Cole, John W.

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.

BACKGROUND AND PURPOSE: Ischemic stroke is a heterogeneous complex disease that results from the interaction between numerous genetic and environmental risk factors. Stroke heritability is approximately 40% in the overall population, similar to that of cancer, although there may be important differences in the etiologies of young (<49 years old) versus older onset ischemic stroke. The goal of this study was to conduct a mechanistic genome-wide association (GWA) analysis contrasting the gene pathways involved in young- and old-onset stroke. METHODS: Our analysis included 946 young subjects (448 ischemic stroke subjects and 498 age and sex matched controls, mean age of stroke onset 41 years) and 2558 older subjects (1070 ischemic stroke subjects and 1488 controls, mean age of stroke onset 66.5 years). All subjects were of European ancestry. The young onset cases and controls were genotyped by Illumina Omni1-Quad array with imputation up to 1.8 M SNPs and the old onset cases and controls by a combination of 550 v1 and v3 as well as 610 Illumina arrays with imputation up to 5.0 M SNPs. Single SNP association testing was performed within each contributing study by logistic regression adjusting for age, sex and population structure. Numbers of genes assigned with significant SNPs (P<=0.0001) were 18 and 25 with young- and old-onset groups, respectively, which were used as an input to pathway analysis by the Pathway Studio (Ariadne Genomics, Co.) RESULTS: SNP-level analyses did not reveal any significant associations with each group that met Bonferroni adjusted thresholds for statistical significance. In analyzing the ‘suggestively associated SNPs’, protein-protein interaction pathway analysis in the young-onset group revealed a strong segregation of significant hub proteins (CAT, RUNX1, and EIF4E) with more than 100 connections, while in the old-onset group the interaction networks were dispersed without much segregation. We observed that protein-protein interaction networks have a power-law connectivity distribution in both young- and old-onset groups. We identified 3 significant and focused pathways, ROS metabolism, melanogenesis, and IGF1R->CdEBPA/FOXO1A signaling, enriched for young-onset stroke (P<0.04). In the old-onset group, 10 pathways were identified involving broader signaling pathways. CONCLUSIONS: Young-onset stroke may involve genetic predisposition characterized by a few distinct and finite pathways. Our data suggests that young onset stroke may involve a few pathways that relate to a small number of key hub proteins, including ROS metabolism, melanogenesis, and IGF1R->CEBPA/FOXO1A signaling. Our analysis is consistent with the hypothesis that different pathways may be involved in young versus old onset stroke.

Introduction: Genetic studies of early-onset disease have been an effective strategy to identify novel pathways and drug targets generalizable also to later-onset disease. Few studies have investigated the sex-specific genetic associations with early-onset ischemic stroke even though several features of ischemic stroke differ between males and females. We hypothesized that stratifying the GWAS by sex would reveal novel stroke loci. Methods: We performed a transethnic ischemic stroke GWAS of 3,056 female cases and 4,462 male cases < 60 years-old and 16,192 and 16,048 sex-matched controls, respectively, from the Early Onset Stroke Genetics Consortium. Results: We identified a significant association in women with a locus in close proximity to TMX1 , a transmembrane platelet protein that inhibits platelet function. Additionally, we identified 2 other suggestive (P < 5 x 10 -6 ) loci in females (see Table), i.e., at APOH , which encodes beta2-glycoprotein I, an established GWAS locus for lipoprotein(a), and LRFN2 which has been previously reported to associate with obesity-related measures and type II diabetes. We observed suggestive evidence for association in males with MMP3/MMP12 , a known stroke susceptibility locus. Conclusions: Despite a very modest sample size, sex-specific analyses identified suggestive associations at biologically important novel loci in females and a known stroke locus in males. Further studies of sex-specific associations in both early- and later-onset ischemic stroke are needed.

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 &gt;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.

Background and Purpose: Family-based methods for estimating heritability cannot discriminate between shared genetic and shared environmental exposures. Recently, methods have been developed for estimating heritability in population samples using genome-wide SNPs. We used the approach developed by Visscher and colleagues to estimate the heritability of ischemic stroke in Caucasian subjects. In addition to evaluating the overall heritability of ischemic stroke, we assessed whether stroke heritability varies by age, gender, and stroke subtype. Methods: Using publicly available software (GCTA and PLINK), we estimated ischemic stroke heritability stratified by age and gender using genome-wide association (GWA) data from three Caucasian ischemic stroke studies: Ischemic Stroke Genetics Study (ISGS), Bio-Repository of DNA in Stroke (BRAINS), and Genetics of Early-Onset Stroke (GEOS). Weighted means of site-specific heritability point estimates were combined according to a standard fixed effects model. Results: Conclusions: A SNP-based approach may be useful in discerning differences in ischemic stroke heritability between different cohorts and subtypes. Overall, our analysis estimated ischemic stroke heritability to be 31% (SE = 7%), with a suggestion of higher heritability for younger cases. Small vessel stroke showed the highest heritability (58 ± 19%), with cardioembolic showing the lowest heritability (16 ± 14%). It should be emphasized that heritability estimates are population-specific and that the method used only reflects the heritability captured by common SNP variants measured in GWA studies, and not phenotypic variability explained by rare variants.

Background: Fundamental advances in stroke care will require pooling imaging phenotype data from multiple centers, to complement the current aggregation of genomic, environmental, and clinical information. Sharing clinically acquired MRI data from multiple hospitals is challenging due to inherent heterogeneity of clinical data, where the same MRI series may be labeled differently depending on vendor and hospital. Furthermore, the de-identification process may remove data describing the MRI series, requiring human review. However, manually annotating the MRI series is not only laborious and slow but prone to human error. In this work, we present a recurrent convolutional neural network (RCNN) for automated classification of the MRI series. Methods: We randomly selected 1000 subjects from the MRI-GENetics Interface Exploration study and partitioned them into 800 training, 100 validation and 100 testing subjects. We categorized the MRI series into 24 groups (see Table). The RCNN used a modified AlexNet to extract features from 2D slices. AlexNet was pretrained on ImageNet photographs. Since clinical MRI are 3D and 4D, a gated recurrent unit neural network was used to aggregate information from multiple 2D slices to make the final prediction. Results: We achieved a classification accuracy (correct/total cases) of 99.8%, 98.5% and 97.5% on the training, validation and testing set, respectively. The averaged F1-score (percent overlap between predicted cases and actual cases) over all categories were 99.8% 98.2% and 94.4% on the training, validation and testing set. Conclusion: We showed that automated annotation of MRI series by repurposing deep-learning techniques used for photographic image recognition tasks is feasible. Such methods can be used to facilitate high throughput curation of MRI data acquired across multiple centers and enable scientifically productive collaboration by researchers and, ultimately enhancing big data stroke research.

Introduction: Even though neuroinflammation is increasingly recognized as an essential contributor to ischemic brain injury, the exact underlying mechanisms remain unclear. Higher expression of the vascular cell adhesion molecule 1 (VCAM-1) increases leukocyte-brain endothelium interaction and has been associated with larger infarct size following acute ischemic stroke (AIS). The activation of the TGF-β signaling pathways can down-regulate VCAM-1 expression and ameliorate deleterious tissue outcome during neuroinflammation. Hypothesis: We sought to investigate whether genetic variation in the TGF-beta pathway and adhesion molecule genes is associated with acute stroke lesion size. Methods: We completed genome-wide association (GWA) testing and diffusion-weighted imaging lesion volume (DWIv) analysis in a discovery cohort of 532 AIS patients of European ancestry enrolled within 48 hours of symptom onset. An independent European ancestry cohort of 724 AIS patients with GWA data and automated DWIv served as replication cohort. GWA testing per SNP was performed using linear regression modeling of natural log-transformed DWIv adjusted for age, sex and relevant principal components. We selected 42 inflammatory genes in the TGF-beta pathway for a gene-based analysis using VEGAS (Versatile Gene-based -Association Study) software. A pre-specified discovery phase Bonferroni-corrected threshold was set at p<0.001. In the replication phase, 14 SNPs overlapping were tested at the Bonferroni-corrected p-value threshold of p<0.004. Results: Of all genes in the TGF-beta pathway, VCAM1 (p=0.0006) was significantly associated with DWIv in the discovery AIS cohort (age: 66 ± 14.9 years, sex: 63.4% male, DWIv: 2.2cm 3 (IQR: 0.6-11.7cm 3 )). A single SNP within the VCAM1 gene boundaries (rs3176876 (BETA=0.2341, p=0.003)) was significantly associated with DWIv in the replication AIS cohort (age: 65 ± 14.1 years, sex: 68.6% male, DWIv: 4cm 3 (IQR: 1.3-17.2cm 3 )). Conclusion: The genetic variant rs3176876 in the VCAM1 gene is associated with larger infarct lesion size measured on brain MRI of AIS patients. These findings suggest genetic contribution to the pro-inflammatory mechanisms in acute cerebral ischemia and warrant further investigation.