105 research outputs found

    Neuroimaging of ischemia and infarction

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    Meeting Report: Consensus Statement—Parkinson’s Disease and the Environment: Collaborative on Health and the Environment and Parkinson’s Action Network (CHE PAN) Conference 26–28 June 2007

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    BackgroundParkinson's disease (PD) is the second most common neurodegenerative disorder. People with PD, their families, scientists, health care providers, and the general public are increasingly interested in identifying environmental contributors to PD risk.MethodsIn June 2007, a multidisciplinary group of experts gathered in Sunnyvale, California, USA, to assess what is known about the contribution of environmental factors to PD.ResultsWe describe the conclusions around which they came to consensus with respect to environmental contributors to PD risk. We conclude with a brief summary of research needs.ConclusionsPD is a complex disorder, and multiple different pathogenic pathways and mechanisms can ultimately lead to PD. Within the individual there are many determinants of PD risk, and within populations, the causes of PD are heterogeneous. Although rare recognized genetic mutations are sufficient to cause PD, these account for < 10% of PD in the U.S. population, and incomplete penetrance suggests that environmental factors may be involved. Indeed, interplay among environmental factors and genetic makeup likely influences the risk of developing PD. There is a need for further understanding of how risk factors interact, and studying PD is likely to increase understanding of other neurodegenerative disorders

    Improved Outcome Prediction Using CT Angiography in Addition to Standard Ischemic Stroke Assessment: Results from the STOPStroke Study

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    Purpose: To improve ischemic stroke outcome prediction using imaging information from a prospective cohort who received admission CT angiography (CTA). Methods: In a prospectively designed study, 649 stroke patients diagnosed with acute ischemic stroke had admission NIH stroke scale scores, noncontrast CT (NCCT), CTA, and 6-month outcome assessed using the modified Rankin scale (mRS) scores. Poor outcome was defined as mRS.2. Strokes were classified as ‘‘major’ ’ by the (1) Alberta Stroke Program Early CT Score (ASPECTS+) if NCCT ASPECTS was#7; (2) Boston Acute Stroke Imaging Scale (BASIS+) if they were ASPECTS+ or CTA showed occlusion of the distal internal carotid, proximal middle cerebral, or basilar arteries; and (3) NIHSS for scores.10. Results: Of 649 patients, 253 (39.0%) had poor outcomes. NIHSS, BASIS, and age, but not ASPECTS, were independent predictors of outcome. BASIS and NIHSS had similar sensitivities, both superior to ASPECTS (p,0.0001). Combining NIHSS with BASIS was highly predictive: 77.6 % (114/147) classified as NIHSS.10/BASIS+ had poor outcomes, versus 21.5 % (77/358) with NIHSS#10/BASIS2 (p,0.0001), regardless of treatment. The odds ratios for poor outcome is 12.6 (95 % CI: 7.9 to 20.0

    Vascular contributions to cognitive impairment and dementia including Alzheimer's disease

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    AbstractScientific evidence continues to demonstrate the linkage of vascular contributions to cognitive impairment and dementia such as Alzheimer's disease. In December, 2013, the Alzheimer's Association, with scientific input from the National Institute of Neurological Disorders and Stroke and the National Heart, Lung and Blood Institute from the National Institutes of Health, convened scientific experts to discuss the research gaps in our understanding of how vascular factors contribute to Alzheimer's disease and related dementia. This manuscript summarizes the meeting and the resultant discussion, including an outline of next steps needed to move this area of research forward

    Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

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    Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research

    A call for transparent reporting to optimize the predictive value of preclinical research

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    The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress

    Warfarin, Aspirin, and Intracranial Vascular Disease

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