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Diffusion MRI in 2-Month-Old Mouse Brain Predicts Alzheimer's Pathology Genotype.

Maria Fatima Falangola1,2, Bryan Granger3, Joshua Voltin1,2

  • 1Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA.

NMR in Biomedicine
|March 4, 2025
PubMed
Summary

Diffusion MRI (dMRI) detects brain changes in Alzheimer's disease (AD) models. Fractional anisotropy (FA) and other dMRI metrics accurately differentiate between AD mice and controls, achieving 95% classification accuracy.

Keywords:
3xTg‐AD mouseAlzheimer's diseasebiomarkersdiffusion MRIdiffusional kurtosis imagingelastic net

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Alzheimer's Disease Research

Background:

  • Diffusion MRI (dMRI) is a non-invasive technique for assessing brain tissue microstructure.
  • Previous studies showed dMRI's sensitivity to microstructural alterations in 3xTg-AD mice, including myelin abnormalities and neuronal changes.
  • This study extends prior work by profiling dMRI metrics in key AD-relevant brain regions of young 3xTg-AD mice.

Purpose of the Study:

  • To establish the dMRI profile in specific brain regions of 2-month-old 3xTg-AD mice and age-matched controls.
  • To evaluate the effectiveness of dMRI metrics in predicting genotype using elastic net (EN) logistic regression.
  • To identify the most sensitive dMRI metrics for distinguishing between AD model mice and controls.

Main Methods:

  • Utilized diffusion MRI (dMRI) to acquire microstructural data from 2-month-old triple transgenic (3xTg-AD) mice and wild-type controls.
  • Analyzed dMRI metrics including fractional anisotropy (FA), radial diffusivity (D⊥), mean diffusivity (MD), and radial kurtosis (K⊥) in regions like the corpus callosum (CC), ventral hippocampus (VH), fimbria (Fi), and subiculum (Sub).
  • Employed elastic net (EN) logistic regression modeling to predict group genotype based on dMRI metrics.

Main Results:

  • Significant group differences were observed in multiple regions, with the corpus callosum (CC) showing the most sensitive metrics: FA, radial diffusivity (D⊥), and radial kurtosis (K⊥).
  • Fractional anisotropy (FA) in the ventral hippocampus (VH) and fimbria (Fi), and mean diffusivity (MD) and D⊥ in the subiculum (Sub) also significantly differentiated the groups.
  • The EN model achieved a high classification accuracy of 0.95 for 3xTg-AD mice, with sensitivity of 0.96 and specificity of 0.94, identifying FA in VH, CC, and cingulate cortex (Ctx-Cg) as key predictors.

Conclusions:

  • dMRI metrics, particularly FA, D⊥, and K⊥ in the corpus callosum, are highly sensitive for detecting early microstructural changes in the 3xTg-AD mouse model.
  • dMRI combined with elastic net modeling provides an accurate and effective method for classifying AD model mice based on brain microstructure.
  • These findings highlight the potential of dMRI as a valuable tool for early detection and monitoring of AD-related neuropathology.