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Abstract Objective To systematically evaluate which lesion‐based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets. Methods We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes. Results We found that prediction models trained on small single‐center datasets could perform well using within‐dataset cross‐validation, but results did not generalize to independent datasets (median R2N1 = 0.2%). Performance across independent datasets improved using large single‐center training data (R2N2 = 15.8%) and improved further using multicenter training data (R2N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P

ObjectiveThis study aimed to determine whether brain volume at the time of ischemic stroke injury is a better biomarker for predicting functional outcomes than brain atrophy.BackgroundBrain parenchymal fraction (BPF) has been used as a surrogate measure of global brain atrophy and a neuroimaging biomarker of brain reserve in studies evaluating clinical outcomes after brain injury. Brain volume itself is affected by natural aging, cardiovascular risk factors, and biological sex, among other factors. Recent studies have shown that brain volume at the time of injury can influence functional outcomes, with larger brain volumes being associated with better outcomes.MethodsAcute ischemic stroke cases at a single center between 2003 and 2011, with neuroimaging obtained within 48 h of presentation were eligible for the study. Functional outcomes represented by the modified Rankin Scale (mRS) score at 90 days post-admission (mRS score ≤ 2 deemed a favorable outcome) were obtained through patient interviews or per chart review. Deep learning–enabled automated segmentation pipelines were used to calculate brain volume, intracranial volume, and BPF on the acute neuroimaging data. Patient outcomes were modeled through logistic regressions, and a model comparison was conducted using the Bayes information criterion (BIC).ResultsA total of 467 patients with arterial ischemic stroke were included in the analysis, with a median age of 65.8 years and 65.3% of the participants being male. In both models, age and a larger stroke lesion volume were associated with worse functional outcomes. Higher BPF and a larger brain volume were associated with favorable functional outcomes; however, a comparison of both models suggested that the brain volume model (BIC = 501) better explains the data than the BPF model (BIC = 511).ConclusionThe extent of global brain atrophy (and its surrogate biomarker BPF) has been regarded as an important biomarker for predicting functional post-stroke outcomes and resilience to acute injury. In this study, we demonstrate that a higher global brain volume at the time of injury better explains favorable functional outcomes, which can be directly measured in a clinical setting.

Introduction The mantra ‘time is brain’ cannot be overstated for patients suffering from acute ischemic stroke. This is especially true for those with large vessel occlusions (LVO) requiring transfer to an endovascular thrombectomy (EVT) capable center. We sought to evaluate the spoke hospital door in‐door out (DIDO) times for patients transferred to our hub center for EVT. Methods Individuals who first presented with LVO to a spoke hospital and were then transferred to the hub for EVT were retrospectively identified from a prospectively maintained database from January 2019 to November 2022. DIDO was defined as the time between spoke hospital door in arrival and door out exit. Baseline characteristics, treatments, and outcomes were compared, dichotomizing DIDO at 90 min based in the American Heart Association goal for DIDO ≤90 min for 50% of transfers. Multivariable regression analyses were performed for determinants of 90‐day ordinal modified Ranking Scale (mRS) and DIDO. Results We identified 194 patients transferred for EVT with available DIDO. The median age was 67 (IQR 57‐80), and 46% were female. The median NIHSS was 16 (10‐20), 50% were treated with intravenous thrombolysis at a spoke, and TICI 2B‐3 reperfusion was achieved in 87% at the hub. The median DIDO was 120 min (97‐149), with DIDO ≤90 min achieved in 18%. DIDO was a significant determinant of 90‐day ordinal mRS (B=0.007, 95%CI=0.001‐0.012, p=0.013), even when accounting for last known well‐to‐spoke door in, spoke door out‐to‐hub arrival, hub arrival‐to‐puncture, puncture‐to‐first pass, age, NIHSS, intravenous thrombolysis, TICI 2B‐3, and symptomatic intracranial hemorrhage. Importantly, determinants of DIDO included Black race or Hispanic ethnicity (B=0.918, 95%CI=0.010‐1.826, p=0.048), atrial fibrillation or heart failure (B=0.793, 95%CI=0.257‐1.329, p=0.004), and basilar LVO location (B=2.528, 95%CI=1.154‐3.901, p

Introduction Patients with baseline disability account for one‐third of stroke presentations. However, there remains controversy in treatment selection for endovascular thrombectomy (EVT). We compared long‐term outcomes and likelihood of transitioning to comfort care for large vessel occlusion (LVO) patients with severe pre‐stroke disability treated with EVT versus medical management at a single center from 2017‐2020. Methods Individuals who presented with LVO were identified retrospectively from a prospectively maintained database. Severe baseline disability was defined as modified Rankin Scale (mRS) 3‐5. Delta mRS was defined as the difference between baseline and 90‐day mRS. Logistic and ordinal regressions were performed to evaluate the relationships between EVT and outcomes. A mixed‐methods analysis was performed to assess rates and reasons for transitions to comfort care. Results A total of 175/1008 (17%) were identified with severe baseline disability. The median age was 82 (IQR 70‐89), and 59% were female. Thirty‐two (18%) with severe baseline disability were treated with EVT. EVT was independently associated with improved delta mRS (B=‐1.048; 95%CI=‐1.777,‐0.318; p=0.005) accounting for age and NIHSS. However, EVT did not reduce the odds of transitioning to comfort care (aOR=0.794; 95%CI=0.347,1.818; p=0.585) accounting for age and NIHSS. Seventy‐six (43%) with severe baseline disability were transitioned to comfort care. Of the 99 not transitioned to comfort care, 18 were treated with EVT, and EVT was independently associated with improved delta mRS (B=‐2.794; 95%CI=‐4.002,‐1.586; p

Introduction Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. The epidemiology and pathophysiology of coma in ischemic stroke has been underexplored. We sought to characterize the incidence and clinical features of coma as a presentation of large vessel occlusion (LVO) stroke. Methods Individuals who presented with LVO were retrospectively identified from July 2018 to December 2020. Coma was defined as an unresponsive state of impaired arousal and awareness, operationalized as a score of 3 on NIH Stroke Scale (NIHSS) item 1a. Results A total of 28/637 (4.4%) patients with LVO stroke were identified as presenting with coma. The median NIHSS was 32 (IQR 29‐34) for those with coma versus 11 (5‐18) for those without (p

Bayesian modeling of anatomical lesion patterns and clinical language outcomes in a cohort of 1401 stroke patients identifies sex-biased lesion-outcome effects after stroke.

Introduction As more large vessel occlusion stroke patients are treated with endovascular thrombectomy (EVT), understanding the pathophysiology of reperfusion injury and the risks of hemorrhagic transformation (HT) are increasingly important. Pre‐EVT infarct topography may have implications for acute interventional treatments such as stenting, and post thrombectomy care such as antithrombotic choice. We sought to quantify region‐specific volumes of infarcted tissue on pre‐EVT MRI, understand their importance for HT, and identify associations with clinical and imaging characteristics. Methods Patients with pre‐EVT MRI were identified retrospectively from a prospectively maintained database. Each patient’s diffusion weighted sequence underwent manual infarct delineation and was registered to a standard space for overlay with cortical, subcortical, and white matter atlases. Structure‐specific lesion volumes were determined. HT was defined as PH1 or PH2 hemorrhage by ECASS criteria. Variables with p< 0.10 in univariate analyses were included in multivariable models. Logistic regression was performed for associations with hemorrhagic transformation and linear regressions for infarct volumes. Results 165 participants [median age 69 years (interquartile range, IQR 56–79), 56% women] were identified. Risk factors included hypertension (70%), diabetes (20%), atrial fibrillation (34%), and prior stroke/TIA (13%). 52% were treated with intravenous alteplase; 70% achieved TICI 2b‐3 reperfusion. HT occurred in 8%. Pre‐EVT infarct volumes [median (IQR)] were 22 cc (12‐43 cc) for total, 11 cc (6‐19 cc) for white matter, 5 cc (1‐19 cc) for cortex, and 3 cc (1‐6 cc) for basal ganglia. Pre‐EVT infarcts [median (IQR)] were made up of 48% (38‐60%) white matter, 23% (6‐47%) cortex, and 15% (4‐28%) basal ganglia. Paramagnetic sequences showed 3% had petechial hemorrhage and 40% had susceptibility vessel sign. Basal ganglia infarct volume was independently associated with HT (OR = 1.342, 95%CI = 1.002,1.797) in a model including white matter infarct volume, cortex infarct volume, smoking, and puncture‐recanalization time. Basal ganglia infarct volume was linked to susceptibility vessel sign (Beta = 0.233, p = 0.006) and NIHSS (Beta = 0.220, p = 0.012), when controlling for total infarct volume. Conclusions In this cohort, greater basal ganglia infarct volume was associated with a higher risk of hemorrhagic transformation, even when accounting for infarct volume in other regions. Susceptibility vessel sign was associated with basal ganglia infarct volume, which may be related to acute middle cerebral artery thrombus occlusion of perforators. These findings require further study in larger cohorts.

Background Intravenous thrombolysis (IVT) before mechanical thrombectomy (MT) for large‐vessel occlusion (LVO) stroke is increasingly controversial. Recent trials suggest MT without IVT is reasonable for patients presenting directly to MT‐capable “hub” centers. However, bypassing IVT has not been evaluated for patients presenting to IVT‐capable “spoke” hospitals that require hub transfer for MT. A perceived lack of efficacy of IVT to result in LVO early recanalization (ER) is often cited to support bypassing IVT, but data for IVT in patients who require interhospital transfer are limited. Here, we examined LVO ER rates after spoke‐administered IVT in our hub‐and‐spoke stroke network. Methods Patients presenting to 25 spokes before hub transfer for MT consideration from 2018 to 2020 were retrospectively identified from a prospectively maintained database. Inclusion criteria were pretransfer computed tomography angiography–defined LVO, Alberta Stroke Program Early Computed Tomography Score ≥6, and posttransfer repeat vessel imaging. Results Of 167 patients, median age was 69, and 51% were women. Seventy‐six received spoke IVT, and 91 did not. Alteplase was the only IVT used in this study. Comorbidities and National Institutes of Health Stroke Scale were similar between groups. ER frequency was increased 7.2‐fold in patients who received spoke IVT (12/76 [15.8%] versus 2/91 [2.2%]; P

Background For patients with large vessel occlusion (LVO) stroke, time to treatment with endovascular thrombectomy is crucial to prevent infarction and improve outcomes. We sought to evaluate the hub arrival‐to‐puncture times and outcomes for transferred patients accepted directly to the angio‐suite (LVO to operating room, LVO2OR) versus those accepted through the emergency department in a hub‐and‐spoke telestroke network. Methods Consecutive patients transferred for endovascular thrombectomy with spoke computed tomography angiography–confirmed LVO, spoke Alberta Stroke Program Early Computed Tomography score >6, and last known well–to–hub arrival

Background Access 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. Methods Consecutive 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. Results Among 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%; P

Background purposeA substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort.Materials and methodsAnalyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome.ResultsWe analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, pFDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, pFDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL.ConclusionMultiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.

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.

Background The treatment of stroke has been undergoing rapid changes. As treatment options progress, prediction of those under risk for complications becomes more important. Available models have, however, frequently been built based on data no longer representative of today’s care, in particular with respect to acute stroke management. Our aim was to build and validate prediction models for 4 clinically important, severe outcomes after stroke. Methods and Results We used German registry data from 152 710 patients with acute ischemic stroke obtained in 2016 (development) and 2017 (validation). We took into account potential predictors that were available at admission and focused on in‐hospital mortality, intracranial mass effect, secondary intracerebral hemorrhage, and deep vein thrombosis as outcomes. Validation cohort prediction and calibration performances were assessed using the following 4 statistical approaches: logistic regression with backward selection, l1‐regularized logistic regression, k‐nearest neighbor, and gradient boosting classifier. In‐hospital mortality and intracranial mass effects could be predicted with high accuracy (both areas under the curve, 0.90 [95% CI, 0.90–0.90]), whereas the areas under the curve for intracerebral hemorrhage (0.80 [95% CI, 0.80–0.80]) and deep vein thrombosis (0.73 [95% CI, 0.73–0.73]) were considerably lower. Stroke severity was the overall most important predictor. Models based on gradient boosting achieved better performances than those based on logistic regression for all outcomes. However, area under the curve estimates differed by a maximum of 0.02. Conclusions We validated prediction models for 4 severe outcomes after acute ischemic stroke based on routinely collected, recent clinical data. Model performance was superior to previously proposed approaches. These predictions may help to identify patients at risk early after stroke and thus facilitate an individualized level of care.

Introduction: The care of emergent large vessel occlusion (ELVO) stroke patients has been revolutionized by endovascular thrombectomy (EVT). Given its robust efficacy, it is crucial to optimize delivery to eligible patients. 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 reasons EVT candidates become ineligible after transfer for treatment. 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: 258 patients were identified with median age 70 years (IQR 60–81) and 50% female. 44% 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

Objective: To personalize the prognostication of post-stroke outcome using MRI-detected cerebrovascular pathology, we sought to investigate the association between the excessive white matter hyperintensity (WMH) burden unaccounted for by the traditional stroke risk profile of individual patients and their long-term functional outcomes after a stroke.Methods: We included 890 patients who survived after an acute ischemic stroke from the MRI-Genetics Interface Exploration (MRI-GENIE) study, for whom data on vascular risk factors (VRFs), including age, sex, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, smoking, prior stroke history, as well as acute stroke severity, 3- to−6-month modified Rankin Scale score (mRS), WMH, and brain volumes, were available. We defined the unaccounted WMH (uWMH) burden via modeling of expected WMH burden based on the VRF profile of each individual patient. The association of uWMH and mRS score was analyzed by linear regression analysis. The odds ratios of patients who achieved full functional independence (mRS < 2) in between trichotomized uWMH burden groups were calculated by pair-wise comparisons.Results: The expected WMH volume was estimated with respect to known VRFs. The uWMH burden was associated with a long-term functional outcome (β = 0.104, p < 0.01). Excessive uWMH burden significantly reduced the odds of achieving full functional independence after a stroke compared to the low and average uWMH burden [OR = 0.4, 95% CI: (0.25, 0.63), p < 0.01 and OR = 0.61, 95% CI: (0.42, 0.87), p < 0.01, respectively].Conclusion: The excessive amount of uWMH burden unaccounted for by the traditional VRF profile was associated with worse post-stroke functional outcomes. Further studies are needed to evaluate a lifetime brain injury reflected in WMH unrelated to the VRF profile of a patient as an important factor for stroke recovery and a plausible indicator of brain health.

Abstract Introduction Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. Methods Surveys, teleconferences, and an in‐person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. Results A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness‐neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. Conclusions The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.

Memory is hypothesised to depend on different brain regions that interact in a network. Here, the authors use case studies of stroke patients with amnesia from the literature to identify brain regions that are part of this network.

Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.

ObjectiveNeuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.MethodsWe analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. 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 stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).ResultsRadiomic features were 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-valuesCV1–6 < 0.001, p-valueCV7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.ConclusionRadiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients’ brain health.

White matter hyperintensities of presumed vascular origin (WMH) are a prevalent form of cerebral small-vessel disease and an important risk factor for post-stroke cognitive dysfunction. Despite this prevalence, it is not well understood how WMH contributes to post-stroke cognitive dysfunction. Preliminary findings suggest that increasing WMH volume is associated with total hippocampal volume in chronic stroke patients. The hippocampus, however, is a complex structure with distinct subfields that have varying roles in the function of the hippocampal circuitry and unique anatomical projections to different brain regions. For these reasons, an investigation into the relationship between WMH and hippocampal subfield volume may further delineate how WMH predispose to post-stroke cognitive dysfunction. In a prospective study of acute ischemic stroke patients with moderate/severe WMH burden, we assessed the relationship between quantitative WMH burden and hippocampal subfield volumes. Patients underwent a 3T MRI brain within 2–5 days of stroke onset. Total WMH volume was calculated in a semi-automated manner. Mean cortical thickness and hippocampal volumes were measured in the contralesional hemisphere. Total and subfield hippocampal volumes were measured using an automated, high-resolution, ex vivo computational atlas. Linear regression analyses were performed for predictors of total and subfield hippocampal volumes. Forty patients with acute ischemic stroke and moderate/severe white matter hyperintensity burden were included in this analysis. Median WMH volume was 9.0 cm3. Adjusting for intracranial volume and stroke laterality, age (β = −3.7, P < 0.001), hypertension (β = −44.7, P = 0.04), WMH volume (β = −0.89, P = 0.049), and mean cortical thickness (β = 286.2, P = 0.006) were associated with total hippocampal volume. In multivariable analysis, age (β = −3.3, P < 0.001) and cortical thickness (β = 205.2, P = 0.028) remained independently associated with total hippocampal volume. In linear regression for predictors of hippocampal subfield volume, increasing WMH volume was associated with decreased hippocampal-amygdala transition area volume (β = −0.04, P = 0.001). These finding suggest that in ischemic stroke patients, increased WMH burden is associated with selective hippocampal subfield degeneration in the hippocampal-amygdala transition area.

Background: Magnetic resonance imaging (MRI) serves as a cornerstone in defining stroke phenotype and etiological subtype through examination of ischemic stroke lesion appearance and is therefore an essential tool in linking genetic traits and stroke. Building on baseline MRI examinations from the centralized and structured radiological assessments of ischemic stroke patients in the Stroke Genetics Network, the results of the MRI-Genetics Interface Exploration (MRI-GENIE) study are described in this work.Methods: The MRI-GENIE study included patients with symptoms caused by ischemic stroke (N = 3,301) from 12 international centers. We established and used a structured reporting protocol for all assessments. Two neuroradiologists, using a blinded evaluation protocol, independently reviewed the baseline diffusion-weighted images (DWIs) and magnetic resonance angiography images to determine acute lesion and vascular occlusion characteristics.Results: In this systematic multicenter radiological analysis of clinical MRI from 3,301 acute ischemic stroke patients according to a structured prespecified protocol, we identified that anterior circulation infarcts were most prevalent (67.4%), that infarcts in the middle cerebral artery (MCA) territory were the most common, and that the majority of large artery occlusions 0 to 48 h from ictus were in the MCA territory. Multiple acute lesions in one or several vascular territories were common (11%). Of 2,238 patients with unilateral DWI lesions, 52.6% had left-sided infarct lateralization (P = 0.013 for χ2 test).Conclusions: This large-scale analysis of a multicenter MRI-based cohort of AIS patients presents a unique imaging framework facilitating the relationship between imaging and genetics for advancing the knowledge of genetic traits linked to ischemic stroke.

The MGP single nucleotide polymorphism (SNP) rs1800801 has previously been associated with recurrent ischemic stroke in a Spanish cohort. Here, we tested for association of this SNP with ischemic stroke recurrence in a North American Caucasian cohort. Acute ischemic stroke patients admitted between 10/2009 and 12/2016 at three hospitals within a large healthcare system in the northeastern United States that were enrolled in a healthcare system-wide exome sequencing program were retrospectively reviewed. Patients with recurrent stroke within 1 year after index event were compared to those without recurrence. Of 9,348 suspected acute ischemic strokes admitted between 10/2009 and 12/2016, 1,727 (18.5%) enrolled in the exome-sequencing program. Among those, 1,068 patients had exome sequencing completed and were eligible for inclusion. Recurrent stroke within the first year of stroke was observed in 79 patients (7.4%). In multivariable analysis, stroke prior to the index stroke (OR 9.694, 95% CI 5.793-16.224, p ≤ 0.001), pro-coagulant status (OR = 3.563, 95% CI 1.504-8.443, p = 0.004) and the AA genotype of SNP rs1800801 (OR = 2.408, 95% CI 1.079-4.389, p = 0.004) were independently associated with recurrent stroke within the first year. The AA genotype of the MGP SNP rs1800801 is associated with recurrence within the first year after ischemic stroke in North American Caucasians. Study of stroke subtypes and additional populations will be required to determine if incorporation of allelic status at this SNP into current risk scores improves prediction of recurrent ischemic stroke.

Objective: To determine whether the rich-club organization, essential for information transport in the human connectome, is an important biomarker of functional outcome after acute ischemic stroke (AIS).Methods: Consecutive AIS patients (N = 344) with acute brain magnetic resonance imaging (MRI) (

Purpose: White matter hyperintensity (WMH) is a common phenotype across a variety of neurological diseases, particularly prevalent in stroke patients; however, vascular territory dependent variation in WMH burden has not yet been identified. Here, we sought to investigate the spatial specificity of WMH burden in patients with acute ischemic stroke (AIS).Materials and Methods: We created a novel age-appropriate high-resolution brain template and anatomically delineated the cerebral vascular territories. We used WMH masks derived from the clinical T2 Fluid Attenuated Inverse Recovery (FLAIR) MRI scans and spatial normalization of the template to discriminate between WMH volume within each subject's anterior cerebral artery (ACA), middle cerebral artery (MCA), and posterior cerebral artery (PCA) territories. Linear regression modeling including age, sex, common vascular risk factors, and TOAST stroke subtypes was used to assess for spatial specificity of WMH volume (WMHv) in a cohort of 882 AIS patients.Results: Mean age of this cohort was 65.23 ± 14.79 years, 61.7% were male, 63.6% were hypertensive, 35.8% never smoked. Mean WMHv was 11.58c ± 13.49 cc. There were significant differences in territory-specific, relative to global, WMH burden. In contrast to PCA territory, age (0.018 ± 0.002, p < 0.001) and small-vessel stroke subtype (0.212 ± 0.098, p < 0.001) were associated with relative increase of WMH burden within the anterior (ACA and MCA) territories, whereas male sex (−0.275 ± 0.067, p < 0.001) was associated with a relative decrease in WMHv.Conclusions: Our data establish the spatial specificity of WMH distribution in relation to vascular territory and risk factor exposure in AIS patients and offer new insights into the underlying pathology.

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery.In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p