Background and Objectives To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern–specific ways. Methods MR 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 &gt;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, age 2 , sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. Results A 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. Discussion Higher 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. 

Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome

International Journal of Stroke, 17(3) Stroke is the most common cause of long-term neurological disability worldwide.1 Half of all people who survive are left disabled, with a third relying on others to assist with activities of daily living. A number of articles in this month’s issue of International Journal of Stroke (IJS) address important aspects of stroke recovery research. One obstacle to improved treatments is a lack of understanding of potential pathways which we can target to increase recovery. Genome-wide association (GWAS) genetic studies have proved a powerful technique in identifying mechanisms underlying a whole variety of diseases. They have identified many genetic loci and pathogenic pathways underlying stroke risk.2 A major advantage of the GWAS approach is that it is “agnostic,” that is, it does not depend upon a hypothesis, and therefore associations with completely new genes and mechanistic pathways can be identified. However, applying such techniques to stroke recovery is challenging. Such studies require data from thousands of cases, and data collection and outcomes need to be standardized across these different cohorts.3 An useful framework for design of these studies is presented in a consensus paper from the International Stroke Genetics Consortium in this issue. It makes an important reading for anyone planning to carry out studies in this area.3 A potentially exciting novel treatment approach is environmental enrichment. This has been shown to improve recovery in animal models of stroke, promoting brain plasticity and enhancing sensory motor recovery with the greatest gains made when used in conjunction with motor retraining.4 Such approaches are now being trialed in humans. Janssen and colleagues, in this month’s issue, describe a feasibility study of environmental enrichment using a non-randomized cluster trial design.5 Both individual and communal forms of environmental enrichment were provided, for the duration of stay in the rehabilitation unit. The experimental group spent 7% less time inactive and 9% more time physically and 6% more time socially active than the control group. There was a trend toward better mobility in the experimental group but no other differences in outcome measures. The authors concluded that the increase in activity was modest and the lack of benefit in clinical outcome 3 months after stroke does not provide evidence for an efficacy trial. Perhaps further work is required to develop enrichment interventions that increase activity further. It is also likely that much larger sample sizes will be required to definitively identify treatment effects. Another concern, and potential treatment opportunity, during rehabilitation is the use of commonly used prescription drugs. It has been suggested that a number of these influence motor recovery following a brain lesion. Experimental findings indicate that selective serotonin reuptake inhibitors (SSRIs) may boost practice-dependent motor improvements, although large clinical randomized controlled trials showed no effect on outcome.6 It has been suggested that activation of gamma-aminobutyric acid (GABA) receptors may be detrimental to motor recovery. In this issue, Johnstone and colleagues determined whether common central nervous system–acting drugs altered outcomes in an intensive upper limb rehabilitation program. Previously in the Queen’s Square Upper Limb Recovery Programme, they have demonstrated that specific highdose training in chronic stroke patients can result in clinically significant improvements in upper limb function.7 In a careful retrospective study in 277 stroke or brain injury patients, studied at a mean time since event of 20 months, they determined the effect of prescriptions on outcome.8 There was no evidence that patients prescribed GABA agonists performed worse on high-intensity rehabilitation. Patients on antidepressants, however, performed poorer than expected. The vast majority (about 80%) of antidepressants were selective serotonin reuptake inhibitors (SSRIs), so this was an unexpected observation. Finally, this issue includes a review on an important emerging area on health services research particularly in the elderly, namely, frailty. Frailty describes a distinctive health state in which the ability of older people to cope with acute stressors is compromised by in increased vulnerability brought about by age-associated declines in physiological reserve and function across multiple organ systems. Although closely associated with age, multimorbidity, and disability, frailty is a discrete syndrome and is independently associated with poorer outcomes across a range of medical conditions. It has been reported to occur in about a fifth of all stroke patients, has been independently associated with stroke severity, and may influence stroke outcome although the authors highlight that more work is required in this area. Certainly an area for future research.

https://journals.sagepub.com/doi/pdf/10.1177/17474930221080904

Reducing disability after stroke

Objectives In recent years, the evidence of the relationship between epigenetics and acute ischemic stroke (AIS) were accumulating, however, the epigenetic characteristics that directs specifically towards the aetiology of large-artery atherosclerosis (LAA) remain ambiguous. The aim of this study was to highlight the overall evidence concerning the epigenetic mechanisms associated with the occurrence of LAA. Materials and methods Studies that involve investigations related to epigenetic markers (DNA methylation and RNA modifications) and LAA were retrieved from eleven scientific publication databases. The studies were screened through the pre-set inclusion and exclusion criteria prior to the NOS evaluation. Results Eligible studies (n=25) were evaluated. Of which, six reported on DNA methylation and 19 studies assessed RNA modifications (16 on miRNAs, two on lncRNAs, and one study on circRNA). Hypomethylation of MTRNR2L8 and ERα promoters; microRNAs (miR-7-2-3p, miR-16, miR-34a-5p, miR-126, miR-143, miR-200b, miR-223, miR-503, miR-1908, miR-146a rs2910164 C/G, miR-149 rs2292832 T/C, miR-200b rs7549819 T/C, miR-34a rs2666433); lncRNA of ZFAS1; and circRNA of hsa_circRNA_102488 were associated with LAA significantly. Conclusion Current systematic review highlighted hypomethylation of miRNAs and lncRNA might be the potential biomarkers for LAA.

Epigenetics Modifications in Large-Artery Atherosclerosis: A Systematic Review.

The modified Rankin Scale change score (ΔmRS) is useful for evaluating acute poststroke functional improvement or deterioration. We investigated the relationship between multiple biomarkers and ΔmRS by analyzing data on 6931 patients with acute ischemic stroke (average age 62.3 ± 11.3 years, 2174 (31.4%) female) enrolled from the Third China National Stroke Registry (CNSR-III) and 15 available biomarkers. Worse outcomes at 3 months were defined as ΔmRS3m-discharge ≥1 (ΔmRS3m-discharge = mRS3m-mRSdischarge). Adjusted odds ratios (aORs) and their 95% confidence intervals (CIs) were calculated from logistic regression models. At 3-months poststroke, 1026 (14.8%) patients experienced worse outcomes. The highest quartiles of white blood cells (WBCs) (aOR [95%CI],1.37 [1.12-1.66]), high-sensitivity C-reactive protein (hs-CRP) (1.37 [1.12-1.67]), interleukin-6 (IL-6) (1.43 [1.16-1.76]), interleukin-1 receptor antagonist (IL-1Ra) (1.46 [1.20-1.78]) and YKL-40 (1.31 [1.06-1.63]) were associated with an increased risk of worse outcomes at 3 months. Results remained stable except for YKL-40 when simultaneously adding multiple biomarkers to the basic traditional-risk-factor model. Similar results were observed at 6 and 12 months after stroke. This study indicated that WBCs, hs-CRP, IL-6, IL-1Ra, and YKL-40 were significantly associated with worse outcomes in acute ischemic stroke patients, and all inflammatory biomarkers except YKL-40 were independent predictors of worse outcomes at 3 months.

Associations between admission levels of multiple biomarkers and subsequent worse outcomes in acute ischemic stroke patients.

Introduction Deep learning has allowed for remarkable progress in many medical scenarios. Since deep learning prediction models often require 105-107 examples, 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. Methods Our 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. Results While 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). Conclusions For 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.

Scaling behaviors of deep learning and linear algorithms for the prediction of stroke severity

Numerous biological mechanisms contribute to outcome after stroke, including brain injury, inflammation, and repair mechanisms. Clinical genetic studies have the potential to discover biological mechanisms affecting stroke recovery in humans and identify intervention targets. Large sample sizes are needed to detect commonly occurring genetic variations related to stroke brain injury and recovery. However, this usually requires combining data from multiple studies where consistent terminology, methodology, and data collection timelines are essential. Our group of expert stroke and rehabilitation clinicians and researchers with knowledge in genetics of stroke recovery here present recommendations for harmonizing phenotype data with focus on measures suitable for multicenter genetic studies of ischemic stroke brain injury and recovery. Our recommendations have been endorsed by the International Stroke Genetics Consortium.

/pdf/international-stroke-genetics-consortium-recommendations-for-18drzwr4cf.pdf

International stroke genetics consortium recommendations for studies of genetics of stroke outcome and recovery

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Loo Keat Wei

Author Tools

Chat about Author

Papers

International stroke genetics consortium recommendations for studies of genetics of stroke outcome and recovery