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In our previous studies, we demonstrated the sensitivity of dMRI to capture brain microstructural alterations in the triple transgenic (3xTg\u2010<jats:sc>AD<\/jats:sc>) mice, particularly brain morphological abnormalities in 2\u2010month\u2010old mice, where dMRI was sensitive to myelin abnormalities, to microglia proliferation\/activation, and to the larger number of basal forebrain cholinergic neurons previously described in this model at this young age. In this study, we extend our prior work by establishing the dMRI profile of several brain regions relevant to <jats:sc>AD<\/jats:sc> pathology in 2\u2010month\u2010old 3xTg\u2010<jats:sc>AD<\/jats:sc> and age\u2010matched controls (NC) and by investigating the effectiveness of these dMRI metrics in predicting group genotype using elastic net (EN) logistic regression modeling. EN has been shown to be a high\u2010performance and stable machine learning model for neuroimaging data. Our results demonstrated significant group differences in several ROIs, particularly in the corpus callosum (CC) where fractional anisotropy (FA) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u2009\u22121.87), radial diffusivity (D<jats:sub>\u2534<\/jats:sub>) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u2009\u22121.33), and radial kurtosis (K<jats:sub>\u2534<\/jats:sub>) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u2009\u22121.34) were statistically significant and the most sensitive dMRI metrics to differentiate between the two groups, with large effect sizes (Cohen's <jats:italic>d<\/jats:italic>) values. Moreover, FA in the ventral hippocampus (VH) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u20091.13) and fimbria (Fi) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u2009\u22121.04) as well as mean diffusivity (MD) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u20091.10) and D<jats:sub>\u2534<\/jats:sub> in the subiculum (Sub) (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001; <jats:italic>d<\/jats:italic>\u2009=\u20091.12) were also statistically significant and able to clearly distinguish the two groups. Additionally, our results from the trained EN model indicate that FA in the VH, CC, and cingulate cortex (Ctx\u2010Cg) were the three best dMRI metrics to classify the 3xTg\u2010<jats:sc>AD<\/jats:sc> mice with an accuracy of 0.95. Sensitivity and specificity were also calculated to assess the goodness of prediction, resulting in 0.96 and 0.94, respectively.<\/jats:p>","DOI":"10.1002\/nbm.70018","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T11:38:02Z","timestamp":1741693082000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Diffusion MRI in 2\u2010Month\u2010Old Mouse Brain Predicts Alzheimer's Pathology Genotype"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6349-7734","authenticated-orcid":false,"given":"Maria\u00a0Fatima","family":"Falangola","sequence":"first","affiliation":[{"name":"Department of Neuroscience Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Center for Biomedical Imaging Medical University of South Carolina  Charleston South Carolina 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