DDALAB: Identifying Latent States from Neural Recordings with Nonlinear Causal Analysis

摘要 这项提议的目标是开发DDALAB，一个软件平台，将使研究人员能够 使用延迟来识别潜在的皮质状态并分析大量神经元中的信息流 差分分析(DDA)。虽然DDA可用于分析任何时间序列数据，但我们首先将重点放在 关于头皮的脑电记录和直接来自大脑的iEEG数据记录。除了开发 软件使调查人员可以轻松地分析他们自己的记录，我们还将开发与 录音存储在OpenNeuro档案中，这是一个为大脑倡议提供资金的数据存储库。这些数据可以是 在本地计算机上运行或直接从OpenNeuro导入到中的DDALAB进行分析和可视化 Nemar资源，并通过圣地亚哥超级计算机的神经科学网关(NSG)进行处理 高性能计算中心(SDSC)。我们建议将DDALAB整合到现有的 由大脑倡议支持的生态系统。 延迟差分分析(DDA)是一种非线性的、适用于时间序列波形的时域技术，它 补充了基于线性傅立叶分析的常用频域技术。DDA有一个号码 分析大脑记录的优势： ·DDA能够从录音中提取线性技术看不到的非线性特征。 ·神经记录和其他时间序列可以用几个低阶延迟多项式准确地拟合 术语，通常只有3个参数。这减少了过量填充，并使DDA对大多数 人工制品，允许使用DDA对原始记录进行在线分析，而无需进行预处理。 ·DDA的输出是时间序列的高度压缩版本，因为噪声和伪影 已被忽略。DDA从原始数据中提取和提取大脑信号，以供以后分析。 ·与机器学习相比，指定模型所需的数据要少得多。 ·同一套DDA模型适用于不同受试者的录音，表明DDA捕捉到了基本信息 大脑皮层动力学特性。 ·与频谱窗口相比，移动窗口中需要的时间点更少，从而提高了时间 决议。 DDALAB将提供数据分析，以识别皮质状态的潜在变化，并提供可视化工具，可以 被用来提取大脑区域之间定向信息流的估计。这些方法可以是 被研究界广泛应用于分析广泛的大脑记录，并更好地开发 脑部疾病患者的治疗。在此提案中开发的软件将公开提供 通过具有开放源代码软件许可证的GitHub。用户将不必购买商业软件或 依赖于专有数据格式。

Summary The goal of this proposal is to develop DDALAB, a software platform that will make it possible for researchers to identify latent cortical states and analyze the flow of information in large populations of neurons using Delay Differential Analysis (DDA). Although DDA can be used to analyze any time series data, we will initially focus on EEG recordings from the scalp and iEEG data recordings directly from the brain. In addition to developing software making it easy for an investigator to analyze their own recordings, we will also develop interfaces with recordings stored in OpenNeuro archive, a data repository funded the The BRAIN Initiative. These data can be analyzed and visualized with the DDALAB running on local computers or imported directly from OpenNeuro into the NEMAR resource and processed via the Neuroscience Gateway (NSG) at the San Diego Supercomputer Center (SDSC) for High Performance Computing (HPC). We propose to integrate DDALAB into the existing ecosystem supported by the BRAIN Initiative. Delay Differential Analysis (DDA) is a nonlinear, time-domain technique that fits time series waveforms, which complements commonly used frequency domain techniques based on linear Fourier analysis. DDA has a number of advantages for analyzing brain recordings: • DDA is able to extract nonlinear features in recordings that are invisible to linear techniques. • Neural recordings and other time series can be accurately fit with a few low-order time-delayed polynomial terms, typically having only 3 parameters. This reduces overfilling and makes DDA insensitive to most artifacts, allowing DDA to be used for online analysis of raw recordings without preprocessing. • The output of DDA is a highly compressed version of the time series because noise and artifacts are ignored. DDA extracts and distills brain signals from raw data for later analysis. • Much less data are required to specify a model compared with machine learning. • The same set of DDA models fits recordings across subjects, suggesting that DDA is capturing fundamental properties of cortical dynamics. • Fewer time points are needed in a moving window compared with spectral windows, improving the time resolution. DDALAB will provide data analysis for identifying latent changes in cortical states and visualization tools that can be used to extract estimates for the directed flow of information between brain areas. These methods can be applied by the research community at large to analyze a wide range of brain recordings and to develop better treatments for patients with brain diseases. The software developed in this proposal will be openly available through GitHub with an Open Source Software license. Users will not have to buy commercial software or depend on proprietary data formats.