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BackgroundAccess to endovascular thrombectomy (EVT) is relatively limited. Hub‐and‐spoke networks seek to transfer appropriate large‐vessel occlusion stroke candidates to EVT‐capable hubs. However, some patients are ineligible upon hub arrival, and factors that drive transfer inefficiencies are not well described. We sought to quantify EVT transfer efficiency and identify reasons for EVT ineligibility.MethodsConsecutive EVT candidates presenting to 25 spokes from 2018 to 2020 with pretransfer computed tomography angiography‐defined large‐vessel occlusion and Alberta Stroke Program Early Computed Tomography Score of ≥6 were identified from a prospectively maintained database. Outcomes of interest included hub EVT, reasons for EVT ineligibility, and 90‐day modified Rankin scale score of ≤2.ResultsAmong 258 patients, the median age was 70 years (interquartile range, 60–81 years); 50% were women. A total of 56% were ineligible for EVT after hub arrival. Cited reasons were large established infarct (49%), mild symptoms (33%), recanalization (6%), distal occlusion (5%), subocclusive lesion (3%), and goals of care (3%). Late window patients (last known well >6 hours) were more likely to be ineligible (67% versus 43%;PP=0.04), had lower National Institutes of Health Stroke Scale score (10 versus 16;PPPP=0.04) compared with eligible patients. EVT ineligibility independently reduced the odds of 90‐day modified Rankin scale score of ≤2 (adjusted odds ratio, 0.26; 95% CI, 0.12–0.56;P=0.001) when controlling for age, National Institutes of Health Stroke Scale score, and last known well‐to‐hub arrival time.ConclusionsAmong patients transferred for EVT, there are multiple reasons for ineligibility upon hub arrival, with most excluded for infarct growth and mild symptoms. Understanding factors that drive transfer inefficiencies is important to improve EVT access and outcomes.

Background: Diffusion-weighted imaging lesion reversal (DWIR) is frequently observed after mechanical thrombectomy for acute ischemic stroke, but little is known about age-related differences and impact on outcome. We aimed to compare, in patients Methods: We retrospectively analyzed data of patients treated for an anterior circulation acute ischemic stroke with large vessel occlusion in 2 French hospitals, who underwent baseline and 24-hour follow-up magnetic resonance imaging, with baseline DWI lesion volume ≥10 cc. The percentage of DWIR (DWIR%), was calculated as follows: DWIR%=(DWIR volume/baseline DWI volume)×100. Data on demographics, medical history, and baseline clinical and radiological characteristics were collected. Results: Among 433 included patients (median age, 68 years), median DWIR% after mechanical thrombectomy was 22% (6–35) in patients ≥80, and 19% (interquartile range, 10–34) in patients P =0.948). In multivariable analyses, successful recanalization after mechanical thrombectomy was associated with higher median DWIR% in both ≥80 ( P =0.004) and P =0.002) patients. In subgroup analyses performed on a minority of subjects, collateral vessels status score (n=87) and white matter hyperintensity volume (n=131) were not associated with DWIR% ( P >0.2). In multivariable analyses, DWIR% was associated with increased rates of favorable 3-month outcomes in both ≥80 ( P =0.003) and P =0.013) patients; the effect of DWIR% on outcome was not influenced by the age group ( P interaction=0.185) Conclusions: DWIR might be an important and nonage-dependent effect of arterial recanalization, as it seems to beneficially impact 3-month outcomes of both younger and older subjects treated with mechanical thrombectomy for acute ischemic stroke and large vessel occlusion.

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.

Distal medium vessel occlusions (DMVOs) are increasingly recognized as a next target for endovascular thrombectomy (EVT). Our objective was to investigate safety and clinical outcomes of EVT for DMVO of the middle cerebral artery (MCA).We analyzed data of the Lille Reperfusion Registry from January 2017 to September 2020. Patients with a primary or secondary DMVO of the MCA seen on pretreatment angiogram were included. Only patients with a eTICI score 2b50-2b67 on initial angiogram were considered. Baseline characteristics, angiographic clinical, and safety outcomes were compared between patients treated with EVT or standard medical treatment (no-EVT).Of the 171 patients included, 96 received EVT (46.9% male, 68.7 ± 15.8 years) and 75 received standard medical treatment (44% male, 73.9 ± 13.1 years). EVT patients had a better improvement of the NIHSS score at discharge (adjusted mean difference: 3.71; 95% CI: 1.18-6.24). In the distal M2 occlusions subgroup, EVT was significantly associated with a higher rate of early neurologic improvement (adjusted OR: 3.62 95% CI: 1.31-10.03), NIHSS improvement at discharge (adjusted mean difference: 5.23; 95% CI: 2.18-8.29), and improved modified Rankin Scale score at 3 months (adjusted common OR for 1 point improvement: 3.06; 95% CI: 1.30 to 7.23). Symptomatic intracranial hemorrhage occurred in 3.1% in the EVT group and in 9.5% in the no-EVT group.EVT for DMVO of the MCA appears to be safe and may lead to improved clinical outcomes. This effect was especially pronounced in patients with distal M2 occlusions, warranting randomized trials to validate this result.

Background and ObjectivesThe care of patients with large vessel occlusion (LVO) stroke has been revolutionized by endovascular thrombectomy (EVT). While EVT has a large effect size, most patients treated with EVT remain disabled or die within 90 days. A better understanding of outcomes may influence EVT selection criteria, novel therapies, and prognostication. We sought to identify associations between outcomes and brain regions involved in ischemic lesions.MethodsFor this cohort study, consecutive patients with LVO who were treated with EVT and underwent post-EVT MRI were identified from a tertiary referral center (2011–2019). Acute ischemic lesions were manually segmented from diffusion-weighted imaging and spatially normalized. Individual lesions were parcellated (atlas-defined 94 cortical regions, 14 subcortical nuclei, 20 white matter tracts) and reduced to 10 essential lesion patterns with the use of unsupervised dimensionality reduction techniques. Ninety-day modified Rankin Scale (mRS) score (>2) was modeled via bayesian regression, taking the 10 lesion patterns as inputs and controlling for lesion size, age, sex, acute NIH Stroke Scale (NIHSS) score, alteplase, prior stroke, intracerebral hemorrhage, and good reperfusion (Thrombolysis in Cerebral Infarction 2b–3). In comparative analyses, 90-day mRS score was modeled considering covariates only, and compartment-wise relevances for acute stroke severity and 90-day mRS score were evaluated.ResultsThere were 151 patients with LVO identified (age 68 ± 15 years, 52% female). The median NIHSS score was 16 (interquartile range 13–20); 56% had mRS score >2. Lesion locations predictive of 90-day mRS score involved bilateral but left hemispherically more pronounced precentral and postcentral gyri, insular and opercular cortex, and left putamen and caudate (area under the curve 0.91, highest probability density interval [HPDI] covering 90% certainty 0.90–0.92). The lesion location model outperformed the simpler model relying on covariates only (bayesian model comparison of 97% weight to the model with vs 3% weight to the model without lesion location). While lesions affecting subcortical nuclei had the highest relevance for stroke severity (posterior distribution mean 0.75, 90% HPDI 0.256–1.31), lesions affecting white matter tracts had the highest relevance for 90-day mRS score (0.656, 90% HPDI 0.0864–1.12).DiscussionThese data describe the significance for outcomes of specific brain regions involved in ischemic lesions on MRI after EVT. Future work in additional datasets is needed to confirm these granular findings.

Introduction: Predicting individual outcomes post-stroke with the highest possible accuracy is a crucial steppingstone in the realization of precision medicine. We here evaluated various types of lesion information in their capacity to predict stroke severity in a large cohort of patients with acute ischemic stroke. Methods: A total of 1,075 patients of the MRI-GENIE study [age: 64.2(14.7), 38% women] contributed to analyses (N=792 as train and N=283 as test sample). We employed ridge regression with hyperparameter optimization and nested five-fold cross-validation to predict acute NIHSS-based stroke severity. Our baseline model considered DWI lesion volume. Further models tested structural lesion location, indirect structural and functional lesion connectivity, individually and combined with lesion volume. Data was preprocessed by principal component analysis (PCA), retaining 95% of the variance of the original data. Model performance was compared in terms of explained variance (R-squared) and 95% confidence intervals in the outer loop of the training set and the test set. Results: Structural lesion connectivity enabled the highest prediction performance in the test set ( Figure 1 ). Prediction performance did not change notably with inclusion of lesion volume information. This contrasted with the combination of functional lesion connectivity and lesion volume that achieved a substantially higher prediction performance compared to functional lesion connectivity or lesion volume in isolation. Lesion location resulted in a prediction performance that was in the range of lesion volume alone and significantly lower than the ones of structural or functional connectivity with lesion volume. Conclusions: Structural connectivity facilitated the most convincing prediction of stroke severity. The capacity of functional lesion connectivity was substantially improved by the inclusion of lesion volume, indicating both represent complementary information.

Introduction: Deep learning (DL) has allowed for substantial progress in many, often machine vision-focused medical scenarios. Since DL models typically require 10 5 -10 7 examples, it is currently unknown whether DL can also enhance predictions of outcomes post-stroke in real-world samples of stroke patients that are several magnitudes smaller. We here compared the capacities of linear and DL algorithms regarding their prediction of stroke severity. Methods: Our analyses relied on a total of 1435 patients assembled from the MRI-GENIE study and a Massachusetts General Hospital-based study. The primary outcome was acute NIHSS-based stroke severity, which we predicted by means of lesion location. We automatically derived lesion segmentations from DWI scans, performed spatial normalization and principal component analysis (PCA), retaining 95% of the variance of the original data. We then separated a train, validation, and test set, of sizes 918, 230, and 287 respectively. We downsampled the train set to 100, 300, and 900 and learned to predict the NIHSS score for each sample size with regularized linear regression and an 8-layered neural network. We selected hyperparameters on the validation set and conducted 500 dataset splits. We here report explained variance (R-squared) in the test set. Results: While linear regression performed favorably for a sample size of 100 patients (R-squared=0.28), DL started to significantly outperform linear regression when trained on 900 patients (R-squared=0.34, Figure 1 ). Average prediction performance improved by ~20% when increasing the sample size 9x. Conclusions: For sample sizes of ~1000 patients, DL showed a more convincing prediction performance for stroke severity than typically employed linear methods. Our findings suggest the existence of non-linear relationships between lesion location and outcome post-stroke that can be utilized for an improved prediction performance once samples are reasonably large.

IntroductionDeep learning has allowed for remarkable progress in many medical scenarios. Since deep learning prediction models often require 105-107examples, it is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity.MethodsOur analyses relied on a total of 1,430 patients assembled from the MRI-GENIE collaboration and a Massachusetts General Hospital-based study. The outcome of interest was NIHSS-based stroke severity in the acute phase after ischemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis (PCA) step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation, and test set to investigate the effects of sample size, we subsampled the train set to 100, 300, and 900 and trained the algorithms to predict the NIHSS score for each sample size with regularized linear regression and an 8-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R-squared) in the test set.ResultsWhile linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9x (maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006).ConclusionsFor sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.

BACKGROUND AND PURPOSE Ischemic diffusion-weighted imaging-fluid-attenuated inversion recovery (DWI-FLAIR) mismatch may be useful in guiding acute stroke treatment decisions given its relationship to onset time and parenchymal viability; however, it relies on subjective grading. Radiomics is an emerging image quantification methodology that may objectively represent continuous image characteristics. We propose a novel radiomics approach to characterize DWI-FLAIR mismatch. METHODS Ischemic lesions were visually graded for FLAIR positivity (absent, subtle, obvious) among consecutive large vessel occlusion stroke patients who underwent hyperacute MRI. Radiomic features were extracted from within the lesions on DWI and FLAIR. The DWI-FLAIR mismatch radiomics signature was built with features systematically selected by a cross-validated ElasticNet linear regression model of mismatch. RESULTS We identified 103 patients with mean age 68 ± 16 years; 63% were female. FLAIR hyperintensity was absent in 25%, subtle in 55%, and obvious in 20%. Inter-rater agreement for visual grading was moderate (Κ = .58). The radiomics signature of DWI-FLAIR mismatch included native FLAIR histogram kurtosis and local binary pattern-filtered FLAIR gray-level cluster shade; both correlated with visual grading (ρ = -.42, p

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.

ObjectiveTo determine whether the association between increasing number of clot retrieval attempts (CRA) and unfavorable outcome is due to an increase in emboli to new territory (ENT) and greater infarct growth (IG) in successfully recanalized patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO).MethodsData were extracted from 2 pooled multicentric prospective registries of consecutive patients with anterior AIS-LVO treated with mechanical thrombectomy (MT) between January 2016 and 2019. Patients with pretreatment and 24-hour posttreatment diffusion-weighted imaging (DWI) achieving successful recanalization, defined as expanded Thrombolysis in Cerebral Infarction Scale score of 2B, 2C, or 3, were included. ENT were assessed and IG was measured by voxel-based segmentation after DWI coregistration. Associations between number of CRA, ENT, IG, and 3-month outcome were analyzed.ResultsFour hundred nineteen patients achieving successful recanalization were included. ENT occurrence was strongly correlated with increasing CRA (ρ = 0.73, p = 10−4). In multivariable linear analysis, IG was independently associated with CRA (β = 1.6 per retrieval attempt, 95% confidence interval [CI] 0.97–9.74, p = 0.03) and ENT (β = 2.7 [95% CI 1.21–4.1], p = 0.03). Unfavorable functional outcome (3-month modified Rankin Scale score >2) increased with each additional CRA. IG was an independent predictor of unfavorable outcome (odds ratio 1.05 [95% CI 1.02–1.07] per 1-mL IG increase, p = 10−4) in binary logistic regression analysis.ConclusionsIncreasing number of CRA in acute stroke is correlated with an increased ENT rate and increased IG volume, affecting functional outcome even when successful recanalization is achieved.Classification of EvidenceThis study provides Class II evidence that, for patients with acute stroke undergoing successful recanalization, an increasing number of CRA is associated with poorer functional outcome.

Resume Introduction L’intelligence artificielle prend une place importante dans l’imagerie medicale. Les radiologues ont une perception optimiste de cette evolution, mais manquent d’outils pour comprendre et evaluer l’efficacite des logiciels. Message principal Cet article met en lumiere les 4 points-cles qu’il faut connaitre pour evaluer un logiciel d’intelligence artificielle : la qualite de la base de donnees dite « d’apprentissage », la maniere dont les donnees ont ete « annotees », l’entrainement du modele et l’evaluation des performances du modele. Conclusion Par une approche simple, le radiologue peut ainsi evaluer la qualite d’un logiciel d’intelligence artificielle.

Background: Cerebral microbleeds (CMBs) have been observed in healthy elderly people undergoing systematic brain magnetic resonance imaging. The potential role of acute triggers on the appearance of CMBs remains unknown. We aimed to describe the incidence of new CMBs after transcatheter aortic valve replacement (TAVR) and to identify clinical and procedural factors associated with new CMBs including hemostatic measures and anticoagulation management. Methods: We evaluated a prospective cohort of patients with symptomatic aortic stenosis referred for TAVR for CMBs (METHYSTROKE [Identification of Epigenetic Risk Factors for Ischemic Complication During the TAVR Procedure in the Elderly]). Standardized neurologic assessment, brain magnetic resonance imaging, and analysis of hemostatic measures including von Willebrand factor were performed before and after TAVR. Numbers and location of microbleeds on preprocedural magnetic resonance imaging and of new microbleeds on postprocedural magnetic resonance imaging were reported by 2 independent neuroradiologists blinded to clinical data. Measures associated with new microbleeds and postprocedural outcome including neurologic functional outcome at 6 months were also examined. Results: A total of 84 patients (47% men, 80.9±5.7 years of age) were included. On preprocedural magnetic resonance imaging, 22 patients (26% [95% CI, 17%–37%]) had at least 1 microbleed. After TAVR, new microbleeds were observed in 19 (23% [95% CI, 14%–33%]) patients. The occurrence of new microbleeds was independent of the presence of microbleeds at baseline and of diffusion-weighted imaging hypersignals. In univariable analysis, a previous history of bleeding ( P =0.01), a higher total dose of heparin ( P =0.02), a prolonged procedure ( P =0.03), absence of protamine reversion ( P =0.04), higher final activated partial thromboplastin time ( P =0.05), lower final von Willebrand factor high-molecular-weight:multimer ratio ( P =0.007), and lower final closure time with adenosine–diphosphate ( P =0.02) were associated with the occurrence of new postprocedural microbleeds. In multivariable analysis, a prolonged procedure (odds ratio, 1.22 [95% CI, 1.03–1.73] for every 5 minutes of fluoroscopy time; P =0.02) and postprocedural acquired von Willebrand factor defect (odds ratio, 1.42 [95% CI, 1.08–1.89] for every lower 0.1 unit of high-molecular-weight:multimer ratio; P =0.004) were independently associated with the occurrence of new postprocedural microbleeds. New CMBs were not associated with changes in neurologic functional outcome or quality of life at 6 months. Conclusions: One out of 4 patients undergoing TAVR has CMBs before the procedure and 1 out of 4 patients develops new CMBs. Procedural or antithrombotic management and persistence of acquired von Willebrand factor defect were associated with the occurrence of new CMBs. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02972008.

Background and Purpose: Patient care-seeking has likely changed during the coronavirus disease 2019 (COVID-19) pandemic. In stroke, delayed or avoided care may translate to substantial morbidity. We sought to determine the effect of the pandemic on patterns of stroke patient presentation and quality of care. Methods: We analyzed data from 25 New England hospitals: one urban, academic comprehensive stroke center and telestroke hub, and 24 spoke hospitals in the telestroke network. We included all telestroke consultations from the 24 spokes, and all stroke admissions to the comprehensive stroke center hub from November 1, 2019 through April 30, 2020. We compared rates of presentation, timeliness presentation, and quality of care pre- versus post-March 1, 2020. We examined trends in patient demographics, stroke severity, timeliness, diagnoses including large vessel occlusion, alteplase use, and endovascular thrombectomy among eligible subjects. We compared proportions and bivariate comparisons to examine for changes pre- versus post-March 1, 2020 and used linear regression to examine trends over time. Results: Among 1248 patient presentations (844 telestroke consultations, 404 comprehensive stroke center admissions), telestroke consultations and ischemic stroke patient admissions decreased among the spokes and hub. Age and stroke severity were unchanged over the study period. We found no change in alteplase administration at telestroke spoke hospitals but did note a decrease in both alteplase use and thrombectomy at our comprehensive stroke center. Time metrics for patient presentation and care delivery were unchanged; however, rates of adherence for the quality measures dysphagia screening, early antithrombotic initiation, and early venous thromboembolism prophylaxis were reduced during the pandemic. Conclusions: In this regional analysis, we found decreasing telestroke consultations and ischemic stroke admissions, and reduced performance on stroke quality of care measures during the COVID-19 pandemic. Contrary to prior reports, we did not find an increase in thrombectomy nor decrease in clinical severity that might be expected if patients with milder symptoms avoided hospitalization.

Previous studies have suggested that mechanical revascularization in acute ischemic stroke (AIS) patients could be affected by clot histology. In this 7-T micro-MRI study, we used R2* relaxometry of clot analogs to evaluate the relationship between texture parameters of R2* maps and clot constituents.Twelve AIS clot analogs were experimentally generated to obtain a wide range of red blood cell concentrations. All clots underwent a MR acquisition using a 7-T micro-MR system. A 3D multi-echo gradient-echo sequence was performed and R2* maps were generated. First order and second order statistics of R2* histograms within the clots were calculated. Iron concentration in clots was measured using absorption spectrometry and red blood cell count (RBC) was obtained by histopathological analysis.RBC count was strongly correlated with iron concentration within clots (r=0.87, P.001). Higher RBC count and iron concentration were significantly correlated with first order parameters including: (a) global positive shift of the R2* histogram with higher '10th percentile', 'median', 'mean' and '90th percentile'; (b) increase of the global magnitude of voxel values with higher 'total energy' and 'root mean squared'; (c) greater uniformity of the voxel values with higher 'uniformity' and lower 'entropy'. Second order statistical parameters confirmed that higher RBC count and iron concentration correlated with (a) greater concentration of high gray-level values in the image; (b) more "coarse" texture of R2* maps.Texture analysis of MRI-R2* maps can accurately estimate the red blood cell count and iron content of AIS clot analogs.

BACKGROUND AND PURPOSE: Follow-up MR imaging of brain AVMs currently relies on contrast-enhanced sequences. Noncontrast techniques, including arterial spin-labeling and TOF, may have value in detecting a residual nidus after radiosurgery. The aim of this study was to compare noncontrast with contrast-enhanced MR imaging for the differentiation of residual-versus-obliterated brain AVMs in radiosurgically treated patients. MATERIALS AND METHODS: Twenty-eight consecutive patients with small brain AVMs (

Introduction: The utility of intravenous thrombolysis (IVT) prior to mechanical thrombectomy (MT) for large vessel occlusion (LVO) stroke is controversial. Low early recanalization (ER) rates and potential delays to MT may support bypassing IVT before MT. Recent trials suggest MT alone is reasonable for LVO patients presenting directly to MT-capable centers. However, bypassing IVT has not been evaluated for patients first presenting to “spoke” hospitals capable of administering IVT but requiring transfer to a “hub” hospital for MT. Here we examined how spoke IVT influences LVO ER and spoke-to-hub transfer time (TT) within our hub-and-spoke network. Methods: Patients presenting to 25 spoke hospitals before hub transfer from 2018 to 2020 who were considered possible EVT candidates with pre-transfer CTA-defined LVO and ASPECTS ≥6 were identified from a prospectively maintained database. ER was determined by chart review of vessel imaging on hub arrival. TT was prospectively recorded. Statistics were performed using permutation resampling and linear regression in R. Results: Of 177 patients included, 76 received IVT. Baseline characteristics, NIHSS, and intracerebral hemorrhage were similar between groups. Spoke IVT improved ER frequency [12/76 (15.8%) vs. 3/101 (3.0%), P Conclusion: Within our network, IVT significantly increases ER and decreases TT, which may contribute to improved 90-day outcomes. This retrospective study supports following current recommendations that IVT be given to all eligible patients at spoke hospitals prior to transfer for MT consideration.

Introduction: Endovascular thrombectomy (EVT) has revolutionized the care of emergent large vessel occlusion (ELVO) stroke patients. It is, therefore, crucial to optimize its delivery to eligible candidates. Within hub-and-spoke hospital system models, some patients first present to distant spoke hospitals and require transfer to hub hospitals for EVT. We sought to understand changes in EVT eligibility during transfer. Methods: Consecutive EVT candidates presenting to 25 spokes from 2018 to 2020 with pre-transfer CTA-defined ELVO and Alberta Stroke Program Early CT Score ≥6 were identified from a prospectively maintained database. Outcomes of interest included hub EVT, reasons for EVT ineligibility, and 90-day functional independence (modified Rankin Scale, mRS ≤2). Results: Among 258 patients, the median age was 70 years (IQR 60-81) and 50% were female. Forty-four percent underwent EVT upon hub arrival, of which 87% achieved Thrombolysis in Cerebral Infarction 2b-3 reperfusion. Compared to EVT-eligible patients, ineligible patients were older (73 vs 68 years, p=0.04), had lower NIH Stroke Scale (NIHSS, 10 vs 16, p Conclusions: These data support that approaches to increase EVT eligibility among ELVO transfers may improve long term outcomes. Infarct growth represents the primary reason for ineligibility. Possible interventions include direct field triage to the hub when feasible, improving inter-hospital transfer times, supporting ischemic penumbra before EVT, and developing novel agents to slow infarct growth.

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.

Background and Purpose: Despite initial successful recanalization after mechanical thrombectomy (MT), some patients with large artery occlusion (LAO)-related stroke will experience an early reocclusion of the injured vessel which may worsen their prognosis. We aimed to investigate the prevalence, associated factors and prognosis of early reocclusion after successful MT in a large prospective cohort of stroke patients with LAO. Methods: We included patients from the Lille reperfusion registry with LAO-related stroke involving M1 segment, internal carotid artery terminus or tandem ICA-M1 occlusion, with successful recanalization after MT and available 24 h imaging follow-up. Early reocclusion was defined as internal carotid artery terminus or M1 occlusion on 24 h magnetic resonance imaging (MRI) or computed tomography (CT) vascular imaging. Multivariable logistic regression models were used to investigate factors associated with early reocclusion and its impact on outcomes. Results: Between 2015 and 2020, 62 of 1015 included patients experienced an early reocclusion (6.1%). Age (odds ratio (OR) per 15 years decrease: 1.38 (1.05–1.81)) antiplatelet use (OR: 0.41 (0.19–0.89)), several device passes (OR: 2.13 (1.18–3.83)), atherosclerosis cause (OR: 2.38 (1.19–4.78)), and early clinical worsening (OR: 2.45 (1.18–5.07)) were independently associated with early reocclusion. Early reocclusion was independently associated with poor prognosis (OR: 7.15 (3.49–14.65)) and mortality (OR: 2.05 (1.07–3.91)) at 3 months. Conclusion: Six percent of patients with LAO-related stroke and initial successful recanalization experienced early reocclusion. This event is associated with a 7-fold increased risk of poor functional outcome and a 2-fold increased risk of mortality. Further efforts are warranted to refine early detection of patients at risk of reocclusion and to improve their management.

Given the efficacy of endovascular thrombectomy (EVT), optimizing systems of delivery is crucial. Magnetic resonance imaging (MRI) is the gold standard for evaluating tissue viability but may require more time to obtain and interpret. We sought to identify determinants of arrival-to-puncture time for patients who underwent MRI-based EVT selection in a real-world setting.Patients were identified from a prospectively maintained database from 2011-2019 that included demographics, presentations, treatments, and outcomes. Process times were obtained from the medical charts. MRI times were obtained from time stamps on the first sequence. Linear and logistic regressions were used to infer explanatory variables of arrival-to-puncture times and effects of arrival-to-puncture time on functional outcomes.In this study 192 patients (median age 70 years, 57% women, 12% non-white) underwent MRI-based EVT selection. 66% also underwent computed tomography (CT) at the hub before EVT. General anesthesia was used for 33%. Among the entire cohort, the median arrival-to-puncture was 102 min; however, among those without CT it was 77 min. Longer arrival-to-puncture times independently reduced the odds of 90-day good outcome (∆mRS ≤ 2 from pre-stroke, aOR = 0.990, 95%CI = 0.981-0.999, p = 0.040) when controlling for age, NIHSS, and good reperfusion (TICI 2b-3). Independent determinants of longer arrival-to-puncture were CT plus MRI (β = 0.205, p = 0.003), non-white race/ethnicity (β = 0.162, p = 0.012), coronary disease (β = 0.205, p = 0.001), and general anesthesia (β = 0.364, p 0.0001).Minimizing arrival-to-puncture time is important for outcomes. Real-world challenges exist in an MRI-based EVT selection protocol; avoiding double imaging is key to saving time. Racial/ethnic disparities require further study. Understanding variables associated with delay will inform protocol changes.

ObjectiveTo 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 National Institutes of Health Stroke Scale data at index stroke, as well as modified Rankin Scale (mRS) at 3-6 months post-stroke were obtained from MRI-GENIE study of acute ischemic stroke (AIS) patients. Individual WMH volume was automatically derived from FLAIR-images. Stroke lesions were automatically segmented from DWI-images, spatially normalized and parcellated into atlas-defined brain regions. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS>2) were modeled within a purpose-built machine learning and Bayesian regression framework. In particular, interaction effects between stroke lesions and a high versus low WMH burden were integrated via hierarchical model structures. Models were adjusted for the covariates age, age2, sex, total DWI-lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts.ResultsA total of 928 AIS patients contributed to stroke severity analyses (mean age: 64.8(14.5), 40% women), 698 patients to functional outcome analyses (mean age: 65.9(14.7), 41% women). Individual stroke lesions were represented in five anatomically distinct left-hemispheric and five right-hemispheric lesion patterns. Across all patients, acute stroke severity was substantially explained by three of these patterns, that were particularly focused on bilateral subcortical and left-hemispherically pronounced cortical regions. In high WMH burden patients, two lesion patterns consistently emerged as more pronounced in case of stroke severity: the first pattern was centered on left-hemispheric insular, opercular and inferior frontal regions, while the second pattern combined right-hemispheric temporo-parietal regions. Bilateral subcortical regions were most relevant in explaining long term unfavorable outcome. No WMH-specific lesion patterns of functional outcomes were substantiated. However, a higher overall WMH burden was associated with higher odds of unfavorable outcomes.ConclusionsHigher WMH burden increases stroke severity in case of stroke lesions involving left-hemispheric insular, opercular and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporo-parietal regions (potentially linked to attention). These findings may contribute to augment stroke outcome predictions and motivate a WMH burden and stroke lesion pattern-specific clinical management of AIS patients.

This meeting abstract was removed due to the OA licensing requirements of this journal. The full abstract is listed here : https://www.svin.org/files/SVIN_2021_Abstracts_for_Web.pdf

While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological brain age. We here first examine whether radiomics analysis of the texture of brain T2-FLAIR MRI images can be used to predict brain age in stroke patients. We then assess the clinical determinants of accelerated brain aging and, finally, its impact on post-stroke functional outcomes. Leveraging a multisite cohort of 4,163 ischemic stroke patients, we show that older-appearing patients have more hypertension, diabetes mellitus, prior strokes, and smoking history and are more likely to develop worse post-stroke outcomes than their younger-appearing counterparts. Our results strengthen the importance of preventive medicine for maintaining brain health in stroke patients as they age and suggest a novel methodology to capture previously undescribed prognostic information available on commonly acquired MRI sequences during routine stroke care.

Background: Syndrome of the Trephined (SoT) is an underrecognized complication after decompressive craniectomy. We aimed to investigate SoT incidence, clinical spectrum, risk factors, and the impact of the cranioplasty on neurological recovery.Methods: Patients undergoing a large craniectomy (>80 cm2) and cranioplasty were prospectively evaluated using modified Rankin score (mRS) and cognitive (attention, processing speed, executive function, language, visuospatial neglect), motor (Motricity Index, Jamar dynamometer, postural score, gait assessment), and radiological evaluation within four days before and after cranioplasty. The primary outcome was SoT, diagnosed when a neurological improvement was observed after the cranioplasty. The secondary outcome was good outcome (mRS 0-3) four days and 90 days after the cranioplasty. Logistic regression models were used to evaluate the risk factors for SoT and the impact of cranioplasty timing on neurological recovery.Results: twenty-six patients (65%) developed SoT and improved after cranioplasty. Brain trauma, hemorrhagic lesions, and shifting of brain structures were associated with SoT. After cranioplasty, a shift towards a good outcome was observed within four days (p=0.025) and persisted at 90 days (p=0.005). Increasing delay to cranioplasty was associated with decreased odds of improvement when adjusting for age and baseline disability (odds ratio 0.96; 95% CI, 0.93-0.99 p=0.012).Conclusions: SoT is frequent after craniectomy and interferes with recovery. A high suspicion of SoT should be exercised in patients who fail to progress or have a previous trauma, hemorrhage, or shifting of brain structures. Performing the cranioplasty earlier was associated with improved and quantifiable neurological recovery.

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke., Acute ischemic stroke impacts men and women differently. Here, the authors show how different lesion patterns in men and women are linked to the extent of stroke severity.

Introduction: Patterns of hospital presentation have changed during the COVID-19 pandemic. In stroke, delayed or avoided care may translate to substantial morbidity. We sought to determine the effect of the pandemic on patterns of stroke patient presentation and quality of care. Methods: We analyzed data from 25 New England hospitals: one urban, academic comprehensive stroke center (CSC) and telestroke hub, and 24 spoke hospitals in the telestroke network. We included all telestroke consultations from the 24 spokes and stroke admissions to the CSC from 11/1/2019 through 4/30/2020. We examined trends in stroke presentation including large vessel occlusion (LVO), alteplase use, and endovascular thrombectomy among eligible subjects. We compared proportions and bivariate comparisons to examine for changes pre- vs. post-3/1/2020, and used linear regression to examine trends over time. Results: Among 1248 patient presentations, telestroke consultations (0.4 fewer consults per week, p=0.005) and ischemic stroke patient admissions (decrease of 0.2 patients per week, p=0.04) decreased among the spokes and hub. Age and stroke severity were unchanged over the study period. We found no change in alteplase administration at telestroke spoke hospitals, but did note a decrease in both alteplase use (1.5 per week prior to March 1 st and 1 per week after, p=0.05) and thrombectomy at our CSC (0.1 fewer cases per week, p=0.02). Time metrics for patient presentation and care delivery were unchanged, however, rates of adherence for several quality measures were reduced during the pandemic (Table 1). Conclusions: In this regional analysis, we found decreasing telestroke consultations and ischemic stroke admissions, and reduced performance on stroke quality of care measures during the COVID-19 pandemic. Contrary to prior reports, we did not find an increase in thrombectomy nor decrease in clinical severity that might be expected if patients with milder symptoms avoided hospitalization.

Introduction: As a radiographic signature of end-stage small vessel disease, white matter hyperintensity (WMH) burden impacts recovery and outcomes after acute ischemic stroke (AIS). In this study, we sought to investigate the effect of WMH volume (WMHv) on stroke severity and functional outcomes independent of the infarct size and topography. Methods: We analyzed 503 AIS patients with MRI data obtained on admission for index stroke enrolled in the multi-center MRI-GENIE study (cohort 1), followed by validation of the findings in an independent single-site study of 555 AIS patients (cohort 2). Stroke severity (NIHSS score) at index stroke and the long-term outcome (3-6 months mRS score) were modeled via Bayesian linear regression. Models included WMHv, age, sex, a 10-dimensional spatial ischemic lesion representation, acute infarct (DWI) volume, and common vascular risk factors (hypertension, diabetes mellitus, atrial fibrillation, coronary artery disease). Results: Cohorts did not differ significantly in major clinical characteristics [cohort 1: age: 65.0±14.6, 41% female, NIHSS: 5.5±5.4, mRS: 1(iqr 2); cohort 2: age: 65.0±14.8, 38% female, NIHSS: 5.0±6.0, mRS: 1(iqr 3), p >0.05 for all comparisons]. WMHv did not substantially affect AIS severity ( Fig A ); in contrast, it emerged as an independent predictor of functional outcome in both datasets ( Fig B ). Conclusions: When accounted for AIS lesion topography and stroke volume, total WMH lesion burden did not appear to modulate initial stroke severity but was associated with worse functional post-stroke outcomes. Future studies are needed to explore potential origins of these detrimental effects of pre-existing WMH burden on recovery after AIS.

Introduction: MRI-detected white matter hyperintensity (WMH) burden is linked to the overall brain health and incident stroke outcomes. However, data are limited as to whether specific acute stroke lesion patterns purport greater stroke severity in patients with extensive pre-existing WMH. In this analysis, we sought to investigate lesion pattern-specific WMH effects on AIS severity. Methods: We analyzed clinical and lesion data from 621 AIS patients enrolled in the multi-site MRI-GENIE study. The acute NIHSS was modelled via Bayesian hierarchical regression. Using ten stroke lesion patterns, that served as input variables of main interest, we introduced a hierarchical level differentiating between patients with higher and lower than median WMH volume (WMHv). Lesion pattern posterior distributions for higher and lower WMHv patients were subtracted to infer substantial differences. Results: In this AIS cohort [age: 65.3±14.6, 60% male], a higher WMHv was associated with greater stroke severity only when specific left-hemispheric brain regions were infarcted. This “lesion pattern 3” was mainly characterized by left middle and inferior frontal gyrus, insular and opercular cortex, pre- and post-central gyrus, and subcortical basal ganglia regions ( Fig ). Conclusions: Higher WMH burden appears to enhance the detrimental effect of acute stroke lesions involving left-hemispheric brain regions underlying language and motor functions. This effect might be due to an exacerbated disruption of functional network integrity by the combination of WMH and stroke lesions and could be explored further in functional imaging studies that simultaneously considered information from both lesion types.

Objective: Ability of the brain to recover after an acute ischemic stroke (AIS) is linked to the pre-stroke burden of white matter hyperintensity (WMH), a radiographic marker of brain health. We sought to determine the excessive WMH burden in an AIS population and investigate its association with 3-month stroke outcomes. Data: We used 2,435 subjects from the MRI-GENIE study. Three-month functional outcomes of 872 subjects among those subjects were measured by 90-day modified Ranking Scale (mRS). Methods: We automatically quantified WMH volume (WMHv) on FLAIR images and adjusted for a brain volume. We modeled a trend using the factor analysis (FA) log-linear regression using age, sex, atrial fibrillation, diabetes, hypertension, coronary artery disease and smoking as input variables. We categorized three WMH burden groups based on the conditional probability given by the model (LOW: lower 33%, MED: middle 34%, and HIGH: upper 33%). The subgroups were compared with respect to mRS (median and dichotomized odds ratio (OR) (good/poor: mRS 0-2/3-6)). Results: Five FA components out of seven with significant relationship to WMHv (p2 =0.359). The HIGH group showed higher median (median=2, IQR=2) mRS score than LOW (median=1, IQR=1) and MED (median=1, IQR=1). The odds (OR) of good AIS outcome for LOW and MED were 1.8 (p=0.0001) and 1.6 (p=0.006) times higher than HIGH, respectively. Conclusion: Once accounted for clinical covariates, the excessive WMHv was associated with worse 3-month stroke outcomes. These data suggest that a life-time of injury to the white matter reflected in WMH is an important factor for stroke recovery and an indicator of the brain health.

Introduction: Endovascular thrombectomy (EVT) has revolutionized large vessel occlusion (LVO) stroke care. However, over half remain functionally disabled or die despite treatment. Understanding outcomes may influence EVT selection, novel therapies, and prognostication. We sought to identify associations between outcomes and brain regions involved in ischemic lesions. Methods: For consecutive LVO patients with post-EVT MRI, acute ischemic lesions were manually segmented from DWI and spatially normalized. Individual lesion volumes were automatically parcellated (atlas-defined 94 cortical regions, 14 subcortical nuclei, 20 white matter tracts) and then reduced to ten anatomically plausible lesion patterns using unsupervised dimensionality reduction techniques. Ninety-day modified Rankin Scale (mRS) was modeled via Bayesian regression, taking the ten lesion patterns as inputs and controlling for lesion size, age, sex, acute NIH Stroke Scale, alteplase, and TICI 2b-3 reperfusion. Results: We identified 153 LVO patients with mean age 68±15 years and 51% female. Median NIHSS was 16 (IQR 13-20), 56% received alteplase, and 84% achieved TICI2b-3. The lesion patterns predictive of 90-day mRS involved bilateral subcortical nuclei, pre- and postcentral gyri, insular and opercular cortex, as well as left-sided inferior frontal and angular gyri ( Figure 1A ). Lesions affecting white matter tracts had the highest relevance predicting 90-day mRS ( Figure 1B ). Conclusions: These data describe the significance for outcomes of specific brain regions involved in ischemic lesions on MRI after EVT. Future work in additional datasets is needed to confirm these granular findings.

Introduction: Peak width of skeletonized mean diffusivity (PSMD) is a novel fully automated diffusion tensor imaging (DTI) marker that has been consistently associated with cognition in cerebral small vessel disease (SVD) cohorts, including cerebral amyloid angiopathy (CAA). We hypothesized that PSMD would be more strongly associated with cognitive performance compared to other conventional DTI metrics in our CAA sample. Methods: We recruited non-demented subjects with probable-CAA from a single-center memory-clinic cohort. We analyzed structural MRIs to compute a validated CAA burden score (0-6 points scale, based on the following MRI features: lobar microbleeds, superficial siderosis, perivascular spaces in centrum semiovale, and white matter hyperintensities). PSMD was obtained using a freely available script ( www.psmd-marker.com ). We used the same skeleton-mask to compute: mean of skeletonized mean diffusivity (mean MD) and mean of skeletonized fractional anisotropy (mean FA). We used linear regression analyses to explore relationships with CAA burden score and cognitive composite scores (processing speed, executive function, memory, and language - z-scores adjusted for age, sex and education level). Results: We included 43 subjects (mean age 74.4 ± 5.9 years; 48.8% female; PSMD median [IQR]: 4.05 [3.58 - 4.80] x 10 -4 mm 2 /s). In linear regression models adjusting for age, DTI metrics were significantly associated with CAA burden score (mean FA: β = -0.563, Adj. R 2 : 0.27; p < 0.001; mean MD: β = 0.581; Adj. R 2 : 0.32; p < 0.001; PSMD: β = 0.364, Adj. R 2 : 0.12; p = 0.018). PSMD was significantly associated with cognitive performance, specifically in the domains of executive function ( β = -0.568; Adj. R 2 : 0.25; p < 0.001) and processing speed ( β = -0.447; Adj. R 2 : 0.19; p = 0.004). Other DTI metrics were not significantly associated with cognitive scores. Conclusion: In this CAA sample, all DTI metrics were associated with CAA burden scores, however, only PSMD was significantly associated with cognition, in domains that are commonly affected in vascular cognitive impairment. Our results warrant confirmation in larger samples, but support PSMD as biomarker for cognition in CAA, outperforming other conventional DTI metrics.

Introduction: Severity of leukoaraiosis detected on T2 MRI scans as white matter hyperintensities (WMH) is associated with infarct growth and poor poststroke outcomes in patients with acute ischemic stroke (AIS). Traditional vascular risk factors (VRF) such as age, hypertension (HTN), type 2 diabetes mellitus (T2D), and cigarette smoking are linked to WMH in large population-based studies, yet casual inferences for WMH in AIS patients are limited. We sought to examine the VRFs for evidence of causal relationships with WMH burden in AIS patients using mendelian randomization principles and polygenic risk score (PRS) methods. Method: We examined FLAIR MRIs obtained within 48 hours of AIS onset in 4,362 European Caucasian patients from the MRI-GENetics Interface Exploration (MRI-GENIE) study. WMH volume (WMHv) was measured using a fully automated deep-learning trained algorithm. We considered 13 VRFs: blood pressure (HTN, SBP, DBP, Pulse Pressure), lipid (total cholesterol, HDL, LDL, TG), BMI, T2D, atrial fibrillation, alcohol use and smoking. For each factor, we calculated a weighted PRS for each individual based on the most recent GWAS with various GWAS p-value cutoff. We then used linear regression to estimate associations between each PRS and log transformed WMHv, controlling for age, gender and principal components of genetic ancestries. Strata-specific estimates were combined using inverse-variance weighting based meta-analysis. Results: PRS of both SBP and DBP were positively and robustly associated with WMHv in the meta-analysis (p value of the association ranging from Conclusion: Using mendelian randomization, our results lend further evidence that high blood pressure is a causal risk factor for WMH in AIS patients. This result is consistent with previous epidemiological studies of leukoaraiosis in stroke-free populations, and it supports universal control of HTN as common contributor to WMH burden and the overall brain health.

Introduction: Structural integrity of cerebral parenchyma is an essential radiographic equivalent of brain health; but its assessment usually requires dedicated advanced image acquisitions. Radiomics analyses bear the potential to describe radiophenotypes beyond what meets the naked eye. We sought to: 1) evaluate this novel approach to predict white matter hyperintensity (WMH) burden and 2) uncover latent clinico-radiological associations. Methods: An international, multi-site cohort of 4,164 acute ischemic stroke (AIS) patients with FLAIR MRI (MRI-GENIE study) underwent total brain and WMH lesion segmentation using convolutional neural networks. Radiomic features (n=1905) were extracted from clinical FLAIR images outside of the WMH (brain mask - WMH mask). Prediction of the WMH burden using radiomics was done using LASSO regression. Radiomic signature of WMH was built with the most stable selected features, then compared to the clinical variables using canonical correlation analysis. Results: In this cohort, (mean age=62.8±15.0, median WMH volume=4.2cc IQR 1.4-11.2), radiomic features were highly predictive of WMH burden (R2=0.8±0.012). Radiomic signature of WMH included 68 features. All 7 pairs of extracted canonical variates (CV) were statistically significant with respective canonical correlations of 0.79, 0.64, 0.44, 0.21, 0.16, 0.15 (Bonferroni corrected p-values CV1-6 CV7 =.003). Upon examination, CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes mellitus (DM), CV4 by hypertension, CV5 by atrial fibrillation (AF) and DM, CV6 by coronary artery disease (CAD) and CV7 by CAD and DM. Conclusion: Radiomics extracted from clinical grade FLAIR images of AIS patients seem able to capture structural integrity of the cerebral parenchyma and to correlate with clinical phenotypes. Further research could evaluate radiomics to predict the progression of cerebral small vessel disease on longitudinal data.

IntroductionNeuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of describing the texture of conventional images beyond what meets the naked eye, radiomic analyses hold potential for evaluating brain health. We sought to: 1) evaluate this novel approach to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and 2) uncover associations between predictive radiomic features and patients’ clinical phenotypes.MethodsOur analyses were based on a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images and corresponding deep-learning-generated total brain and WMH segmentation. Radiomic features were extracted from normal-appearing brain tissue (brain mask–WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with the most stable selected features predictive of WMH burden and then related this signature to clinical variables (age, sex, hypertension (HTN), atrial fibrillation (AF), diabetes mellitus (DM), coronary artery disease (CAD), and history of smoking) using canonical correlation analysis.ResultsRadiomic features were highly predictive of WMH burden (R2=0.855±0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-valuesCV1-6CV7=.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and DM, CV4 by HTN, CV5 by AF and DM, CV6 by CAD, and CV7 by CAD and DM.ConclusionRadiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes. Further research could evaluate radiomics to predict the progression of WMH.Research in contextEvidence before this studyWe did a systematic review on PubMed until December 1, 2020, for original articles and reviews in which radiomics were used to characterize stroke or cerebrovascular diseases. Radiomic analyses cover a broad ensemble of high-throughput quantification methods applicable to digitalized medical images that extract high-dimensional data by describing a given region of interest by its size, shape, histogram, and relationship between voxels. We used the search terms “radiomics” or “texture analysis”, and “stroke”, “cerebrovascular disease”, “small vessel disease”, or “white matter hyperintensities”. Our research identified 24 studies, 18 studying radiomics of stroke lesions and 6 studying cerebrovascular diseases. All the latter six studies were based on MRI (T1-FLAIR, dynamic contrast-enhanced imaging, T1 & T2-FLAIR, T2-FLAIR post-contrast, T2-FLAIR, and T2-TSE images). Four studies were describing small vessel disease, and two were predicting longitudinal progression of WMH. The average sample size was small with 96 patients included (maximum: 204). One study on 141 patients identified 7 T1-FLAIR radiomic features correlated with cardiovascular risk factors (age and hyperlipidemia) using univariate correlations. All studies were monocentric and performed on a single MRI scanner.Added value of this studyTo date and to the best of our knowledge, this is the largest radiomics study performed on cerebrovascular disease or any topic, and one of the very few to include a great diversity of participating sites with diverse clinical MRI scanners. This study is the first one to establish a radiomic signature of WMH and to interpret its relationship with common cardiovascular risk factors. Our findings add to the body of evidence that damage caused by small vessel disease extend beyond the visible white matter hyperintensities, but the added value resides in the detection of that subvisible damage on routinely acquired T2-FLAIR imaging. It also suggests that cardiovascular phenotypes might manifest in distinct textural patterns detectable on conventional clinical-grade T2-FLAIR images.Implications of all the available evidenceAssessing brain structural integrity has implications for treatment selection, follow-up, prognosis, and recovery prediction in stroke patients but also other neurological disease populations. Measuring cerebral parenchymal structural integrity usually requires advanced imaging such as diffusion tensor imaging or functional MRI. Translation of those neuroimaging biomarkers remains uncommon in clinical practice mainly because of their time-consuming and costly acquisition. Our study provides a potential novel solution to assess brains’ structural integrity applicable to standard, routinely acquired T2-FLAIR imaging.Future research could, for instance, benchmark this radiomics approach against diffusion or functional MRI metrics in the prediction of cognitive or functional outcomes after stroke.

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.