroimaging
- Neurology
- Biomedical Engineering
Background:
- Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating early and accurate diagnostic methods.
- Identifying individuals with MCI is critical for timely intervention and potential disease modification.
Purpose of the Study:
- To develop and validate a robust method for early identification of MCI using neuroimaging biomarkers.
- To compare the diagnostic performance of combined morphological metrics against individual metrics.
Main Methods:
- Utilized voxel-based, deformation-based, and surface-based morphometry to extract multiple brain morphological metrics.
- Employed a random forest classifier trained on a discovery dataset (ADNI) and validated on independent datasets.
- Evaluated performance using 10-fold cross-validation and external datasets.
Main Results:
- The combined morphological metrics achieved approximately 80% accuracy in identifying MCI across all datasets.
- This performance significantly surpassed models using single metrics (45%-76% accuracy).
- Cortical thickness and gyrification index emerged as key biomarkers for MCI detection.
Conclusions:
- A multi-metric approach using random forest classification offers a reliable and accurate method for early MCI diagnosis.
- Surface-based morphometry metrics, specifically cortical thickness and gyrification index, are promising biomarkers for MCI.
- This method shows potential for assisting clinicians in the early diagnosis of MCI.
