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

基本信息

批准号：
10442179
负责人：
Bradley T. Voytek
金额：
$ 6.43万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-06-30
项目状态：
已结题

项目摘要

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 the success of our neural data analysis toolboxes to allow for time-resolved analysis of aperiodic neural activity. We will test the validity of our tools against real and simulated data. This extension is specifically designed to address the first major oscillation analysis issue outlined above. After testing, this extension will be incorporated into the existing analytic toolboxes developed previously from this grant. We will then leverage this new extension to show, in proof-of-concept, how it can be used to uncover novel results in human visual working memory encoding. All of these will be done using open- source tools, built to industry standards of software development, in a transparent manner. 1

项目摘要认知要求在多个大脑区域之间进行神经合奏的动态协调。这是之一最大的神经科学问题：神经种群如何形成瞬态通信网络认知服务？发生这种情况的一种令人兴奋的候选机制是通过神经的耦合大脑区域之间的振荡。这些振荡是电生理学的无处不在的特征，发生跨物种。尽管他们进行了广泛的研究，但最近的工作还是在分析振荡时突出了许多陷阱，在很大程度上集中在三个主要问题上：1）相对于上的振荡（1/f）应测量振荡背景是因为，严格来说，振荡被定义为功率谱的任何区域上升的区域在1/f背景之上，与认知和疾病相关的背景本身是动态的。 2）大多数用于提取和量化振荡的工具假设它们是正弦的，尽管事实他们很少。此外，这些非肌体特征可能会带有关键的生理信息。 3）尽管有迅速的证据两种振荡模式是不同的，甚至可能扮演不同的功能角色。在这个项目中，我们将大大扩展神经数据分析工具箱的成功，以允许时间分解的神经活性分析。我们将测试工具针对真实和的工具的有效性模拟数据。该扩展名专门设计用于解决第一个主要振荡分析问题上面概述了。测试后，此扩展名将合并到已开发的现有分析工具箱中以前来自这笔赠款。然后，我们将利用这一新扩展名在概念验证中显示如何表现用于发现人类视觉工作记忆编码的新型结果。所有这些都将使用开放源工具，以透明的方式构建为软件开发标准的源工具。 1