Citations

Abstract This study aimed to investigate the influence of stroke lesions in predefined 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 enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. 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. 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 1000 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 effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. 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. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Abstract Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.

Abstract 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.

Intracerebral haemorrhage (ICH) and small vessel ischaemic stroke (SVS) are the most severe manifestations of cerebral small vessel disease. In a cross-phenotype genome-wide association analysis, Chung et al. identify two novel associations at 2q33 and 13q34 plus a previously identified locus at 1q22 for non-lobar ICH and SVS risk.

Background and Purpose— We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms’ performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results— The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P <0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm 3 (0.9–16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P <0.0001) and different topography compared with other stroke subtypes. Conclusions— Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Background and Purpose— We aimed to assess if renal function can aid in risk stratification for ischemic stroke or transient ischemic attack (TIA) recurrence and death in patients with embolic stroke of undetermined source (ESUS). Methods— We pooled 12 ESUS datasets from Europe and America. Renal function was evaluated using the estimated glomerular filtration rate (eGFR) and analyzed in continuous, binary, and categorical way. Cox-regression analyses assessed if renal function was independently associated with the risk for ischemic stroke/TIA recurrence and death. The Kaplan-Meier product limit method estimated the cumulative probability of ischemic stroke/TIA recurrence and death. Results— In 1530 patients with ESUS followed for 3260 patient-years, there were 237 recurrences (15.9%) and 201 deaths (13.4%), corresponding to 7.3 ischemic stroke/TIA recurrences and 5.6 deaths per 100 patient-years, respectively. Renal function was not associated with the risk for ischemic stroke/TIA recurrence when forced into the final multivariate model, regardless if it was analyzed as continuous (hazard ratio, 1.00; 95% CI, 0.99–1.00 for every 1 mL/min), binary (hazard ratio, 1.27; 95% CI, 0.87–1.73) or categorical covariate (likelihood-ratio test 2.59, P =0.63 for stroke recurrence). The probability of ischemic stroke/TIA recurrence across stages of renal function was 11.9% for eGFR ≥90, 16.6% for eGFR 60–89, 21.7% for eGFR 45–59, 19.2% for eGFR 30–44, and 24.9% for eGFR <30 (likelihood-ratio test 2.59, P =0.63). The results were similar for the outcome of death. Conclusions— The present study is the largest pooled individual patient-level ESUS dataset, and does not provide evidence that renal function can be used to stratify the risk of ischemic stroke/TIA recurrence or death in patients with ESUS.