Holmegaard, Lukas

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.

Introduction: White matter hyperintensity volume (WMHv) is an important and highly heritable cerebrovascular phenotype; however, manual or semi-automated approaches to clinically acquired MRI analysis hinder large-scale studies in acute ischemic stroke (AIS). In this work, we develop a high-throughput, fully automated WMHv analysis pipeline for clinical fluid-attenuated inversion recovery (FLAIR) images to facilitate rapid genetic discovery in AIS. Methods: Automated WMHv extraction from multiple subjects relies on significant pre-processing of medical scans, including co-registration of the images. To reduce the effects of anisotropic voxel sizes, each FLAIR image is upsampled using bi-cubic interpolation. Brain extraction is performed using RObust Brain EXtraction (ROBEX). Images are then registered to an in-house FLAIR template using Advanced Normalization Tools (ANTs). The spatial covariation of WMH is learned through principal component analysis (PCA) of manual outlines from 100 subjects. Areas of leukoaraiosis are identified and separated from other lesions using the PCA modes. Volumes are then computed using non-interpolated slices for each subject. Standard deviation (SD) in WMHv (9 subjects; 6 raters each) is calculated as a measure of variability. Good agreement between automated and manual outlines is assessed in 358 subjects (automated WMHv within 3SD of manual WMHv). Results: As part of the MRI - Gen etics I nterface E xploration (MRI-GENIE) study, WMHv were calculated on a set of 2703 FLAIR images of patients from 12 independent AIS cohorts (sites). Results are shown in Figure 1. Comparing manual and automated WMHv shows that 88% of the automated WMHv fall within 3 SD from the manual WMHv, suggesting good agreement. Conclusion: WMHv segmentation using a fully-automated pipeline for analysis of clinical MRIs is both feasible and accurate. Ongoing analysis of the extracted WMHv is expected to advance current knowledge of risks and outcomes in AIS.

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— Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14 549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.