Search

Background and Objectives: Visible perivascular spaces are an MRI marker of cerebral small vessel disease and might predict future stroke. However, results from existing studies vary. We aimed to clarify this through a large collaborative multicenter analysis., Methods: We pooled individual patient data from a consortium of prospective cohort studies. Participants had recent ischemic stroke or transient ischemic attack (TIA), underwent baseline MRI, and were followed up for ischemic stroke and symptomatic intracranial hemorrhage (ICH). Perivascular spaces in the basal ganglia (BGPVS) and perivascular spaces in the centrum semiovale (CSOPVS) were rated locally using a validated visual scale. We investigated clinical and radiologic associations cross-sectionally using multinomial logistic regression and prospective associations with ischemic stroke and ICH using Cox regression., Results: We included 7,778 participants (mean age 70.6 years; 42.7% female) from 16 studies, followed up for a median of 1.44 years. Eighty ICH and 424 ischemic strokes occurred. BGPVS were associated with increasing age, hypertension, previous ischemic stroke, previous ICH, lacunes, cerebral microbleeds, and white matter hyperintensities. CSOPVS showed consistently weaker associations. Prospectively, after adjusting for potential confounders including cerebral microbleeds, increasing BGPVS burden was independently associated with future ischemic stroke (versus 0-10 BGPVS, 11-20 BGPVS: HR 1.19, 95% CI 0.93-1.53; 21+ BGPVS: HR 1.50, 95% CI 1.10-2.06; p = 0.040). Higher BGPVS burden was associated with increased ICH risk in univariable analysis, but not in adjusted analyses. CSOPVS were not significantly associated with either outcome., Discussion: In patients with ischemic stroke or TIA, increasing BGPVS burden is associated with more severe cerebral small vessel disease and higher ischemic stroke risk. Neither BGPVS nor CSOPVS were independently associated with future ICH.

Background: Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods., Methods: We included three cohorts with varying SVD severity (RUN DMC, n  = 503; SCANS, n  = 121; HARMONISATION, n  = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranked by different models., Results: We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature., Conclusions: When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier B.V.)

Background: Poststroke cognitive impairment (PSCI) occurs in about half of stroke survivors. Cumulative evidence indicates that functional outcomes of stroke are worse in women than men. Yet it is unknown whether the occurrence and characteristics of PSCI differ between men and women., Methods: Individual patient data from 9 cohorts of patients with ischemic stroke were harmonized and pooled through the Meta-VCI-Map consortium (n=2343, 38% women). We included patients with visible symptomatic infarcts on computed tomography/magnetic resonance imaging and cognitive assessment within 15 months after stroke. PSCI was defined as impairment in ≥1 cognitive domains on neuropsychological assessment. Logistic regression analyses were performed to compare men to women, adjusted for study cohort, to obtain odds ratios for PSCI and individual cognitive domains. We also explored sensitivity and specificity of cognitive screening tools for detecting PSCI, according to sex (Mini-Mental State Examination, 4 cohorts, n=1814; Montreal Cognitive Assessment, 3 cohorts, n=278)., Results: PSCI was found in 51% of both women and men. Men had a lower risk of impairment of attention and executive functioning (men: odds ratio, 0.76 [95% CI, 0.61-0.96]), and language (men: odds ratio, 0.67 [95% CI, 0.45-0.85]), but a higher risk of verbal memory impairment (men: odds ratio, 1.43 [95% CI, 1.17-1.75]). The sensitivity of Mini-Mental State Examination (<25) for PSCI was higher for women (0.53) than for men (0.27; P =0.02), with a lower specificity for women (0.80) than men (0.96; P =0.01). Sensitivity and specificity of Montreal Cognitive Assessment (<26.) for PSCI was comparable between women and men (0.91 versus 0.86; P =0.62 and 0.29 versus 0.28; P =0.86, respectively)., Conclusions: Sex was not associated with PSCI occurrence but affected domains differed between men and women. The latter may explain why sensitivity of the Mini-Mental State Examination for detecting PSCI was higher in women with a lower specificity compared with men. These sex differences need to be considered when screening for and diagnosing PSCI in clinical practice.

Background and Objectives: The severity of white matter hyperintensities (WMH) at presentation with stroke is associated with poststroke dementia and dependency. However, WMH can decrease or increase after stroke; prediction of cognitive decline is imprecise; and there are few data assessing longitudinal interrelationships among changing WMH, cognition, and function after stroke, despite the clinical importance., Methods: We recruited patients within 3 months of a minor ischemic stroke, defined as NIH Stroke Scale (NIHSS) score <8 and not expected to result in a modified Rankin Scale (mRS) score >2. Participants repeated MRI at 1 year and cognitive and mRS assessments at 1 and 3 years. We ran longitudinal mixed-effects models assessing change in Addenbrooke's Cognitive Examination-Revised (ACE-R) and mRS scores. For mRS score, we assessed longitudinal WMH volumes (cube root; percentage intracranial volume [ICV]), adjusting for age, NIHSS score, ACE-R, stroke subtype, and time to assessment. For ACE-R score, we additionally adjusted for ICV, mRS, premorbid IQ, and vascular risk factors. We then used a multivariate model to jointly assess changing cognition/mRS score, adjusted for prognostic variables, using all available data., Results: We recruited 264 patients; mean age was 66.9 (SD 11.8) years; 41.7% were female; and median mRS score was 1 (interquartile range 1-2). One year after stroke, normalized WMH volumes were associated more strongly with 1-year ACE-R score (β = -0.259, 95% CI -0.407 to -0.111 more WMH per 1-point ACE-R decrease, p = 0.001) compared to subacute WMH volumes and ACE-R score (β = 0.105, 95% CI -0.265 to 0.054, p = 0.195). Three-year mRS score was associated with 3-year ACE-R score (β = -0.272, 95% CI -0.429 to -0.115, p = 0.001). Combined change in baseline-1-year jointly assessed ACE-R/mRS scores was associated with fluctuating WMH volumes ( F = 9.3, p = 0.03)., Discussion: After stroke, fluctuating WMH mean that 1-year, but not baseline, WMH volumes are associated strongly with contemporaneous cognitive scores. Covarying longitudinal decline in cognition and independence after stroke, central to dementia diagnosis, is associated with increasing WMH volumes., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Background: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction., Aims: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline., Methods: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site., Results: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline., Conclusions: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model., Methods: In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal-external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa., Findings: In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88-0·92), inter-rater agreement (0·85-0·87), and intra-rater agreement (for a single rater, 0·95) were all high., Interpretation: To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI., Funding: The Netherlands Organisation for Health Research and Development., Competing Interests: Declaration of interests H-JB reports grants from Bristol-Myers Squibb Korea, Chong Kun Dang Pharmaceutical, Dong-A Pharmaceutical, AstraZeneca Korea, Bayer, Daiichi Sankyo, Yuhan, Jeil Pharmaceutical, Korean Drug, Shinpoong Pharmaceutical, Servier Korea, JLK Inspection, and Samjin Pharmaceutical, grants and personal fees from Shire Korea and Eisai Korea, and personal fees from Amgen Korea and Otsuka Korea, outside the submitted work. DJW reports personal fees from Bayer, Alnylam Pharmaceuticals, and Portola, outside the submitted work. OG reports grants from Amiens University Hospital and the French Ministry of Health, during the conduct of the study; and during the last 5 years OG has served on scientific advisory boards and as a speaker for Novartis, CSL-Behring, Biogen, Genzyme, Lilly, Bristol-Myers Squibb, Boehringer-Ingelheim, Covidien, Teva Santé, and Astra Zeneca, outside the submitted work. MB has received funding for travel and meetings from Teva Santé, Bristol-Myers Squibb, Roche, Pfizer, Sanofi-Aventis France, Isis Perfusion Nord, and Amgen. GJB reports grants from The Netherlands Organisation for Health Research and Development (ZonMW), during the conduct of the study. JMW reports grants from the Wellcome Trust, Row Fogo Charitable Trust, Chest Heart Stroke Scotland, and the UK Medical Research Council, during the conduct of the study; and grants from Fondation Leducq, EU Horizon 2020 (SVDs@target project, grant agreement number 666881), the British Heart Foundation, and the UK Stroke Association, outside the submitted work. HPA reports grants from ZonMW (grant number 842003011), during the conduct of the study. All other authors declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Subtle blood-brain barrier (BBB) permeability increases have been shown in small vessel disease (SVD) using various analysis methods. Following recent consensus recommendations, we used Patlak tracer kinetic analysis, considered optimal in low permeability states, to quantify permeability-surface area product (PS), a BBB leakage estimate, and blood plasma volume (v P ) in 201 patients with SVD who underwent dynamic contrast-enhanced MRI scans. We ran multivariable regression models with a quantitative or qualitative metric of white matter hyperintensity (WMH) severity, demographic and vascular risk factors. PS increased with WMH severity in grey (B = 0.15, Confidence Interval (CI): [0.001,0.299], p = 0.049) and normal-appearing white matter (B = 0.015, CI: [-0.008,0.308], p = 0.062). Patients with more severe WMH had lower v P in WMH (B = -0.088, CI: [-0.138,-0.039], p < 0.001), but higher v P in normal-appearing white matter (B = 0.031, CI: [-0.004,0.065], p = 0.082). PS and v P were lower at older ages in WMH, grey and white matter. We conclude higher PS in normal-appearing tissue with more severe WMH suggests impaired BBB integrity beyond visible lesions indicating that the microvasculature is compromised in normal-appearing white matter and WMH. BBB dysfunction is an important mechanism in SVD, but associations with clinical variables are complex and underlying damage affecting vascular surface area may alter interpretation of tracer kinetic results., (Copyright © 2021. Published by Elsevier Inc.)

Introduction: The Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies., Methods: Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage., Results: Forty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage., Discussion: Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.

Background and Purpose: Insights into evolution of cerebral small vessel disease on neuroimaging might advance knowledge of the natural disease course. Data on evolution of sporadic symptomatic lacunar infarcts are limited. We investigated long-term changes of symptomatic lacunar infarcts and surrounding white matter on structural magnetic resonance imaging., Methods: From 2 nonoverlapping, single-center, prospective observational stroke studies, we selected patients presenting with lacunar stroke symptoms with a recent small subcortical (lacunar) infarct on baseline structural magnetic resonance imaging and with follow-up magnetic resonance imaging available at 1 to 5 years. We assessed changes in imaging characteristics of symptomatic lacunar infarcts and surrounding white matter., Results: We included 79 patients of whom 32 (41%) had complete and 40 (51%) had partial cavitation of the index lesion at median follow-up of 403 (range, 315-1781) days. In 42 of 79 (53%) patients, we observed a new white matter hyperintensity adjacent to the index infarct, either superior (white matter hyperintensity cap, n=17), inferior (white matter hyperintensity track, n=13), or both (n=12)., Conclusions: Half of the sporadic symptomatic lacunar infarcts developed secondary changes in superior and inferior white matter. These white matter hyperintensity caps and tracks may reflect another aspect of cerebral small vessel-related disease progression. The clinical and prognostic values remain to be determined., (© 2018 The Authors.)

Background: Higher dietary salt intake increases the risk of stroke and may increase white matter hyperintensity (WMH) volume. We hypothesized that a long-term higher salt intake may be associated with other features of small vessel disease (SVD)., Methods: We recruited consecutive patients with mild stroke presenting to the Lothian regional stroke service. We performed brain magnetic resonance imaging, obtained a basic dietary salt history, and measured the urinary sodium/creatinine ratio. We also carried out a systematic review to put the study in the context of other studies in the field., Results: We recruited 250 patients, 112 with lacunar stroke and 138 with cortical stroke, with a median age of 67.5 years. After adjustment for risk factors, including age and hypertension, patients who had not reduced their salt intake in the long term were more likely to have lacunar stroke (odds ratio [OR], 1.90; 95% confidence interval [CI], 1.10-3.29), lacune(s) (OR, 2.06; 95% CI, 1.09-3.99), microbleed(s) (OR, 3.4; 95% CI, 1.54, 8.21), severe WMHs (OR, 2.45; 95% CI 1.34-4.57), and worse SVD scores (OR, 2.17; 95% CI, 1.22-3.9). There was limited association between SVD and current salt intake or urinary sodium/creatinine ratio. Our systematic review found no previously published studies of dietary salt and SVD., Conclusion: The association between dietary salt and background SVD is a promising indication of a potential neglected contributory factor for SVD. These results should be replicated in larger, long-term studies using the recognized gold-standard measures of dietary sodium., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To assess factors associated with white matter hyperintensity (WMH) change in a large cohort after observing obvious WMH shrinkage 1 year after minor stroke in several participants in a longitudinal study., Methods: We recruited participants with minor ischemic stroke and performed clinical assessments and brain MRI. At 1 year, we assessed recurrent cerebrovascular events and dependency and repeated the MRI. We assessed change in WMH volume from baseline to 1 year (normalized to percent intracranial volume [ICV]) and associations with baseline variables, clinical outcomes, and imaging parameters using multivariable analysis of covariance, model of changes, and multinomial logistic regression., Results: Among 190 participants (mean age 65.3 years, range 34.3-96.9 years, 112 [59%] male), WMH decreased in 71 participants by 1 year. At baseline, participants whose WMH decreased had similar WMH volumes but higher blood pressure ( p = 0.0064) compared with participants whose WMH increased. At 1 year, participants with WMH decrease (expressed as percent ICV) had larger reductions in blood pressure (β = 0.0053, 95% confidence interval [CI] 0.00099-0.0097 fewer WMH per 1-mm Hg decrease, p = 0.017) and in mean diffusivity in normal-appearing white matter (β = 0.075, 95% CI 0.0025-0.15 fewer WMH per 1-unit mean diffusivity decrease, p = 0.043) than participants with WMH increase; those with WMH increase experienced more recurrent cerebrovascular events (32%, vs 16% with WMH decrease, β = 0.27, 95% CI 0.047-0.50 more WMH per event, p = 0.018)., Conclusions: Some WMH may regress after minor stroke, with potentially better clinical and brain tissue outcomes. The role of risk factor control requires verification. Interstitial fluid alterations may account for some WMH reversibility, offering potential intervention targets., (Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)