Tools for parameterizing and visualizing electrophysiological rhythmic and arrhythmic features

用于参数化和可视化电生理节律和心律失常特征的工具

• 批准号: 10704814
• 负责人: Bradley T. Voytek
• 金额: $ 7.94万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704814
• 关键词: Address; Adoption; Algorithmic Software; Animals; Attention; Behavior; Behavioral; Brain region; Cognition; Cognitive; Communication; Complement; Complex; Computer software; Coupling; Data; Data Analytics; Data Set; Disease; Electroencephalography; Electrophysiology (science); Environment; Equilibrium; Exhibits; Fourier Transform; Frequencies; Human; Industry Standard; Link; Mathematics; Measures; Memory; Mental Depression; Methods; Modality; Neurosciences; Noise; Paper; Parkinson Disease; Peer Review; Periodicity; Physiological; Physiology; Play; Population; Pythons; Reporting; Research; Research Personnel; Resolution; Role; Services; Signal Transduction; Sleep; Standardization; Techniques; Technology; Testing; Time; Visualization; Visualization software; Work; analytical tool; autism spectrum disorder; base; cloud based; computerized data processing; data exploration; data visualization; design; improved; interactive tool; interest; large datasets; novel; open source; open source tool; prototype; relating to nervous system; software development; tool

Project Summary Cognition requires the dynamic coordination of neural ensembles across multiple brain regions. This is one of the biggest neuroscientific questions: how do neural populations form transient communication networks in the service of cognition? One exciting candidate mechanism by which this occurs is through the coupling of neural oscillations between brain regions. These oscillations are a ubiquitous feature of electrophysiology, occurring across species. Despite their wide study, recent work has highlighted many pitfalls in analyzing oscillations, largely centered around three major issues: 1) Oscillations should be measured relative to the aperiodic (1/f) background because, strictly speaking, oscillations are defined as any regions of the power spectrum that rise above the 1/f background, which has itself been shown to be dynamic in relation to both cognition and disease; 2) Most tools for extracting and quantifying oscillations assume that they are sinusoidal despite the fact that they rarely ever are. Further, those non-sinusoidal features may carry critical physiological information; 3) Traditional methods can conflate bursting and non-bursting oscillations, despite the rapidly mounting evidence that the two oscillatory modes are distinct, and may even play different functional roles. In this project we will significantly expand upon analytic software and platforms, developed by my lab, to test the validity of our tools against real and simulated data. These tools are designed specifically to address the three major oscillation analysis issues outlined above. After testing, these analytic toolboxes will then be moved online, to permit cloud-based, large-scale analysis of oscillations, the 1/f background, non-sinusoidal waveform features, and oscillatory bursts. We will then leverage new, dynamic, interactive in-brower visualization tools for data processing and exploration. All of these will be done using open-source tools, built to industry standards of software development, in a transparent manner. 1

项目概要 认知需要跨多个大脑区域的神经整体的动态协调。这是其中之一 最大的神经科学问题：神经群体如何在环境中形成短暂的通信网络 认知服务？发生这种情况的一种令人兴奋的候选机制是通过神经元的耦合 大脑区域之间的振荡。这些振荡是电生理学的普遍特征，发生在 跨物种。尽管进行了广泛的研究，但最近的工作凸显了分析振荡时的许多陷阱， 主要围绕三个主要问题：1) 振荡应该相对于非周期 (1/f) 进行测量 背景，因为严格来说，振荡被定义为功率谱中上升的任何区域 高于 1/f 背景，其本身已被证明与认知和疾病相关； 2) 大多数提取和量化振荡的工具都假设它们是正弦曲线，尽管事实上 他们很少这样做。此外，那些非正弦特征可能携带关键的生理信息； 3） 尽管证据迅速增加，但传统方法可能会将爆裂振荡和非爆裂振荡混为一谈 这两种振荡模式是不同的，甚至可能发挥不同的功能作用。 在这个项目中，我们将显着扩展我的实验室开发的分析软件和平台，以测试 我们的工具针对真实和模拟数据的有效性。这些工具专门设计用于解决 上述三个主要的振荡分析问题。经过测试后，这些分析工具箱将被 在线移动，以允许基于云的大规模振荡分析、1/f 背景、非正弦曲线 波形特征和振荡突发。然后我们将利用新的、动态的、交互式的浏览器 用于数据处理和探索的可视化工具。所有这些都将使用开源工具来完成， 以透明的方式遵守软件开发的行业标准。 1

项目成果

Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity.

磁惊厥疗法和电惊厥疗法会增加非周期性活动。

• 发表时间: 2023-10-27
• 作者: Smith, Sydney E;Kosik, Eena L;van Engen, Quirine;Kohn, Jordan;Hill, Aron T;Zomorrodi, Reza;Blumberger, Daniel M;Daskalakis, Zafiris J;Hadas, Itay;Voytek, Bradley
• 通讯作者: Voytek, Bradley

Methodological considerations for studying neural oscillations.

研究神经振荡的方法学考虑。

• 发表时间: 2022-06
• 作者: Donoghue, Thomas;Schaworonkow, Natalie;Voytek, Bradley
• 通讯作者: Voytek, Bradley

Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity.

磁惊厥疗法和电惊厥疗法会增加非周期性活动。

• 发表时间: 2023-11-16
• 影响因子: 6.8
• 作者: Smith, Sydney E;Kosik, Eena L;van Engen, Quirine;Kohn, Jordan;Hill, Aron T;Zomorrodi, Reza;Blumberger, Daniel M;Daskalakis, Zafiris J;Hadas, Itay;Voytek, Bradley
• 通讯作者: Voytek, Bradley

The data science future of neuroscience theory.

神经科学理论的数据科学未来。

• 发表时间: 2022-11
• 影响因子: 48
• 作者: Voytek, Bradley

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent.

人类电生理频谱指数中注意力和感觉统计的特定模态跟踪。

• 发表时间: 2021-10-21
• 影响因子: 7.7
• 作者: Waschke, Leonhard;Donoghue, Thomas;Fiedler, Lorenz;Smith, Sydney;Garrett, Douglas D;Voytek, Bradley;Obleser, Jonas
• 通讯作者: Obleser, Jonas