Personalized Profiles of Pathology in Pediatric Traumatic Brain Injury

项目摘要/摘要 儿童和青少年的创伤性脑损伤(TBI)发生率在普通人群中最高，但 目前用于根据磁共振数据检查结构性和功能性缺陷的工具有几个关键的局限性。第一, 在成像分析中没有金标准程序来考虑病变，其次，现有的工具 是建立在成年人口的基础上的。我们建议开发一个解决这两个问题的工作流 并支持扩展用于高级多模式分析的新工具，并使用该工作流来 确定与结果相关的因素。个性化的病理档案(P3)将包括注册到 适合年龄的模板和病变分割，允许体素病变症状映射， 形态测量分析、形状分析和网络扩散建模是该套餐的一部分，带有选项 也用于纵向分析。这一工作流程将包括并扩展新的管道。体状病变 症状映射检查病变位置和特定症状之间的对应关系，但 在现有应用中动力不足。网络扩散模型使用来自健康人群的扩散磁共振数据 以个体为模型的病理传播。虽然目前这项技术用于慢性损伤患者 估计损伤的震中，在这个提议中，纵向数据将被用来验证预测 病理学传播了。按区域进行的统计分析同样使用健康数据来预测 根据病变位置断开连接。对称多元线性约简降低高维 成像、认知和临床数据到组件，揭示干扰模式。通过将所有这些包括在内 跨多个站点的单独方法，P3将允许对TBI的影响进行多模式检查 儿科患者具有较大的统计能力。在目标1中，我们将在8个队列中开发和测试P3 网站。来自神经学、康复、神经心理学、放射学和大脑的临床专家的意见 开发，以及来自数学、计算机科学和神经成像分析的技术专业知识，我们 将确保P3在统计和计算上是有效的，并具有临床相关性。在目标2中，我们将提取 来自不同队列的不同尺度的共同神经心理学终点，并将这些测量与 由P3生成的大脑指标，以确定与结果相关的因素，并确定 病人群体。在Aim 3中，我们将把P3分发到一个测试站点网络，以便在本地运行，从而允许 进一步改进和验证，并向研究界传播P3。通过荟萃分析或 协调与大型分析相结合，我们将结合不同站点的影响，并检查 效果大小、位置和方向。教育将通过国内和国际的教程进行。 会议、现场访问和书面文件。P3将在网上提供，并将继续 来自开发团队的支持。P3的最终目标是更好地理解 受伤后的结果，以指导未来的治疗发展。

Project summary/abstract Children and adolescents have the highest rate of traumatic brain injury (TBI) in the general population, but current tools for examining structural and functional deficits from MR data have several key limitations. First, there is no gold standard procedure for considering lesions in imaging analysis, and second, existing tools have been built on adult populations. We propose to develop a workflow that addresses both of these issues and supports the extension of novel tools for advanced, multimodal analysis, and to use that workflow to identify factors associated with outcome. Personalized Profiles of Pathology (P3) will include registration to age-appropriate templates and lesion segmentation, allowing for voxelwise lesion symptom mapping, morphometric analyses, shape analysis, and network diffusion modeling as part of the package, with options for longitudinal analysis as well. This workflow will include and extend novel pipelines. Voxelwise lesion symptom mapping examines the correspondence between lesion location and specific symptoms, but has been underpowered in existing applications. Network diffusion modeling uses diffusion MRI data from healthy individuals to model the spread of pathology. While this is currently used on chronically injured patients to estimate the epicenter of injury, in this proposal, longitudinal data will be used to validate predictions of pathology spread. Tract-wise statistical analysis similarly uses healthy data to predict the degree of disconnection based on lesion location. Symmetric multivariate linear reduction reduces high dimensional imaging, cognitive, and clinical data to components, revealing patterns of disruption. By including all of these individual approaches across multiple sites, P3 will allow for multi-modal examination of the impact of TBI on pediatric patients with greater statistical power. In Aim 1, we will develop and test P3 on cohorts from eight sites. With input from clinical experts in neurology, rehabilitation, neuropsychology, radiology, and brain development, and technical expertise from mathematics, computer science, and neuroimaging analytics, we will ensure that P3 is statistically and computationally valid and clinically relevant. In Aim 2, we will extract common neuropsychological endpoints from disparate scales across cohorts and use these measures with brain metrics generated by P3 to determine factors associated with outcome and identify subgroups within the patient population. In Aim 3, we will distribute P3 to a network of beta-testing sites to run locally, allowing for further improvement and validation, and disseminate P3 to the research community. Through meta-analysis or harmonization paired with mega-analysis, we will combine effects across sites and examine consistency in effect size, location, and direction. Education will occur through tutorials at national and international conferences, site visits, and through written documentation. P3 will be made available online, with continuing support from the development team. The ultimate goal of P3 is to better understand heterogeneity in post-injury outcome to inform future treatment development.