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

推进二次数据分析：ENIGMA 脑损伤数据协调计划

• 批准号: 10266848
• 负责人: Emily Larsen Dennis
• 金额: $ 92.15万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266848
• 关键词: Address; Age; Archives; Attenuated; Behavioral; Big Data; Biological Markers; Brain; Brain Diseases; Brain Injuries; Calibration; Clinical; Code; Cognitive; Collaborations; Communities; Data; Data Aggregation; Data Analyses; Data Collection; Data Set; Decentralization; Decision Trees; Development; Disease; Environment; Epilepsy; Functional Magnetic Resonance Imaging; Genetic; Goals; Guidelines; Heterogeneity; Image; Informatics; Injury; International; Machine Learning; Magnetic Resonance Imaging; Measures; Memory; Mental Depression; Meta-Analysis; Modeling; Neuronal Plasticity; Neuropsychology; Neurosciences; Outcome; Patient Self-Report; Patient-Focused Outcomes; Patients; Pattern; Performance; Phase; Phenotype; Population Study; Post-Traumatic Stress Disorders; Procedures; Process; Protocols documentation; Public Health; Quality Control; Research; Research Personnel; Rest; Sample Size; Sampling; Science; Severities; Site; Socioeconomic Status; Standardization; Stream; Structure; Techniques; Testing; Time; Traumatic Brain Injury; Validation; base; behavior test; behavioral construct; clinical heterogeneity; cohort; collaborative environment; combat; computerized data processing; cost; data acquisition; data analysis pipeline; data curation; data harmonization; data ingestion; data pipeline; data quality; data sharing; design; flexibility; heterogenous data; image processing; imaging study; insight; multimodal data; neuroimaging; novel; open data; open source; patient population; patient subsets; portability; prognostic value; response; satisfaction; sharing platform; stroke recovery; success; tool; virtual; virtual environment; working group

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.

项目总结/摘要 创伤性脑损伤（TBI）是全球主要的公共卫生问题，虽然神经成像在治疗创伤性脑损伤方面是有用的。 了解损伤后大脑结构和功能的破坏，有许多因素会减弱 其预测能力。例如，受伤后的结果存在巨大的异质性， 部分原因是受伤严重。成本经常限制神经影像学研究的样本量，但考虑到 已证明影响患者结局的多种因素（年龄、损伤严重程度、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效力严重不足。一 解决方案是联合收割机数据和创建新的数据共享平台，和增强神经成像遗传学 通过荟萃分析（ENIGMA）联盟已经支持这种合作超过十年， 一系列临床疾病本提案的目标是开发工具和数据处理程序， 在ENIGMA脑损伤工作组中使用。在R61阶段，我们的目标是开发和测试一个工作流程， 协调处理行为数据（目标1）以及结构和功能（静息状态）MRI数据 (Aim 2）。对于R61的目标1，目标是提供一个依赖于数据的程序决策树， 研究人员在收集一系列神经心理学数据的队列中建立共同的认知终点， 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南 从不同的行为测试组合中提取共同认知终点的最佳实践（R61 Aim 1）。 在R61的目标2中，我们开发了一个名为协调和聚合的图像处理管道， 功能和结构成像数据PIPEline; HAF-PIPE），允许聚集非等效 成像数据。一个主要目标是去中心化ComBat，一个开源数据协调工具，这样它就可以 用于虚拟共享环境。满足R61 Go/No-Go标准后， 对包括13个队列的数据集进行管理，提取常见的认知终点，并创建HAF- PIPE，我们将进入R33阶段。在R33阶段，我们将利用大型统一数据集， 应用机器学习技术（CorEx -相关性解释）来识别每个患者群中的患者群 研究的患者人群。HAF管道和R61阶段的程序和指南将被 扩展到其他患者人群，并提供给其他ENIGMA工作组。的 统一的数据沿着创建数据的工具和程序将向研究人员开放 在提案提交和批准后作为策展数据集。随着成功，这一提议有望 显著推进临床神经科学中的数据管理、协调和共享。我们预计 我们的建议将大大提高我们对受伤后影响结果的因素的理解， 产生一个在神经影像学界有用的工具。