Statin Therapy for Secondary Prevention in Ischemic Stroke Patients With Cerebral Microbleeds.

BACKGROUND AND OBJECTIVES: The association between statin use and the risk of intracranial hemorrhage (ICrH) following ischemic stroke (IS) or transient ischemic attack (TIA) in patients with cerebral microbleeds (CMBs) remains uncertain. This study investigated the risk of recurrent IS and ICrH in patients receiving statins based on the presence of CMBs. METHODS: We conducted a pooled analysis of individual patient data from the Microbleeds International Collaborative Network, comprising 32 hospital-based prospective studies fulfilling the following criteria: adult patients with IS or TIA, availability of appropriate baseline MRI for CMB quantification and distribution, registration of statin use after the index stroke, and collection of stroke event data during a follow-up period of ≥3 months. The primary endpoint was the occurrence of recurrent symptomatic stroke (IS or ICrH), while secondary endpoints included IS alone or ICrH alone. We calculated incidence rates and performed Cox regression analyses adjusting for age, sex, hypertension, atrial fibrillation, previous stroke, and use of antiplatelet or anticoagulant drugs to explore the association between statin use and stroke events during follow-up in patients with CMBs. RESULTS: In total, 16,373 patients were included (mean age 70.5 ± 12.8 years; 42.5% female). Among them, 10,812 received statins at discharge, and 4,668 had 1 or more CMBs. The median follow-up duration was 1.34 years (interquartile range: 0.32-2.44). In patients with CMBs, statin users were compared with nonusers. Compared with nonusers, statin therapy was associated with a reduced risk of any stroke (incidence rate [IR] 53 vs 79 per 1,000 patient-years, adjusted hazard ratio [aHR] 0.68 [95% CI 0.56-0.84]), a reduced risk of IS (IR 39 vs 65 per 1,000 patient-years, aHR 0.65 [95% CI 0.51-0.82]), and no association with the risk of ICrH (IR 11 vs 16 per 1,000 patient-years, aHR 0.73 [95% CI 0.46-1.15]). The results in aHR remained consistent when considering anatomical distribution and high burden (≥5) of CMBs. DISCUSSION: These observational data suggest that secondary stroke prevention with statins in patients with IS or TIA and CMBs is associated with a lower risk of any stroke or IS without an increased risk of ICrH. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that for patients with IS or TIA and CMBs, statins lower the risk of any stroke or IS without increasing the risk of ICrH.

Clinical Associations and Prognostic Value of MRI-Visible Perivascular Spaces in Patients With Ischemic Stroke or TIA: A Pooled Analysis

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.

Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models.

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.

Sex Differences in Poststroke Cognitive Impairment: A Multicenter Study in 2343 Patients With Acute Ischemic Stroke.

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.

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke.

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/.

Impact of Small Vessel Disease Progression on Long-term Cognitive and Functional Changes After Stroke.

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 (ß = -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 (ß = 0.105, 95% CI -0.265 to 0.054, p = 0.195). Three-year mRS score was associated with 3-year ACE-R score (ß = -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.

Tracer kinetic assessment of blood-brain barrier leakage and blood volume in cerebral small vessel disease: Associations with disease burden and vascular risk factors.

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 (vP) 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 vP in WMH (B = -0.088, CI: [-0.138,-0.039], p < 0.001), but higher vP in normal-appearing white matter (B = 0.031, CI: [-0.004,0.065], p = 0.082). PS and vP 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.

Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts.

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.

The Meta VCI Map consortium for meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping: Design and multicenter pilot study.

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.

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.

Long-Term Morphological Changes of Symptomatic Lacunar Infarcts and Surrounding White Matter on Structural Magnetic Resonance Imaging.

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.

Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI.

Background and Purpose: The T1-weighted dynamic contrast enhanced (DCE)-MRI is an imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters characterizing microvasculature of tissues. For the present study, we propose a new machine learning (ML) based approach to directly estimate the PK parameters from the acquired DCE-MRI image-time series that is both more robust and faster than conventional model fitting. Materials and Methods: We specifically utilize deep convolutional neural networks (CNNs) to learn the mapping between the image-time series and corresponding PK parameters. DCE-MRI datasets acquired from 15 patients with clinically evident mild ischaemic stroke were used in the experiments. Training and testing were carried out based on leave-one-patient-out cross- validation. The parameter estimates obtained by the proposed CNN model were compared against the two tracer kinetic models: (1) Patlak model, (2) Extended Tofts model, where the estimation of model parameters is done via voxelwise linear and nonlinear least squares fitting respectively. Results: The trained CNN model is able to yield PK parameters which can better discriminate different brain tissues, including stroke regions. The results also demonstrate that the model generalizes well to new cases even if a subject specific arterial input function (AIF) is not available for the new data. Conclusion: A ML-based model can be used for direct inference of the PK parameters from DCE image series. This method may allow fast and robust parameter inference in population DCE studies. Parameter inference on a 3D volume-time series takes only a few seconds on a GPU machine, which is significantly faster compared to conventional non-linear least squares fitting.

Small Vessel Disease and Dietary Salt Intake: Cross-Sectional Study and Systematic Review.

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.

White matter hyperintensity reduction and outcomes after minor stroke.

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 (ß = 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 (ß = 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, ß = 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.

Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: results of a prospective cohort study.

BACKGROUND: White matter hyperintensities (WMHs) are commonly seen on in brain imaging and are associated with stroke and cognitive decline. Therefore, they may provide a relevant intermediate outcome in clinical trials. WMH can be measured as a volume or visually on the Fazekas scale. We investigated predictors of WMH progression and design of efficient studies using WMH volume and Fazekas score as an intermediate outcome. METHODS: We prospectively recruited 264 patients with mild ischaemic stroke and measured WMH volume, Fazekas score, age and cardiovascular risk factors at baseline and 1 year. We modelled predictors of WMH burden at 1 year and used the results in sample size calculations for hypothetical randomised controlled trials with different analysis plans and lengths of follow-up. RESULTS: Follow-up WMH volume was predicted by baseline WMH: a 0.73-ml (95% CI 0.65-0.80, p < 0.0001) increase per 1-ml baseline volume increment, and a 2.93-ml increase (95% CI 1.76-4.10, p < 0.0001) per point on the Fazekas scale. Using a mean difference of 1 ml in WMH volume between treatment groups, 80% power and 5% alpha, adjusting for all predictors and 2-year follow-up produced the smallest sample size (n = 642). Other study designs produced samples sizes from 2054 to 21,270. Sample size calculations using Fazekas score as an outcome with the same power and alpha, as well as an OR corresponding to a 1-ml difference, were sensitive to assumptions and ranged from 2504 to 18,886. CONCLUSIONS: Baseline WMH volume and Fazekas score predicted follow-up WMH volume. Study size was smallest using volumes and longer-term follow-up, but this must be balanced against resources required to measure volumes versus Fazekas scores, bias due to dropout and scanner drift. Samples sizes based on Fazekas scores may be best estimated with simulation studies.

Integrity of normal-appearing white matter: Influence of age, visible lesion burden and hypertension in patients with small-vessel disease.

White matter hyperintensities accumulate with age and occur in patients with stroke, but their pathogenesis is poorly understood. We measured multiple magnetic resonance imaging biomarkers of tissue integrity in normal-appearing white matter and white matter hyperintensities in patients with mild stroke, to improve understanding of white matter hyperintensities origins. We classified white matter into white matter hyperintensities and normal-appearing white matter and measured fractional anisotropy, mean diffusivity, water content (T1-relaxation time) and blood-brain barrier leakage (signal enhancement slope from dynamic contrast-enhanced magnetic resonance imaging). We studied the effects of age, white matter hyperintensities burden (Fazekas score) and vascular risk factors on each biomarker, in normal-appearing white matter and white matter hyperintensities, and performed receiver-operator characteristic curve analysis. Amongst 204 patients (34.3-90.9 years), all biomarkers differed between normal-appearing white matter and white matter hyperintensities ( P < 0.001). In normal-appearing white matter and white matter hyperintensities, mean diffusivity and T1 increased with age ( P < 0.001), all biomarkers varied with white matter hyperintensities burden ( P < 0.001; P = 0.02 signal enhancement slope), but only signal enhancement slope increased with hypertension ( P = 0.028). Fractional anisotropy showed complex age-white matter hyperintensities-tissue interactions; enhancement slope showed white matter hyperintensities-tissue interactions. Mean diffusivity distinguished white matter hyperintensities from normal-appearing white matter best at all ages. Blood-brain barrier leakage increases with hypertension and white matter hyperintensities burden at all ages in normal-appearing white matter and white matter hyperintensities, whereas water mobility and content increase as tissue damage accrues, suggesting that blood-brain barrier leakage mediates small vessel disease-related brain damage.

Cerebral blood flow in small vessel disease: A systematic review and meta-analysis.

White matter hyperintensities are frequent on neuroimaging of older people and are a key feature of cerebral small vessel disease. They are commonly attributed to chronic hypoperfusion, although whether low cerebral blood flow is cause or effect is unclear. We systematically reviewed studies that assessed cerebral blood flow in small vessel disease patients, performed meta-analysis and sensitivity analysis of potential confounders. Thirty-eight studies (n = 4006) met the inclusion criteria, including four longitudinal and 34 cross-sectional studies. Most cerebral blood flow data were from grey matter. Twenty-four cross-sectional studies (n = 1161) were meta-analysed, showing that cerebral blood flow was lower in subjects with more white matter hyperintensity, globally and in most grey and white matter regions (e.g. mean global cerebral blood flow: standardised mean difference-0.71, 95% CI -1.12, -0.30). These cerebral blood flow differences were attenuated by excluding studies in dementia or that lacked age-matching. Four longitudinal studies (n = 1079) gave differing results, e.g., more baseline white matter hyperintensity predated falling cerebral blood flow (3.9 years, n = 575); cerebral blood flow was low in regions that developed white matter hyperintensity (1.5 years, n = 40). Cerebral blood flow is lower in subjects with more white matter hyperintensity cross-sectionally, but evidence for falling cerebral blood flow predating increasing white matter hyperintensity is conflicting. Future studies should be longitudinal, obtain more white matter data, use better age-correction and stratify by clinical diagnosis.

Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability.

There is evidence that subtle breakdown of the blood-brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) combined with tracer kinetic modelling is widely used for assessing permeability and perfusion in brain tumours and body tissues where contrast agents readily accumulate in the extracellular space. However, in diseases where leakage is subtle, the optimal approach for measuring BBB integrity is likely to differ since the magnitude and rate of enhancement caused by leakage are extremely low; several methods have been reported in the literature, yielding a wide range of parameters even in healthy subjects. We hypothesised that the Patlak model is a suitable approach for measuring low-level BBB permeability with low temporal resolution and high spatial resolution and brain coverage, and that normal levels of scanner instability would influence permeability measurements. DCE-MRI was performed in a cohort of mild stroke patients (n=201) with a range of cerebral small vessel disease severity. We fitted these data to a set of nested tracer kinetic models, ranking their performance according to the Akaike information criterion. To assess the influence of scanner drift, we scanned 15 healthy volunteers that underwent a "sham" DCE-MRI procedure without administration of contrast agent. Numerical simulations were performed to investigate model validity and the effect of scanner drift. The Patlak model was found to be most appropriate for fitting low-permeability data, and the simulations showed vp and K(Trans) estimates to be reasonably robust to the model assumptions. However, signal drift (measured at approximately 0.1% per minute and comparable to literature reports in other settings) led to systematic errors in calculated tracer kinetic parameters, particularly at low permeabilities. Our findings justify the growing use of the Patlak model in low-permeability states, which has the potential to provide valuable information regarding BBB integrity in a range of diseases. However, absolute values of the resulting tracer kinetic parameters should be interpreted with extreme caution, and the size and influence of signal drift should be measured where possible.

Blood pressure and sodium: Association with MRI markers in cerebral small vessel disease.

Dietary salt intake and hypertension are associated with increased risk of cardiovascular disease including stroke. We aimed to explore the influence of these factors, together with plasma sodium concentration, in cerebral small vessel disease (SVD). In all, 264 patients with nondisabling cortical or lacunar stroke were recruited. Patients were questioned about their salt intake and plasma sodium concentration was measured; brain tissue volume and white-matter hyperintensity (WMH) load were measured using structural magnetic resonance imaging (MRI) while diffusion tensor MRI and dynamic contrast-enhanced MRI were acquired to assess underlying tissue integrity. An index of added salt intake (P = 0.021), pulse pressure (P = 0.036), and diagnosis of hypertension (P = 0.0093) were positively associated with increased WMH, while plasma sodium concentration was associated with brain volume (P = 0.019) but not with WMH volume. These results are consistent with previous findings that raised blood pressure is associated with WMH burden and raise the possibility of an independent role for dietary salt in the development of cerebral SVD.