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.
The impact of stroke is devastating to those who suffer from it, their families, and the society as a whole. This study aims to facilitate development of the novel clinical applications that personalize stroke risk and outcome prediction in each individual patient with brain MRI and genetic data, paving the way for the future cutting-edge diagnostics and therapeutics in ischemic stroke.
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| Etherton, Mark R; Rost, Natalia S; Wu, Ona (2018) Infarct topography and functional outcomes. J Cereb Blood Flow Metab 38:1517-1532 |
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| Zhang, Miaomiao; Wells 3rd, William M; Golland, Polina (2017) Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms. Med Image Anal 41:55-62 |
| Hong, Yi; Golland, Polina; Zhang, Miaomiao (2017) Fast Geodesic Regression for Population-Based Image Analysis. Med Image Comput Comput Assist Interv 10433:317-325 |
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| Zhang, Miaomiao; Liao, Ruizhi; Dalca, Adrian V et al. (2017) Frequency Diffeomorphisms for Efficient Image Registration. Inf Process Med Imaging 10265:559-570 |
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