Advancing Secondary Data Analysis: the ENIGMA Brain Injury Data Harmonization Initiative

项目摘要/摘要 创伤性脑损伤(TBI)是全球一个主要的公共卫生问题，虽然神经成像在 了解损伤后大脑结构和功能的破坏，有许多因素可以减弱 它的预测能力。例如，受伤后的结局存在巨大的异质性，这只是 部分原因是受伤的严重程度。在神经成像研究中，成本经常限制样本大小，但考虑到 已被证明影响患者预后的因素有很多(年龄、受伤严重程度、社会经济地位)， 小样本和大规模单变量检验往往导致许多研究的动力严重不足。一 解决方案是将数据结合起来，创建新的数据共享平台，并增强神经成像遗传学 通过Meta分析(Enigma)联盟支持这种合作已有十多年 一系列临床疾病。该提案的目标是开发工具和数据处理程序，以 用于谜脑损伤工作组。在R61阶段，我们的目标是开发和测试以下工作流 协调处理行为数据(目标1)以及结构和功能(静息状态)磁共振数据 (目标2)。对于R61的目标1，目标是提供依赖于数据的过程决策树，从而允许 研究人员在收集了一系列神经心理学数据的队列中建立共同的认知终点 和临床措施。该提案将创建可共享的程序、灵活的工具和可推广的指导方针 了解从不同的行为测试电池中提取共同认知端点的最佳实践(R61目标1)。 在R61的目标2中，我们开发了一个称为协调和聚合的图像处理流水线，用于 功能和结构成像数据流水线；HIF-PIPE)，其允许聚集非等价的 成像数据。一个主要目标是分散战斗，这是一个开放源码的数据协调工具，这样它就可以 在虚拟共享环境中使用。在满足R61通过/不通过标准后，即 对包括13个队列的数据集进行整理，提取共同的认知终点，并创建HAF- PIPE，我们将进入R33阶段。在R33阶段，我们将利用大型、协调的数据集和 应用机器学习技术(Corex相关解释)来识别每个组内的患者群 研究了患者群体。HAF-PIPE以及R61阶段的程序和指南 扩大到更多的患者群体，并向其他谜团工作组提供。这个 研究人员将可以访问统一的数据以及创建数据的工具和程序 在提案提交和批准后，将其作为一个经过策划的数据集。如果成功，这项提议将带来希望 显著推进临床神经科学中的数据管理、协调和共享。我们期待着 我们的建议将大大提高我们对影响受伤后结果的因素的理解，并将 提供一个在整个神经成像社区都有用的工具。

Project Summary/Abstract Traumatic brain injury (TBI) is a major public health issue globally, and while neuroimaging has been useful in understanding disruption in brain structure and function after injury, there are a number of factors that attenuate its prognostic ability. For example, there is tremendous heterogeneity in outcome after injury which is only partially explained by injury severity. Cost frequently limits sample size in neuroimaging studies, yet given the myriad factors that have been shown to influence patient outcome (age, injury severity, socioeconomic status), small samples and mass univariate testing often result in many studies being grossly under-powered. One solution is to combine data and create novel data sharing platforms, and the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium has supported this kind of collaboration for over a decade across a range of clinical disorders. The goal of this proposal is to develop tools and data processing procedures for use in the ENIGMA Brain Injury working group. In the R61 phase, we aim to develop and test a workflow for harmonized processing of behavioral data (Aim 1) as well as structural and functional (resting-state) MRI data (Aim 2). For Aim 1 of the R61, the goal is to offer a decision tree of procedures that is data-dependent, allowing investigators to establish common cognitive endpoints across cohorts that collect a range of neuropsychological and clinical measures. This proposal will create sharable procedures, flexible tools, and generalizable guidelines for best practices for extracting common cognitive endpoints from distinct behavioral test batteries (R61 Aim 1). In Aim 2 of the R61, we develop an image processing pipeline called Harmonization and Aggregation for Functional and structural imaging data PIPEline; HAF-PIPE) that allows for aggregation of non-equivalent imaging data. A primary goal is to decentralize ComBat, an open-source data harmonization tool, so that it can be used in a virtual sharing environment. Following satisfaction of the R61 Go/No-Go criteria, which is the curation of the dataset including 13 cohorts, extraction of common cognitive endpoints, and creation of HAF- PIPE, we will move to the R33 phase. In the R33 phase, we will leverage the large, harmonized dataset and apply a machine learning technique (CorEx - Correlation Explanation) to identify patient clusters within each patient population studied. HAF-PIPE and the procedures and guidelines from the R61 phase will then be extended to additional patient populations and made available to other ENIGMA working groups. The harmonized data, along with the tools and procedures for creating them, will be accessible to researchers following proposal submission and approval as a curated dataset. With success, this proposal holds the promise of significantly advancing data curation, harmonization, and sharing in the clinical neurosciences. We anticipate that our proposal will significantly advance our understanding of factors that impact outcome after injury and will yield a tool that will be useful across the neuroimaging community.