Stress, Depressive Symptoms, and Physical Strength Impact Brain Perivascular Spaces: A Cohort Study with Insights from First-Degree Relatives

Stress, Depressive Symptoms, and Physical Strength Impact Brain Perivascular Spaces: A Cohort Study with Insights from First-Degree Relatives | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Stress, Depressive Symptoms, and Physical Strength Impact Brain Perivascular Spaces: A Cohort Study with Insights from First-Degree Relatives Maria Valdés Hernández, Alexandra Morozova, Roberto Duarte Coello, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6589799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Stress, depressive symptoms, cardiovascular health, and physical strength are well-recognised factors that contribute to microcirculatory changes in the brain. However, there is limited understanding of their impact on perivascular waste clearance. In this study, we used the Stratifying Depression and Resilience Longitudinally (STRADL) family-based cohort, subgroup of the Generation Scotland Scottish Family Health Study, to assess the association between these factors, familial influences, and perivascular space (PVS) characteristics. Using an automated segmentation method on brain magnetic resonance images, we generated PVS volumes, count, density (i.e., count per unit volume), and median length in separate brain regions. Our findings indicate that PVS burden increases with age, hypertension and current depressive symptoms in basal ganglia and centrum semiovale regions, and with higher hair cortisol levels and weaker hand grip only in the centrum semiovale. Regional differences in PVS characteristics observed between the centrum semiovale and basal ganglia could provide insights into the underlying pathophysiology of PVS. Additionally, a familial component was identified, in keeping with a genetic influence on PVS morphology. These results highlight the multifactorial nature of perivascular spaces and emphasize the need for longitudinal studies to observe individual long-term perivascular changes and their impact on brain health. Health sciences/Biomarkers/Predictive markers Health sciences/Diseases/Psychiatric disorders/Depression perivascular spaces (PVS) depression stress hair cortisol hand grip test QIDS (Quick Inventory of Depressive Symptomatology) Figures Figure 1 Figure 2 Figure 3 Introduction Stress is a well-established pro-inflammatory trigger and a significant risk factor for numerous diseases [ 1 ], with cardiovascular and neurodegenerative disorders being particularly affected [ 2 ]. Chronic stress disrupts microcirculatory function [ 3 ] and induces a neuroinflammatory response [ 4 ]. It also exacerbates depression and anxiety [ 5 ], which have also been associated with neuroinflammation [ 5 , 6 , 7 ] and decreased cerebral blood flow [ 8 ], possibly leading to alterations in microcirculation. A key factor in maintaining cardiovascular health is physical strength [ 9 ]. Reduced muscular strength has been linked to increased cardiometabolic risks, as well as higher cardiovascular-related and overall mortality [ 10 ]. Microvascular changes are thought to be reflected in the enlargement of perivascular spaces (PVS), visible in magnetic resonance imaging (MRI) [ 11 ]. PVS are physiological compartments surrounding the perforating blood vessels of the brain, lying at the interface of the vasculature and the cerebral parenchyma. They serve as a critical conduit for interstitial solute clearance [ 11 ], functioning similarly to the lymphatic system – hence the term ‘glymphatic’, where ‘g’ stands for the glial cells forming the boundary between the brain tissue and the outer vessel wall. PVS are thought to be part of the pathway for clearing waste metabolites [ 11 ], contribute to maintaining brain homeostasis [ 12 , 13 ] and facilitating a neuroimmune response [ 14 ]. PVS may be considered a structural proxy for healthy brain function, and their enlargement on MRI has been associated with advancing age [ 15 , 16 , 17 , 18 ], and the presence of neurovascular, neurodegenerative, and neuropsychiatric conditions, including multiple sclerosis [ 19 , 20 ], dementia [ 11 , 21 , 22 ], cerebral small vessel disease [ 23 , 24 ], and many more. Several factors may impact the perivascular architecture of the brain, with age and hypertension being consistent associates of PVS enlargement across most studies [ 25 ]. PVS morphology exhibits high individual variability [ 13 ], with genetics being a substantial predisposing factor, as demonstrated by several research groups [ 26 , 27 ], including recent large genome-wide association studies [ 28 , 29 ]. To date, only a limited number of studies have explored the impact of neuropsychiatric factors on PVS. One study [ 30 ] identified a correlation between traumatic events and PVS volume increase in patients with depression as well as in healthy subjects, suggesting that PVS enlargement may be secondary to stress or trauma-related changes, such as brain atrophy. Another study [ 31 ] confirmed this association, showing that neuroinflammation is a potential mediating link between stress and PVS in an elderly population with cognitive impairment. A third study [ 32 ] showed that higher PVS burden correlates with post-stroke depression using a visual rating scale. While the effect of neuropsychiatric factors on microvasculature is apparent, to what extent they influence perivascular morphology and glymphatic clearance – an essential process for maintaining healthy brain function – remains unclear. Furthermore, although the heritability of PVS burden is well-established [ 28 , 29 ], the familial impact on perivascular morphometry is yet to be determined. We here investigate the associations between neuropsychiatric factors – stress assessed through hair cortisol, depressive symptoms assessed through the Quick Inventory of Depressive Symptomatology [ 33 ] (QIDS) scale, physical factors as through the hand grip strength test [ 34 ] – and perivascular characteristics in a population-based cohort and explore the potential familial impact on PVS through first-degree relatives. QIDS is considered a trusted measure to briefly assess one’s mental health. Hair cortisol measurement is one of the most effective ways to quantify stress, seen as a reliable biomarker of chronic stress exposure [ 35 , 36 ]. And the hand grip test is recognised as an indicator of cardiovascular health [ 37 ], cognitive function [ 38 ], and even mortality risk [ 39 ]. We hypothesize that advancing age, hypertension, higher QIDS score and hair cortisol will be associated with high PVS burden, while stronger hand grip will be associated with reduced PVS load. Furthermore, we hypothesize that, due to demonstrated genetic associations, family membership will lead to greater similarity in MRI-visible PVS burden and morphometric characteristics among first-degree relatives. To the best of our knowledge, this is the first study to examine the relationship between hand grip strength and PVS, as well as to measure hair cortisol and use the QIDS scale in this context. Methods Sample The dataset comes from the Stratifying Depression and Resilience Longitudinally (STRADL) study [40], a population-based longitudinal study examining the biological and psychological mechanisms of depression and resilience. For the purpose of this study, 1183 subjects were included, with all relevant data available, from a dataset of 1188 participants with MRI assessment performed at two locations, Aberdeen (51%) and Dundee (49%), in Scotland, United Kingdom, using 3T magnetic resonance imaging scanners. From them, 859 individuals were unrelated, and 324 were part of 136 families of between 2 and 6 members – all first degree relatives. PVS Assessment The neuroimaging protocol of the primary study that provided data for this analysis is published [40]. We used previously defaced T1-weighted, T2-weighted and fluid attenuated inversion recovery (FLAIR) magnetic resonance images (MRI), as well as the brain parcellation done by freesurfer v 6.0 (https://surfer.nmr.mgh.harvard.edu/), all provided by the Generation Scotland Data Access Committee (https://genscot.ed.ac.uk/for-researchers/access), together with the rest of the variables used in the analyses. We co-registered all MRI sequences to the nearly-isotropic (1 x 0.94 x 0.94 mm 3 ) T1-weighted space, where the freesurfer segmentations are, and obtained the intracranial volume using FLIRT [41] and BET2 [42]. We applied Gaussian clustering to the multisequence (4D) volume obtained by concatenating the three co-registered MRI sequences to extract the brain tissue separately from the pure CSF, the meningeal layers and main venous pathways. We obtained initial priors of the basal ganglia (BG) and white matter regions (mainly centrum semiovale (CSO), excluding the internal and external capsules that were included in the BG region) from freesurfer segmentations. We refined them to ensure that venous pathways, membranes and CSF were not included, by applying the masks generated from the multispectral Gaussian clustering segmentations. Then, we mapped our (3D) regions of interest (ROIs) to the T2-weighted space, of higher spatial resolution (i.e., 0.5 x 0.48 x 0.48 mm 3 ), where we performed the PVS segmentations using the thresholded output from the Frangi filter as described in Ballerini et al. [43]. From the PVS segmentations we calculated the percentage volume and the density of PVS in each ROI (PVS counts in a ROI volume), and determined the average PVS length per ROI using the Bezier curves approximation [44]. The step-by-step pipeline followed is described and downloadable from https://doi.org/10.7488/ds/7486. Overview of methods for glucocorticoid extraction / grip strength / QIDS/ hypertension Demographic data (age and sex), were obtained within the Generation Scotland: Scottish Family Health Study [45] between 2006 and 2011. Grip strength, hair sample, blood pressure, and QIDS were obtained at the same time as the MRI in an in-person assessment wave in 2019 for the STRADL study. Grip strength was determined using a Patterson Medical Jamar hand dynamometer. A small sample (i.e., approximately 3mm diameter) of hair was collected from the posterior vertex region of the head for extracting cumulative cortisol using organic solvents in pulverised hair and processed using liquid chromatography-mass spectroscopy. Statistical Analysis We applied a linear mixed effects model (LME) to account for possible correlations in the PVS characteristics among first-degree relatives. A single LME model was built for each PVS measurement (PVS volume %, count, density, and median length) as an outcome in both the centrum semiovale (CSO) and the basal ganglia (BG). Age, hypertension, hair cortisol, grip strength, and QIDS score were entered as fixed effects to examine their influence on the PVS characteristic. Family ID was included as a random intercept to model shared variance within families. This enabled to assess both within-family and between-family variation, including comparisons between family members and unrelated individuals, providing insight into the contribution of familial factors on the outcome. Since the dataset contained both individuals and families, a sensitivity analysis was carried out by repeating the analysis on a sample that excluded individuals without family members (i.e., restricting the sample to families with at least two members). Family ID was added as a random intercept, as in the primary model, while age was added as a random covariate to assess potential differences in its effect across families. All statistical analyses were performed using the Stata software package (version 18, StataCorp. 2023. Stata Statistical Software: Release 18 . College Station, TX: StataCorp LLC.). No correction for multiple comparisons was applied. We report coefficient estimates, standard errors, and p-values. Standardised effect sizes were also determined. Results Descriptive Statistics The demographic, clinical, and neuroimaging data of the study cohort are presented in Table 1 . The distribution of family sizes is summarized in Table 2 . Each family size represents the number of members per family. Table 1 . Study data overview Mean SD Age (years) 59.6 10.1 Grip strength (kg) 29.7 10.3 QIDS score 4.7 3.7 Median IQR Hair cortisol (pg/mg) 3.7 4.24 CSO PVS volume (ml) 3.55 3.59 CSO PVS volume (% within ROI) 1.4 1.4 CSO PVS count 865.5 690 CSO PVS density (count/ROI in ml) 6 4.7 CSO PVS length (mm) 5.52 6.5 BG PVS volume (ml) 1.25 0.83 BG PVS volume (% within ROI) 2.3 1.5 BG PVS count 221 158 BG PVS density (count/ROI in ml) 4.2 2.9 BG PVS length (mm) 5.75 7.66 Overall number of subjects (n) 1183 Subjects without hypertension (n) 869 (73.5%) Subjects with relatives within the cohort (n) 324 (27.4%) Table 2 . Distribution of Family Cluster Sizes in the Sample (First-Degree Relatives Only). Family Size Number of Families Percentage 1 859 72.6 % 2 102 8.62% 3 21 1.78% 4 9 0.76% 5 3 0.25% 6 1 0.09% Primary Analyses All Linear Mixed Effects (LME) models, one for each PVS measurement (PVS volume %, count, density, and median length) as an outcome in both the centrum semiovale (CSO) and the basal ganglia (BG), are summarised in Table 3 . As the most comprehensive PVS characteristic, PVS volume as percentage in the ROIs was selected to visualise the associations between statistically significant covariates and the PVS burden ( Figures 1 and 2) . Table 3. Linear mixed effects models’ summary results Centrum Semiovale Basal Ganglia PVS volume % Est., (SE) PVS count Est., (SE) PVS density Est., (SE) PVS median length Est., (SE) PVS volume % Est., (SE) PVS count Est., (SE) PVS density Est., (SE) PVS median length Est., (SE) Fixed Effect Intercept 0.005 (0.312) 169.96 (177.0) 1.44 (1.21) 4.35 (0.28) 1.086 (0.317) 191.47 (40.21) 3.1 (0.72) 4.86 (0.26) Age 0.03 (0.004)*** 11.22(2.42)*** 0.092 (0.017)*** 0.022 (0.004)*** 0.025 (0.004)*** n.s. 0.028(0.01)*** 0.017 (0.004)*** Hypertension 0.193 (0.105) n.s. n.s. n.s. 0.404 (0.102)*** 32.11 (12.91)* 0.624 (0.232)** 0.26 (0.084)** Hair cortisol 0.097 (0.045)* 61.43 (25.58)* 0.41(0 .175)* n.s. n.s. n.s. n.s. n.s. Grip strength -0.012 (0.004)** n.s. -0.0375 (0.017)* n.s. n.s. n.s. n.s. n.s. QIDS score 0.026 (0.011)* 14.57 (6.42)* 0.118 (0.044)** n.s. 0.029 (0.012)* 4.15(1.45)** 0.083 (0.026)** n.s. Random Effect Family ID 0.355 (0.148)* n.s. n.s. 0.336 (0.098)*** n.s. n.s. n.s. n.s. * p < 0.05, ** p < 0.01, *** p < 0.001. Age was a highly significant predictor of the PVS burden in the CSO across all models (p<0.001 for PVS volume (% in CSO ROI, Figure 1A ), count, density, and median length), with standardised effect size of 0.24 for PVS volume, 0.17 for PVS count, 203.1 for PVS density, and 0.2 for PVS median length. It is worth noting that the standard deviation of PVS density is very low: 0.0046, therefore the large values for standardized effect sizes in this parameter. In the BG, age correlated with the PVS volume (% in BG ROI, Figure 1B ) (p<0.001), PVS density (p=0.005), and PVS median length (p<0.001), with standardized effect size of 0.21 for PVS volume, 105.6 for PVS density, and 0.18 for PVS median length. Hypertension showed a marginally significant association with the percentage of PVS volume in the CSO (p=0.057, Figure 1C ). In the BG, hypertension was statistically significantly associated with all PVS morphometric features (p<0.001 for PVS volume (% in ROI, Figure 1D ), p=0.013 for PVS count, p=0.007 for PVS density, and p=0.002 for PVS median length). The QIDS score was associated with the same PVS morphometric features in the CSO and the BG: PVS volume (p=0.021 for CSO, Figure 2A (standardised effect size 0.08), p=0.012 for BG ( Figure 2B ), with standardized effect size of 0.27), PVS count (p=0.023 for CSO (standardised effect size 0.08), p=0.004 for BG, with standardised effect size of 0.1), PVS density (p=0.007 for CSO with standardised effect size of 94.45; and p=0.002 for BG, with standardised effect size of 112.2). There were no associations between the QIDS score and PVS median length in either of the brain regions (p>0.05 for both). Grip strength was negatively associated with increased percentage of PVS volume ( Figure 2C ) and PVS density in the CSO (p=0.009, and p=0.028, respectively; with standardised effect size of -0.1 for PVS volume and -84.7 for PVS density). It was not associated with any PVS measurements in the BG (p>0.05 for all measurements). Hair cortisol level was associated with CSO PVS measurements (p=0.032 for PVS volume (% in ROI, Figure 2D ), p=0.016 for PVS count, p=0.02 for PVS density, with standardised effect size of 0.075 for PVS volume, 0.09 for PVS count, and 84.9 for PVS density). There were no associations between hair cortisol and the BG PVS burden (p>0.05 for all measurements). The variance component for Family ID was significant in the CSO with the percentage of PVS volume in this region as the outcome (p=0.01), and PVS median length (p<0.001), indicating that a substantial portion of the variance is explained by familial factors. In the BG, Family ID effect was non-significant. Figure 3 shows an example of PVS segmentation in the CSO in 3 representatives of three generations of the same family. Sensitivity Analysis The sensitivity analysis was carried out for the two models with the significant random effect of Family ID – the model with the PVS volume (% in ROIs) as an outcome, and the model with the PVS median length (see Table 3). The analysis consisted of two parts. In the first part, we replicated the original model but restricted the sample to families with at least two members, excluding individuals without family members. This allowed us to assess whether being in the same family affects the outcome. In the second part, we added age as a random covariate to the model, which revealed that age had substantially different effects across families. The random effect of Family ID remained significant in both parts of the sensitivity analysis for both models (p=0.011 vs p=0.014 for PVS volume (% in ROIs), and p=0.001 vs p=0.002 for PVS median length), and the model fit was very similar (Akaike Information Criterion (AIC) 677.7 vs 679.87 for PVS volume %, and AIC 697.5 vs 694.7), with the model including age as a random covariate showing slightly more significance. Discussion We explored the associations between age, hypertension, grip strength, hair cortisol, QIDS score and PVS characteristics in BG and CSO 3D ROIs, including the PVS volume as percentage in the ROIs, count, density, and median length, in the CSO and the BG. Age was highly significantly associated with all PVS measurements in the CSO, as well as with the PVS volume (% in ROI), density, and median length in the BG. Our results are in accordance with the existing literature, as age is consistently shown to be a crucial factor related to PVS enlargement [15, 16, 17, 18]. The only measurement that did not yield substantial findings was the PVS count in the BG, which may be due to a lower average number of PVS present in this region compared to the larger area of the CSO. However, all other BG PVS morphometric features investigated in our study confirm the effect of age on the PVS burden. Hypertension is another well-established factor influencing PVS [11, 25]. In our study, hypertension was significantly associated with all PVS measurements in the BG, while its association with PVS volume (% in ROI) in the CSO was only marginally significant. Hypertension-related vascular changes have previously been demonstrated to particularly affect PVS in the BG [46, 47]. Hypertension decreases cerebrovascular reactivity [48, 49] and leads to blood-brain barrier dysfunction [47]. These factors, compounded by atherosclerosis in the small cerebral vessels [46], may contribute to impaired metabolic waste clearance and, in turn, to PVS enlargement, particularly in the BG [50], although this is yet to be confirmed. Overall, pathological vascular changes are likely not specific to the BG but occur throughout the brain’s vasculature [49], with their impact first observed in the small vessels of the BG, where PVS may have a reduced capacity to compensate for pathophysiological changes compared to the PVS in the CSO [51]. This may be due to differences in venous drainage, as the CSO has multiple drainage routes [52], which enhances its compensatory capacity when increased perivascular clearance is required, such as during the pathological vascular changes mentioned above. This may explain our findings, as we observed a weaker association between hypertension and PVS volume % in the CSO (at the trend level only). This could also be attributed to the nature of our data, where the analysis used a binary variable (presence or absence of hypertension) rather than blood pressure measurements for each participant. Indeed, cumulative blood pressure measurements might provide a more precise estimate of the impact on perivascular clearance [53], regardless of the hypertension diagnosis, which reflects the continuous nature of the underlying pathophysiology. Chronic stress promotes neuroinflammation [4], which leads to blood-brain barrier disruption and immune cell accumulation in the PVS, resulting in an increased PVS burden [31]. We used hair cortisol as a biomarker of chronic stress exposure [35] and found positive associations between hair cortisol and PVS volume %, count, and density in the CSO. However, no associations were found between cortisol and the PVS burden in the BG. This may be due to cortical areas being the primary target of cortisol [54]. As cortisol promotes neuroinflammation, it affects the perivascular clearance system that drains the subcortical white matter, including the CSO. In contrast, the BG, being deep gray matter structures, are less sensitive to cortisol-driven changes. Chronic increased cortisol exposure has been shown to have a profound impact on the cortical architecture in preclinical models [55], promoting neurodegenerative and cognitive changes, which are also observed with increased PVS burden [11, 53]. Our findings are in agreement with those obtained by [30], who found associations between stress exposure and increased PVS burden both in the healthy subjects as well as in individuals with depression. Being a psychiatric disorder, depression is thought to be exacerbated by neuroinflammation [56]. In our study, we utilised the Quick Inventory of Depressive Symptomatology to assess the mental wellbeing of participants, and discovered an association between higher scores – indicating more severe symptoms – and PVS volume (as % in ROIs), count, and density in both the CSO and the BG. While the link between PVS morphology and clearance abnormalities is yet to be fully established, the consistent correlation with all PVS morphometric features in our cohort suggests that depressive symptomatology may be associated with alterations in the clearance system. Brain metabolism has been shown to be substantially reduced in major depressive disorder [57], suggesting a slower turnover of brain waste metabolites, which may result in increased PVS burden. In our study, participants were evaluated based on symptom severity rather than a formal depression diagnosis, yielding arguably a more precise estimate, as depression is commonly underdiagnosed [58]. Our findings align with Liang et al. [32], who reported more PVS in post-stroke depression. Del Brutto et al., however, did not find associations between depression and PVS [59], likely due to their smaller sample size and the methodology used, as they rated PVS as either present or absent, while other studies that found the correlation used more detailed PVS quantification methods [30, 32]. Another novel correlate of PVS morphology explored in our study was the hand grip test. We found a significant negative association between hand grip strength and percentage of PVS volume and PVS density in the CSO, suggesting that physical fitness may be associated with improved microvascular health. Although the hand grip test has not been previously studied in the context of perivascular morphology, it is a well-established marker of overall physical health in ageing populations [60], and has been linked to cardiovascular health [37], neurodegeneration, and cognitive decline [38] – factors known to impair PVS clearance [11]. Therefore, if our results are replicated in other cohorts, the hand grip test could serve as a simple and useful proxy for assessing microvascular health, and, in turn, vessel plasticity and perivascular clearance. Finally, we found a significant familial factor in the percentage of PVS volume and median length in the CSO, with age being a substantial contributing factor. This suggests that while age-related perivascular changes are universally observed, their trajectory may vary across families. Genetics has previously been shown to associate with perivascular morphology [28, 29]. The vascular architecture of the brain has a strong genetic component and is also influenced by epigenetic factors [61]. Therefore, it appears that perivascular spaces that follow the perforating arterioles of the brain are influenced by familial factors. Given the high variability of PVS in the general population [13], longitudinal tracing of PVS would be highly valuable in establishing the dynamics of perivascular changes over time and their relation to progression of other markers of adverse vascular health [62]. The study is subject to limitations. Cross-sectional study design limits the ability to infer causal relationships between the factors assessed and perivascular space characteristics. Additionally, the familial component was evaluated only in a subset of the cohort, which may restrict the generalisability of these findings to the broader population. Longitudinal studies are needed to better understand the causal dynamics and the impact of familial factors on PVS morphological characteristics. Another limitation is that, from our analyses, we cannot conclude on the association between PVS burden and clinical diagnosis of depression. There were only 105/1188 (i.e., 8.8 %) participants from this cohort diagnosed with mayor depressive disorder at baseline (years 2006-2011), while the MRI assessment took place years later during the third assessment (recruitment ending in 2019). QIDS being a self-reported continuous scale applied at the time of the MRI provides more robust statistics and accounts for potentially undiagnosed subjects. Moreover, longitudinal and repeatability studies on PVS measurements that inform on the dynamics of PVS across time are still to be conducted. Therefore, it would have been rather speculative to derive conclusions on the putative association between PVS burden and clinical diagnosis of depression from analyzing the data available acquired more than 9 years apart. In conclusion, our study demonstrates the association of age, hypertension, stress, depressive symptoms, and physical strength on PVS burden and morphometry. We found that PVS volume increases with age, hypertension, depressive symptomatology, and higher hair cortisol, while a better hand grip strength is associated with a lesser PVS burden. Additionally, we observed substantial regional differences in PVS burden between the CSO and the BG. Furthermore, a familial component was identified, consistent with known genetic and epigenetic factors that may contribute to individual variations in perivascular dynamics. These findings emphasize the multifactorial nature of PVS development and demonstrate its role as a potential biomarker of healthy brain functioning. Declarations For the primary study that provided data for these analyses, consent for linkage of participant data and samples to routine NHS records was obtained as part of the original GS:SFHS (05/S1401/89), and subsequent procedures were conducted following an independent, but linked, ethics application (14/SS/0039). All data used was fully anonymised and following the declaration of Helsinki. Conflicts of Interests Authors declare no conflict of interest Acknowledgements We would like to thank all of the Generation Scotland participants for their contribution to this study. We also thank the research assistants, clinicians and technicians for their help in collecting the data, and Generation Scotland administration for making it available for research. This study is funded by the Hilary and Galen Weston Foundation under the Novel Biomarkers 2019 scheme [ref UB190097l] administered by the Weston Brain Institute, the Fondation Leducq Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05), and the UK Dementia Research Institute [award number UK DRI-4002] through UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006] and is currently supported by the Wellcome Trust [216767/Z/19/Z]. This study was also supported and funded by the Wellcome Trust Strategic Award ‘Stratifying Resilience and Depression Longitudinally’ (STRADL) [References 104036/Z/14/Z and 220857/Z/20/Z]. We acknowledge the support of the British Heart Foundation [RE/18/5/34216], the Row Fogo Charitable Trust [BRO-D.FID3668413] (MVH, JMW), and Charles University grants to AM: Cooperatio 36 - Medical Diagnostics and Basic Medical Sciences, and Cooperatio – Neurosciences. References Liu YZ, Wang YX, Jiang CL. Inflammation: The Common Pathway of Stress-Related Diseases. Front Hum Neurosci. 2017 Jun 20;11:316. doi: 10.3389/fnhum.2017.00316. Cohen S, Janicki-Deverts D, Miller GE. Psychological stress and disease. JAMA. 2007 Oct 10;298(14):1685-7. doi: 10.1001/jama.298.14.1685. Burrage E, Marshall KL, Santanam N, Chantler PD. Cerebrovascular dysfunction with stress and depression. Brain Circ. 2018 Apr-Jun;4(2):43-53. doi: 10.4103/bc.bc_6_18. Peng Z, Peng S, Lin K, Zhao B, Wei L, Tuo Q, Liao D, Yuan T, Shi Z. Chronic stress-induced depression requires the recruitment of peripheral Th17 cells into the brain. J Neuroinflammation. 2022 Jul 14;19(1):186. Ross RA, Foster SL, Ionescu DF. The Role of Chronic Stress in Anxious Depression. Chronic Stress (Thousand Oaks). 2017 Feb 17;1:2470547016689472. doi: 10.1177/2470547016689472. Kim IB, Lee JH, Park SC. The Relationship between Stress, Inflammation, and Depression. Biomedicines. 2022 Aug 9;10(8):1929. doi: 10.3390/biomedicines10081929. Won E, Kim YK. Neuroinflammation-Associated Alterations of the Brain as Potential Neural Biomarkers in Anxiety Disorders. Int J Mol Sci. 2020 Sep 7;21(18):6546. doi: 10.3390/ijms21186546. Chiappelli J, Adhikari BM, Kvarta MD, Bruce HA, Goldwaser EL, Ma Y, Chen S, Ament S, Shuldiner AR, Mitchell BD, Kochunov P, Wang DJ, Hong LE. Depression, stress and regional cerebral blood flow. J Cereb Blood Flow Metab. 2023 May;43(5):791-800. doi: 10.1177/0271678X221148979. Tikkanen E, Gustafsson S, Ingelsson E. Associations of Fitness, Physical Activity, Strength, and Genetic Risk With Cardiovascular Disease: Longitudinal Analyses in the UK Biobank Study. Circulation. 2018 Jun 12;137(24):2583-2591. doi: 10.1161/CIRCULATIONAHA.117.032432. Carbone S, Kirkman DL, Garten RS, Rodriguez-Miguelez P, Artero EG, Lee DC, Lavie CJ. Muscular Strength and Cardiovascular Disease: AN UPDATED STATE-OF-THE-ART NARRATIVE REVIEW. J Cardiopulm Rehabil Prev. 2020 Sep;40(5):302-309. doi: 10.1097/HCR.0000000000000525. Wardlaw JM, Benveniste H, Nedergaard M, Zlokovic BV, Mestre H, Lee H, Doubal FN, Brown R, Ramirez J, MacIntosh BJ, Tannenbaum A, Ballerini L, Rungta RL, Boido D, Sweeney M, Montagne A, Charpak S, Joutel A, Smith KJ, Black SE; colleagues from the Fondation Leducq Transatlantic Network of Excellence on the Role of the Perivascular Space in Cerebral Small Vessel Disease. Perivascular spaces in the brain: anatomy, physiology and pathology. Nat Rev Neurol. 2020 Mar;16(3):137-153. doi: 10.1038/s41582-020-0312-z. Zong X, Lian C, Jimenez J, Yamashita K, Shen D, Lin W. Morphology of perivascular spaces and enclosed blood vessels in young to middle-aged healthy adults at 7T: Dependences on age, brain region, and breathing gas. Neuroimage. 2020 Sep;218:116978. doi: 10.1016/j.neuroimage.2020.116978. Morozova A, Španiel F, Škoch A, Brabec M, Zolotarov G, Musil V, Zach P. Enlarged brain perivascular spaces correlate with blood plasma osmolality in the healthy population: A longitudinal study. Neuroimage. 2024 Oct 15;300:120871. doi: 10.1016/j.neuroimage.2024.120871. Ineichen BV, Okar SV, Proulx ST, Engelhardt B, Lassmann H, Reich DS. Perivascular spaces and their role in neuroinflammation. Neuron. 2022 Nov 2;110(21):3566-3581. doi: 10.1016/j.neuron.2022.10.024. Zhu YC, Dufouil C, Soumaré A, Mazoyer B, Chabriat H, Tzourio C. High degree of dilated Virchow-Robin spaces on MRI is associated with increased risk of dementia. J Alzheimers Dis. 2010;22(2):663-72. doi: 10.3233/JAD-2010-100378. Francis F, Ballerini L, Wardlaw JM. Perivascular spaces and their associations with risk factors, clinical disorders and neuroimaging features: A systematic review and meta-analysis. Int J Stroke. 2019 Jun;14(4):359-371. doi: 10.1177/1747493019830321. Huang P, Zhu Z, Zhang R, Wu X, Jiaerken Y, Wang S, Yu W, Hong H, Lian C, Li K, Zeng Q, Luo X, Xu X, Yu X, Yang Y, Zhang M. Factors Associated With the Dilation of Perivascular Space in Healthy Elderly Subjects. Front Aging Neurosci. 2021 Mar 26;13:624732. doi: 10.3389/fnagi.2021.624732. Park CH, Shin NY, Nam Y, Yoon U, Ahn K, Lee SK. Characteristics of perivascular space dilatation in normal aging. Hum Brain Mapp. 2023 Jun 1;44(8):3232-3240. doi: 10.1002/hbm.26277. Achiron A, Faibel M. Sandlike appearance of Virchow-Robin spaces in early multiple sclerosis: a novel neuroradiologic marker. AJNR Am J Neuroradiol. 2002 Mar;23(3):376-80. Granberg T, Moridi T, Brand JS, Neumann S, Hlavica M, Piehl F, Ineichen BV. Enlarged perivascular spaces in multiple sclerosis on magnetic resonance imaging: a systematic review and meta-analysis. J Neurol. 2020 Nov;267(11):3199-3212. doi: 10.1007/s00415-020-09971-5. Javierre-Petit C, Schneider JA, Kapasi A, Makkinejad N, Tamhane AA, Leurgans SE, Mehta RI, Barnes LL, Bennett DA, Arfanakis K. Neuropathologic and Cognitive Correlates of Enlarged Perivascular Spaces in a Community-Based Cohort of Older Adults. Stroke. 2020 Sep;51(9):2825-2833. doi: 10.1161/STROKEAHA.120.029388. Romero JR, Pinheiro A, Aparicio HJ, DeCarli CS, Demissie S, Seshadri S. MRI-Visible Perivascular Spaces and Risk of Incident Dementia: The Framingham Heart Study. Neurology. 2022 Dec 5;99(23):e2561-e2571. doi: 10.1212/WNL.0000000000201293. Doubal FN, MacLullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010 Mar;41(3):450-4. doi: 10.1161/STROKEAHA.109.564914. Brown R, Benveniste H, Black SE, Charpak S, Dichgans M, Joutel A, Nedergaard M, Smith KJ, Zlokovic BV, Wardlaw JM. Understanding the role of the perivascular space in cerebral small vessel disease. Cardiovasc Res. 2018 Sep 1;114(11):1462-1473. doi: 10.1093/cvr/cvy113. Waymont JMJ, Valdés Hernández MDC, Bernal J, Duarte Coello R, Brown R, Chappell FM, Ballerini L, Wardlaw JM. Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain. Neuroimage. 2024 Aug 15;297:120685. doi: 10.1016/j.neuroimage.2024.120685. Duperron MG, Tzourio C, Sargurupremraj M, Mazoyer B, Soumaré A, Schilling S, Amouyel P, Chauhan G, Zhu YC, Debette S. Burden of Dilated Perivascular Spaces, an Emerging Marker of Cerebral Small Vessel Disease, Is Highly Heritable. Stroke. 2018 Feb;49(2):282-287. doi: 10.1161/STROKEAHA.117.019309. Choi Y, Nam Y, Choi Y, Kim J, Jang J, Ahn KJ, Kim BS, Shin NY. MRI-visible dilated perivascular spaces in healthy young adults: A twin heritability study. Hum Brain Mapp. 2020 Dec 15;41(18):5313-5324. doi: 10.1002/hbm.25194. Duperron MG, Knol MJ, Le Grand Q, Evans TE, Mishra A, Tsuchida A, Roshchupkin G, Konuma T, Trégouët DA, Romero JR, Frenzel S, Luciano M, Hofer E, Bourgey M, Dueker ND, Delgado P, Hilal S, Tankard RM, Dubost F, Shin J, Saba Y, Armstrong NJ, Bordes C, Bastin ME, Beiser A, Brodaty H, Bülow R, Carrera C, Chen C, Cheng CY, Deary IJ, Gampawar PG, Himali JJ, Jiang J, Kawaguchi T, Li S, Macalli M, Marquis P, Morris Z, Muñoz Maniega S, Miyamoto S, Okawa M, Paradise M, Parva P, Rundek T, Sargurupremraj M, Schilling S, Setoh K, Soukarieh O, Tabara Y, Teumer A, Thalamuthu A, Trollor JN, Valdés Hernández MC, Vernooij MW, Völker U, Wittfeld K, Wong TY, Wright MJ, Zhang J, Zhao W, Zhu YC, Schmidt H, Sachdev PS, Wen W, Yoshida K, Joutel A, Satizabal CL, Sacco RL, Bourque G; CHARGE consortium; Lathrop M, Paus T, Fernandez-Cadenas I, Yang Q, Mazoyer B, Boutinaud P, Okada Y, Grabe HJ, Mather KA, Schmidt R, Joliot M, Ikram MA, Matsuda F, Tzourio C, Wardlaw JM, Seshadri S, Adams HHH, Debette S. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease. Nat Med. 2023 Apr;29(4):950-962. doi: 10.1038/s41591-023-02268-w. Ran L, Fang Y, Cheng C, He Y, Shao Z, Kong Y, Huang H, Xu S, Luo X, Wang W, Hao X, Wang M. Genome-wide and phenome-wide studies provided insights into brain glymphatic system function and its clinical associations. Sci Adv. 2025 Jan 17;11(3):eadr4606. doi: 10.1126/sciadv.adr4606. Ranti DL, Warburton AJ, Rutland JW, Dullea JT, Markowitz M, Smith DA, Kligler SZK, Rutter S, Langan M, Arrighi-Allisan A, George I, Verma G, Murrough JW, Delman BN, Balchandani P, Morris LS. Perivascular spaces as a marker of psychological trauma in depression: A 7-Tesla MRI study. Brain Behav. 2022 Jul;12(7):e32598. doi: 10.1002/brb3.2598. Sibilia F, Sheikh-Bahaei N, Mack WJ, Barisano G, Choupan J. Neuroinflammation modifies the relationship between stress and perivascular spaces in an elderly population with different levels of cognitive impairment. Front Cell Neurosci. 2024 Nov 13;18:1480405. doi: 10.3389/fncel.2024.1480405. Liang Y, Chan YL, Deng M, Chen YK, Mok V, Wang F, Ungvari GS, Chu CW, Tang WK. Enlarged perivascular spaces in the centrum semiovale are associated with poststroke depression: A 3-month prospective study. J Affect Disord. 2018 Mar 1;228:166-172. doi: 10.1016/j.jad.2017.11.080. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003 Sep 1;54(5):573-83. doi: 10.1016/s0006-3223(02)01866-8. Lee SY. Handgrip Strength: An Irreplaceable Indicator of Muscle Function. Ann Rehabil Med. 2021 Jun;45(3):167-169. doi: 10.5535/arm.21106. Russell E, Koren G, Rieder M, Van Uum S. Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions. Psychoneuroendocrinology. 2012 May;37(5):589-601. doi: 10.1016/j.psyneuen.2011.09.009. Green C, Stolicyn A, Harris MA, Shen X, Romaniuk L, Barbu MC, Hawkins EL, Wardlaw JM, Steele JD, Waiter GD, Sandu AL, Campbell A, Porteous DJ, Seckl JR, Lawrie SM, Reynolds RM, Cavanagh J, McIntosh AM, Whalley HC. Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland. Transl Psychiatry. 2021 Oct 12;11(1):523. doi: 10.1038/s41398-021-01644-9. Lawman HG, Troiano RP, Perna FM, Wang CY, Fryar CD, Ogden CL. Associations of Relative Handgrip Strength and Cardiovascular Disease Biomarkers in U.S. Adults, 2011-2012. Am J Prev Med. 2016 Jun;50(6):677-683. doi: 10.1016/j.amepre.2015.10.022. Shaughnessy KA, Hackney KJ, Clark BC, Kraemer WJ, Terbizan DJ, Bailey RR, McGrath R. A Narrative Review of Handgrip Strength and Cognitive Functioning: Bringing a New Characteristic to Muscle Memory. J Alzheimers Dis. 2020;73(4):1265-1278. doi: 10.3233/JAD-190856. Chai L, Zhang D, Fan J. Comparison of grip strength measurements for predicting all-cause mortality among adults aged 20+ years from the NHANES 2011-2014. Sci Rep. 2024 Nov 25;14(1):29245. doi: 10.1038/s41598-024-80487-y. Habota T, Sandu AL, Waiter GD, McNeil CJ, Steele JD, Macfarlane JA, Whalley HC, Valentine R, Younie D, Crouch N, Hawkins EL, Hirose Y, Romaniuk L, Milburn K, Buchan G, Coupar T, Stirling M, Jagpal B, MacLennan B, Priba L, Harris MA, Hafferty JD, Adams MJ, Campbell AI, MacIntyre DJ, Pattie A, Murphy L, Reynolds RM, Elliot R, Penton-Voak IS, Munafò MR, Evans KL, Seckl JR, Wardlaw JM, Lawrie SM, Haley CS, Porteous DJ, Deary IJ, Murray AD, McIntosh AM. Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study: A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments. Wellcome Open Res. 2021 Jul 16;4:185. doi: 10.12688/wellcomeopenres.15538.2. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002 Oct;17(2):825-41. doi: 10.1016/s1053-8119(02)91132-8. M. Jenkinson, M. Pechaud, and S. Smith. BET2: MR-based estimation of brain, skull and scalp surfaces. In Eleventh Annual Meeting of the Organization for Human Brain Mapping, 2005. Ballerini L, Lovreglio R, Valdés Hernández MDC, Ramirez J, MacIntosh BJ, Black SE, Wardlaw JM. Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering. Sci Rep. 2018 Feb 1;8(1):2132. doi: 10.1038/s41598-018-19781-5. R. Duarte Coello, M. Valdés Hernández, J. Bernal Moyano, J. Wardlaw. Cubic Bézier curve approximation for the estimation of perivascular spaces measurements in MRI brain scans. In G. Waiter, T. Lambrou, G. Leontidis, N. Oren, T. Morris, S. Gordon, J. McGowan, & C. Nicolson (Eds.) Proceedings of the 27th Conference on Medical Image Understanding and Analysis 2023, Frontiers Media, SA (2023), pp. 168-173, 10.3389/978-2-8325-1231-9. Smith BH, Campbell H, Blackwood D, Connell J, Connor M, Deary IJ, Dominiczak AF, Fitzpatrick B, Ford I, Jackson C, Haddow G, Kerr S, Lindsay R, McGilchrist M, Morton R, Murray G, Palmer CN, Pell JP, Ralston SH, St Clair D, Sullivan F, Watt G, Wolf R, Wright A, Porteous D, Morris AD. Generation Scotland: the Scottish Family Health Study; a new resource for researching genes and heritability. BMC Med Genet. 2006 Oct 2;7:74. doi: 10.1186/1471-2350-7-74. Selvarajah J, Scott M, Stivaros S, Hulme S, Georgiou R, Rothwell N, Tyrrell P, Jackson A. Potential surrogate markers of cerebral microvascular angiopathy in asymptomatic subjects at risk of stroke. EurRadiol. 2009 Apr;19(4):1011-8. doi: 10.1007/s00330-008-1202-8. Charidimou A, Meegahage R, Fox Z, Peeters A, Vandermeeren Y, Laloux P, Baron JC, Jäger HR, Werring DJ. Enlarged perivascular spaces as a marker of underlying arteriopathy in intracerebral haemorrhage: a multicentre MRI cohort study. J NeurolNeurosurg Psychiatry. 2013 Jun;84(6):624-9. doi: 10.1136/jnnp-2012-304434. Hajjar I, Zhao P, Alsop D, Novak V. Hypertension and cerebral vasoreactivity: a continuous arterial spin labeling magnetic resonance imaging study. Hypertension. 2010 Nov;56(5):859-64. doi: 10.1161/HYPERTENSIONAHA.110.160002. Santisteban MM, Iadecola C, Carnevale D. Hypertension, Neurovascular Dysfunction, and Cognitive Impairment. Hypertension. 2023 Jan;80(1):22-34. doi: 10.1161/HYPERTENSIONAHA.122.18085. Libecap TJ, Bauer CE, Zachariou V, Pappas CA, Raslau FD, Liu P, Lu H, Gold BT. Association of Baseline Cerebrovascular Reactivity and Longitudinal Development of Enlarged Perivascular Spaces in the Basal Ganglia. Stroke. 2023 Nov;54(11):2785-2793. doi: 10.1161/STROKEAHA.123.043882. Yu Q, Wang H, Zhang W, Zhang X, Zhao J, Gong L, Liu X. MRI-visible enlarged perivascular spaces in basal ganglia rather than centrum semiovale was associated with aneurysmal subarachnoid hemorrhage. Front Neurol. 2024 Jan 15;15:1341499. doi: 10.3389/fneur.2024.1341499. Zhang K, Zhou Y, Zhang W, Li Q, Sun J, Lou M. MRI-visible perivascular spaces in basal ganglia but not centrum semiovale or hippocampus were related to deep medullary veins changes. J Cereb Blood Flow Metab. 2022 Jan;42(1):136-144. doi: 10.1177/0271678X211038138. Shi H, Cui L, Hui Y, Wu S, Li X, Shu R, Song H, Wang J, Yu P, Chen S, Li J, Yang L, Wang Z, Yang Q, Gao Y. Enlarged Perivascular Spaces in Relation to Cumulative Blood Pressure Exposure and Cognitive Impairment. Hypertension. 2023 Oct;80(10):2088-2098. doi: 10.1161/HYPERTENSIONAHA.123.21453. Harrewijn A, Vidal-Ribas P, Clore-Gronenborn K, Jackson SM, Pisano S, Pine DS, Stringaris A. Associations between brain activity and endogenous and exogenous cortisol - A systematic review. Psychoneuroendocrinology. 2020 Oct;120:104775. doi: 10.1016/j.psyneuen.2020.104775. Cook SC, Wellman CL. Chronic stress alters dendritic morphology in rat medial prefrontal cortex. J Neurobiol. 2004 Aug;60(2):236-48. doi: 10.1002/neu.20025. Han KM, Ham BJ. How Inflammation Affects the Brain in Depression: A Review of Functional and Structural MRI Studies. J Clin Neurol. 2021 Oct;17(4):503-515. doi: 10.3988/jcn.2021.17.4.503. Su L, Cai Y, Xu Y, Dutt A, Shi S, Bramon E. Cerebral metabolism in major depressive disorder: a voxel-based meta-analysis of positron emission tomography studies. BMC Psychiatry. 2014 Nov 19;14:321. doi: 10.1186/s12888-014-0321-9. Williams SZ, Chung GS, Muennig PA. Undiagnosed depression: A community diagnosis. SSM Popul Health. 2017 Jul 28;3:633-638. doi: 10.1016/j.ssmph.2017.07.012.Barnes JN, Pearson AG, Corkery AT, Eisenmann NA, Miller KB. Exercise, Arterial Stiffness, and Cerebral Vascular Function: Potential Impact on Brain Health. J IntNeuropsychol Soc. 2021 Sep;27(8):761-775. doi: 10.1017/S1355617721000394. Del Brutto OH, Mera RM, Del Brutto VJ, Castillo PR. Enlarged basal ganglia perivascular spaces and sleep parameters. A population-based study. Clin Neurol Neurosurg. 2019 Jul;182:53-57. doi: 10.1016/j.clineuro.2019.05.002. Vaishya R, Misra A, Vaish A, Ursino N, D'Ambrosi R. Hand grip strength as a proposed new vital sign of health: a narrative review of evidences. J Health Popul Nutr. 2024 Jan 9;43(1):7. doi: 10.1186/s41043-024-00500-y. Jones EA, le Noble F, Eichmann A. What determines blood vessel structure? Genetic prespecification vs. hemodynamics. Physiology (Bethesda). 2006 Dec;21:388-95. doi: 10.1152/physiol.00020.2006. Menze I, Bernal J, Kaya P, Aki Ç, Pfister M, Geisendörfer J, Yakupov R, Coello RD, Valdés-Hernández MDC, Heneka MT, Brosseron F, Schmid MC, Glanz W, Incesoy EI, Butryn M, Rostamzadeh A, Meiberth D, Peters O, Preis L, Lammerding D, Gref D, Priller J, Spruth EJ, Altenstein S, Lohse A, Hetzer S, Schneider A, Fliessbach K, Kimmich O, Vogt IR, Wiltfang J, Bartels C, Schott BH, Hansen N, Dechent P, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Kilimann I, Goerss D, Laske C, Munk MH, Sanzenbacher C, Hinderer P, Scheffler K, Spottke A, Roy-Kluth N, Lüsebrink F, Neumann K, Wardlaw J, Jessen F, Schreiber S, Düzel E, Ziegler G. Perivascular space enlargement accelerates in ageing and Alzheimer's disease pathology: evidence from a three-year longitudinal multicentre study. Alzheimers Res Ther. 2024 Oct 31;16(1):242. doi: 10.1186/s13195-024-01603-8. 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Murray","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"","lastName":"Murray","suffix":""},{"id":465142714,"identity":"7fd18a1b-3241-4659-92d3-3e93a61a793c","order_by":11,"name":"Joanna Wardlaw","email":"","orcid":"https://orcid.org/0000-0002-9812-6642","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Wardlaw","suffix":""}],"badges":[],"createdAt":"2025-05-04 18:35:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6589799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6589799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83914802,"identity":"041a0086-7c10-4bcc-bd08-ccf2b222265a","added_by":"auto","created_at":"2025-06-04 12:35:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346707,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between age (in years) and PVS volume (as % of the region of interest) in the centrum semiovale (A), and basal ganglia (B), and association between hypertension (yes=1 and no=0) and PVS volume (as %in the region of interest) in the centrum semiovale (C), and basal ganglia (D).\u003c/p\u003e","description":"","filename":"MorozovaSTRADLPVSFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6589799/v1/3374e448ec0c554709e5c130.png"},{"id":83914804,"identity":"2f6bacef-6a2b-4e32-b8d6-3532f15c540a","added_by":"auto","created_at":"2025-06-04 12:35:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":401663,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between the QIDS score and PVS volume (as % of the region of interest) in the centrum semiovale (A), and basal ganglia (B), and association between the grip strength (in Kg) (C) and hair cortisol (in ng/mg) (D) and PVS volume (as % of the region of interest) in the centrum semiovale.\u003c/p\u003e","description":"","filename":"MorozovaSTRADLPVSFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6589799/v1/c7bf8ee3ca3faf1a9c2130ed.png"},{"id":83916556,"identity":"36f391c7-e5ca-447a-9566-a211c1d95130","added_by":"auto","created_at":"2025-06-04 12:51:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2200378,"visible":true,"origin":"","legend":"\u003cp\u003eEquivalent axial T2-weighted MRI slice in the centrum semiovale, with the PVS segmented (bottom row) in red, in three consecutive generations of the same family (younger to older: left to right). Observe similar number of PVS counted regardless of age differences, and similarities in the topological (i.e., spatial) distribution patterns in the [two pairs of] consecutive generations.\u003c/p\u003e","description":"","filename":"STRADLimageRoberto3generations.png","url":"https://assets-eu.researchsquare.com/files/rs-6589799/v1/352b616908f83f28afa15c78.png"},{"id":83917422,"identity":"fe52e197-3cac-4b2a-a49e-829f24581817","added_by":"auto","created_at":"2025-06-04 12:59:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5931121,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6589799/v1/c0d31f0b-8460-47bb-9783-f9254e95ec76.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Stress, Depressive Symptoms, and Physical Strength Impact Brain Perivascular Spaces: A Cohort Study with Insights from First-Degree Relatives","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStress is a well-established pro-inflammatory trigger and a significant risk factor for numerous diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with cardiovascular and neurodegenerative disorders being particularly affected [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Chronic stress disrupts microcirculatory function [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and induces a neuroinflammatory response [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It also exacerbates depression and anxiety [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which have also been associated with neuroinflammation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and decreased cerebral blood flow [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], possibly leading to alterations in microcirculation. A key factor in maintaining cardiovascular health is physical strength [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Reduced muscular strength has been linked to increased cardiometabolic risks, as well as higher cardiovascular-related and overall mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicrovascular changes are thought to be reflected in the enlargement of perivascular spaces (PVS), visible in magnetic resonance imaging (MRI) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. PVS are physiological compartments surrounding the perforating blood vessels of the brain, lying at the interface of the vasculature and the cerebral parenchyma. They serve as a critical conduit for interstitial solute clearance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], functioning similarly to the lymphatic system \u0026ndash; hence the term \u0026lsquo;glymphatic\u0026rsquo;, where \u0026lsquo;g\u0026rsquo; stands for the glial cells forming the boundary between the brain tissue and the outer vessel wall. PVS are thought to be part of the pathway for clearing waste metabolites [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], contribute to maintaining brain homeostasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and facilitating a neuroimmune response [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PVS may be considered a structural proxy for healthy brain function, and their enlargement on MRI has been associated with advancing age [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and the presence of neurovascular, neurodegenerative, and neuropsychiatric conditions, including multiple sclerosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], dementia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], cerebral small vessel disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and many more.\u003c/p\u003e \u003cp\u003eSeveral factors may impact the perivascular architecture of the brain, with age and hypertension being consistent associates of PVS enlargement across most studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. PVS morphology exhibits high individual variability [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with genetics being a substantial predisposing factor, as demonstrated by several research groups [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], including recent large genome-wide association studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To date, only a limited number of studies have explored the impact of neuropsychiatric factors on PVS. One study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] identified a correlation between traumatic events and PVS volume increase in patients with depression as well as in healthy subjects, suggesting that PVS enlargement may be secondary to stress or trauma-related changes, such as brain atrophy. Another study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] confirmed this association, showing that neuroinflammation is a potential mediating link between stress and PVS in an elderly population with cognitive impairment. A third study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] showed that higher PVS burden correlates with post-stroke depression using a visual rating scale. While the effect of neuropsychiatric factors on microvasculature is apparent, to what extent they influence perivascular morphology and glymphatic clearance \u0026ndash; an essential process for maintaining healthy brain function \u0026ndash; remains unclear. Furthermore, although the heritability of PVS burden is well-established [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the familial impact on perivascular morphometry is yet to be determined.\u003c/p\u003e \u003cp\u003eWe here investigate the associations between neuropsychiatric factors \u0026ndash; stress assessed through hair cortisol, depressive symptoms assessed through the Quick Inventory of Depressive Symptomatology [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (QIDS) scale, physical factors as through the hand grip strength test [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] \u0026ndash; and perivascular characteristics in a population-based cohort and explore the potential familial impact on PVS through first-degree relatives. QIDS is considered a trusted measure to briefly assess one\u0026rsquo;s mental health. Hair cortisol measurement is one of the most effective ways to quantify stress, seen as a reliable biomarker of chronic stress exposure [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. And the hand grip test is recognised as an indicator of cardiovascular health [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], cognitive function [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and even mortality risk [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. We hypothesize that advancing age, hypertension, higher QIDS score and hair cortisol will be associated with high PVS burden, while stronger hand grip will be associated with reduced PVS load. Furthermore, we hypothesize that, due to demonstrated genetic associations, family membership will lead to greater similarity in MRI-visible PVS burden and morphometric characteristics among first-degree relatives. To the best of our knowledge, this is the first study to examine the relationship between hand grip strength and PVS, as well as to measure hair cortisol and use the QIDS scale in this context.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSample\u003c/p\u003e\n\u003cp\u003eThe dataset comes from the Stratifying Depression and Resilience Longitudinally (STRADL) study [40], a population-based longitudinal study examining the biological and psychological mechanisms of depression and resilience. For the purpose of this study, 1183 subjects were included, with all relevant data available, from a dataset of 1188 participants with MRI assessment performed at two locations, Aberdeen (51%) and Dundee (49%), in Scotland, United Kingdom, using 3T magnetic resonance imaging scanners. From them, 859 individuals were unrelated, and 324 were part of 136 families of between 2 and 6 members \u0026ndash; all first degree relatives.\u003c/p\u003e\n\u003cp\u003ePVS Assessment\u003c/p\u003e\n\u003cp\u003eThe neuroimaging protocol of the primary study that provided data for this analysis is published [40]. We used previously defaced T1-weighted, T2-weighted and fluid attenuated inversion recovery (FLAIR) magnetic resonance images (MRI), as well as the brain parcellation done by freesurfer v 6.0 (https://surfer.nmr.mgh.harvard.edu/), all provided by the Generation Scotland Data Access Committee (https://genscot.ed.ac.uk/for-researchers/access), together with the rest of the variables used in the analyses. We co-registered all MRI sequences to the nearly-isotropic (1 x 0.94 x 0.94 mm\u003csup\u003e3\u003c/sup\u003e) T1-weighted space, where the freesurfer segmentations are, and obtained the intracranial volume using FLIRT [41] and BET2 [42]. We applied Gaussian clustering to the multisequence (4D) volume obtained by concatenating the three co-registered MRI sequences to extract the brain tissue separately from the pure CSF, the meningeal layers and main venous pathways. We obtained initial priors of the basal ganglia (BG) and white matter regions (mainly centrum semiovale (CSO), excluding the internal and external capsules that were included in the BG region) from freesurfer segmentations. We refined them to ensure that venous pathways, membranes and CSF were not included, by applying the masks generated from the multispectral Gaussian clustering segmentations. Then, we mapped our (3D) regions of interest (ROIs) to the T2-weighted space, of higher spatial resolution (i.e., 0.5 x 0.48 x 0.48 mm\u003csup\u003e3\u003c/sup\u003e), where we performed the PVS segmentations using the thresholded output from the Frangi filter as described in Ballerini et al. [43]. From the PVS segmentations we calculated the percentage volume and the density of PVS in each ROI (PVS counts in a ROI volume), and determined the average PVS length per ROI using the Bezier curves approximation [44]. The step-by-step pipeline followed is described and downloadable from https://doi.org/10.7488/ds/7486.\u003c/p\u003e\n\u003cp\u003eOverview of methods for glucocorticoid extraction / grip strength / QIDS/ hypertension\u003c/p\u003e\n\u003cp\u003eDemographic data (age and sex), were obtained within the Generation Scotland: Scottish Family Health Study [45] between 2006 and 2011. Grip strength, hair sample, blood pressure, and QIDS were obtained at the same time as the MRI in an in-person assessment wave in 2019 for the STRADL study. Grip strength was determined using a Patterson Medical Jamar hand dynamometer. A small sample (i.e., approximately 3mm diameter) of hair was collected from the posterior vertex region of the head for extracting cumulative cortisol using organic solvents in pulverised hair and processed using liquid chromatography-mass spectroscopy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eWe applied a linear mixed effects model (LME) to account for possible correlations in the PVS characteristics among first-degree relatives. A single LME model was built for each PVS measurement (PVS volume %, count, density, and median length) as an outcome in both the centrum semiovale (CSO) and the basal ganglia (BG). Age, hypertension, hair cortisol, grip strength, and QIDS score were entered as fixed effects to examine their influence on the PVS characteristic. Family ID was included as a random intercept to model shared variance within families. This enabled to assess both within-family and between-family variation, including comparisons between family members and unrelated individuals, providing insight into the contribution of familial factors on the outcome.\u003c/p\u003e\n\u003cp\u003eSince the dataset contained both individuals and families, a sensitivity analysis was carried out by repeating the analysis on a sample that excluded individuals without family members (i.e., restricting the sample to families with at least two members). Family ID was added as a random intercept, as in the primary model, while age was added as a random covariate to assess potential differences in its effect across families.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using the Stata software package (version 18, StataCorp. 2023. \u003cem\u003eStata Statistical Software: Release 18\u003c/em\u003e. College Station, TX: StataCorp LLC.). No correction for multiple comparisons was applied. We report coefficient estimates, standard errors, and p-values. Standardised effect sizes were also determined.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive Statistics\u003c/p\u003e\n\u003cp\u003eThe demographic, clinical, and neuroimaging data of the study cohort are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e. The distribution of family sizes is summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e. Each family size represents the number of members per family.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Study data overview\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGrip strength (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eQIDS score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHair cortisol (pg/mg)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCSO PVS volume (ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCSO PVS volume (% within ROI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCSO PVS count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e865.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;CSO PVS density (count/ROI in ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCSO PVS length (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBG PVS volume (ml)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBG PVS volume (% within ROI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBG PVS count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBG PVS density (count/ROI in ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBG PVS length (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3029%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOverall number of subjects (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40.2145%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubjects without hypertension (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40.2145%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e869 (73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubjects with relatives within the cohort (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40.2145%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e324 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Distribution of Family Cluster Sizes in the Sample (First-Degree Relatives Only).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Size\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Families\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e72.6 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e8.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e1.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e0.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e0.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9513%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5415%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5072%;\"\u003e\n \u003cp\u003e0.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePrimary Analyses\u003c/p\u003e\n\u003cp\u003eAll Linear Mixed Effects (LME) models, one for each PVS measurement (PVS volume %, count, density, and median length) as an outcome in both the centrum semiovale (CSO) and the basal ganglia (BG), are summarised in \u003cstrong\u003eTable 3\u003c/strong\u003e. As the most comprehensive PVS characteristic, PVS volume as percentage in the ROIs was selected to visualise the associations between statistically significant covariates and the PVS burden (\u003cstrong\u003eFigures 1 and 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTable 3. Linear mixed effects models\u0026rsquo; summary results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1032\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCentrum Semiovale\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBasal Ganglia\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS volume %\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS count\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS density Est., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS median length Est., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS volume %\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS count\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS density\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVS median length Est., (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 1032px;\"\u003e\n \u003cp\u003e\u003cem\u003eFixed Effect\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.005 (0.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e169.96 (177.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.44 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.35 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.086 (0.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e191.47 (40.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.1 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.86 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03 (0.004)***\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.22(2.42)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.092 (0.017)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.022 (0.004)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.025 (0.004)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.028(0.01)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.017 (0.004)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.193 (0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.404 (0.102)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.11 (12.91)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.624 (0.232)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26 (0.084)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHair cortisol\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.097 (0.045)*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e61.43 (25.58)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.41(0 .175)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGrip strength\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.012 (0.004)**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.0375 (0.017)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eQIDS score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.026 (0.011)* \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.57 (6.42)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.118 (0.044)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.029 (0.012)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.15(1.45)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.083 (0.026)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 1032px;\"\u003e\n \u003cp\u003e\u003cem\u003eRandom Effect\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFamily ID\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.355 (0.148)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.336 (0.098)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAge was a highly significant predictor of the PVS burden in the CSO across all models (p\u0026lt;0.001 for PVS volume (% in CSO ROI, \u003cstrong\u003eFigure 1A\u003c/strong\u003e), count, density, and median length), with standardised effect size of 0.24 for PVS volume, 0.17 for PVS count, 203.1 for PVS density, and 0.2 for PVS median length. It is worth noting that the standard deviation of PVS density is very low: 0.0046, therefore the large values for standardized effect sizes in this parameter. In the BG, age correlated with the PVS volume (% in BG ROI, \u003cstrong\u003eFigure 1B\u003c/strong\u003e) (p\u0026lt;0.001), PVS density (p=0.005), and PVS median length (p\u0026lt;0.001), with standardized effect size of 0.21 for PVS volume, 105.6 for PVS density, and 0.18 for PVS median length.\u003c/p\u003e\n\u003cp\u003eHypertension showed a marginally significant association with the percentage of PVS volume in the CSO (p=0.057, \u003cstrong\u003eFigure 1C\u003c/strong\u003e). In the BG, hypertension was statistically significantly associated with all PVS morphometric features (p\u0026lt;0.001 for PVS volume (% in ROI, \u003cstrong\u003eFigure 1D\u003c/strong\u003e), p=0.013 for PVS count, p=0.007 for PVS density, and p=0.002 for PVS median length).\u003c/p\u003e\n\u003cp\u003eThe QIDS score was associated with the same PVS morphometric features in the CSO and the BG: PVS volume (p=0.021 for CSO, \u003cstrong\u003eFigure 2A\u003c/strong\u003e (standardised effect size 0.08), p=0.012 for BG (\u003cstrong\u003eFigure 2B\u003c/strong\u003e), with standardized effect size of 0.27), PVS count (p=0.023 for CSO (standardised effect size 0.08), p=0.004 for BG, with standardised effect size of 0.1), PVS density (p=0.007 for CSO with standardised effect size of 94.45; and p=0.002 for BG, with standardised effect size of 112.2). There were no associations between the QIDS score and PVS median length in either of the brain regions (p\u0026gt;0.05 for both).\u003c/p\u003e\n\u003cp\u003eGrip strength was negatively associated with increased percentage of PVS volume (\u003cstrong\u003eFigure 2C\u003c/strong\u003e) and PVS density in the CSO (p=0.009, and p=0.028, respectively; with standardised effect size of -0.1 for PVS volume and -84.7 for PVS density). It was not associated with any PVS measurements in the BG (p\u0026gt;0.05 for all measurements).\u003c/p\u003e\n\u003cp\u003eHair cortisol level was associated with CSO PVS measurements (p=0.032 for PVS volume (% in ROI, \u003cstrong\u003eFigure 2D\u003c/strong\u003e), p=0.016 for PVS count, p=0.02 for PVS density, with standardised effect size of 0.075 for PVS volume, 0.09 for PVS count, and 84.9 for PVS density). There were no associations between hair cortisol and the BG PVS burden (p\u0026gt;0.05 for all measurements).\u003c/p\u003e\n\u003cp\u003eThe variance component for Family ID was significant in the CSO with the percentage of PVS volume in this region as the outcome (p=0.01), and PVS median length (p\u0026lt;0.001), indicating that a substantial portion of the variance is explained by familial factors. In the BG, Family ID effect was non-significant. \u003cstrong\u003eFigure 3\u003c/strong\u003e shows an example of PVS segmentation in the CSO in 3 representatives of three generations of the same family.\u003c/p\u003e\n\u003cp\u003eSensitivity Analysis\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis was carried out for the two models with the significant random effect of Family ID \u0026ndash; the model with the PVS volume (% in ROIs) as an outcome, and the model with the PVS median length (see Table 3). The analysis consisted of two parts. In the first part, we replicated the original model but restricted the sample to families with at least two members, excluding individuals without family members. This allowed us to assess whether being in the same family affects the outcome. In the second part, we added age as a random covariate to the model, which revealed that age had substantially different effects across families. The random effect of Family ID remained significant in both parts of the sensitivity analysis for both models (p=0.011 vs p=0.014 for PVS volume (% in ROIs), and p=0.001 vs p=0.002 for PVS median length), and the model fit was very similar (Akaike Information Criterion (AIC) 677.7 vs 679.87 for PVS volume %, and AIC 697.5 vs 694.7), with the model including age as a random covariate showing slightly more significance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe explored the associations between age, hypertension, grip strength, hair cortisol, QIDS score and PVS characteristics in BG and CSO 3D ROIs, including the PVS volume as percentage in the ROIs, count, density, and median length, in the CSO and the BG.\u003c/p\u003e\n\u003cp\u003eAge was highly significantly associated with all PVS measurements in the CSO, as well as with the PVS volume (% in ROI), density, and median length in the BG. Our results are in accordance with the existing literature, as age is consistently shown to be a crucial factor related to PVS enlargement [15, 16, 17, 18]. The only measurement that did not yield substantial findings was the PVS count in the BG, which may be due to a lower average number of PVS present in this region compared to the larger area of the CSO. However, all other BG PVS morphometric features investigated in our study confirm the effect of age on the PVS burden.\u003c/p\u003e\n\u003cp\u003eHypertension is another well-established factor influencing PVS [11, 25]. In our study, hypertension was significantly associated with all PVS measurements in the BG, while its association with PVS volume (% in ROI) in the CSO was only marginally significant. Hypertension-related vascular changes have previously been demonstrated to particularly affect PVS in the BG [46, 47]. Hypertension decreases cerebrovascular reactivity [48, 49] and leads to blood-brain barrier dysfunction [47]. These factors, compounded by atherosclerosis in the small cerebral vessels [46], may contribute to impaired metabolic waste clearance and, in turn, to PVS enlargement, particularly in the BG [50], although this is yet to be confirmed. Overall, pathological vascular changes are likely not specific to the BG but occur throughout the brain\u0026rsquo;s vasculature [49], with their impact first observed in the small vessels of the BG, where PVS may have a reduced capacity to compensate for pathophysiological changes compared to the PVS in the CSO [51]. This may be due to differences in venous drainage, as the CSO has multiple drainage routes [52], which enhances its compensatory capacity when increased perivascular clearance is required, such as during the pathological vascular changes mentioned above. This may explain our findings, as we observed a weaker association between hypertension and PVS volume % in the CSO (at the trend level only). This could also be attributed to the nature of our data, where the analysis used a binary variable (presence or absence of hypertension) rather than blood pressure measurements for each participant. Indeed, cumulative blood pressure measurements might provide a more precise estimate of the impact on perivascular clearance [53], regardless of the hypertension diagnosis, which reflects the continuous nature of the underlying pathophysiology.\u003c/p\u003e\n\u003cp\u003eChronic stress promotes neuroinflammation [4], which leads to blood-brain barrier disruption and immune cell accumulation in the PVS, resulting in an increased PVS burden [31]. We used hair cortisol as a biomarker of chronic stress exposure [35] and found positive associations between hair cortisol and\u0026nbsp;PVS volume %, count, and density in the CSO. However, no associations were found between cortisol and the PVS burden in the BG. This may be due to cortical areas being the primary target of cortisol [54]. As cortisol promotes neuroinflammation, it affects the perivascular clearance system that drains the subcortical white matter, including the CSO. In contrast, the BG, being deep gray matter structures, are less sensitive to cortisol-driven changes. Chronic increased cortisol exposure has been shown to have a profound impact on the cortical architecture in preclinical models [55], promoting neurodegenerative and cognitive changes, which are also observed with increased PVS burden [11, 53]. Our findings are in agreement with those obtained by [30], who found associations between stress exposure and increased PVS burden both in the healthy subjects as well as in individuals with depression.\u003c/p\u003e\n\u003cp\u003eBeing a psychiatric disorder, depression is thought to be exacerbated by neuroinflammation [56]. In our study, we utilised the Quick Inventory of Depressive Symptomatology to assess the mental wellbeing of participants, and discovered an association between higher scores \u0026ndash; indicating more severe symptoms \u0026ndash; and PVS volume (as % in ROIs), count, and density in both the CSO and the BG. While the link between PVS morphology and clearance abnormalities is yet to be fully established, the consistent correlation with all PVS morphometric features in our cohort suggests that depressive symptomatology may be associated with alterations in the clearance system.\u003c/p\u003e\n\u003cp\u003eBrain metabolism has been shown to be substantially reduced in major depressive disorder [57], suggesting a slower turnover of brain waste metabolites, which may result in increased PVS burden. In our study, participants were evaluated based on symptom severity rather than a formal depression diagnosis, yielding arguably a more precise estimate, as depression is commonly underdiagnosed [58].\u0026nbsp;Our findings align with Liang et al.\u0026nbsp;[32], who reported more PVS in post-stroke depression.\u0026nbsp;Del Brutto et al., however, did not find associations between depression and PVS [59], likely due to their smaller sample size and the methodology used, as they rated PVS as either present or absent, while other studies that found the correlation used more detailed PVS quantification methods [30, 32].\u003c/p\u003e\n\u003cp\u003eAnother novel correlate of PVS morphology explored in our study was the hand grip test. We found a significant negative association between hand grip strength and percentage of PVS volume and PVS density in the CSO, suggesting that physical fitness may be associated with improved microvascular health. Although the hand grip test has not been previously studied in the context of perivascular morphology, it is a well-established marker of overall physical health in ageing populations [60], and has been linked to cardiovascular health [37], neurodegeneration, and cognitive decline [38] \u0026ndash; factors known to impair PVS clearance [11]. Therefore, if our results are replicated in other cohorts, the hand grip test could serve as a simple and useful proxy for assessing microvascular health, and, in turn, vessel plasticity and perivascular clearance.\u003c/p\u003e\n\u003cp\u003eFinally, we found a significant familial factor in the percentage of PVS volume and median length in the CSO, with age being a substantial contributing factor. This suggests that while age-related perivascular changes are universally observed, their trajectory may vary across families. Genetics has previously been shown to associate with perivascular morphology [28, 29]. The vascular architecture of the brain has a strong genetic component and is also influenced by epigenetic factors [61]. Therefore, it appears that perivascular spaces that follow the perforating arterioles of the brain are influenced by familial factors. Given the high variability of PVS in the general population [13], longitudinal tracing of PVS would be highly valuable in establishing the dynamics of perivascular changes over time and their relation to progression of other markers of adverse vascular health [62].\u003c/p\u003e\n\u003cp\u003eThe study is subject to limitations. Cross-sectional study design limits the ability to infer causal relationships between the factors assessed and perivascular space characteristics. Additionally, the familial component was evaluated only in a subset of the cohort, which may restrict the generalisability of these findings to the broader population. Longitudinal studies are needed to better understand the causal dynamics and the impact of familial factors on PVS morphological characteristics. Another limitation is that, from our analyses, we cannot conclude on the association between PVS burden and clinical diagnosis of depression. There were only 105/1188 (i.e., 8.8 %) participants from this cohort diagnosed with mayor depressive disorder at baseline (years 2006-2011), while the MRI assessment took place years later during the third assessment (recruitment ending in 2019). QIDS being a self-reported continuous scale applied at the time of the MRI provides more robust statistics and accounts for potentially undiagnosed subjects. Moreover, longitudinal and repeatability studies on PVS measurements that inform on the dynamics of PVS across time are still to be conducted. Therefore, it would have been rather speculative to derive conclusions on the putative association between PVS burden and clinical diagnosis of depression from analyzing the data available acquired more than 9 years apart.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study demonstrates the association of age, hypertension, stress, depressive symptoms, and physical strength on PVS burden and morphometry. We found that PVS volume increases with age, hypertension, depressive symptomatology, and higher hair cortisol, while a better hand grip strength is associated with a lesser PVS burden. Additionally, we observed substantial regional differences in PVS burden between the CSO and the BG. Furthermore, a familial component was identified, consistent with known genetic and epigenetic factors that may contribute to individual variations in perivascular dynamics. These findings emphasize the multifactorial nature of PVS development and demonstrate its role as a potential biomarker of healthy brain functioning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eFor the primary study that provided data for these analyses, consent for linkage of participant data and samples to routine NHS records was obtained as part of the original GS:SFHS (05/S1401/89), and subsequent procedures were conducted following an independent, but linked, ethics application (14/SS/0039). All data used was fully anonymised and following the declaration of Helsinki.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interests\u003c/h2\u003e \u003cp\u003eAuthors declare no conflict of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank all of the Generation Scotland participants for their contribution to this study. We also thank the research assistants, clinicians and technicians for their help in collecting the data, and Generation Scotland administration for making it available for research. This study is funded by the Hilary and Galen Weston Foundation under the Novel Biomarkers 2019 scheme [ref UB190097l] administered by the Weston Brain Institute, the Fondation Leducq Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05), and the UK Dementia Research Institute [award number UK DRI-4002] through UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer\u0026rsquo;s Society, and Alzheimer\u0026rsquo;s Research UK. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006] and is currently supported by the Wellcome Trust [216767/Z/19/Z]. This study was also supported and funded by the Wellcome Trust Strategic Award \u0026lsquo;Stratifying Resilience and Depression Longitudinally\u0026rsquo; (STRADL) [References 104036/Z/14/Z and 220857/Z/20/Z]. We acknowledge the support of the British Heart Foundation [RE/18/5/34216], the Row Fogo Charitable Trust [BRO-D.FID3668413] (MVH, JMW), and Charles University grants to AM: Cooperatio 36 - Medical Diagnostics and Basic Medical Sciences, and Cooperatio \u0026ndash; Neurosciences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu YZ, Wang YX, Jiang CL. Inflammation: The Common Pathway of Stress-Related Diseases. Front Hum Neurosci. 2017 Jun 20;11:316. doi: 10.3389/fnhum.2017.00316.\u003c/li\u003e\n\u003cli\u003eCohen S, Janicki-Deverts D, Miller GE. Psychological stress and disease. JAMA. 2007 Oct 10;298(14):1685-7. doi: 10.1001/jama.298.14.1685.\u003c/li\u003e\n\u003cli\u003eBurrage E, Marshall KL, Santanam N, Chantler PD. Cerebrovascular dysfunction with stress and depression. Brain Circ. 2018 Apr-Jun;4(2):43-53. doi: 10.4103/bc.bc_6_18.\u003c/li\u003e\n\u003cli\u003ePeng Z, Peng S, Lin K, Zhao B, Wei L, Tuo Q, Liao D, Yuan T, Shi Z. Chronic stress-induced depression requires the recruitment of peripheral Th17 cells into the brain. J Neuroinflammation. 2022 Jul 14;19(1):186.\u003c/li\u003e\n\u003cli\u003eRoss RA, Foster SL, Ionescu DF. The Role of Chronic Stress in Anxious Depression. Chronic Stress (Thousand Oaks). 2017 Feb 17;1:2470547016689472. doi: 10.1177/2470547016689472.\u003c/li\u003e\n\u003cli\u003eKim IB, Lee JH, Park SC. The Relationship between Stress, Inflammation, and Depression. Biomedicines. 2022 Aug 9;10(8):1929. doi: 10.3390/biomedicines10081929.\u003c/li\u003e\n\u003cli\u003eWon E, Kim YK. Neuroinflammation-Associated Alterations of the Brain as Potential Neural Biomarkers in Anxiety Disorders. Int J Mol Sci. 2020 Sep 7;21(18):6546. doi: 10.3390/ijms21186546.\u003c/li\u003e\n\u003cli\u003eChiappelli J, Adhikari BM, Kvarta MD, Bruce HA, Goldwaser EL, Ma Y, Chen S, Ament S, Shuldiner AR, Mitchell BD, Kochunov P, Wang DJ, Hong LE. Depression, stress and regional cerebral blood flow. J Cereb Blood Flow Metab. 2023 May;43(5):791-800. doi: 10.1177/0271678X221148979.\u003c/li\u003e\n\u003cli\u003eTikkanen E, Gustafsson S, Ingelsson E. Associations of Fitness, Physical Activity, Strength, and Genetic Risk With Cardiovascular Disease: Longitudinal Analyses in the UK Biobank Study. Circulation. 2018 Jun 12;137(24):2583-2591. doi: 10.1161/CIRCULATIONAHA.117.032432.\u003c/li\u003e\n\u003cli\u003eCarbone S, Kirkman DL, Garten RS, Rodriguez-Miguelez P, Artero EG, Lee DC, Lavie CJ. Muscular Strength and Cardiovascular Disease: AN UPDATED STATE-OF-THE-ART NARRATIVE REVIEW. J Cardiopulm Rehabil Prev. 2020 Sep;40(5):302-309. doi: 10.1097/HCR.0000000000000525.\u003c/li\u003e\n\u003cli\u003eWardlaw JM, Benveniste H, Nedergaard M, Zlokovic BV, Mestre H, Lee H, Doubal FN, Brown R, Ramirez J, MacIntosh BJ, Tannenbaum A, Ballerini L, Rungta RL, Boido D, Sweeney M, Montagne A, Charpak S, Joutel A, Smith KJ, Black SE; colleagues from the Fondation Leducq Transatlantic Network of Excellence on the Role of the Perivascular Space in Cerebral Small Vessel Disease. Perivascular spaces in the brain: anatomy, physiology and pathology. Nat Rev Neurol. 2020 Mar;16(3):137-153. doi: 10.1038/s41582-020-0312-z.\u003c/li\u003e\n\u003cli\u003eZong X, Lian C, Jimenez J, Yamashita K, Shen D, Lin W. Morphology of perivascular spaces and enclosed blood vessels in young to middle-aged healthy adults at 7T: Dependences on age, brain region, and breathing gas. Neuroimage. 2020 Sep;218:116978. doi: 10.1016/j.neuroimage.2020.116978.\u003c/li\u003e\n\u003cli\u003eMorozova A, \u0026Scaron;paniel F, \u0026Scaron;koch A, Brabec M, Zolotarov G, Musil V, Zach P. Enlarged brain perivascular spaces correlate with blood plasma osmolality in the healthy population: A longitudinal study. Neuroimage. 2024 Oct 15;300:120871. doi: 10.1016/j.neuroimage.2024.120871.\u003c/li\u003e\n\u003cli\u003eIneichen BV, Okar SV, Proulx ST, Engelhardt B, Lassmann H, Reich DS. Perivascular spaces and their role in neuroinflammation. Neuron. 2022 Nov 2;110(21):3566-3581. doi: 10.1016/j.neuron.2022.10.024.\u003c/li\u003e\n\u003cli\u003eZhu YC, Dufouil C, Soumar\u0026eacute; A, Mazoyer B, Chabriat H, Tzourio C. High degree of dilated Virchow-Robin spaces on MRI is associated with increased risk of dementia. J Alzheimers Dis. 2010;22(2):663-72. doi: 10.3233/JAD-2010-100378.\u003c/li\u003e\n\u003cli\u003eFrancis F, Ballerini L, Wardlaw JM. Perivascular spaces and their associations with risk factors, clinical disorders and neuroimaging features: A systematic review and meta-analysis. Int J Stroke. 2019 Jun;14(4):359-371. doi: 10.1177/1747493019830321.\u003c/li\u003e\n\u003cli\u003eHuang P, Zhu Z, Zhang R, Wu X, Jiaerken Y, Wang S, Yu W, Hong H, Lian C, Li K, Zeng Q, Luo X, Xu X, Yu X, Yang Y, Zhang M. Factors Associated With the Dilation of Perivascular Space in Healthy Elderly Subjects. Front Aging Neurosci. 2021 Mar 26;13:624732. doi: 10.3389/fnagi.2021.624732.\u003c/li\u003e\n\u003cli\u003ePark CH, Shin NY, Nam Y, Yoon U, Ahn K, Lee SK. Characteristics of perivascular space dilatation in normal aging. Hum Brain Mapp. 2023 Jun 1;44(8):3232-3240. doi: 10.1002/hbm.26277.\u003c/li\u003e\n\u003cli\u003eAchiron A, Faibel M. Sandlike appearance of Virchow-Robin spaces in early multiple sclerosis: a novel neuroradiologic marker. AJNR Am J Neuroradiol. 2002 Mar;23(3):376-80.\u003c/li\u003e\n\u003cli\u003eGranberg T, Moridi T, Brand JS, Neumann S, Hlavica M, Piehl F, Ineichen BV. Enlarged perivascular spaces in multiple sclerosis on magnetic resonance imaging: a systematic review and meta-analysis. J Neurol. 2020 Nov;267(11):3199-3212. doi: 10.1007/s00415-020-09971-5.\u003c/li\u003e\n\u003cli\u003eJavierre-Petit C, Schneider JA, Kapasi A, Makkinejad N, Tamhane AA, Leurgans SE, Mehta RI, Barnes LL, Bennett DA, Arfanakis K. Neuropathologic and Cognitive Correlates of Enlarged Perivascular Spaces in a Community-Based Cohort of Older Adults. Stroke. 2020 Sep;51(9):2825-2833. doi: 10.1161/STROKEAHA.120.029388.\u003c/li\u003e\n\u003cli\u003eRomero JR, Pinheiro A, Aparicio HJ, DeCarli CS, Demissie S, Seshadri S. MRI-Visible Perivascular Spaces and Risk of Incident Dementia: The Framingham Heart Study. Neurology. 2022 Dec 5;99(23):e2561-e2571. doi: 10.1212/WNL.0000000000201293.\u003c/li\u003e\n\u003cli\u003eDoubal FN, MacLullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010 Mar;41(3):450-4. doi: 10.1161/STROKEAHA.109.564914.\u003c/li\u003e\n\u003cli\u003eBrown R, Benveniste H, Black SE, Charpak S, Dichgans M, Joutel A, Nedergaard M, Smith KJ, Zlokovic BV, Wardlaw JM. Understanding the role of the perivascular space in cerebral small vessel disease. Cardiovasc Res. 2018 Sep 1;114(11):1462-1473. doi: 10.1093/cvr/cvy113.\u003c/li\u003e\n\u003cli\u003eWaymont JMJ, Vald\u0026eacute;s Hern\u0026aacute;ndez MDC, Bernal J, Duarte Coello R, Brown R, Chappell FM, Ballerini L, Wardlaw JM. Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain. Neuroimage. 2024 Aug 15;297:120685. doi: 10.1016/j.neuroimage.2024.120685.\u003c/li\u003e\n\u003cli\u003eDuperron MG, Tzourio C, Sargurupremraj M, Mazoyer B, Soumar\u0026eacute; A, Schilling S, Amouyel P, Chauhan G, Zhu YC, Debette S. Burden of Dilated Perivascular Spaces, an Emerging Marker of Cerebral Small Vessel Disease, Is Highly Heritable. Stroke. 2018 Feb;49(2):282-287. doi: 10.1161/STROKEAHA.117.019309.\u003c/li\u003e\n\u003cli\u003eChoi Y, Nam Y, Choi Y, Kim J, Jang J, Ahn KJ, Kim BS, Shin NY. MRI-visible dilated perivascular spaces in healthy young adults: A twin heritability study. Hum Brain Mapp. 2020 Dec 15;41(18):5313-5324. doi: 10.1002/hbm.25194.\u003c/li\u003e\n\u003cli\u003eDuperron MG, Knol MJ, Le Grand Q, Evans TE, Mishra A, Tsuchida A, Roshchupkin G, Konuma T, Tr\u0026eacute;gou\u0026euml;t DA, Romero JR, Frenzel S, Luciano M, Hofer E, Bourgey M, Dueker ND, Delgado P, Hilal S, Tankard RM, Dubost F, Shin J, Saba Y, Armstrong NJ, Bordes C, Bastin ME, Beiser A, Brodaty H, B\u0026uuml;low R, Carrera C, Chen C, Cheng CY, Deary IJ, Gampawar PG, Himali JJ, Jiang J, Kawaguchi T, Li S, Macalli M, Marquis P, Morris Z, Mu\u0026ntilde;oz Maniega S, Miyamoto S, Okawa M, Paradise M, Parva P, Rundek T, Sargurupremraj M, Schilling S, Setoh K, Soukarieh O, Tabara Y, Teumer A, Thalamuthu A, Trollor JN, Vald\u0026eacute;s Hern\u0026aacute;ndez MC, Vernooij MW, V\u0026ouml;lker U, Wittfeld K, Wong TY, Wright MJ, Zhang J, Zhao W, Zhu YC, Schmidt H, Sachdev PS, Wen W, Yoshida K, Joutel A, Satizabal CL, Sacco RL, Bourque G; CHARGE consortium; Lathrop M, Paus T, Fernandez-Cadenas I, Yang Q, Mazoyer B, Boutinaud P, Okada Y, Grabe HJ, Mather KA, Schmidt R, Joliot M, Ikram MA, Matsuda F, Tzourio C, Wardlaw JM, Seshadri S, Adams HHH, Debette S. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease. Nat Med. 2023 Apr;29(4):950-962. doi: 10.1038/s41591-023-02268-w.\u003c/li\u003e\n\u003cli\u003eRan L, Fang Y, Cheng C, He Y, Shao Z, Kong Y, Huang H, Xu S, Luo X, Wang W, Hao X, Wang M. Genome-wide and phenome-wide studies provided insights into brain glymphatic system function and its clinical associations. Sci Adv. 2025 Jan 17;11(3):eadr4606. doi: 10.1126/sciadv.adr4606.\u003c/li\u003e\n\u003cli\u003eRanti DL, Warburton AJ, Rutland JW, Dullea JT, Markowitz M, Smith DA, Kligler SZK, Rutter S, Langan M, Arrighi-Allisan A, George I, Verma G, Murrough JW, Delman BN, Balchandani P, Morris LS. Perivascular spaces as a marker of psychological trauma in depression: A 7-Tesla MRI study. Brain Behav. 2022 Jul;12(7):e32598. doi: 10.1002/brb3.2598.\u003c/li\u003e\n\u003cli\u003eSibilia F, Sheikh-Bahaei N, Mack WJ, Barisano G, Choupan J. Neuroinflammation modifies the relationship between stress and perivascular spaces in an elderly population with different levels of cognitive impairment. Front Cell Neurosci. 2024 Nov 13;18:1480405. doi: 10.3389/fncel.2024.1480405.\u003c/li\u003e\n\u003cli\u003eLiang Y, Chan YL, Deng M, Chen YK, Mok V, Wang F, Ungvari GS, Chu CW, Tang WK. Enlarged perivascular spaces in the centrum semiovale are associated with poststroke depression: A 3-month prospective study. J Affect Disord. 2018 Mar 1;228:166-172. doi: 10.1016/j.jad.2017.11.080.\u003c/li\u003e\n\u003cli\u003eRush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003 Sep 1;54(5):573-83. doi: 10.1016/s0006-3223(02)01866-8.\u003c/li\u003e\n\u003cli\u003eLee SY. Handgrip Strength: An Irreplaceable Indicator of Muscle Function. Ann Rehabil Med. 2021 Jun;45(3):167-169. doi: 10.5535/arm.21106.\u003c/li\u003e\n\u003cli\u003eRussell E, Koren G, Rieder M, Van Uum S. Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions. Psychoneuroendocrinology. 2012 May;37(5):589-601. doi: 10.1016/j.psyneuen.2011.09.009.\u003c/li\u003e\n\u003cli\u003eGreen C, Stolicyn A, Harris MA, Shen X, Romaniuk L, Barbu MC, Hawkins EL, Wardlaw JM, Steele JD, Waiter GD, Sandu AL, Campbell A, Porteous DJ, Seckl JR, Lawrie SM, Reynolds RM, Cavanagh J, McIntosh AM, Whalley HC. Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland. Transl Psychiatry. 2021 Oct 12;11(1):523. doi: 10.1038/s41398-021-01644-9.\u003c/li\u003e\n\u003cli\u003eLawman HG, Troiano RP, Perna FM, Wang CY, Fryar CD, Ogden CL. Associations of Relative Handgrip Strength and Cardiovascular Disease Biomarkers in U.S. Adults, 2011-2012. Am J Prev Med. 2016 Jun;50(6):677-683. doi: 10.1016/j.amepre.2015.10.022.\u003c/li\u003e\n\u003cli\u003eShaughnessy KA, Hackney KJ, Clark BC, Kraemer WJ, Terbizan DJ, Bailey RR, McGrath R. A Narrative Review of Handgrip Strength and Cognitive Functioning: Bringing a New Characteristic to Muscle Memory. J Alzheimers Dis. 2020;73(4):1265-1278. doi: 10.3233/JAD-190856.\u003c/li\u003e\n\u003cli\u003eChai L, Zhang D, Fan J. Comparison of grip strength measurements for predicting all-cause mortality among adults aged 20+ years from the NHANES 2011-2014. Sci Rep. 2024 Nov 25;14(1):29245. doi: 10.1038/s41598-024-80487-y.\u003c/li\u003e\n\u003cli\u003eHabota T, Sandu AL, Waiter GD, McNeil CJ, Steele JD, Macfarlane JA, Whalley HC, Valentine R, Younie D, Crouch N, Hawkins EL, Hirose Y, Romaniuk L, Milburn K, Buchan G, Coupar T, Stirling M, Jagpal B, MacLennan B, Priba L, Harris MA, Hafferty JD, Adams MJ, Campbell AI, MacIntyre DJ, Pattie A, Murphy L, Reynolds RM, Elliot R, Penton-Voak IS, Munaf\u0026ograve; MR, Evans KL, Seckl JR, Wardlaw JM, Lawrie SM, Haley CS, Porteous DJ, Deary IJ, Murray AD, McIntosh AM. Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study: A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments. Wellcome Open Res. 2021 Jul 16;4:185. doi: 10.12688/wellcomeopenres.15538.2.\u003c/li\u003e\n\u003cli\u003eJenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002 Oct;17(2):825-41. doi: 10.1016/s1053-8119(02)91132-8.\u003c/li\u003e\n\u003cli\u003eM. Jenkinson, M. Pechaud, and S. Smith. BET2: MR-based estimation of brain, skull and scalp surfaces. In Eleventh Annual Meeting of the Organization for Human Brain Mapping, 2005.\u003c/li\u003e\n\u003cli\u003eBallerini L, Lovreglio R, Vald\u0026eacute;s Hern\u0026aacute;ndez MDC, Ramirez J, MacIntosh BJ, Black SE, Wardlaw JM. Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering. Sci Rep. 2018 Feb 1;8(1):2132. doi: 10.1038/s41598-018-19781-5.\u003c/li\u003e\n\u003cli\u003eR. Duarte Coello, M. Vald\u0026eacute;s Hern\u0026aacute;ndez, J. Bernal Moyano, J. Wardlaw. Cubic B\u0026eacute;zier curve approximation for the estimation of perivascular spaces measurements in MRI brain scans. In G. Waiter, T. Lambrou, G. Leontidis, N. Oren, T. Morris, S. Gordon, J. McGowan, \u0026amp; C. Nicolson (Eds.) Proceedings of the 27th Conference on Medical Image Understanding and Analysis 2023, Frontiers Media, SA (2023), pp. 168-173, 10.3389/978-2-8325-1231-9.\u003c/li\u003e\n\u003cli\u003eSmith BH, Campbell H, Blackwood D, Connell J, Connor M, Deary IJ, Dominiczak AF, Fitzpatrick B, Ford I, Jackson C, Haddow G, Kerr S, Lindsay R, McGilchrist M, Morton R, Murray G, Palmer CN, Pell JP, Ralston SH, St Clair D, Sullivan F, Watt G, Wolf R, Wright A, Porteous D, Morris AD. Generation Scotland: the Scottish Family Health Study; a new resource for researching genes and heritability. BMC Med Genet. 2006 Oct 2;7:74. doi: 10.1186/1471-2350-7-74.\u003c/li\u003e\n\u003cli\u003eSelvarajah J, Scott M, Stivaros S, Hulme S, Georgiou R, Rothwell N, Tyrrell P, Jackson A. Potential surrogate markers of cerebral microvascular angiopathy in asymptomatic subjects at risk of stroke. EurRadiol. 2009 Apr;19(4):1011-8. doi: 10.1007/s00330-008-1202-8.\u003c/li\u003e\n\u003cli\u003eCharidimou A, Meegahage R, Fox Z, Peeters A, Vandermeeren Y, Laloux P, Baron JC, J\u0026auml;ger HR, Werring DJ. Enlarged perivascular spaces as a marker of underlying arteriopathy in intracerebral haemorrhage: a multicentre MRI cohort study. J NeurolNeurosurg Psychiatry. 2013 Jun;84(6):624-9. doi: 10.1136/jnnp-2012-304434.\u003c/li\u003e\n\u003cli\u003eHajjar I, Zhao P, Alsop D, Novak V. Hypertension and cerebral vasoreactivity: a continuous arterial spin labeling magnetic resonance imaging study. Hypertension. 2010 Nov;56(5):859-64. doi: 10.1161/HYPERTENSIONAHA.110.160002.\u003c/li\u003e\n\u003cli\u003eSantisteban MM, Iadecola C, Carnevale D. Hypertension, Neurovascular Dysfunction, and Cognitive Impairment. Hypertension. 2023 Jan;80(1):22-34. doi: 10.1161/HYPERTENSIONAHA.122.18085.\u003c/li\u003e\n\u003cli\u003eLibecap TJ, Bauer CE, Zachariou V, Pappas CA, Raslau FD, Liu P, Lu H, Gold BT. Association of Baseline Cerebrovascular Reactivity and Longitudinal Development of Enlarged Perivascular Spaces in the Basal Ganglia. Stroke. 2023 Nov;54(11):2785-2793. doi: 10.1161/STROKEAHA.123.043882.\u003c/li\u003e\n\u003cli\u003eYu Q, Wang H, Zhang W, Zhang X, Zhao J, Gong L, Liu X. MRI-visible enlarged perivascular spaces in basal ganglia rather than centrum semiovale was associated with aneurysmal subarachnoid hemorrhage. Front Neurol. 2024 Jan 15;15:1341499. doi: 10.3389/fneur.2024.1341499. \u003c/li\u003e\n\u003cli\u003eZhang K, Zhou Y, Zhang W, Li Q, Sun J, Lou M. MRI-visible perivascular spaces in basal ganglia but not centrum semiovale or hippocampus were related to deep medullary veins changes. J Cereb Blood Flow Metab. 2022 Jan;42(1):136-144. doi: 10.1177/0271678X211038138. \u003c/li\u003e\n\u003cli\u003eShi H, Cui L, Hui Y, Wu S, Li X, Shu R, Song H, Wang J, Yu P, Chen S, Li J, Yang L, Wang Z, Yang Q, Gao Y. Enlarged Perivascular Spaces in Relation to Cumulative Blood Pressure Exposure and Cognitive Impairment. Hypertension. 2023 Oct;80(10):2088-2098. doi: 10.1161/HYPERTENSIONAHA.123.21453.\u003c/li\u003e\n\u003cli\u003eHarrewijn A, Vidal-Ribas P, Clore-Gronenborn K, Jackson SM, Pisano S, Pine DS, Stringaris A. Associations between brain activity and endogenous and exogenous cortisol - A systematic review. Psychoneuroendocrinology. 2020 Oct;120:104775. doi: 10.1016/j.psyneuen.2020.104775.\u003c/li\u003e\n\u003cli\u003eCook SC, Wellman CL. Chronic stress alters dendritic morphology in rat medial prefrontal cortex. J Neurobiol. 2004 Aug;60(2):236-48. doi: 10.1002/neu.20025.\u003c/li\u003e\n\u003cli\u003eHan KM, Ham BJ. How Inflammation Affects the Brain in Depression: A Review of Functional and Structural MRI Studies. J Clin Neurol. 2021 Oct;17(4):503-515. doi: 10.3988/jcn.2021.17.4.503.\u003c/li\u003e\n\u003cli\u003eSu L, Cai Y, Xu Y, Dutt A, Shi S, Bramon E. Cerebral metabolism in major depressive disorder: a voxel-based meta-analysis of positron emission tomography studies. BMC Psychiatry. 2014 Nov 19;14:321. doi: 10.1186/s12888-014-0321-9.\u003c/li\u003e\n\u003cli\u003eWilliams SZ, Chung GS, Muennig PA. Undiagnosed depression: A community diagnosis. SSM Popul Health. 2017 Jul 28;3:633-638. doi: 10.1016/j.ssmph.2017.07.012.Barnes JN, Pearson AG, Corkery AT, Eisenmann NA, Miller KB. Exercise, Arterial Stiffness, and Cerebral Vascular Function: Potential Impact on Brain Health. J IntNeuropsychol Soc. 2021 Sep;27(8):761-775. doi: 10.1017/S1355617721000394.\u003c/li\u003e\n\u003cli\u003eDel Brutto OH, Mera RM, Del Brutto VJ, Castillo PR. Enlarged basal ganglia perivascular spaces and sleep parameters. A population-based study. Clin Neurol Neurosurg. 2019 Jul;182:53-57. doi: 10.1016/j.clineuro.2019.05.002.\u003c/li\u003e\n\u003cli\u003eVaishya R, Misra A, Vaish A, Ursino N, D\u0026apos;Ambrosi R. Hand grip strength as a proposed new vital sign of health: a narrative review of evidences. J Health Popul Nutr. 2024 Jan 9;43(1):7. doi: 10.1186/s41043-024-00500-y.\u003c/li\u003e\n\u003cli\u003eJones EA, le Noble F, Eichmann A. What determines blood vessel structure? Genetic prespecification vs. hemodynamics. Physiology (Bethesda). 2006 Dec;21:388-95. doi: 10.1152/physiol.00020.2006. \u003c/li\u003e\n\u003cli\u003eMenze I, Bernal J, Kaya P, Aki \u0026Ccedil;, Pfister M, Geisend\u0026ouml;rfer J, Yakupov R, Coello RD, Vald\u0026eacute;s-Hern\u0026aacute;ndez MDC, Heneka MT, Brosseron F, Schmid MC, Glanz W, Incesoy EI, Butryn M, Rostamzadeh A, Meiberth D, Peters O, Preis L, Lammerding D, Gref D, Priller J, Spruth EJ, Altenstein S, Lohse A, Hetzer S, Schneider A, Fliessbach K, Kimmich O, Vogt IR, Wiltfang J, Bartels C, Schott BH, Hansen N, Dechent P, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Kilimann I, Goerss D, Laske C, Munk MH, Sanzenbacher C, Hinderer P, Scheffler K, Spottke A, Roy-Kluth N, L\u0026uuml;sebrink F, Neumann K, Wardlaw J, Jessen F, Schreiber S, D\u0026uuml;zel E, Ziegler G. Perivascular space enlargement accelerates in ageing and Alzheimer\u0026apos;s disease pathology: evidence from a three-year longitudinal multicentre study. Alzheimers Res Ther. 2024 Oct 31;16(1):242. doi: 10.1186/s13195-024-01603-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"perivascular spaces (PVS), depression, stress, hair cortisol, hand grip test, QIDS (Quick Inventory of Depressive Symptomatology)","lastPublishedDoi":"10.21203/rs.3.rs-6589799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6589799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStress, depressive symptoms, cardiovascular health, and physical strength are well-recognised factors that contribute to microcirculatory changes in the brain. However, there is limited understanding of their impact on perivascular waste clearance. In this study, we used the Stratifying Depression and Resilience Longitudinally (STRADL) family-based cohort, subgroup of the Generation Scotland Scottish Family Health Study, to assess the association between these factors, familial influences, and perivascular space (PVS) characteristics. Using an automated segmentation method on brain magnetic resonance images, we generated PVS volumes, count, density (i.e., count per unit volume), and median length in separate brain regions. Our findings indicate that PVS burden increases with age, hypertension and current depressive symptoms in basal ganglia and centrum semiovale regions, and with higher hair cortisol levels and weaker hand grip only in the centrum semiovale. Regional differences in PVS characteristics observed between the centrum semiovale and basal ganglia could provide insights into the underlying pathophysiology of PVS. Additionally, a familial component was identified, in keeping with a genetic influence on PVS morphology. These results highlight the multifactorial nature of perivascular spaces and emphasize the need for longitudinal studies to observe individual long-term perivascular changes and their impact on brain health.\u003c/p\u003e","manuscriptTitle":"Stress, Depressive Symptoms, and Physical Strength Impact Brain Perivascular Spaces: A Cohort Study with Insights from First-Degree Relatives","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 12:35:42","doi":"10.21203/rs.3.rs-6589799/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cafc4d6e-d127-40e6-bf66-4d2a04196a2a","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49375800,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":49375801,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"}],"tags":[],"updatedAt":"2025-06-04T12:35:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 12:35:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6589799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6589799","identity":"rs-6589799","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}