Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition

Abstract

White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to aging and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging (NODDI), to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau, and white matter hyperintensities), and cognition. We also compared NODDI findings to the older Diffusion Tensor Imaging model-based findings. 328 participants (264 cognitively unimpaired, 57 mild cognitive impairment, and 7 dementia with a mean age of 68.3 ±13.1 years) from the Mayo Clinic Study of Aging with multi-shell diffusion imaging, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and NODDI. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid, and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from NODDI was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter aging and white matter changes due to age-related pathological processes and were associated with cognition. NODDI and Diffusion Tensor Imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. NODDI provides additional information about synaptic density, organization, and free water content which may aid in providing mechanistic insights into disease progression.

Subjective cognitive decline predicts lower cingulo-opercular network functional connectivity in individuals with lower neurite density in the forceps minor

“…Our analyses revealed that the neurite density of the forceps minor was negatively correlated with age, in line with previous reports for fractional anisotropy and mean diffusivity (de Groot et al, 2015), voxelwise neurite density (Merluzzi et al, 2016), and overall diffusion metrics from atlas-based tracts of interest (Raghavan et al, 2021). Neither education nor sex was associated with the neurite density of the forceps minor in the present sample, also in line with previous reports (e.g., Raghavan et al, 2021).…”

“…Aging is associated with a high load of white matter hyperintensities, which tend to accumulate, mostly, in the frontal lobes (Raz et al, 2007). White matter hyperintensities are indicative of small vessel disease (Wardlaw et al, 2015) and have been shown to be associated with lower neurite density of, mainly, corpus callosum (i.e., including the forceps minor) and association (e.g., superior longitudinal fasciculus) fibers (Raghavan et al, 2021). Accordingly, it appears plausible that vascular risk factors or particular age-related vascular damage in frontal regions might contribute to a neurite density reduction in the forceps minor.…”

Characterization of white matter over 1–2 years in small vessel disease using MR-based quantitative susceptibility mapping and free-water mapping

“…This might be the reason why WMLs regress in SVD over time. In addition, a recent study using neurite orientation dispersion and density imaging demonstrated that increased FW was the strongest predictor of cognitive function in participants in the Mayo Clinic Study of Aging (Raghavan et al, 2021). However, no significant correlations were identified in our SVD study.…”

Early structural connectivity within the sensorimotor network: deviations related to prematurity and association to neurodevelopmental outcome

“…Additionally, previous studies suggested the accuracy of NODDI to assess WM maturation, with NDI being quite sensitive to WM microstructural developmental changes (Batalle et al, 2017; Kimpton et al, 2021). Besides, ODI describes the orientational dispersion of fibers within a tract, which is highly variable across tracts and might change during the maturation of crossing fibers (Raghavan et al, 2021). Nevertheless, additional analyses (not in the scope of this article) might be performed to evaluate the potential correlations between all these metrics, as performed in previous studies (Kunz et al, 2014).…”