Capturing the nature of events and event context using hierarchical event descriptors (HED).
Capturing the nature of events and event context using hierarchical event descriptors (HED).
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使用分层事件描述符(HED)捕获事件的性质和事件上下文。
DOI：
10.1016/j.neuroimage.2021.118766
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发表时间：
2021-12-15
期刊：
影响因子：
5.7
通讯作者：
Makeig S
中科院分区：
文献类型：
--
作者：
Robbins K;Truong D;Appelhoff S;Delorme A;Makeig S
事件相关数据分析在脑电和脑磁图(MEEG)以及包括功能磁共振成像(FMRI)在内的其他神经成像手段中发挥着核心作用。关于报告哪些事件以及如何注释其全部性质的选择，显著影响用于进一步分析和元分析或超分析的神经成像数据集的价值、可靠性和可重复性。使用新的第三代分层事件描述符(HED)框架和工具(hedtag s.org)的强大注释策略以人类可读和机器可操作的形式将健壮的事件描述与实验设计和元数据的细节相结合，使事件注释与全方位的神经成像和其他时间序列数据相关。本文以著名的多主体、多模式的Wakeman和Henson数据集为例，研究了事件的设计和标注过程，该数据集由Wakeman和Henson的作者提供，作为脑成像数据结构(BIDS)数据集(BIDS.NeuroImaging.io)。我们提出了一套以自然方式集成到BIDS元数据文件体系结构中的事件注释的最佳实践和指导方针，检查了事件设计决策的影响，并提供了在MEEG和其他神经成像数据中组织事件的工作示例。我们演示了使用HED的注释如何记录神经成像实验期间发生的事件以及它们之间的相互关系，从而提供机器可操作的注释，从而实现实验内和实验间的自动化分析和比较。我们讨论了HED软件工具的演变，并提供了附带HED注释的出价格式的Wakeman和Henson数据集MEEG数据的版本(OpenNeual.org，ds003645)。
Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made available an accompanying HED-annotated BIDS-formated edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645).
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影响因子：
2.9
作者：
Henson RN;Wakeman DG;Litvak V;Friston KJ
通讯作者：
Friston KJ
影响因子：
5.7
作者：
Bigdely-Shamlo, Nima;Touryan, Jonathan;Robbins, Kay
通讯作者：
Robbins, Kay
影响因子：
3
作者：
Makeig, Scott;Gramann, Klaus;Jung, Tzyy-Ping;Sejnowski, Terrence J.;Poizner, Howard
通讯作者：
Poizner, Howard
影响因子：
3.5
作者：
Bigdely-Shamlo N;Cockfield J;Makeig S;Rognon T;La Valle C;Miyakoshi M;Robbins KA
通讯作者：
Robbins KA
影响因子：
9.8
作者：
Holdgraf, Christopher;Appelhoff, Stefan;Hermes, Dora
通讯作者：
Hermes, Dora