Keywords: Large Animals, Nonhuman Primates, Aging, Bonnet Macaque, Support Vector Classification, Microstructure
Motivation: Age-related changes in the brain involve multiple biological processes, thus requiring advanced analysis approaches of multi-variate MRI maps are necessary.
Goal(s): We applied a support vector machine classifier to multivariate MRI maps in order to identify groups of metrics most predictive of age-related change.
Approach: High-resolution MRI was acquired in seven female bonnet macaque post-mortem brain specimens ranging in age from 10-34 years. Voxel-wise binary classification was performed with all-metrics, diffusion-only, and relaxometry-only co-registered datasets.
Results: Age classification using all-metrics dataset achieved high accuracy (>90%). The insula had significant age accuracy classification (59%) and were explained by R2* and RD maps.
Impact: Multi-variate approaches, such as a support vector machine classifier are advantageous when assessing aging and show potential in being applied to neurodegenerative disease studies.
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