Association between perivascular diffusion and white matter microstructural integrity, free water, Aβ burden, and cognition: diffusion tensor vs. kurtosis tensor

TL;DR

This study compares two MRI techniques to assess brain fluid flow and finds that one method better tracks cognitive decline and amyloid buildup.

Contribution

DKI-ALPS is shown to be more accurate than DTI-ALPS for assessing glymphatic system function and its link to cognitive impairment.

Findings

CI individuals had significantly lower DKI-ALPS values compared to healthy controls.

Longitudinal DKI-ALPS trajectories showed stronger correlations with Aβ burden and cognitive decline.

Both DTI-ALPS and DKI-ALPS were negatively correlated with white matter microstructural integrity and free water.

Abstract

Perivascular diffusion holds great potential for the non-invasive assessment of the glymphatic system (GS). However, Gaussian model-based diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) is limited by microstructural alterations. This study aimed to compare cross-sectional and longitudinal trajectories of diffusion kurtosis imaging ALPS (DKI-ALPS) and DTI-ALPS and investigate their association with white matter (WM) microstructural integrity, free water (FW), Aβ burden, and cognitive impairment (CI).

This study included 128 healthy controls (HCs) and 83 individuals with cognitive impairment (CI) who underwent multi-shell diffusion-weighted magnetic resonance imaging (dMRI). Four dMRI indices were quantified: DTI-ALPS and DKI-ALPS to assess the GS function; peak width of skeletonized mean diffusivity (PSMD) to evaluate the WM microstructural integrity; and FW-WM to quantify the extracellular fluid accumulation in WM. Cohen’s d was reported as the measure of effect size, with generalized linear models (GLMs) adjusting for confounding factors. Functional principal component analysis (FPCA) was used to determine the trajectories of dMRI indices.

CIs exhibited significantly lower DTI-ALPS (1.28 vs. 1.37; p = 0.007; Cohen’s d = 0.383) and DKI-ALPS (1.37 vs. 1.63, p < 0.001; Cohen’s d = 0.770) than HCs. GLMs confirmed significant group differences in DKI-ALPS indices. DTI-ALPS was positively correlated with DKI-ALPS (r = 0.551; p < 0.001), with stronger associations in HCs than in those with CIs (r = 0.628 vs. 0.370; all p < 0.05). Both DTI-ALPS and DKI-ALPS were negatively correlated with PSMD (r = −0.327 and −0.251; all p < 0.05) and FW-WM (r = −0.317 and −0.393; all p < 0.05). The FPCA revealed distinct trajectories of DTI-ALPS, DKI-ALPS, PSMD, and FW-WM between HCs and CIs, and Cohen’s d of the first FPC score was 0.685, 0.977, 0.573, and 1.004, respectively (all p < 0.001). Compared with baseline dMRI measurements, the trajectory patterns exhibited stronger correlations with Aβ burden (DTI-ALPS, 0.277 vs. −0.217; DKI-ALPS, 0.552 vs. −0.468; PSMD, 0.278 vs. 0.201; FW-WM, 0.313 vs. 0.113) and cognitive performance.

Our study indicated that DKI-ALPS provides an accurate assessment of GS function compared with DTI-ALPS. Longitudinal trajectories, particularly the trajectory of DKI-ALPS, demonstrate stronger associations with Aβ burden and cognitive decline.

Figures