Personalized Profiles of Pathology in Pediatric Traumatic Brain Injury

小儿创伤性脑损伤的个性化病理学概况

• 批准号: 10377732
• 负责人: Emily Larsen Dennis
• 金额: $ 70.73万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10377732
• 关键词: Accounting; Address; Adolescent; Adolescent Development; Adult; Age; Algorithms; Anatomy; Atrophic; Attention; Brain; Cause of Death; Characteristics; Child; Childhood; Childhood Injury; Chronic; Clinical; Clinical Data; Cognitive; Communities; Consensus; Craniocerebral Trauma; Data; Data Set; Developed Countries; Development; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Documentation; Education; Ensure; Future; General Population; Genetic; Goals; Gold; Heterogeneity; Image; Image Analysis; Imaging Device; Incidence; Individual; Injury; International; Lesion; Link; Location; Mathematics; Measures; Meta-Analysis; Modeling; Monitor; Morphology; Nerve Degeneration; Neurology; Neuropsychology; Outcome; Outcome Measure; Pathology; Patients; Pattern; Pediatric Radiology; Physical Medicine; Population; Positioning Attribute; Procedures; Process; Prognosis; Public Health; Radiology Specialty; Recovery; Rehabilitation therapy; Research; Research Personnel; Resources; Running; Sample Size; Severities; Site; Site Visit; Statistical Data Interpretation; Subgroup; Symptoms; Technical Expertise; Techniques; Testing; Thick; Time; Traumatic Brain Injury; Validation; Work; analysis pipeline; base; brain volume; clinically relevant; cohort; computational neuroscience; computer science; cost effective; disability; experience; high dimensionality; improved; injured; interest; longitudinal analysis; morphometry; multidisciplinary; multimodality; neurodevelopment; neuroimaging; novel; outcome prediction; patient population; patient subsets; pediatric patients; pediatric traumatic brain injury; personalized medicine; psychologic; sex; shape analysis; software development; symposium; therapy development; tool; tractography; web site; working group

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.

项目概要/摘要 儿童和青少年在一般人群中创伤性脑损伤（TBI）的发病率最高， 目前用于从MR数据检查结构和功能缺陷的工具具有几个关键的局限性。第一、 在成像分析中没有考虑病变的金标准程序，第二，现有的工具 都是建立在成年人的基础上的我们建议开发一个解决这两个问题的工作流程 并支持扩展用于高级多模态分析的新工具，并使用该工作流程 确定与结果相关的因素。个性化病理学概况（P3）将包括登记， 适合年龄的模板和病变分割，允许体素病变症状映射， 形态分析、形状分析和网络扩散建模作为软件包的一部分，带有选项 纵向分析也是如此。该工作流程将包括并扩展新的管道。体素损伤 症状映射检查病变位置和特定症状之间的对应关系，但 在现有的应用程序中，网络扩散建模使用来自健康人的扩散MRI数据 个人来模拟病理学的传播。虽然这是目前用于慢性受伤的病人， 估计伤害的震中，在这个建议中，纵向数据将用于验证预测， 病理扩散。Tract-wise统计分析类似地使用健康数据来预测 根据病变位置断开连接。对称多元线性约简减少了高维 成像，认知和临床数据的组成部分，揭示破坏模式。把这些都包括进去 P3将允许对TBI的影响进行多模式检查， 儿科患者具有更大的统计功效。在目标1中，我们将在八个队列中开发和测试P3， 网站.在神经病学、康复学、神经心理学、放射学和脑的临床专家的投入下， 开发，以及数学，计算机科学和神经成像分析的技术专长，我们 将确保P3在统计学和计算上有效且具有临床相关性。在目标2中，我们将提取 来自不同队列的不同量表的共同神经心理学终点，并使用这些测量方法， 由P3生成的大脑指标，以确定与结果相关的因素，并确定 患者人群。在Aim 3中，我们将把P3分发到beta测试站点的网络中，以便在本地运行， 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，我们将联合收割机的影响，并检查一致性， 效果大小、位置和方向。教育将通过在国家和国际上的教程进行。 会议、实地考察和书面文件。P3将在线提供， 开发团队的支持。P3的最终目标是更好地理解 损伤后的结果，以告知未来的治疗发展。