Cloud based neuroimaging analysis for identifying traumatic braininjuries and related changes

基于云的神经影像分析，用于识别创伤性脑损伤和相关变化

• 批准号: 10827676
• 负责人: KENT A KIEHL
• 金额: $ 26.98万
• 依托单位: LOVELACE BIOMEDICAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10827676
• 关键词: Acceleration; Administrative Supplement; Algorithms; Award; Brain; Brain imaging; Classification; Clinical; Clinical assessments; Cloud Computing; Cloud Service; Communities; Data; Data Collection; Data Set; Databases; Detection; Development; Evaluation; Forensic Medicine; Functional Magnetic Resonance Imaging; Funding; General Population; Goals; Grant; Hour; Human; Image; Impaired cognition; Imprisonment; Incidence; Individual; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measures; Memory; Methodology; Modality; Motion; National Institute of Neurological Disorders and Stroke; Neurocognitive; Neuropsychology; Outcome; Pathology; Performance; Population; Population Heterogeneity; Process; Protocols documentation; Recording of previous events; Running; Sampling; Site; System; Testing; Time; Traumatic Brain Injury; United States National Institutes of Health; Validation; Woman; brain based; brain volume; classification algorithm; cloud based; comorbidity; computational platform; computerized data processing; computing resources; cost; data analysis pipeline; feasibility testing; feature selection; high dimensionality; high risk; high risk men; high risk population; imaging modality; improved; men; mild traumatic brain injury; multimodal neuroimaging; neural; neuroimaging; neuroimaging marker; parent grant; pediatric trauma; predictive modeling; processing speed; prototype; service providers; substance use; tool; trait

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.

项目摘要（最多 30 行） 该提案概述了评估基于云的数据处理的性能和实用性的计划 基于 MRI 的脑成像数据的计算要求较高的分析。这项行政补充将 以最近获奖的 R01 的目标为基础，该项目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。 在过去的十年中，我们的团队不断得到 NIH 的资助，收集详细的临床和神经影像资料 来自 4000 多名高危男性和女性的协议。我们现有的数据包括多模式神经影像协议 （sMRI、fMRI、DTI）、全面的临床评估、神经心理学评估和 TBI 病史。这 当前项目的目标是从社区样本中推广现有的 mTBI 分类算法 高风险法医样本，并改进基于神经影像的客观认知能力下降测量。 在传统平台上，这些基于神经影像的分类工具涉及数十万个 潜在的功能，并且需要几周的运行时间，即使对于相对较少的受试者也是如此。 考虑到该项目所需分析的计算复杂性，基于云的计算平台 在效率方面可能具有很大优势。我们建议，首先，将我们定制的容器化 用于预处理的神经成像管道，然后实施我们当前本地实施的 分类算法。基于云的解决方案将使我们能够探索几种算法方法 与使用基于本地服务器的解决方案相比，特征选择和合并的时间更短。为了测试 基于云处理的可行性和优势，我们将构建数据处理管道并对其进行验证 使用现有数据。具体来说，我们希望建立算法方法原型来检测特征相关的 神经连接的变化，并使用在 NIH 支持下和公开收集的现有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 奖的目的之一就是测试 我们的算法对 FITBIR 中数据的通用性（随时可用）。该测试可能会尽快开始 补充已收到。基于云的平台与基于本地服务器的处理将在 数据处理速度和成本（包括人工工作时间）。这些客观措施将给 我们清楚地了解大规模实施基于云的处理（包括应用程序）的价值 当前赠款的纵向目标。