BRAIN Initiative: Hierarchical Event Descriptors (HED): a system to characterize events in neurobehavioral data

这个为期两年的项目将推进、集成、记录和促进分层结构的使用 用于描述人类神经成像和行为数据中的事件的事件描述符(HED)系统 来自研究、实验和其他来源的足够详细的资料，以支持比较分析 不同研究中的人脑动力学。将记录的数据动态与时间上的- 主体经验、行动和认知方面的可指定变化是的主要目标(和挑战) 包括神经成像和生物力学成像。规范记录(或帖子)的注释 特定标识)这些数据集上的事件-记录在不同的感觉环境中，包括 不同的参与者任务和/或任务条件--以机器可操作的方式 对档案数据进行系统的可重现的比较分析，以实现发现和建模 系统水平的大脑功能以及大脑/行为功能的生物标志物。《赫德》 据我们所知，系统是解决定义问题的唯一本体论系统 实验人体神经成像和其他研究中的经验和行为事件 记录行为数据。第三代HED的发布代表着在 HED的可用性和批注功能，包括简单地对 实验设计和实验结构，以记录实验刺激和 受试者在数据中以机器可操作的形式进行响应。这个项目将会有所改善 支持基础设施，规范HED治理和维护流程，支持 为专业子领域开发图书馆词汇的用户社区，并开发 支持在通用分析平台上使用HED进行分析的其他工具。相当可观 我们将致力于开发实用教程、案例研究和大量开放源码软件 HED带注释的数据集。进一步的努力将致力于探索HED并将其融入 更广泛的人类神经信息学生态系统，包括大脑倡议赞助的OpenNeuro 存档和Nemar数据、工具和计算资源。

This two-year project will advance, integrate, document, and promote the use of the Hierarchical Event Descriptor (HED) system to describe events in human neuroimaging and behavioral data from research experiments and other sources in sufficient detail to support comparative analysis of human brain dynamics across studies. Relating the recorded data dynamics to temporally- specifiable changes in subject experience, action, and cognition is a major goal (and challenge) for both neuroimaging and biomechanical imaging. Standardizing the annotation of recorded (or post hoc identified) events across such data sets — recorded in diverse sensory environments involving different participant tasks and/or task conditions — in a ‘machine-actionable’ way is essential for systematic reproducible comparative analysis of archival data to enable discovery and modeling of systems-level brain function as well as biomarkers of brain/behavioral function. The HED system is to our knowledge the only ontological system addressing the problem of defining experiential and behavioral events in experimental human neuroimaging and other studies recording behavioral data. The release of third-generation HED represents a dramatic advance in HED usability and annotation capabilities, including the capacity to simply encode the experimental design and experimental structure as well to document experimental stimuli and subject responses within the data in a machine-actionable form. This project will improve supporting infrastructure, formalize HED governance and maintenance processes, support a community of users developing library vocabularies for specialized subfields, and develop additional tools for supporting analysis using HED on common analysis platforms. Substantial effort will be expended in developing practical tutorials, case studies, and a body of open-source HED annotated datasets. Further effort will be devoted to exploring and integrating HED into the wider human neuroinformatics ecosystem including the BRAIN Initiative sponsored OpenNeuro archive and the NEMAR data, tools, and compute resource.