Paranasal sinus occupancy assessed from magnetic resonance images-associations with clinical indicators in patients with systemic lupus erythematosus.

OBJECTIVES: Nasal, paranasal sinus and mucosal disorders are common symptoms in autoimmune rheumatic diseases. Soft tissue changes and fluid accumulation in the osteomeatal complexes and paranasal sinuses manifest as opaqueness on radiological images which can be assessed using visual scoring and computational methods on CT scans, but their results do not always correlate. Using MRI, we investigate the applicability of different image analysis methods in SLE. METHODS: We assessed paranasal sinus opaqueness on MRI from 51 SLE patients, using three visual scoring systems and expert-delineated computational volumes, and examined their association with markers of disease activity, inflammation, endothelial dysfunction and common small vessel disease (SVD) indicators, adjusting for age and sex-at-birth. RESULTS: The average paranasal sinus volume occupation was 4.55 (6.47%) [median (interquartile range) = 0.67 (0.25-2.65) ml], mainly in the maxillary and ethmoid sinuses. It was highly correlated with Lund-Mackay (LM) scores modified at 50% opaqueness cut-off (Spearman's ρ: 0.71 maxillary and 0.618 ethmoids, P < 0.001 in all), and with more granular variations of the LM system. The modified LM scores were associated with SVD scores (0: B = 5.078, s.e. = 1.69, P = 0.0026; 2: B = -0.066, s.e. = 0.023, P = 0.0045) and disease activity (anti-dsDNA: B = 4.59, s.e. = 2.22, P = 0.045; SLEDAI 3-7: 2.86 < B < 4.30; 1.38 < s.e. < 1.63; 0.0083 ≤ P ≤ 0.0375). Computationally derived percent opaqueness yielded similar results. CONCLUSION: In patients with SLE, MRI computational assessment of sinuses opaqueness and LM scores modified at a 50% cut-off may be useful tools in understanding the relationships among paranasal sinus occupancy, disease activity and SVD markers.

Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces.

BACKGROUND: Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified. NEW METHOD: We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T. RESULTS: The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter. COMPARISON WITH EXISTING METHODS: Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given. CONCLUSIONS: Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.

Characterizing the Neurobiological Mechanisms of Action of Exercise and Cognitive-Behavioral Interventions for Rheumatoid Arthritis Fatigue: A Magnetic Resonance Imaging Brain Study.

OBJECTIVE: Chronic fatigue is a major clinical unmet need among patients with rheumatoid arthritis (RA). Current therapies are limited to nonpharmacological interventions, such as personalized exercise programs (PEPs) and cognitive-behavioral approaches (CBAs); however, most patients still continue to report severe fatigue. To inform more effective therapies, we conducted a magnetic resonance imaging (MRI) brain study of PEPs and CBAs, nested within a randomized controlled trial (RCT), to identify their neurobiological mechanisms of fatigue reduction in RA. METHODS: A subgroup of patients with RA (n = 90), participating in an RCT of PEPs and CBAs for fatigue, undertook a multimodal MRI brain scan following randomization to either usual care (UC) alone or in addition to PEPs and CBAs and again after the intervention (six months). Brain regional volumetric, functional, and structural connectivity indices were curated and then computed employing a causal analysis framework. The primary outcome was fatigue improvement (Chalder fatigue scale). RESULTS: Several structural and functional connections were identified as mediators of fatigue improvement in both PEPs and CBAs compared to UC. PEPs had a more pronounced effect on functional connectivity than CBAs; however, structural connectivity between the left isthmus cingulate cortex (L-ICC) and left paracentral lobule (L-PCL) was shared, and the size of mediation effect ranked highly for both PEPs and CBAs (ßAverage = -0.46, SD 0.61; ßAverage = -0.32, SD 0.47, respectively). CONCLUSION: The structural connection between the L-ICC and L-PCL appears to be a dominant mechanism for how both PEPs and CBAs reduce fatigue among patients with RA. This supports its potential as a substrate of fatigue neurobiology and a putative candidate for future targeting.

Sleep quality, perivascular spaces and brain health markers in ageing - A longitudinal study in the Lothian Birth Cohort 1936.

BACKGROUND: Sleep is thought to play a major role in brain health and general wellbeing. However, few longitudinal studies have explored the relationship between sleep habits and imaging markers of brain health, particularly markers of brain waste clearance such as perivascular spaces (PVS), of neurodegeneration such as brain atrophy, and of vascular disease, such as white matter hyperintensities (WMH). We explore these associations using data collected over 6 years from a birth cohort of older community-dwelling adults in their 70s. METHOD: We analysed brain MRI data from ages 73, 76 and 79 years, and self-reported sleep duration, sleep quality and vascular risk factors from community-dwelling participants in the Lothian Birth Cohort 1936 (LBC1936) study. We calculated sleep efficiency (at age 76), quantified PVS burden (at age 73), and WMH and brain volumes (age 73 to 79), calculated the white matter damage metric, and used structural equation modelling (SEM) to explore associations and potential causative pathways between indicators related to brain waste cleaning (i.e., sleep and PVS burden), brain and WMH volume changes during the 8th decade of life. RESULTS: Lower sleep efficiency was associated with a reduction in normal-appearing white matter (NAWM) volume (ß = 0.204, P = 0.009) from ages 73 to 79, but not concurrent volume (i.e. age 76). Increased daytime sleep correlated with less night-time sleep (r = -0.20, P < 0.001), and with increasing white matter damage metric (ß = -0.122, P = 0.018) and faster WMH growth (ß = 0.116, P = 0.026). Shorter night-time sleep duration was associated with steeper 6-year reduction of NAWM volumes (ß = 0.160, P = 0.011). High burden of PVS at age 73 (volume, count, and visual scores), was associated with faster deterioration in white matter: reduction of NAWM volume (ß = -0.16, P = 0.012) and increasing white matter damage metric (ß = 0.37, P < 0.001) between ages 73 and 79. On SEM, centrum semiovale PVS burden mediated 5% of the associations between sleep parameters and brain changes. CONCLUSION: Sleep impairments, and higher PVS burden, a marker of impaired waste clearance, were associated with faster loss of healthy white matter and increasing WMH in the 8th decade of life. A small percentage of the effect of sleep in white matter health was mediated by the burden of PVS consistent with the proposed role for sleep in brain waste clearance.

Retinal capillary microvessel morphology changes are associated with vascular damage and dysfunction in cerebral small vessel disease.

Cerebral small vessel disease (SVD) is a cause of stroke and dementia. Retinal capillary microvessels revealed by optical coherence tomography angiography (OCTA) are developmentally related to brain microvessels. We quantified retinal vessel density (VD) and branching complexity, investigating relationships with SVD lesions, white matter integrity on diffusion tensor imaging (DTI) and cerebrovascular reactivity (CVR) to CO2 in patients with minor stroke. We enrolled 123 patients (mean age 68.1 ± SD 9.9 years), 115 contributed retinal data. Right (R) and left (L) eyes are reported. After adjusting for age, eye disease, diabetes, blood pressure and image quality, lower VD remained associated with higher mean diffusivity (MD) (standardized ß; R -0.16 [95%CI -0.32 to -0.01]) and lower CVR (L 0.17 [0.03 to 0.31] and R 0.19 [0.02 to 0.36]) in normal appearing white matter (NAWM). Sparser branching remained associated with sub-visible white matter damage shown by higher MD (R -0.24 [-0.08 to -0.40]), lower fractional anisotropy (FA) (L 0.17 [0.01 to 0.33]), and lower CVR (R 0.20 [0.02 to 0.38]) in NAWM. OCTA-derived metrics provide evidence of microvessel abnormalities that may underpin SVD lesions in the brain.

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols.

Vast quantities of Magnetic Resonance Images (MRI) are routinely acquired in clinical practice but, to speed up acquisition, these scans are typically of a quality that is sufficient for clinical diagnosis but sub-optimal for large-scale precision medicine, computational diagnostics, and large-scale neuroimaging collaborative research. Here, we present a critic-guided framework to upsample low-resolution (often 2D) MRI full scans to help overcome these limitations. We incorporate feature-importance and self-attention methods into our model to improve the interpretability of this study. We evaluate our framework on paired low- and high-resolution brain MRI structural full scans (i.e., T1-, T2-weighted, and FLAIR sequences are simultaneously input) obtained in clinical and research settings from scanners manufactured by Siemens, Phillips, and GE. We show that the upsampled MRIs are qualitatively faithful to the ground-truth high-quality scans (PSNR = 35.39; MAE = 3.78E-3; NMSE = 4.32E-10; SSIM = 0.9852; mean normal-appearing gray/white matter ratio intensity differences ranging from 0.0363 to 0.0784 for FLAIR, from 0.0010 to 0.0138 for T1-weighted and from 0.0156 to 0.074 for T2-weighted sequences). The automatic raw segmentation of tissues and lesions using the super-resolved images has fewer false positives and higher accuracy than those obtained from interpolated images in protocols represented with more than three sets in the training sample, making our approach a strong candidate for practical application in clinical and collaborative research.

Measuring axial length of the eye from magnetic resonance brain imaging.

BACKGROUND: Metrics derived from the human eye are increasingly used as biomarkers and endpoints in studies of cardiovascular, cerebrovascular and neurological disease. In this context, it is important to account for potential confounding that can arise from differences in ocular dimensions between individuals, for example, differences in globe size. METHODS: We measured axial length, a geometric parameter describing eye size from T2-weighted brain MRI scans using three different image analysis software packages (Mango, ITK and Carestream) and compared results to biometry measurements from a specialized ophthalmic instrument (IOLMaster 500) as the reference standard. RESULTS: Ninety-three healthy research participants of mean age 51.0 ± SD 5.4 years were analyzed. The level of agreement between the MRI-derived measurements and the reference standard was described by mean differences as follows, Mango - 0.8 mm; ITK - 0.5 mm; and Carestream - 0.1 mm (upper/lower 95% limits of agreement across the three tools ranged from 0.9 mm to - 2.6 mm). Inter-rater reproducibility was between - 0.03 mm and 0.45 mm (ICC 0.65 to 0.93). Intra-rater repeatability was between 0.0 mm and - 0.2 mm (ICC 0.90 to 0.95). CONCLUSIONS: We demonstrate that axial measurements of the eye derived from brain MRI are within 3.5% of the reference standard globe length of 24.1 mm. However, the limits of agreement could be considered clinically significant. Axial length of the eye obtained from MRI is not a replacement for the precision of biometry, but in the absence of biometry it could provide sufficient accuracy to act as a proxy. We recommend measuring eye axial length from MRI in studies that do not have biometry but use retinal imaging to study neurodegenerative changes so as to control for differing eye size across individuals.

Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland.

Introduction: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. MS prevalence varies geographically and is notably high in Scotland. Disease trajectory varies significantly between individuals and the causes for this are largely unclear. Biomarkers predictive of disease course are urgently needed to allow improved stratification for current disease modifying therapies and future targeted treatments aimed at neuroprotection and remyelination. Magnetic resonance imaging (MRI) can detect disease activity and underlying damage non-invasively in vivo at the micro and macrostructural level. FutureMS is a prospective Scottish longitudinal multi-centre cohort study, which focuses on deeply phenotyping patients with recently diagnosed relapsing-remitting MS (RRMS). Neuroimaging is a central component of the study and provides two main primary endpoints for disease activity and neurodegeneration. This paper provides an overview of MRI data acquisition, management and processing in FutureMS. FutureMS is registered with the Integrated Research Application System (IRAS, UK) under reference number 169955. Methods and analysis: MRI is performed at baseline (N=431) and 1-year follow-up, in Dundee, Glasgow and Edinburgh (3T Siemens) and in Aberdeen (3T Philips), and managed and processed in Edinburgh. The core structural MRI protocol comprises T1-weighted, T2-weighted, FLAIR and proton density images. Primary imaging outcome measures are new/enlarging white matter lesions (WML) and reduction in brain volume over one year. Secondary imaging outcome measures comprise WML volume as an additional quantitative structural MRI measure, rim lesions on susceptibility-weighted imaging, and microstructural MRI measures, including diffusion tensor imaging and neurite orientation dispersion and density imaging metrics, relaxometry, magnetisation transfer (MT) ratio, MT saturation and derived g-ratio measures. Conclusions: FutureMS aims to reduce uncertainty around disease course and allow for targeted treatment in RRMS by exploring the role of conventional and advanced MRI measures as biomarkers of disease severity and progression in a large population of RRMS patients in Scotland.

Sources of systematic error in DCE-MRI estimation of low-level blood-brain barrier leakage.

PURPOSE: Dynamic contrast-enhanced (DCE) -MRI with Patlak model analysis is increasingly used to quantify low-level blood-brain barrier (BBB) leakage in studies of pathophysiology. We aimed to investigate systematic errors due to physiological, experimental, and modeling factors influencing quantification of the permeability-surface area product PS and blood plasma volume vp , and to propose modifications to reduce the errors so that subtle differences in BBB permeability can be accurately measured. METHODS: Simulations were performed to predict the effects of potential sources of systematic error on conventional PS and vp quantification: restricted BBB water exchange, reduced cerebral blood flow, arterial input function (AIF) delay and B1+ error. The impact of targeted modifications to the acquisition and processing were evaluated, including: assumption of fast versus no BBB water exchange, bolus versus slow injection of contrast agent, exclusion of early data from model fitting and B1+ correction. The optimal protocol was applied in a cohort of recent mild ischaemic stroke patients. RESULTS: Simulation results demonstrated substantial systematic errors due to the factors investigated (absolute PS error ≤ 4.48 × 10-4 min-1 ). However, these were reduced (≤0.56 × 10-4 min-1 ) by applying modifications to the acquisition and processing pipeline. Processing modifications also had substantial effects on in-vivo normal-appearing white matter PS estimation (absolute change ≤ 0.45 × 10-4 min-1 ). CONCLUSION: Measuring subtle BBB leakage with DCE-MRI presents unique challenges and is affected by several confounds that should be considered when acquiring or interpreting such data. The evaluated modifications should improve accuracy in studies of neurodegenerative diseases involving subtle BBB breakdown.

Brain network reorganisation and spatial lesion distribution in systemic lupus erythematosus.

OBJECTIVE: This work investigates network organisation of brain structural connectivity in systemic lupus erythematosus (SLE) relative to healthy controls and its putative association with lesion distribution and disease indicators. METHODS: White matter hyperintensity (WMH) segmentation and connectomics were performed in 47 patients with SLE and 47 healthy age-matched controls from structural and diffusion MRI data. Network nodes were divided into hierarchical tiers based on numbers of connections. Results were compared between patients and controls to assess for differences in brain network organisation. Voxel-based analyses of the spatial distribution of WMH in relation to network measures and SLE disease indicators were conducted. RESULTS: Despite inter-individual differences in brain network organization observed across the study sample, the connectome networks of SLE patients had larger proportion of connections in the peripheral nodes. SLE patients had statistically larger numbers of links in their networks with generally larger fractional anisotropy weights (i.e. a measure of white matter integrity) and less tendency to aggregate than those of healthy controls. The voxels exhibiting connectomic differences were coincident with WMH clusters, particularly the left hemisphere's intersection between the anterior limb of the internal and external capsules. Moreover, these voxels also associated more strongly with disease indicators. CONCLUSION: Our results indicate network differences reflective of compensatory reorganization of the neural circuits, reflecting adaptive or extended neuroplasticity in SLE.

Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing.

BACKGROUND: Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. METHODS: We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. RESULTS: The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. CONCLUSIONS: Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage.

The brain health index: Towards a combined measure of neurovascular and neurodegenerative structural brain injury.

Background A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia. Aim We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index. Materials and methods We computed brain health index in N = 288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N = 157; lupus, N = 51) and against measures of brain atrophy in healthy participants (N = 80) using multiple regression. We evaluated associations with Addenbrooke's Cognitive Exam Revised in patients and with reaction time in healthy participants. Results The brain health index (standard beta = 0.20-0.59, P < 0.05) was significantly and more strongly associated with Addenbrooke's Cognitive Exam Revised, including at one year follow-up, than white matter hyperintensity volume (standard beta = 0.04-0.08, P > 0.05) and small vessel disease score (standard beta = 0.02-0.27, P > 0.05) alone in both patient groups. Further, the brain health index (standard beta = 0.57-0.59, P < 0.05) was more strongly associated with reaction time than measures of brain atrophy alone (standard beta = 0.04-0.13, P > 0.05) in healthy participants. Conclusions The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease.

Cognitive abilities, brain white matter hyperintensity volume, and structural network connectivity in older age.

OBJECTIVE: To assess brain structural connectivity in relation to cognitive abilities in healthy ageing, and the mediating effects of white matter hyper-intensity (WMH) volume. METHODS: MRI data were analysed in 558 members of the Lothian Birth Cohort 1936. Brains were segmented into 85 regions and combined with tractography to generate structural connectomes. WMH volume was quantified. Relationships between whole-brain connectivity, assessed using graph theory metrics, and four major domains of cognitive ability (visuospatial reasoning, verbal memory, information processing speed and crystallized ability) were investigated, as was the mediating effects of WMH volume on these relationships. RESULTS: Visuospatial reasoning was associated with network strength, mean shortest path length, and global efficiency. Memory was not associated with any network connectivity metric. Information processing speed and crystallized ability were associated with all network measures. Some relationships were lost when adjusted for mean network FA. WMH volume mediated 11%-15% of the relationships between most network measures and information processing speed, even after adjusting for mean network FA. CONCLUSION: Brain structural connectivity relates to visuospatial reasoning, information processing speed and crystallized ability, but not memory, in this relatively healthy age-homogeneous cohort of 73 year olds. When adjusted for mean FA across the network, most relationships are lost, except with information processing speed suggesting that the underlying topological network structure is related to this cognitive domain. Moreover, the connectome-processing speed relationship is partly mediated by WMH volume in this cohort. Hum Brain Mapp 39:622-632, 2018. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Cerebral Small Vessel Disease Burden Is Increased in Systemic Lupus Erythematosus.

BACKGROUND AND PURPOSE: Systemic lupus erythematosus (SLE) increases stroke risk, but the mechanism is uncertain. This study aimed to determine the association between SLE and features on neuroimaging of cerebral small vessel disease (SVD), a risk factor for stroke. METHODS: Consecutive patients attending a clinic for SLE were recruited. All patients underwent brain magnetic resonance imaging; had blood samples taken for markers of inflammation, endothelial dysfunction, cholesterol, and autoantibodies; and underwent cognitive and psychiatric testing. The data were compared with sex- and age-matched healthy controls and patients with minor stroke. Features of SVD were measured, a total SVD score calculated, and associations sought with vascular risk factors, cognition, SLE activity, and disease duration. RESULTS: Fifty-one SLE patients (age: 48.8 years; SD: 14.3 years) had a greater total SVD score compared with healthy controls (1 versus 0; P<0.0001) and stroke patients (1 versus 0; P=0.02). There were higher perivascular spaces and deep white matter hyperintensity scores and more superficial brain atrophy in SLE patients versus healthy controls. Despite fewer vascular risk factors than similarly aged stroke patients, SLE patients had similar or more of some SVD features. The total SVD score was not associated with SLE activity, cognition, disease duration, or any blood measure. CONCLUSIONS: In this data set, SLE patients had a high burden of SVD features on magnetic resonance imaging, particularly perivascular spaces. A larger longitudinal study is warranted to determine the causes of SVD features in SLE and clinical implications.

Cerebrovascular Disease in Rheumatic Diseases: A Systematic Review and Meta-Analysis.

BACKGROUND AND PURPOSE: Some rheumatic diseases are associated with stroke. Less is known about associations with stroke subtypes or stroke risk by age. We quantified the association between stroke, its subtypes, and rheumatic diseases and identified when stroke risk is greatest. METHODS: Searches of EMBASE (from 1980) and MEDLINE (from inception) to end 2014 and manual search of reference lists for studies of stroke and stroke subtypes in rheumatic diseases as well as studies measuring cerebrovascular disease from magnetic resonance imaging. RESULTS: Prior published meta-analyses and new pooled analyses of any stroke in rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, gout, and psoriasis show an excess risk of stroke over the general population with odds ratio (OR) ranging from 1.51 (95% confidence interval: 1.39-1.62) to 2.13 (1.53-2.98). New meta-analyses of stroke subtypes in rheumatoid arthritis [ischemic: OR, 1.64 (1.32-2.05); hemorrhagic: OR, 1.68 (1.11-2.53)] and systemic lupus erythematosus [ischemic: OR, 2.11 (1.66-2.67); hemorrhagic: OR, 1.82 (1.07-3.09)] show an excess risk of stroke over the general population. Stroke risk across rheumatic diseases is highest in those aged <50 years [OR, 1.79 (1.46-2.20)] and reduces relatively with ageing [>65 years: OR, 1.14 (0.94-1.38); difference P<0.007]. Inflammatory arthropathies conveyed higher stroke risk than noninflammatory diseases (OR, 1.3, 1.2-1.3). It was not possible to adjust ORs for risk factors or treatments. CONCLUSIONS: Risk of any stroke is higher in most rheumatic diseases than in the general population, particularly <50 years. Rheumatoid arthritis and systemic lupus erythematosus increase ischemic and hemorrhagic stroke risk by 60% to 100% relative to the general population.

Plasma Biomarkers of Inflammation, Endothelial Function and Hemostasis in Cerebral Small Vessel Disease.

BACKGROUND: The cause of lacunar ischemic stroke, a clinical feature of cerebral small vessel disease (SVD), is largely unknown. Inflammation and endothelial dysfunction have been implicated. Plasma biomarkers could provide mechanistic insights but current data are conflicting. White matter hyperintensities (WMHs) are an important imaging biomarker of SVD. It is unknown if plasma biomarkers add predictive capacity beyond age and vascular risk factors in explaining WMH. METHODS: We prospectively recruited patients presenting with non-disabling ischemic stroke, classifying them clinically and with the help of MRI as lacunar or cortical. We measured biomarkers of inflammation, endothelial dysfunction and hemostasis for >1 month after stroke and compared biomarker levels between stroke subtypes. We quantitatively calculated WMH. We used multiple linear regression analysis to model WMH as a function of age, sex, hypertension and smoking (the baseline model). We fitted exploratory models using plasma biomarkers as predictor variables to assess model improvement over baseline. RESULTS: We recruited 125 patients. The lacunar group (n = 65) had lower tissue plasminogen activator (t-PA) levels in unadjusted (7.39 vs. 8.59 ng/ml, p = 0.029) and adjusted (p = 0.035) analyses compared with the cortical group (n = 60). There were no significant differences in the other plasma biomarkers. The results for t-PA were consistent with an updated meta-analysis, although the effect remains non-significant (standardized mean difference -0.08 (95% CI -0.25 to 0.09)). The baseline regression model explained 29% of the variance in quantitative WMH (R2 0.289). Inflammatory biomarkers showed minor improvement over baseline (R2 0.291), but the other plasma biomarkers did not improve the baseline model. CONCLUSION: Plasma t-PA levels appear to differ between lacunar and cortical stroke subtypes, late after stroke, independent of age, sex and vascular risk factors and may reflect endothelial dysfunction. Except for a minor additional predictive effect of inflammatory markers, plasma biomarkers do not relate to WMH severity in this small stroke population.