CARE4Kids: Imaging Biomarker Core

项目摘要/摘要-成像生物标记核心 CARE4儿童无墙中心项目旨在发现持续后继发的可测量生物标记物 脑震荡症状(PPCS)，并使用这些标志物来表征将 告知预后和可能的治疗方法。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和 迄今为止影像研究的概括性。成像生物标记核心的总体目标是收集 最大限度地提供有关儿童和青少年结局的信息的最先进的多模式脑成像数据 脑震荡。多模式成像将仅在基线时在开发队列中收集，并用于 开发和测试将在验证队列中检验的结果预测模型。神经成像 协议旨在最大限度地提高数据质量，并最大限度地减少受伤、年轻患者群体的不适 并基于现有的多站点努力(即ABCD、UK Biobank)和NINDS成像建议。这个 成像Biomarker Core有以下具体目标：具体目标1：发现哪种多模式大脑 影像测量最好地预测脑震荡的临床结果(单独，以及当与其他 生物标志物和临床措施)。具体目标2：神经成像站点资格和培训-站点 使用试点数据和协调培训的资格鉴定将为收集高质量数据做好准备。特定目标 3：收集高质量的多模式脑MRI-360名参与者将在开发中的6个地点进行扫描 一群人。具体目标4：持续数据质量控制--通过幻影、志愿者、 数据审查将确保高质量的数据在不同站点之间具有最大程度的可比性。具体目标5：分析 数据：将图像和派生措施传输到犹他大学数据协调中心(U-DCC)，以 向研究界传播。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。测量区域脑体积、皮质几何形状、白质 组织、功能连通性、灌注和神经病理(即微出血和脑白质 高信号)将在不同组之间进行比较，并检查其与结果测量的相关性。在……里面 除了这些更简单的方法，与U-DCC合作，我们将使用机器学习方法来 确定成像、人口统计学和临床测量的哪种组合最能预测功能结果。

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.