Longitudinal evidence for a mutually reinforcing relationship between white matter hyperintensities and cortical thickness in cognitively unimpaired older adults

Abstract

Background: For over three decades, the concomitance of cortical neurodegeneration and white matter hyperintensities (WMH) has sparked discussions about their coupled temporal dynamics. Longitudinal studies supporting this hypothesis nonetheless remain scarce.

Methods: We applied global and regional bivariate latent growth curve modelling to determine the extent to which WMH and cortical thickness were interrelated over a four-year period. For this purpose, we leveraged longitudinal MRI data from 451 cognitively unimpaired participants (DELCODE; median age 69.71 [IQR 65.51, 75.50] years; 52.32% female). Participants underwent MRI sessions annually over a four-year period (1815 sessions in total, with roughly four MRI sessions per participant). We adjusted all models for demographics and cardiovascular risk.

Results: Our findings were three-fold. First, larger WMH volumes were linked to lower cortical thickness (σ = -0.165, SE = 0.047, Z = -3.515, P < 0.001). Second, individuals with higher WMH volumes experienced more rapid cortical thinning (σ = -0.226, SE = 0.093, Z = -2.443, P = 0.007), particularly in temporal, cingulate, and insular regions. Similarly, those with lower initial cortical thickness had faster WMH progression (σ = -0.141, SE = 0.060, Z = -2.336, P = 0.009), with this effect being most pronounced in temporal, cingulate, and insular cortices. Third, faster WMH progression was associated with accelerated cortical thinning (σ = -0.239, SE = 0.139, Z = -1.710, P = 0.044), particularly in frontal, occipital, and insular cortical regions.

Conclusions: Our study suggests that cortical thinning and WMH progression could be mutually reinforcing rather than parallel, unrelated processes, which become entangled before cognitive deficits are detectable.

Trial registration: German Clinical Trials Register (DRKS00007966, 04/05/2015).

Keywords: Cortical Thickness; Latent Growth Curve Model; Longitudinal Modelling; Structural Magnetic Resonance Imaging; White Matter Hyperintensities.

Fig. 1

BLGCM to probe the coupling of cortical thickness and WMH over repeated measures. Illustration of the longitudinal structural equation modelling (SEM) model. We employed the conventional notation with squared variables indicating observed and measured variables (manifest variables) and circular ones referring to latent (unobserved) variables. Single-headed solid arrows illustrate a modelled relationship between two variables, with the arrow pointing towards the dependent variable. Single-headed dashed arrows signify a relationship between two variables, where the weight is fixed. Double-headed arrows represent the covariance (hyperparameter) between two variables. Grey triangles represent latent intercept estimates. We further adjusted latent intercepts and slopes for age, sex, years of education, total cardiovascular risk factors, and TICV. We omitted these paths for visualisation purposes

Fig. 2

Changes in WMH volumes and cortical thickness over four years. We obtained latent intercepts and slopes for each individual through the application of univariate LGCM to WMH volumes and cortical thickness (separate models for each neuroimaging feature). We used them to compute latent growth curve parameters and predict individual trajectories, corrected for age, sex, years of education, total cardiovascular risk scores, and TICV. Prior to plotting and to enhance interpretability, we back-transformed all predicted measurements. A Total WMH volume trajectories, as predicted by the model. Light blue lines represent the predicted trajectories and the dark blue one the average one. B Back-transformed individual factor scores of latent slopes for WMH, summarised in the density plots, indicate that WMH volumes generally increased over time. We adjusted density plots such that the modes attain the highest value, irrespective of the actual frequency. The rate of change varied substantially across individuals in both cases. C Mean cortical thickness trajectories, as predicted by the model. Light purple lines represent the predicted trajectories and the dark purple one the average one. D Back-transformed individual factor scores of cortical thicknesses across the considered brain regions. The variability in change rates indicated significant inter-individual differences in regional cortical thinning

Fig. 3

Relationship between latent growth parameters from global and regional BLGCMs. We employed longitudinal BLGCMs to characterise the spatiotemporal interrelation between WMH volumes and cortical thickness over the span of four years. We adjusted latent intercepts and slopes for age, sex, years of education, total cardiovascular risk scores, and TICV. (A) Relationship between latent growth curve parameters obtained from the global model. At baseline, individuals with larger WMH volumes had lower cortical thickness. Over time, those experiencing rapid cortical thinning initially had large total WMH volumes. Similarly, those with rapid WMH progression had thinner cortices at baseline. In general, faster WMH progression was linked to more rapid cortical thinning (Q4). (B) Regional analyses suggest cross-domain associations have regional specificities. We applied FDR correction to account for multiple comparisons. In regions highlighted in red, we found a statistically significant covariance between latent growth curve parameters after FDR correction ($P_{\mathit{FDR}}<0.05$)

Fig. 4

Cross-domain intercept-slope and slope-slope associations.A Predicted four-year changes in cortical thickness and WMH trajectories, stratified by baseline WMH volumes and cortical thickness, respectively. We categorised individuals based on whether their latent intercepts were below the 25th or above the 75th percentile, respectively. B Relationship between predicted changes in cortical thickness and WMH volumes over four years. We back-transformed all predicted measurements to plotting for interpretability purposes. We adjusted latent intercepts and slopes for age, sex, years of education, total cardiovascular risk scores, and TICV