NODDI in gray matter is a sensitive marker of aging and early AD changes

Abstract

Introduction: Age-related and Alzheimer's disease (AD) dementia-related neurodegeneration impact brain health. While morphometric measures from T1-weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker.

Methods: We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography.

Results: Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age-related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures.

Discussion: These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD.

Highlights: Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD).Free water fraction has higher sensitivity to normal brain aging.Free water fraction has stronger associations with early AD and regional tau deposition.

Keywords: brain aging; diffusion magnetic resonance imaging; early Alzheimer's disease dementia; neurite orientation dispersion and density imaging.

FIGURE 1

A, Heatmap of Spearman correlations between regional imaging measurements and chronological age within CU participants. Spearman correlations were reported as mean (lower bound of 95% CI, upper bound of 95% CI) from 100 bootstrapping runs. Heatmap colors were coded based on the absolute value of the mean correlation coefficients. Only regions with Spearman correlations in the top quartile are colored for clarity. Yellow color indicates correlations with high magnitude. B, Visual representation of regional Spearman correlations as illustrated in (A). CI, confidence interval; CT, cortical thickness and gray matter volume; CU, cognitively unimpaired; FWF, free water fraction; NDI, neurite density index; ODI, orientation dispersion index.

FIGURE 2

Heatmap displaying average effect sizes (Cohen dz) indicating the disparity in microstructural and morphometric measures between age‐matched CU and participants with MCI (left), as well as between CU and AD dementia participants (right). Effect sizes were reported as mean (lower bound of 95% CI, upper bound of 95% CI) from 100 bootstrapping runs. Heatmap colors were assigned based on the absolute value of effect sizes. Only regions with absolute Cohen dz values in the top quartile are color‐coded for clarity. Yellow color indicates larger effect size, thus more significant change between the CU group and the compared group. AD, Alzheimer's disease; CI, confidence interval; CT, cortical thickness and gray matter volume; CU, cognitively unimpaired; FWF, free water fraction; MCI, mild cognitive impairment; NDI, neurite density index; ODI, orientation dispersion index.

FIGURE 3

A, Illustrations of Braak ROIs I, III–VI. B, Multivariable linear regression models are fit to each Braak stage ROI. For each ROI, imaging parameters serve as regressors and tau deposition as the target. Colors are assigned based on the absolute value of linear coefficients. CT, cortical thickness and gray matter volume; FWF, free water fraction; NDI, neurite density index; ODI, orientation dispersion index; ROI, region of interest.