MRI-GENetics Interface Exploration_MRI-GENIE_Study

 描述(申请人提供)：中风对患者、他们的家庭和整个社会的影响是毁灭性的。此外，中风事件本身是 仅仅是脑血管疾病总负担的粗略指标，以及整体功能 残疾在很大程度上取决于大脑中发生的伴随变化，如脑白质高密度(WMH)和急性脑梗塞面积，这可以通过脑MRI可靠地评估。卒中相关脑损伤的更详细的特征可以提供一个强大的和生物学相关的底物来研究新的疾病途径和药物靶点，以改善卒中后的结局和二级卒中预防。遗传学处于开发中风诊断和治疗的新策略的前沿；然而，由于缺乏精确的表型特征和统计能力不足，中风的基因发现一直受到阻碍。具有临床意义的MRI特征，如WMH负荷和弥散加权成像(DWI)上的急性脑梗塞面积，与中风风险和预后密切相关，也是高度可遗传的。我们建议在NINDS卒中遗传学网络(SIGN)的3,385例精确表型卒中病例中表征这些新的MRI衍生特征与卒中亚型的关系，然后使用已经生成的基因数据来识别这些特征的遗传决定因素。MRI数据将使用一种新颖的、多模式的、基于图像的高通量分析管道获得，随后将详细描述中风表型、它们与中风后结果的关系以及它们的潜在遗传结构。这项建议利用了中风神经学家、遗传学家和神经影像分析师社区内正在进行的大规模、多中心、由NIH资助的合作，他们提供了广泛的专业知识和独特的贡献，以确保这项建议的可行性和科学严谨性。这项研究的成功实施将为临床相关的脑血管MRI表型分析创造一条管道，将提供给更广泛的研究社区，并将加快基因发现的步伐，推进中风风险和结果预测的临床应用程序的开发。

 DESCRIPTION (provided by applicant): The impact of stroke is devastating to those who suffer from it, their families, and the society as a whole. Furthermore, the stroke event itself is only a crude indicator of the total burden of cerebrovascular disease, and the overall functionally disability depends largely on the accompanying changes that occur in the brain, such as white matter hyperintensity (WMH) and acute cerebral infarct size, which can be assessed reliably by brain MRI. A more detailed characterization of stroke-related brain lesions can provide a powerful and biologically relevant substrate to examine novel disease pathways and drug targets for improving post-stroke outcomes and secondary stroke prevention. Genetics are at the cutting-edge of these novel strategies for developing stroke diagnostics and therapeutics; however, genetic discovery in stroke has been hindered by the lack of precise phenotype characterization and insufficient statistical power. Clinically meaningful MRI traits, such as WMH burden and acute infarct size on diffusion-weighted imaging (DWI), which are strongly related to stroke risk and outcomes, are also highly heritable. We propose to characterize the relation of these novel MRI- derived traits to stroke subtypes in 3,385 exquisitely phenotyped stroke cases from the NINDS Stroke Genetics Network (SiGN), and then to identify genetic determinants of these traits using already generated genotype data. The MRI data will be obtained using a novel, multimodal high-throughput image-based analysis pipeline, followed by detailed characterization of the stroke phenotypes, their association with post-stroke outcomes, and their underlying genetic architecture. This proposal takes advantage of the ongoing large-scale, multi- center, NIH-funded collaboration within the community of stroke neurologists, geneticists, and neuroimaging analysts, who provide a broad spectrum of expertise and unique contributions to ensure feasibility and scientific rigor of this proposal. Successful execution of this study will create a pipeline for the clinically relevant cerebrovascular MRI phenotype analysis that will be made available to the broader research community and that will accelerate the pace of genetic discoveries and advance the development of clinical applications in risk and outcome prediction in stroke.