Assessment of Mendelian and risk-factor genes in Alzheimer disease: A prospective nationwide clinical utility study and recommendations for genetic screening.

PURPOSE: To assess the likely pathogenic/pathogenic (LP/P) variants rates in Mendelian dementia genes and the moderate-to-strong risk factors rates in patients with Alzheimer disease (AD). METHODS: We included 700 patients in a prospective study and performed exome sequencing. A panel of 28 Mendelian and 6 risk-factor genes was interpreted and returned to patients. We built a framework for risk variant interpretation and risk gradation and assessed the detection rates among early-onset AD (EOAD, age of onset (AOO) ≤65 years, n = 608) depending on AOO and pedigree structure and late-onset AD (66 < AOO < 75, n = 92). RESULTS: Twenty-one patients carried a LP/P variant in a Mendelian gene (all with EOAD, 3.4%), 20 of 21 affected APP, PSEN1, or PSEN2. LP/P variant detection rates in EOAD ranged from 1.7% to 11.6% based on AOO and pedigree structure. Risk factors were found in 69.5% of the remaining 679 patients, including 83 (12.2%) being heterozygotes for rare risk variants, in decreasing order of frequency, in TREM2, ABCA7, ATP8B4, SORL1, and ABCA1, including 5 heterozygotes for multiple rare risk variants, suggesting non-monogenic inheritance, even in some autosomal-dominant-like pedigrees. CONCLUSION: We suggest that genetic screening should be proposed to all EOAD patients and should no longer be prioritized based on pedigree structure.

Determinants of disability at 6 months after stroke: The GRECogVASC Study.

BACKGROUND AND PURPOSE: The aim of this study was to determine the contributions of background disorders responsible for participation restriction as indexed by a structured interview for the modified Rankin Scale (mRS-SI). METHODS: A subset of 256 patients was assessed at 6 months after stroke using the National Institutes of Health Stroke Scale (NIHSS), gait score, comprehensive cognitive battery (yielding a global cognitive Z-score), behavioral dysexecutive disorders (DDs), anxiety and depressive symptoms, epilepsy, and headache. Following bivariate analyses, determinants of participation restriction were selected using ordinal regression analysis with partial odds. RESULTS: Poststroke participation restriction (mRS-SI score > 1) was observed in 59% of the patients. In bivariate analyses, mRS-SI score was associated with prestroke mRS-SI score, 6-month NIHSS score, gait score, global cognitive Z-score, behavioral DDs, and presence of anxiety and depression (all: p = 0.0001; epilepsy: p =0.3; headache: p = 0.7). After logistic regression analysis, NIHSS score was associated with increasing mRS-SI score (p = 0.00001). Prestroke mRS-SI score (p = 0.00001), behavioral DDs (p = 0.0008) and global cognitive Z-score (p = 0.01) were associated with both mRS-SI score > 1 and mRS-SI score > 2. In addition, gait score was associated with mRS-SI score > 2 (p = 0.00001). This model classified 85% of mRS-SI scores correctly (p = 0.001). Structural equation modeling showed the contributions of gait limitation (standardized coefficient [SC]: 0.68; p = 0.01), prestroke mRS-SI (SC: 0.41; p = 0.01), severity of neurological impairment (SC: 0.16; p = 0.01), global cognitive Z-score (SC: -0.14; p = 0.05), and behavioral DDs (SC: 0.13; p = 0.01). CONCLUSION: These results provide a statistical model of weights of determinants responsible for poststroke participation restriction and highlight a new independent determinant: behavioral DDs.

In Individuals With Subjective Cognitive Decline, Age, Memory and Speed Scores at Baseline Predict Progression to Cognitive Impairment.

Some patients with subjective cognitive decline (SCD) progress to neurocognitive disorders (NCD), whereas others remain stable; however, the neuropsychological determinants of this progression have not been identified. Our objective was to examine baseline neuropsychological indicators that could discriminate between stable SCD Versus progression toward an NCD. We retrospectively included patients consulting for SCD at a university medical center's memory center (Amiens, France) who had undergone 3 or more neuropsychological assessments. Among the 80 patients with SCD, 11 had progressed to an NCD. The combination of age, memory, and speed scores at the baseline assessment predicted the progression of SCD with a sensitivity of 91%, and a negative predictive value of 98%. The present results constitute a first step (pending prospective studies) toward helping physicians to identify cases of SCD at risk of progression and, in particular, identifying patients with SCD who will not progress by examining baseline neuropsychological indicators. ClinicalTrials.gov ID: NCT04880252.

Neuroimaging Determinants of Poststroke Cognitive Performance.

Background and Purpose- We aimed to define the neuroimaging determinants of poststroke cognitive performance and their relative contributions among a spectrum of magnetic resonance imaging markers, including lesion burden and strategic locations. Methods- We prospectively included patients with stroke from the GRECogVASC study (Groupe de Réflexion pour l'Évaluation Cognitive Vasculaire) who underwent 3-T magnetic resonance imaging and a comprehensive standardized battery of neuropsychological tests 6 months after the index event. An optimized global cognitive score and neuroimaging markers, including stroke characteristics, cerebral atrophy markers, and small vessel diseases markers, were assessed. Location of strategic strokes was determined using a specifically designed method taking into account stroke size and cerebral atrophy. A stepwise multivariable linear regression model was used to identify magnetic resonance imaging determinants of cognitive performance. Results- Data were available for 356 patients (mean age: 63.67±10.6 years; 326 [91.6%] of the patients had experienced an ischemic stroke). Six months poststroke, 50.8% of patients presented with a neurocognitive disorder. Strategic strokes (right corticospinal tract, left antero-middle thalamus, left arcuate fasciculus, left middle frontal gyrus, and left postero-inferior cerebellum; R2=0.225; P=0.0001), medial temporal lobe atrophy ( R2=0.077; P=0.0001), total brain tissue volume ( R2=0.028; P=0.004), and stroke volume ( R2=0.013; P=0.005) were independent determinants of cognitive performance. Strategic strokes accounted for the largest proportion of the variance in the cognitive score (22.5%). The white matter hyperintensity burden, brain microbleeds, and dilated perivascular spaces were not independent determinants. Conclusions- Optimized global cognitive score and combined approach of both quantitative measures related to structure loss and qualitative measures related to the presence of strategic lesion are required to improve the determination of structure-function relationship of cognitive performance after stroke.

Prevalence of Poststroke Neurocognitive Disorders Using National Institute of Neurological Disorders and Stroke-Canadian Stroke Network, VASCOG Criteria (Vascular Behavioral and Cognitive Disorders), and Optimized Criteria of Cognitive Deficit.

BACKGROUND AND PURPOSE: The prevalence of poststroke neurocognitive disorder (NCD) has yet to be accurately determined. The primary objective of the present study was to optimize operationalization of the criterion for NCD by using an external validity criterion. METHODS: The GRECOG-VASC cohort (Groupe de Réflexion pour l'Évaluation Cognitive Vasculaire) of 404 stroke patients with cerebral infarct (91.3%) or hemorrhage (18.7%) was assessed 6 months poststroke and 1003 healthy controls, with the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network standardized battery. Three dimensions of the criterion for cognitive impairment were systematically examined by using the false-positive rate as an external validity criterion. Diagnosis of mild and major NCD was based on the VASCOG criteria (Vascular Behavioral and Cognitive Disorders). The mechanisms of functional decline were systematically assessed. RESULTS: The optimal criterion for cognitive impairment was the shortened summary score (ie, averaged performance for action speed, executive functions, and language) because it was associated with the highest (P=0.0001) corrected true-positive rate (43.5%) and a false-positive rate ≤5%. Using this criterion, the mean (95% confidence interval) prevalence of poststroke NCD was 49.5% (44.6-54.4), most of which corresponded to mild NCD (39.1%; 95% confidence interval, 34.4-43.9) rather than dementia (10.4%; 95% confidence interval, 7.4-13.4). CONCLUSIONS: This study is the first to have optimized the operationalization of the criterion for poststroke cognitive impairment. It documented the prevalence of poststroke NCD in the GRECOG-VASC cohort and showed that mild cognitive impairment accounts for 80% of the affected patients. Finally, the method developed in the present study offers a means of harmonizing the diagnosis of NCD. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01339195.

Systematic Review and Meta-Analysis of Prevalence in Post-Stroke Neurocognitive Disorders in Hospital-Based Studies.

BACKGROUND/AIMS: Post-stroke neurocognitive disorders (post-stroke NCD) have been reported with a very variable prevalence. METHODS: Based on a systematic literature search, hospital-based studies published between January 1990 and September 2015 were selected when they reported the prevalence of total, mild, and major post-stroke NCD diagnosed by using specified criteria. Factors affecting prevalence were assessed using meta-regression analysis. RESULTS: Among the 7,440 references evaluated, 16 hospital-based studies were selected, corresponding to a total of 3,087 patients. The overall prevalence of total post-stroke NCD was 53.4% (95% CI: 46.9-59.8): 36.4% for mild post-stroke NCD (95% CI: 29-43.8) and 16.5% (95% CI: 12.1-20.8) for major post-stroke NCD. The overall prevalence was mainly influenced by the threshold score used for categorization (p = 0.0001) and, in the subgroup of studies using a conservative threshold (i.e., ≤7th percentile), by the recurrent stroke rate (p = 0.0005). The prevalence of major post-stroke NCD was mainly influenced by age (p = 0.003). CONCLUSION: More than half of stroke survivors experience post-stroke NCD, corresponding to mild post-stroke NCD in two-thirds of cases and major post-stroke NCD in one-third of cases. Harmonization of stroke assessment and cognitive score thresholds is urgently needed to allow more accurate estimation of post-stroke NCD prevalence, especially mild post-stroke NCD.

Functional architecture of executive processes: Evidence from verbal fluency and lesion mapping in stroke patients.

The functional organization and related anatomy of executive functions are still largely unknown and were examined in the present study using a verbal fluency task. The objective of this study was to determine the cognitive architecture of a fluency task and related voxelwise anatomy in the GRECogVASC cohort and fMRI based meta-analytical data. First, we proposed a model of verbal fluency in which two control processes, lexico-semantic strategic search process and attention process, interact with semantic and lexico-phonological output processes. This model was assessed by testing 404 patients and 775 controls for semantic and letter fluency, naming, and processing speed (Trail Making test part A). Regression (R2 = .276 and .3, P = .0001, both) and structural equation modeling (CFI: .88, RMSEA: .2, SRMR: .1) analyses supported this model. Second, voxelwise lesion-symptom mapping and disconnectome analyses demonstrated fluency to be associated with left lesions of the pars opercularis, lenticular nucleus, insula, temporopolar region, and a large number of tracts. In addition, a single dissociation showed specific association of letter fluency with the pars triangularis of F3. Disconnectome mapping showed the additional role of disconnection of left frontal gyri and thalamus. By contrast, these analyses did not identify voxels specifically associated with lexico-phonological search processes. Third, meta-analytic fMRI data (based on 72 studies) strikingly matched all structures identified by the lesion approach. These results support our modeling of the functional architecture of verbal fluency based on two control processes (strategic search and attention) operating on semantic and lexico-phonologic output processes. Multivariate analysis supports the prominent role of the temporopolar area (BA 38) in semantic fluency and the F3 triangularis area (BA 45) in letter fluency. Finally, the lack of voxels specifically dedicated to strategic search processes could be due to a distributed organization of executive functions warranting further studies.

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/.

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.

Profile of and risk factors for poststroke cognitive impairment in diverse ethnoregional groups.

OBJECTIVE: To address the variability in prevalence estimates and inconsistencies in potential risk factors for poststroke cognitive impairment (PSCI) using a standardized approach and individual participant data (IPD) from international cohorts in the Stroke and Cognition Consortium (STROKOG) consortium. METHODS: We harmonized data from 13 studies based in 8 countries. Neuropsychological test scores 2 to 6 months after stroke or TIA and appropriate normative data were used to calculate standardized cognitive domain scores. Domain-specific impairment was based on percentile cutoffs from normative groups, and associations between domain scores and risk factors were examined with 1-stage IPD meta-analysis. RESULTS: In a combined sample of 3,146 participants admitted to hospital for stroke (97%) or TIA (3%), 44% were impaired in global cognition and 30% to 35% were impaired in individual domains 2 to 6 months after the index event. Diabetes mellitus and a history of stroke were strongly associated with poorer cognitive function after covariate adjustments; hypertension, smoking, and atrial fibrillation had weaker domain-specific associations. While there were no significant differences in domain impairment among ethnoracial groups, some interethnic differences were found in the effects of risk factors on cognition. CONCLUSIONS: This study confirms the high prevalence of PSCI in diverse populations, highlights common risk factors, in particular diabetes mellitus, and points to ethnoracial differences that warrant attention in the development of prevention strategies.

Differentiating between Alzheimer's Disease and Vascular Cognitive Impairment: Is the "Memory Versus Executive Function" Contrast Still Relevant?

BACKGROUND: The contrast between memory versus executive function impairments is commonly used to differentiate between neurocognitive disorders (NCDs) due to Alzheimer's disease (AD) and vascular cognitive impairment (VCI). We reconsidered this question because of the current use of AD biomarkers and the recent revision of the criteria for AD, VCI, and dysexecutive syndrome. OBJECTIVE: To establish and compare the neuropsychological profiles in AD (i.e., with positive CSF biomarkers) and in VCI. METHODS: We included 62 patients with mild or major NCDs due to pure AD (with positive CSF biomarker assays), and 174 patients (from the GRECogVASC cohort) with pure VCI. The neuropsychological profiles were compared after stratification for disease severity (mild or major NCD). We defined a memory-executive function index (the mean z score for the third free recall and the delayed free recall in the Free and Cued Selective Reminding Test minus the mean z score for category fluency and the completion time in the Trail Making Test part B) and determined its diagnostic accuracy. RESULTS: Compared with VCI patients, patients with AD had significantly greater memory impairments (p = 0.001). Executive function was impaired to a similar extent in the two groups (p = 0.11). Behavioral executive disorders were more prominent in the AD group (p = 0.001). Although the two groups differed significant with regard to the memory-executive function index (p < 0.001), the latter's diagnostic accuracy was only moderate (sensitivity: 63%, specificity: 87%). CONCLUSION: Although the contrast between memory and executive function impairments was supported at the group level it does not reliably discriminate between AD and VCI at the individual level.

Is VLSM a valid tool for determining the functional anatomy of the brain? Usefulness of additional Bayesian network analysis.

OBJECTIVES: The ability of voxel-based lesion-symptom mapping (VLSM) to define the functional anatomy of the human brain has not been fully assessed. With a view to assessing VLSM's validity, the present study analyzed the technique's ability to determine the known clinical-anatomic correlates of hemiparesis in stroke patients. DESIGN: Lesions (damaged in at least 5 patients) associated with transformed limb motor score (after adjustment on lesion volume) at 6 months were examined in 272 patients using VLSM. The value of additional multivariable linear, logistic and Bayesian analyses was examined. RESULTS: We first checked that motor hemiparesis was fully accounted for by corticospinal tract (CST) lesions (sensitivity = 100%; p = 0.0001). Conventional VLSM analysis flagged up 2 regions corresponding to the CST, but also 8 regions located outside the CST. All 10 brain regions achieving statistical significance in the VLSM analysis were submitted to 3 additional analyses. The backward linear regression analysis selected 5 regions, one only corresponding to the CST (R2: 0.03, p = 0.0008). The logistic regression analysis selected correctly the CST (OR: 2.39, 95%CI: 1.44-3.96; 0.001). The Bayesian network analysis selected regions including the CST (in 92% of 3000 bootstrap replications) and identified the source of multicollinearity. These lesions evaluated by structural equation modeling resulted in an excellent fit (p-value = 0.228, chi/df = 1.19, RMSEA = 0.032, CFI = 0.999). Analyses of confusion factors showed that conventional VLSM analyses were strongly influenced by lesion frequency (R2 = 0.377; p = 0.0001) and multicollinearity. CONCLUSIONS: Conventional VLSM analyses are sensitive but weakened by a type I error due to the combined effects of multicollinearity and lesion frequency. We demonstrate that the addition of a Bayesian network analysis, and to a lesser extent of logistic regression, controlled for this type I error and constituted a reliable means of defining the functional anatomy of the motor system in stroke patients.

Who should undergo a comprehensive cognitive assessment after a stroke? A cognitive risk score.

OBJECTIVE: To validate the ability of a specifically developed cognitive risk score to identify patients at risk of poststroke neurocognitive disorders (NCDs) who are eligible for a comprehensive cognitive assessment. METHODS: After assessing 404 patients (infarct 91.3%) in the Groupe de Réflexion pour l'Evaluation Cognitive VASCulaire (GRECogVASC) cross-sectional study with the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network battery 6 months after stroke, we used multivariable logistic regression and bootstrap analyses to determine factors associated with NCDs. Independent, internally validated factors were included in a cognitive risk score. RESULTS: Cognitive impairment was present in 170 of the 320 patients with a Rankin Scale score ≥1. The backward logistic regression selected 4 factors (≥73% of the permutations): NIH Stroke Scale score on admission ≥7 (odds ratio [OR] 2.73, 95% confidence interval [CI] 1.29-4.3, p = 0.005), multiple strokes (OR 3.78, 95% CI 1.6-8, p = 0.002), adjusted Mini-Mental State Examination (MMSEadj) score ≤27 (OR 6.69, 95% CI 3.9-11.6, p = 0.0001), and Fazekas score ≥2 (OR 2.34, 95% CI 1.3-4.2, p = 0.004). The cognitive risk score computed with these 4 factors provided good calibration, discrimination (overoptimism-corrected C = 0.793), and goodness of fit (Hosmer-Lemeshow test p = 0.99). A combination of Rankin Scale score ≥1, cognitive risk score ≥1, and MMSEadj score ≥21 selected 230 (56.9%) of the 404 patients for a comprehensive assessment. This procedure yielded good sensitivity (96.5%) and moderate specificity (43%; positive predictive value 0.66, negative predictive value 0.91) and was more accurate (p ≤ 0.03 for all) than the sole use of screening tests (MMSE or Montréal Cognitive Assessment). CONCLUSION: The GRECogVASC cognitive risk score comprises 4 easily documented factors; this procedure helps to identify patients at risk of poststroke NCDs who must therefore undergo a comprehensive assessment. CLINICALTRIALSGOV IDENTIFIER: NCT01339195.

STROKOG (stroke and cognition consortium): An international consortium to examine the epidemiology, diagnosis, and treatment of neurocognitive disorders in relation to cerebrovascular disease.

INTRODUCTION: The Stroke and Cognition consortium (STROKOG) aims to facilitate a better understanding of the determinants of vascular contributions to cognitive disorders and help improve the diagnosis and treatment of vascular cognitive disorders (VCD). METHODS: Longitudinal studies with ≥75 participants who had suffered or were at risk of stroke or TIA and which evaluated cognitive function were invited to join STROKOG. The consortium will facilitate projects investigating rates and patterns of cognitive decline, risk factors for VCD, and biomarkers of vascular dementia. RESULTS: Currently, STROKOG includes 25 (21 published) studies, with 12,092 participants from five continents. The duration of follow-up ranges from 3 months to 21 years. DISCUSSION: Although data harmonization will be a key challenge, STROKOG is in a unique position to reuse and combine international cohort data and fully explore patient level characteristics and outcomes. STROKOG could potentially transform our understanding of VCD and have a worldwide impact on promoting better vascular cognitive outcomes.