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、整合、记录和推广层次结构的使用 事件描述符 (HED) 系统用于描述人类神经影像和行为数据中的事件 来自研究实验和其他来源的足够详细信息以支持比较分析 跨研究的人类大脑动力学。将记录的数据动态与时间相关 主体经验、行动和认知的具体变化是一个主要目标（和挑战） 神经影像和生物力学影像。标准化记录（或帖子）的注释 此类数据集中的事件——记录在不同的感官环境中，涉及 不同的参与者任务和/或任务条件——以“机器可操作”的方式对于 对档案数据进行系统性、可重复的比较分析，以实现发现和建模 系统级大脑功能以及大脑/行为功能的生物标志物。哈德 据我们所知，系统是解决定义问题的唯一本体论系统 实验人类神经影像学和其他研究中的经验和行为事件 记录行为数据。第三代 HED 的发布代表了技术的巨大进步 HED 可用性和注释功能，包括简单编码的能力 实验设计和实验结构以及记录实验刺激和 数据中的主体响应以机器可操作的形式。该项目将改善 支持基础设施、规范 HED 治理和维护流程、支持 用户社区为专业子领域开发图书馆词汇表，并开发 支持在通用分析平台上使用 HED 进行分析的附加工具。实质性 将致力于开发实用教程、案例研究和一系列开源代码 HED 带注释的数据集。我们将进一步努力探索并将 HED 融入到 更广泛的人类神经信息学生态系统，包括 BRAIN Initiative 赞助的 OpenNeuro 存档以及 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.