ArticleNMR in biomedicine2025
Diffusion MRI in 2-Month-Old Mouse Brain Predicts Alzheimer's Pathology Genotype.
Article in NMR in biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Diffusion MRI measures detect brain microstructure changes due to early treatment with neurotrophic peptide mimetic P021 in the 3xTg-AD mouse model of Alzheimer's disease.Magnetic resonance imaging · 2026Article
- Female 3xTg-AD mice demonstrate hyperexcitability phenotype of Alzheimer's disease in structure-function and function-behavior relationships.Network neuroscience (Cambridge, Mass.) · 2025Article
- Humanized APP is the primary determinant of regional brain volume in humanized APOE knock-in mice: a cross-sectional ex vivo MRI study.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Diffusion MRI (dMRI) is widely used as a non-invasive means of detecting changes in brain tissue microstructure. In our previous studies, we demonstrated the sensitivity of dMRI to capture brain microstructural alterations in the triple transgenic (3xTg-AD) mice, particularly brain morphological abnormalities in 2-month-old 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 AD pathology in 2-month-old 3xTg-AD and age-matched 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-performance 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) (p < 0.0001; d = -1.87), radial diffusivity (D
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Registered trials
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