Tools for parameterizing and visualizing electrophysiological rhythmic and arrhythmic features

项目摘要 认知需要跨多个大脑区域的神经集合的动态协调。这是 最大的神经科学问题：神经群体如何在大脑中形成短暂的通信网络？ 认知服务？发生这种情况的一个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， 跨越物种。尽管他们进行了广泛的研究，但最近的工作突出了分析振荡的许多陷阱， 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 因为严格地说，振荡被定义为功率谱的任何区域， 高于1/f背景，1/f背景本身已被证明与认知和疾病都是动态的; 2)大多数用于提取和量化振荡的工具都假定它们是正弦的，尽管事实上 他们很少是。此外，那些非正弦特征可以携带关键的生理信息; 3） 传统的方法可以混淆突发和非突发振荡，尽管迅速增加的证据， 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，我们将大大扩展我们的神经数据分析工具箱的成功， 非周期性神经活动的时间分辨分析。我们将测试我们的工具对真实的和 模拟数据此扩展专门用于解决第一个主要的振荡分析问题 上面概述的。经过测试，该扩展将被纳入现有的分析工具箱开发 此前，该基金。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 用于揭示人类视觉工作记忆编码的新结果。所有这些都将通过开放式- 源代码工具，以透明的方式构建软件开发的行业标准。 1

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