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

基本信息

批准号：
10618768
负责人：
Emily Larsen Dennis
金额：
$ 89.86万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10618768
关键词：
Acceleration Address Age Attenuated Behavioral Big Data Biological Markers Brain Brain Diseases Brain Injuries Calibration Clinical Code Cognitive Collaborations Communities Darkness Data Data Aggregation Data Analyses Data Collection Data Set Decentralization Decision Trees Development Disease Environment Epilepsy Functional Imaging Functional Magnetic Resonance Imaging Genetic Goals Guidelines Heterogeneity Image Informatics Injury International Machine Learning Magnetic Resonance Imaging Measures Memory Mental Depression Meta-Analysis Methods 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 TBI Patients Techniques Testing Time Traumatic Brain Injury Validation 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 reduction data sharing design flexibility heterogenous data image processing imaging study insight machine learning framework multimodal data neuroimaging novel open data open source patient population patient subsets portability prognostic value response satisfaction sharing platform stroke recovery structural imaging success tool virtual virtual environment working group

项目摘要

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.

项目摘要/摘要创伤性脑损伤（TBI）在全球范围内是一个主要的公共卫生问题，尽管神经影像却很有用了解受伤后大脑结构和功能的破坏，有许多因素减弱它的预后能力。例如，受伤后的结果存在巨大的异质性部分用伤害严重程度解释。成本经常限制神经影像学研究中的样本量，但鉴于已经显示出影响患者结果的无数因素（年龄，伤害严重程度，社会经济地位），小样本和质量单变量测试通常会导致许多研究严重低估。一解决方案是结合数据并创建新颖的数据共享平台以及增强的神经影像学遗传学通过荟萃分析（Enigma）财团支持了这种合作，已有十多年的历史一系列临床疾病。该提案的目的是为开发工具和数据处理程序在谜脑损伤工作组中使用。在R61阶段，我们旨在开发和测试一个工作流程行为数据的协调处理（AIM 1）以及结构和功能（静止状态）MRI数据（目标2）。对于R61的AIM 1，目标是提供一个依赖数据的程序的决策树，允许研究人员建立跨同类群体的常见认知终点，这些终点收集一系列神经心理学和临床措施。该建议将创建可共享的程序，灵活的工具和可推广的准则从不同行为测试电池中提取常见的认知终点的最佳实践（R61 AIM 1）。在R61的AIM 2中，我们开发了一个图像处理管道，称为协调和聚合功能和结构成像数据管道； haf-pipe）允许汇总非等效成像数据。一个主要目标是分散战斗，这是一种开源数据协调工具，以便它可以在虚拟共享环境中使用。在满足R61 GO/No-Go标准之后数据集的策展，包括13个队列，共同认知终点的提取以及创建HAF- 管道，我们将移至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-相关说明）来识别每个人的患者群集患者人数研究了。然后，HAF-Pipe以及R61阶段的过程和指南将是扩展到其他患者人群，并提供给其他谜团工作组。这研究人员可以访问协调数据，以及创建它们的工具和程序根据提案提交和批准作为策划数据集。有了成功，该提议有望在临床神经科学中显着提高数据策展，协调和共享。我们期待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，并将产生一个在神经影像社区中有用的工具。