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，我们将显着扩展我们的神经数据分析工具箱的成功，以允许非周期性神经活动的时间分辨分析。我们将根据实际情况测试我们工具的有效性模拟数据。该扩展是专门为解决第一个主要振荡分析问题而设计的如上所述。经过测试，该扩展将被纳入现有开发的分析工具箱中之前从这笔赠款中获得。然后，我们将利用这个新的扩展来展示，在概念验证中，它是如何用于发现人类视觉工作记忆编码的新结果。所有这些都将使用开放式完成源工具，以透明的方式按照软件开发的行业标准构建。 1