Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Abstract

The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intra-cellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extra-cellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

“…While our age prediction was based on several diffusion models known to be sensitive to WM aging [73,84,89], it is possible that specific estimates of WM hyperintensities could yield APOE -sensitive CMR associations. Furthermore, a recent UK Biobank study revealed region-and metric-specific effects of age and sex on WM microstructure [148]. Although age prediction models combine a rich variety of WM characteristics into single estimates, global BAG estimates do not provide specific information about regional WM connections.…”

A Component or Multiple Components of Bleeding Gums May Ameliorate Both Glaucoma and Alzheimer’s Disease

“…Data processing was performed on Minerva, a Linux mainframe with Centos 7.6, at the Icahn School of Medicine at Mount Sinai. We used the UK Biobank Data Parser (ukbb parser), a python-based package that allows easy interfacing with the large UK Biobank dataset [ 16 ]. Statistical analysis (Fisher’s exact test and logistic regression) was done using SPSS 25 and R.…”

White matter microstructure shows sex differences in late childhood: Evidence from 6,797 children

“…RSI, in comparison, can resolve complex fiber configurations and provides more refined microstructure measures, offering greater insight into the underlying neurobiology than DTI (White, Leergaard, et al, 2013; White et al, 2014; White, McDonald, et al, 2013). Prior work has together indicated that the relative sensitivity of conventional and advanced dMRI metrics may also depend on the specific neurobiology underlying the scientific question of interest, allowing for the possibility that DTI and RSI may exhibit significantly different sensitivity to effects beyond participant sex (Lawrence et al, 2021; Nir et al, 2019; Pines et al, 2020).…”

“…Such in vivo neuroimaging studies have used both conventional and advanced diffusion-weighted magnetic resonance imaging (dMRI) models to extensively characterize sex differences in large-scale adult samples (Cox et al, 2016; Lawrence et al, 2021; Ritchie et al, 2018). These analyses firmly established white matter sex differences across the human brain, with some regional variability in the magnitude of such differences and the reported effect sizes indicating mean sex differences coupled with overlapping distributions in men and women (Cox et al, 2016; Lawrence et al, 2021; Ritchie et al, 2018). White matter microstructure in adulthood has also been associated with cognitive and behavioral variability, as well as a range of brain-based disorders that exhibit sex differences in their prevalence or presentation (Favre et al, 2019; Kelly et al, 2018; Meyer & Lee, 2019; Paus, Keshavan, & Giedd, 2008; Roberts, Anderson, & Husain, 2013; Salminen et al, 2022; van Velzen et al, 2020).…”

“…Sex differences in white matter microstructure are consistently shown in the adult human brain (Jahanshad & Thompson, 2017; Salminen et al, 2022). Such in vivo neuroimaging studies have used both conventional and advanced diffusion-weighted magnetic resonance imaging (dMRI) models to extensively characterize sex differences in large-scale adult samples (Cox et al, 2016; Lawrence et al, 2021; Ritchie et al, 2018). These analyses firmly established white matter sex differences across the human brain, with some regional variability in the magnitude of such differences and the reported effect sizes indicating mean sex differences coupled with overlapping distributions in men and women (Cox et al, 2016; Lawrence et al, 2021; Ritchie et al, 2018).…”