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分析。这一行政补充将 建立在最近授予的R01的目标之上，该目标开发了用于识别和跟踪的分类算法 在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，我们的团队一直由NIH资助，收集详细的临床和神经成像 4000多名高风险男女的协议。我们现存的数据包括多模式神经成像协议 (sMRI、fMRI、DTI）、全面的临床评估、神经心理学评估和TBI病史。的 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，并改善基于神经成像的认知能力下降的客观测量。 在传统平台上，这些基于神经成像的分类工具涉及数十万个 潜在的功能并且需要数周的运行时间，即使对于相对较少的受试者也是如此。 鉴于该项目所需分析的计算复杂性，基于云的计算平台 在效率方面是非常有利的。我们建议，首先，将我们定制的 神经成像管道进行预处理，然后实施我们目前在本地实施的 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，在更短的时间内完成特征选择和联合。为了测试 基于云的处理的可行性和优势，我们将建立数据处理管道并验证它们 使用现有数据。具体来说，我们想原型算法的方法来检测性状相关 神经连接的变化，并使用NIH支持下收集的现有数据和来自公共 可用的神经影像学数据库（例如FITBIR）。事实上，我们R01奖的目的之一是测试 我们的算法在FITBIR（现成的）数据的推广性。测试可以开始， 已收到补充。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）。这些客观措施将使 我们清楚地了解了在更大范围内实施基于云的处理的价值，包括应用程序 为当前赠款的纵向目标。