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September 17, 2025

Identifying Early Diffusion MRI Biomarkers in 2-month-old 3xTg-AD mice via the Elastic Net

Key Points

The elastic net model achieved an accuracy of 0.95 in classifying 3xTg-AD mice.
Key metrics included sensitivity of 0.96 and specificity of 0.94 for early Alzheimer's detection.
Diffusion MRI measurements were taken from brain regions in 2-month-old 3xTg-AD mice, using a logistic regression approach.
Findings highlight diffusion MRI's ability to detect early microstructural changes in Alzheimer's disease.

Abstract

Motivation: To establish diffusion MRI (dMRI) biomarkers for early detection of Alzheimer's disease (AD). Goal(s): To investigate the effectiveness of dMRI metrics in classifying 3xTg-AD mice using elastic net (EN). Approach: Multiple dMRI metrics were measured in several brain regions of 2-month-old 3xTg-AD and control mice, and an EN logistic regression model was applied to determine the best predictor for classifying mouse group genotype. Results: The EN model yielded an accuracy of 0.95, sensitivity of 0.96, and specificity of 0.94. The fractional anisotropies of the ventral hippocampus, corpus callosum, and cingulate cortex were identified as the three best dMRI metrics for classifying 3xTg-AD mice. Impact: Diffusion MRI can accurately distinguish 3xTg-AD mice from control mice at 2 months, which is prior to the development of amyloid plaques and tau pathology. This demonstrates dMRI's high sensitivity to microstructural changes occurring early in the disease process.

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