Topological relationships between perivascular spaces and progression of white matter hyperintensities: A pilot study in a sample of the Lothian Birth Cohort 1936

Abstract

Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesized to also have topological proximity. However, this and the influence of their spatial proximity on WMH progression are unknown. We analyzed longitudinal MRI data from 29 out of 32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive aging, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (waves−1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal, and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (waves−1–2: 73%; 2–3: 72%). Formal statistical comparisons of severity of each of these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest some deep WMH clusters may form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear.

Perivascular space enlargement accelerates with hypertension, white matter hyperintensities, chronic inflammation, and Alzheimer’s disease pathology: evidence from a three-year longitudinal multicentre study

“…We observed that presumably healthy individuals with higher initial WMH volumes tend to exhibit greater PVS volumes, and that, over the subsequent years, WMH can form around PVS (see Fig. 1E ), emphasising their interdependence and potentially shared underlying cardiovascular or ageing-related mechanisms(18,54,66). Additionally, baseline WMH and PVS rates of change of CSO and BG, respectively, correlated with one another, suggesting that future studies should explore the hypothesis of recurrent time-lagged associations.…”

“…Numerous cross-sectional studies support the positive association between PVS and WMH(2) and a few even provide evidence of WMH developing in the proximity of PVS(54). We used correlational analysis to determine whether baseline regional WMH volumes explained part of the variance in regional PVS rates of change in the presumably healthy subsample, after adjusting for age, sex, and years of education.…”

“…Though elusive, the scale at which enlargement occurs over short time periods, limitations in imaging resolution (e.g. partial volume effects), and methodological confounds caused by the presence or formation of WMH around PVS(54) may help to explain this situation. Accordingly, we tested for linear and quadratic effects of age on PVS measurements in the presumably healthy subsample, to assess supposedly pathology-free ageing processes.…”

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

“…In the same cohort of community-dwelling individuals (LBC1936), the spatial proximity of WMHs with PVS was evaluated longitudinally ( n = 29, mean age = 71.9 years old), with images collected over three sessions at 3-year intervals ( Barnes et al, 2022 ). WMHs appeared to be adjacent to or co-localized with PVS, however, statistical analyses revealed no significant associations ( Barnes et al, 2022 ).…”

“…Ramirez et al, 2015Berezuk et al, 2015Cai et al, 2015Zong et al, 2016Wang et al, 2016Niazi et al, 2018Piantino et al, 2020Ballerini et al, 2020aBallerini et al, 2020bValdés Hernández et al, 2020Zong et al, 2020Barisano et al, 2021bSepehrband et al, 2021 Ramirez et al, 2021Shen et al, 2021George et al, 2021Piantino et al, 2021Donahue et al, 2021 Langan et al, 2022Ranti et al, 2022Lynch et al, 2022, Preprint Barisano et al, 2022bHupfeld et al, 2022Barnes et al, 2022Kamagata et al, 2022Donahue et al, 2022Ramirez et al, 2022Jokinen et al, 2020Zong et al, 2020Wang et al, 2021Huang et al, 2021 Spijkerman et al, 2022 PVS measures can be broadly categorized as quantitative metrics (volume, count, and volume fractions), and morphological metrics (length, width, solidity, and tortuosity). Listed first are the methods that utlize classical image filtering technique.…”