CARE4Kids: Imaging Biomarker Core

项目总结/摘要-成像生物标志物核心 CARE 4Kids Center Without Walls项目旨在发现可测量的持续性后 脑震荡症状（PPCS），并使用这些标志物来表征亚组（内表型）， 告知预后和潜在治疗。先进的神经成像技术为揭示 脑震荡影响大脑结构和功能，但相对较小的样本量限制了可靠性， 到目前为止，成像研究的普遍性。成像生物标志物核心的总体目标是收集 最先进的多模态脑成像数据，最大限度地提供有关儿童和青少年结局的信息 脑震荡将在开发队列中仅在基线时采集多模态成像，并用于 开发和测试将在验证队列中检查的结果预测模型。神经成像 该方案旨在最大限度地提高数据质量，并最大限度地减少我们受伤的年轻患者人群的不适 并且基于现有的多站点努力（即，ABCD，UK Biobank）和NINDS成像建议。的 成像生物标志物核心具有以下具体目标：具体目标1：发现哪种多模式大脑 影像学方法最能预测脑震荡的临床结果（单独使用，以及与其他 生物标志物和临床测量）。具体目标2：神经影像学研究中心资格认证和培训-研究中心 使用试点数据和协调培训进行资格认证将使各地点为收集高质量数据做好准备。具体目标 3：收集高质量的多模态脑MRI - 360名参与者将在开发的6个站点进行扫描 队列。具体目标4：持续的数据质量控制-通过体模、志愿者、 数据审查将确保高质量的数据，最大限度地在不同地点之间进行比较。具体目标5：分析 数据：将图像和衍生测量传输至犹他州大学数据协调中心（U-DCC）， 传播到研究界。我们将使用标准化的、经过验证的、公开可用的协议， 处理神经成像数据测量局部脑容量、皮质几何学、白色物质 组织、功能连接、灌注和神经病理学（即，微出血和白色物质 高强度）将在组之间进行比较，并检查与结果测量的相关性。在 除了这些更简单的方法外，与U-DCC合作，我们还将采用机器学习方法， 确定哪种影像学、人口统计学和临床指标的组合最能预测功能结局。

PROJECT SUMMARY/ABSTRACT – Imaging Biomarker Core The CARE4Kids Center Without Walls Project aims to discover measurable biomarkers of persistent post- concussive symptoms (PPCS) and use these markers to characterize subgroups (endophenotypes) that will inform prognosis and potential treatment. Advanced neuroimaging offers great promise in revealing how concussion affects brain structure and function, but relatively small sample sizes have limited the reliability and generalizability of imaging studies to date. The overall goal of the Imaging Biomarker Core is to collect state-of- the-art multimodal brain imaging data that is maximally informative about outcomes in children and adolescents with concussion. Multimodal imaging will be collected in the Development Cohort, at baseline only, and used to develop and test models of outcome prediction that will be examined in the Validation Cohort. The neuroimaging protocol is designed to maximize data quality and minimize discomfort for our injured, younger patient population and is based on existing multi-site efforts (i.e., ABCD, UK Biobank) and NINDS imaging recommendations. The Imaging Biomarker Core has the following Specific Aims: Specific Aim 1: Discover which multimodal brain imaging measures best predict clinical outcomes in concussion (separately, and when combined with other biomarkers and clinical measures). Specific Aim 2: Neuroimaging site qualification and training – site qualification using pilot data and coordinated training will prepare sites to collect high-quality data. Specific Aim 3: Collect high-quality multimodal brain MRI – 360 participants will be scanned across 6 sites in the Development Cohort. Specific Aim 4: Ongoing data quality control – ongoing quality control through phantoms, volunteers, and data review will ensure high-quality data that is maximally comparable across sites. Specific Aim 5: Analyze Data: Transfer images and derived measures to the University of Utah Data Coordinating Center (U-DCC) to disseminate to the research community. We will use standardized, validated, publicly-available protocols to process the neuroimaging data. Measures of regional brain volumes, cortical geometry, white matter organization, functional connectivity, perfusion, and neuropathology (i.e., microbleeds and white matter hyperintensities) will be compared between groups and examined for correlation with outcome measures. In addition to these simpler approaches, working with the U-DCC, we will employ machine learning approaches to identify which combination of imaging, demographic, and clinical measures best predicts functional outcome.