Abstract
A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.
Highlights
White matter alterations have been linked to age-related cognitive decline and implicated in neurodegenerative diseases such as Alzheimer’s disease (Bennett & Madden, 2014; Pievani et al, 2014)
We found that age and participant sex was significantly related to many white matter properties across the brain, and advanced diffusion-weighted magnetic resonance imaging (dMRI) models detected age and sex effects the most sensitively
The following white matter indices were calculated from mean apparent propagator MRI (MAPMRI): return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-toplane probability (RTPP); RTOP is a zero-displacement probability that can be decomposed into RTAP and RTPP, which reflect restrictive barriers in the radial and axial orientations, respectively
Summary
White matter alterations have been linked to age-related cognitive decline and implicated in neurodegenerative diseases such as Alzheimer’s disease (Bennett & Madden, 2014; Pievani et al, 2014). The conventional modeling approach applied to dMRI data, known as diffusion tensor imaging (DTI), fits a single-tensor to single-shell dMRI data and typically reflects hindered diffusion (Basser et al, 1994; Jones, 2008). A more advanced single-shell model is the tensor distribution function (TDF), which addresses well-established limitations of DTI by using a continuous mixture of tensors to capture multiple underlying fiber populations (Leow et al, 2009; Nir et al, 2017; Zhan et al, 2009). Compared to single-shell models, multi-shell dMRI models may allow for a more nuanced depiction of the underlying microstructural environment by using multi-shell dMRI data, which allows both hindered and restricted diffusion to be captured. Multi-shell diffusion models include, among others, the biophysical model neurite orientation dispersion and density imaging (NODDI) and the signal-based model mean apparent propagator MRI (MAPMRI). NODDI is a multi-compartment model that separately models restricted, hindered, and free water
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