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、记录和促进分层 事件描述符（HED）系统，用于描述人类神经成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的资料，以支持比较分析 人类大脑动力学的研究。将记录的数据动态与时间相关- 主体经验、行动和认知的可指定变化是一个主要目标（和挑战） 神经成像和生物力学成像。规范录音（或录音带）的注释 在不同的感官环境中记录的这些数据集上的事件， 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、可重复的比较分析，以实现发现和建模 以及大脑/行为功能的生物标志物。述HED 系统是我们所知的唯一的本体论系统解决的问题， 实验性人类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的发布代表了 HED可用性和注释功能，包括简单编码 实验设计和实验结构以及记录实验刺激， 以机器可操作的形式在数据内进行主题响应。该项目将改善 支持基础设施，规范HED治理和维护流程，支持 用户社区为专门的子领域开发图书馆词汇表， 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 将努力开发实用教程、案例研究和一套开源软件， HED注释数据集。我们会进一步研究如何把高等教育融入 更广泛的人类神经信息学生态系统，包括BRAIN Initiative赞助的OpenNeuro 归档和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.