Makeig Scott, Robbins Kay
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, United States.
Department of Computer Science, University of Texas San Antonio, San Antonio, TX, United States.
Front Neuroinform. 2024 May 23;18:1292667. doi: 10.3389/fninf.2024.1292667. eCollection 2024.
The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between themselves, that unfold through time, and that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.
大脑是一个复杂的动态系统,其当前状态与对过去、当前以及预期未来的威胁和机遇的认知紧密相连,这些威胁和机遇不断影响着认知以及行为目标和决策。大脑活动在多个时间尺度上由不断演变的感觉、本体感觉和自身感觉体验流驱动。神经成像实验旨在分离并聚焦于这些复杂动态的某些方面,以便更好地理解大脑活动如何支持人类体验、认知、行为和健康。在此,我们考虑一种事件相关数据建模方法,该方法试图将体验和行为解析为一组有时间界定的事件。我们区分随着时间展开的事件本身,以及记录事件开始、结束和任何其他事件阶段转换的实验时间线潜伏期。对实验事件(感觉、运动或其他)的精确描述使参与者的体验和行为能够在事件本身或所有或任何实验事件的背景下得到解释。我们讨论了神经成像实验中的事件如何过去已被识别和表示、目前如何识别和表示以及应如何最好地识别和表示,重点强调了对事件和事件背景进行建模对于有意义地解释大脑动态、体验和行为之间关系的重要性。我们展示了使用分层事件描述符(HED;https://www.hedtags.org)系统对时间序列神经成像数据进行文本注释如何比当前的数据注释实践更能充分地对事件及其不断演变的背景的作用进行建模,从而有助于数据分析、元分析和大数据分析。最后,我们讨论了HED系统必须继续扩展以满足神经成像研究不断变化的需求的方式。