Cloud based neuroimaging analysis for identifying traumatic braininjuries and related changes

项目摘要(最多30行) 本建议书概述了评估基于云的数据处理的性能和效用的计划 基于MRI的脑成像数据的计算需求分析。这一行政副刊将 建立在最近获奖的R01的目标之上，该R01开发用于识别和跟踪的分类算法 高危人群中与轻度创伤性脑损伤(MTBI)相关的进行性病理学。 在过去的十年里，我们的团队一直得到美国国立卫生研究院的资助，收集详细的临床和神经成像 来自4000多名高危男女的治疗方案。我们现有的数据包括多模式神经成像方案 (sMRI、fMRI、DTI)、全面的临床评估、神经心理评估和脑外伤病史。这个 本项目的目标是从社区样本中推广现有的mTBI分类算法 对高危法医样本进行研究，并改进基于神经成像的认知功能下降的客观测量方法。 在传统平台上，这些基于神经成像的分类工具涉及数十万 潜在的特征，需要几周的运行时间，即使对于相对较少的受试者。 鉴于此项目所需分析的计算复杂性，基于云的计算平台 在效率方面可能是非常有利的。我们建议，首先，将我们定制的 对神经影像流水线进行前处理，然后实施我们目前在当地实施的 分类算法。基于云的解决方案将允许我们探索以下几种算法方法 与使用基于本地服务器的解决方案相比，在更短的时间内完成功能选择和合并。为了测试 云处理的可行性和优势，我们将建立数据处理管道并进行验证 使用现有数据。具体地说，我们想要原型算法方法来检测与特征相关的 神经连接的变化，并使用在NIH支持下收集的现有数据和从公共部门收集的数据来测试这些变化 可用的神经影像数据库(例如FITBIR)。事实上，我们R01奖项的目的之一是测试 我们的算法对FITBIR中的数据的普适性(容易获得)。这项测试最快可能在 收到了补充材料。基于云平台与基于本地服务器的处理的对比将在 数据处理速度和成本方面(包括人工工作时间)。这些客观措施将给 美国清楚地了解在更大范围内实施基于云的处理(包括应用程序)的价值 用于当前赠款的纵向目标。

Project Summary (30 lines max) This proposal outlines plans to evaluate the performance and utility of cloud-based data processing for computationally demanding analysis of MRI-based brain imaging data. This administrative supplement would build on the aims of a recently awarded R01 which develops classification algorithms for identifying and tracking progressive pathology associated with mild traumatic brain injury (mTBI) in a population of high-risk individuals. Over the last decade, our team has been continuously funded by NIH to collect detailed clinical and neuroimaging protocols from over 4000 high-risk men and women. Our extant data include multimodal neuroimaging protocols (sMRI, fMRI, DTI), thorough clinical assessments, neuropsychological evaluations, and histories of TBI. The aims of the current project are to generalize existing classification algorithms for mTBI from community samples to high-risk forensic samples and to improve on an objective neuroimaging-based measure of cognitive decline. On traditional platforms, these neuroimaging-based classification tools involve hundreds of thousands of potential features and require running times of several weeks, even for relatively small numbers of subjects. Given the computational complexity of the analyses required for this project, cloud-based computing platforms could be highly advantageous in terms of efficiency. We propose, first, to containerize our customized neuroimaging pipelines for pre-processing, followed by implementation of our current locally implemented classification algorithms. A cloud-based solution will allow us to explore several algorithmic approaches towards feature selection and union in a shorter time frame than using a local server-based solution. In order to test the feasibility and advantages of cloud-based processing, we will build data processing pipelines and validate them using existing data. Specifically, we would like to prototype algorithmic approaches towards detecting trait related changes in neural connectivity and test these using extant data collected under NIH support and from publicly available neuroimaging databases (e.g. FITBIR). Indeed, one of the aims of our R01 award is to test the generalizability of our algorithms to data in FITBIR (readily available). This testing could begin as soon as supplement was received. The cloud-based platform versus local-server-based processing will be evaluated in terms of data processing speed and costs (including human working hours). These objective measures will give us a clear picture of the value of implementing cloud-based processing on a larger scale, including applications for the longitudinal aims of the current grant.