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：
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
项目类别：
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

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.

项目概要/摘要创伤性脑损伤 (TBI) 是全球范围内的一个重大公共卫生问题，而神经影像学在了解受伤后大脑结构和功能的破坏后，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，这只是部分原因是受伤严重程度。成本经常限制神经影像学研究中的样本量，但考虑到多种因素已被证明会影响患者的治疗结果（年龄、损伤严重程度、社会经济地位），小样本和大规模单变量测试常常导致许多研究的效力严重不足。一解决方案是结合数据并创建新颖的数据共享平台，以及增强神经影像遗传学通过荟萃分析 (ENIGMA) 联盟十多年来一直支持这种合作一系列临床疾病。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行为数据（目标 1）以及结构和功能（静息态）MRI 数据的统一处理（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，允许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和临床措施。该提案将创建可共享的程序、灵活的工具和通用的指南了解从不同行为测试组中提取常见认知终点的最佳实践（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管道功能和结构成像数据 PIPEline； HAF-PIPE），允许聚合非等价的成像数据。主要目标是去中心化 ComBat（一种开源数据协调工具），以便它能够在虚拟共享环境中使用。满足 R61 Go/No-Go 标准后，即数据集的管理（包括 13 个队列）、提取常见认知终点以及创建 HAF- PIPE，我们将进入 R33 阶段。在 R33 阶段，我们将利用大型、统一的数据集和应用机器学习技术（CorEx - 相关性解释）来识别每个患者群体中的患者群体研究的患者群体。 HAF-PIPE 以及 R61 阶段的程序和指南将在扩展到更多患者群体并提供给其他 ENIGMA 工作组。这研究人员将可以访问统一的数据以及创建这些数据的工具和程序提案提交并批准为精选数据集后。如果成功的话，这个提议是有希望的显着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极大地增进我们对影响受伤后结果的因素的理解，并将产生一个对整个神经影像界有用的工具。