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This study aimed to investigate the influence of stroke lesions in pre-defined highly interconnected (rich club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance.We analyzed MRI data recorded at index stroke and ∼3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke (AIS) enrolled in the multisite MRI-GENIE study. Structural stroke lesions were spatially normalized and parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS>2) was modeled in a Bayesian logistic regression framework that relied on both lesion location, as well as the covariates: age, sex, total DWI lesion volume and comorbidities. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. Via model comparisons, we first tested whether the rich club region model was superior to a baseline model considering clinical covariates and lesion volume only. In spatial specificity analyses, we randomized the split into “rich club” and “non-rich club” regions and compared the effect of the actual rich club regions to the distribution of effects from 1,000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club region effects.A total of 822 patients (age: 64.7 (standard deviation: 15.0), 39% women, 27.7% with mRS>2) were analyzed. The rich club model substantially outperformed the baseline model (weights of model comparison: rich club model: 0.96; baseline: 0.04). Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations (15/1,000 random constellations with higher mean posterior values). Among the these 15 random constellations with higher means, the most frequently selected regions were the inferior temporal gyrus (posterior division, 8/15), the putamen (8/15), the cingulate gyrus (7/15) and the superior parietal lobule (6/15). Rich club regions were substantially more important in explaining long-term outcome in women than in men (mean of the difference distribution:-0.107, 90%-HDPI:-0.193 to -0.0124).Lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific, i.e., the majority of random combinations of six regions had comparably smaller effects on long-term outcome. Effects were substantially more pronounced in women.

Background and ObjectivesTo examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern–specific ways.MethodsMR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3–6 months after stroke were obtained from the MRI–Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning–based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts.ResultsA total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location–specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location.DiscussionHigher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.

Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

International audience; BACKGROUND AND PURPOSE: To determine the influence of the cerebral small vessel disease (SVD) burden on collateral recruitment in patients treated with mechanical thrombectomy (MT) for anterior circulation acute ischemic stroke (AIS). METHODS: Patients with AIS due to large vessel occlusion (LVO) from the Thrombectomie des Artères Cérébrales (THRACE) trial and prospective cohorts from 2 academic comprehensive stroke centers treated with MT were pooled and retrospectively analyzed. Collaterals' adequacy was assessed using the American Society of Interventional and Therapeutic Radiology/Society of Interventional Radiology (ASITN/SIR) score on initial digital subtraction angiography and dichotomized as good (3,4) versus poor (0-2) collaterals. The SVD burden was rated with the global SVD score on MRI. Multivariable logistic regression analyses were used to determine relationships between SVD and ASITN/SIR scores. RESULTS: A total of 312 participants were included (53.2% males, mean age 67.8 ± 14.9 years). Two hundred and seven patients had poor collaterals (66.4%), and 133 (42.6%) presented with any SVD signature. In multivariable analysis, patients demonstrated worse leptomeningeal collaterality with increasing SVD burden before and after adjustment for SVD risk factors (adjusted odds ratio [aOR] 0.69; 95%CI [0.52-0.89] and aOR 0.66; 95%CI [0.5-0.88], respectively). Using individual SVD markers, poor collaterals were significantly associated with the presence of lacunes (aOR 0.40, 95% CI [0.20-0.79]). CONCLUSION: Our study provides evidence that in patients with AIS due to LVO treated with MT, the burden of SVD assessed by pre-treatment MRI is associated with poorer recruitment of leptomeningeal collaterals.

The aim of the current study was to explore the whole‐brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long‐term stroke severity. We investigated resting‐state functional MRI‐based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re‐occurring dynamic connectivity configurations were obtained using a sliding window approach and k‐means clustering. We evaluated differences in dynamic patterns between three NIHSS‐stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90‐day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post‐stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three‐level ANOVA: p, By employing dynamic functional connectivity analyses, Bonkhoff et al. demonstrate that severe acute ischemic stroke is linked to transiently increased isolated information processing in multiple functional domains. Additionally, they show that dynamic connectivity involving default mode network components significantly correlates with recovery in the first three months poststroke.

Objectives Despite the proven efficacy of endovascular thrombectomy (EVT) for large vessel occlusion stroke, over half treated remain functionally disabled or die. Infarct topography may have implications for prognostication, patient selection, and the development of tissue-specific neuroprotective agents. We sought to quantify white matter injury in anterior circulation acute infarcts post-EVT to understand its significance and identify its determinants. Materials and Methods Demographics, history, presentations, and outcomes for consecutive patients treated with EVT were recorded in a prospectively maintained database at a single center. Acute infarct masks were coregistered to standard space. Standard atlases of white matter, cortex, and basal ganglia were used to determine region-specific infarct volumes. Results 167 individuals were identified with median age 69 years and 53% women. 85% achieved adequate reperfusion (TICI 2b-3) after EVT; 43% achieved 90-day functional independence (mRS 0-2). Median infarct volumes were 45cc (IQR 18-122) for total, 17cc (6-49) for white matter, 21cc (4-53) for cortex, and 5cc (1-8) for basal ganglia. The odds of 90-day mRS 0-2 were reduced in patients with larger white matter infarct volume (cc, OR=0.89, 95%CI=0.81-0.96), independent of cortex infarct volume, basal ganglia infarct volume, age, NIHSS, and TICI 2b-3 reperfusion. Reperfusion-to-MRI time was associated with white matter infarct volume (hr, β=0.119, p=0.017), but not cortical or basal ganglia infarct volume. Conclusions These data quantitatively describe region-specific infarct volumes after EVT and suggest the clinical relevance of white matter infarct volume as a predictor of long-term outcomes. Further study is warranted to examine delayed white matter infarction and the significance of specific white matter tracts.

Background and purpose Endovascular thrombectomy (EVT) has revolutionized large vessel occlusion stroke care. However, not all patients with good endovascular results achieve good outcomes. We sought to understand the clinical significance of magnetic resonance imaging defined infarct growth despite adequate reperfusion and identify associated clinical and radiographic variables. Methods History, presentation, treatments, and outcomes for consecutive EVT patients at a referral center were collected. Adequate reperfusion was defined as thrombolysis in cerebral infarction (TICI) score 2b-3. Region-specific infarct volumes in white matter, cortex, and basal ganglia were determined on diffusion-weighted imaging. Infarct growth was defined as post-EVT minus pre-EVT volume. Good outcome was defined as 90-day modified Rankin Scale ≤2. Results Forty-four patients with adequate reperfusion were identified with median age 72 years; 64% were women. Each region showed infarct growth: white matter (median pre-EVT 7 cubic centimeters [cc], post-EVT 16 cc), cortex (4 cc, 15 cc), basal ganglia (2 cc, 4 cc), total (20 cc, 39 cc). In multivariable regression, total infarct growth independently decreased the odds of good outcome (odds ratio = .946, 95% CI = .897, .998). Further multivariable analyses for determinants of infarct growth identified female sex was associated with less total growth (β = -.294, P = .042), TICI 3 was associated with less white matter growth (β = -.277, P = .048) and cortical growth (β = -.335, P = .017), and both female sex (β = -.332, P = .015) and coronary disease (β = -.337, P = .015) were associated with less cortical growth. Conclusions Infarct growth occurred despite adequate reperfusion, disproportionately in the cortex, and independently decreased the odds of good outcome. Infarct growth occurred while patients were hospitalized and may represent a therapeutic target. Potential determinants of region-specific infarct growth were identified that require confirmation in larger studies.

Background and PurposeTo explore the whole-brain dynamic functional network connectivity patterns in acute ischemic stroke (AIS) patients and their relation to stroke severity in the short and long term.MethodsWe investigated large-scale dynamic functional network connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we established correlation analyses between dynamic connectivity estimates and AIS severity as well as neurological recovery within the first 90 days after stroke (DNIHSS). Finally, we built Bayesian hierarchical models to predict acute ischemic stroke severity and examine the inter-relation of dynamic connectivity and clinical measures, with an emphasis on white matter hyperintensity lesion load.ResultsWe identified three distinct dynamic connectivity configurations in the early post-acute stroke phase. More severely affected patients (NIHSS 10–21) spent significantly more time in a highly segregated dynamic connectivity configuration that was characterized by particularly strong connectivity (three-level ANOVA: ppr = –0.68, pConclusionsOur findings demonstrate transiently increased segregation between multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first three months post-stroke.

Background and Purpose— Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods— We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHA 2 DS 2 -VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record–Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism by calculating C statistics and sensitivity/specificity cutoffs for cardioembolic stroke. Results— Of 1431 individuals with ischemic stroke (age, 65±15; 40% women), 323 (22.6%) had cardioembolism. AF risk was significantly associated with cardioembolism (CHA 2 DS 2 -VASc: odds ratio [OR] per SD, 1.69 [95% CI, 1.49–1.93]; Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score: OR, 2.22 [95% CI, 1.90–2.60]; electronic Health Record–Based AF: OR, 2.55 [95% CI, 2.16–3.04]). Discrimination was greater for Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score (C index, 0.695 [95% CI, 0.663–0.726]) and Electronic Health Record–Based AF score (0.713 [95% CI, 0.681–0.744]) versus CHA 2 DS 2 -VASc (C index, 0.651 [95% CI, 0.619–0.683]). Examination of AF scores across a range of thresholds indicated that AF risk may facilitate identification of individuals at low likelihood of cardioembolism (eg, negative likelihood ratios for Electronic Health Record–Based AF score ranged 0.31–0.10 at sensitivity thresholds 0.90–0.99). Conclusions— AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.

We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolution research-quality scans. In contrast to research-quality scans, clinical scans are often sparse, missing up to 86% of the slices available in research-quality scans. Existing methods for registering these sparse images are either inaccurate or extremely slow. We present a learning-based registration method, SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods. To our knowledge, it is the first method to use deep learning specifically tailored to registering clinical images. We demonstrate our method on a clinically-acquired MRI dataset of stroke patients and on a simulated sparse MRI dataset. Our code is available as part of the VoxelMorph package at http://voxelmorph.mit.edu/., Comment: This version was accepted to CHIL. It builds on the previous version of the paper and includes more experimental results

Background: Few prospective cohort studies collect detailed information on stroke characteristics among individuals who experience ischemic stroke, including white matter hyperintensity volume, and thus cannot explore how prospectively collected biomarkers prior to the stroke influence white matter hyperintensity volume. We explored the association between a large panel of prospectively collected lipid and inflammatory biomarkers and white matter hyperintensity volume among participants in the Women's Health Study with incident ischemic stroke. Methods: Among Women's Health Study participants with first ischemic stroke who had baseline serum biomarkers and available magnetic resonance imaging, we measured white matter hyperintensity volume using a validated semi-automated method. Linear regression was used to explore the associations between biomarkers and log-transformed white matter hyperintensity volume. Results: After multivariate adjustment, a 1% increment in HbA1c% was associated with an increase in white matter hyperintensity volume (P value = .05). Evidence of a nonlinear association between high density lipoprotein cholesterol levels and ApoA1 levels with white matter hyperintensity volume was noted (P values for nonlinearity = .01 and .001, respectively). No other biomarkers were significantly associated with white matter hyperintensity volume. Conclusions: Chronic hyperglycemia as evidenced by HbA1c levels measured years prior to stroke is associated with white matter hyperintensity volume at the time of stroke. Additional research is needed to explain why low levels of high density lipoprotein cholesterol levels and ApoA1 may be associated with similar white matter hyperintensity volume as high levels.

X-linked adrenoleukodystrophy is a neurodegenerative disorder caused by mutations in ABCD1. By applying a vascular model to conventional MR perfusion imaging, Lauer et al. demonstrate that ABCD1 deficiency causes alterations in microvascular flow in white matter areas and developmental stages with the highest probability for conversion to cerebral disease., Cerebral X-linked adrenoleukodystrophy is a devastating neurodegenerative disorder caused by mutations in the ABCD1 gene, which lead to a rapidly progressive cerebral inflammatory demyelination in up to 60% of affected males. Selective brain endothelial dysfunction and increased permeability of the blood–brain barrier suggest that white matter microvascular dysfunction contributes to the conversion to cerebral disease. Applying a vascular model to conventional dynamic susceptibility contrast magnetic resonance perfusion imaging, we demonstrate that lack of ABCD1 function causes increased capillary flow heterogeneity in asymptomatic hemizygotes predominantly in the white matter regions and developmental stages with the highest probability for conversion to cerebral disease. In subjects with ongoing inflammatory demyelination we observed a sequence of increased capillary flow heterogeneity followed by blood–brain barrier permeability changes in the perilesional white matter, which predicts lesion progression. These white matter microvascular alterations normalize within 1 year after treatment with haematopoietic stem cell transplantation. For the first time in vivo, our studies unveil a model to assess how ABCD1 alters white matter microvascular function and explores its potential as an earlier biomarker for monitoring disease progression and response to treatment.

Background: Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. Methods: To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from −3 to −8 in a prior independent genetic association study. Results: Incident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13–1.46; P =1.5×10 −4 ) to 1.67 (25 variants; 95% confidence interval, 1.47–1.90; P =9.3×10 −15 ). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629–0.811; maximum ΔC statistic from clinical score alone, 0.009–0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39–4.58; P =2.7×10 −3 ). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20–4.40; P =0.01). Conclusions: Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms.

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.

Background: White matter hyperintensity (WMH) is a common manifestation of chronic ischemic microvascular disease that heralds greater risk of functional disability, stroke, and dementia. SPRINT MIND recently reported that intensive blood pressure reduction resulted in lower rates of mild cognitive impairment and WMH progression, suggesting that medical interventions could have a measurable impact on WMH. We conducted an anonymous survey of providers in the NINDS StrokeNet to better understand neurologist attitudes about asymptomatic WMH. Methods: We sent a 7-question survey to the 29 Regional Coordinating Centers of the StrokeNet, whose coordinators disseminated the survey to providers “involved in the care of a patient after their stroke.” Results: We received 136 responses. For stroke prevention therapies, including aspirin and statin therapy and blood pressure target, there was substantial equipoise, with no single option receiving >50% endorsement and between 15-32% of respondents choosing the option of “not sure.” 83% of respondents indicated high or moderate enthusiasm for a trial targeting this patient population. The clinical outcomes of reduction in ischemic stroke, cognitive impairment, or dementia were high importance (>70% endorsement), while the remaining radiographic, safety, and clinical endpoints all failed to reach 50% endorsement. Conclusions: Our survey establishes meaningful neurologist attitudes that can inform future WMH research. There is considerable equipoise regarding optimal medical treatment for patients with asymptomatic WMH and providers in StrokeNet, who would be a vital stakeholder in WMH research in the United States, enthusiastically support a clinical trial to resolve open questions on optimal medical management.

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans. We introduce a generative model that captures fine-scale anatomical structure across subjects in clinical image collections and derive an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago ., Accepted at IEEE Transactions on Medical Imaging (\c{opyright} 2018 IEEE)

Background and Purpose— We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms’ performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results— The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P 3 (0.9–16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P Conclusions— Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Comment inThe yin and yang of magnesium and calcium: New genetic insights for stroke? [Neurology. 2019]; International audience; Objective To determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach. Methods Analyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases). Results In standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10 −4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10 −4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype. Conclusions This study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.

Background and purposeFunctional outcomes after ischaemic stroke are worse in women, despite adjusting for differences in comorbidities and treatment approaches. White matter microvascular integrity represents one risk factor for poor long-term functional outcomes after ischaemic stroke. The aim of the study is to characterise sex-specific differences in microvascular integrity in individuals with acute ischaemic stroke.MethodsA retrospective analysis of subjects with acute ischaemic stroke and brain MRI with diffusion-weighted (DWI) and dynamic-susceptibility contrast-enhanced (DSC) perfusion-weighted imaging obtained within 9 hours of last known well was performed. In the hemisphere contralateral to the acute infarct, normal-appearing white matter (NAWM) microvascular integrity was measured using the K2 coefficient and apparent diffusion coefficient (ADC) values. Regression analyses for predictors of K2 coefficient, DWI volume and good outcome (90-day modified Rankin scale (mRS) score Results105 men and 79 women met inclusion criteria for analysis. Despite no difference in age, women had increased NAWM K2 coefficient (1027.4 vs 692.7×10–6/s; p=0.006). In women, atrial fibrillation (β=583.6; p=0.04) and increasing NAWM ADC (β=4.4; p=0.02) were associated with increased NAWM K2 coefficient. In multivariable regression analysis, the K2 coefficient was an independent predictor of DWI volume in women (β=0.007; p=0.01) but not men.ConclusionsIn women with acute ischaemic stroke, increased NAWM K2 coefficient is associated with increased infarct volume and chronic white matter structural integrity. Prospective studies investigating sex-specific differences in white matter microvascular integrity are needed.

Background and Purpose— Increased systolic blood pressure variability (BPV) is associated with worse outcome after acute ischemic stroke and may also have a negative impact after intracerebral hemorrhage. We sought to determine whether increased BPV was detrimental in the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial. Methods— The primary outcome of our study was a 3-month follow-up modified Rankin Scale of 3 to 6, and the secondary outcome was a utility-weighted modified Rankin Scale. We calculated blood pressure mean and variability using systolic blood pressure from the acute period (2–24 hours postrandomization) and subacute period (days 2, 3, and 7). Results— The acute period included 913 patients and the subacute included 877. For 5 different statistical measures of systolic BPV, there was a consistent association between increased BPV and worse neurological outcome in both the acute and subacute periods. This association was not found for systolic blood pressure mean. Conclusions— In this secondary analysis of ATACH-2, we show that increased systolic BPV is associated with worse long-term neurological outcome. Additional research is needed to find techniques that allow early identification of patients with an expected elevation of BPV and to study pharmacological or protocol-based approaches to minimize BPV.

Background and Purpose— Oxidative stress is an early response to cerebral ischemia and is likely to play an important role in the pathogenesis of cerebral ischemic injury. We sought to evaluate whether hyperacute plasma concentrations of biomarkers of oxidative stress, inflammation, and tissue damage predict infarct growth (IG). Methods— We prospectively measured plasma F2-isoprostane (F2-isoP), urinary 8-oxo-7,8-dihydro-2′-deoxyguoanosine, plasma oxygen radical absorbance capacity assay, high sensitivity C reactive protein, and matrix metalloproteinase 2 and 9 in consecutive patients with acute ischemic stroke presenting within 9 hours of symptom onset. Patients with baseline diffusion-weighted magnetic resonance imaging and follow-up diffusion-weighted imaging or computed tomographic scan were included to evaluate the final infarct volume. Baseline diffusion-weighted imaging volume and final infarct volume were analyzed using semiautomated volumetric method. IG volume was defined as the difference between final infarct volume and baseline diffusion-weighted imaging volume. Results— A total of 220 acute ischemic stroke subjects were included in the final analysis. One hundred seventy of these had IG. Baseline F2-isoP significantly correlated with IG volume (Spearman ρ=0.20; P =0.005) and final infarct volume (Spearman ρ=0.19; P =0.009). In a multivariate binary logistic regression model, baseline F2-isoP emerged as an independent predictor of the occurrence of IG (odds ratio, 2.57; 95% confidence interval, 1.37–4.83; P =0.007). In a multivariate linear regression model, baseline F2-isoP was independently associated with IG volume (B, 0.38; 95% confidence interval, 0.04–0.72; P =0.03). Conclusions— Elevated hyperacute plasma F2-isoP concentrations independently predict the occurrence of IG and IG volume in patients with acute ischemic stroke. If validated in future studies, measuring plasma F2-isoP might be helpful in the acute setting to stratify patients with acute ischemic stroke for relative severity of ischemic injury and expected progression.

We sought to investigate the relationship between blood–brain barrier (BBB) permeability and microstructural white matter integrity, and their potential impact on long-term functional outcomes in patients with acute ischemic stroke (AIS). We studied 184 AIS subjects with perfusion-weighted MRI (PWI) performed