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

总结 该提案的目标是开发DDALAB，这是一个软件平台，可以让研究人员 识别潜在的皮层状态，并使用Delay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，但我们将首先关注 来自头皮的EEG记录和直接来自大脑的iEEG数据记录。除了开发 软件，使调查人员更容易分析自己的记录，我们还将开发接口， 记录存储在OpenNeuro档案中，这是一个由大脑计划资助的数据存储库。这些数据可以 通过在本地计算机上运行的DDALAB进行分析和可视化，或直接从OpenNeuro导入到 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（HPC）中心（SDSC）。我们建议把DDALAB纳入现有的 由BRAIN倡议支持的生态系统。 延迟微分分析（DDA）是一种非线性的时域技术，用于拟合时间序列波形， 补充了常用的基于线性傅立叶分析的频域技术。副检察官有个号码 分析大脑记录的优势： · DDA能够在记录中提取线性技术不可见的非线性特征。 ·神经记录和其他时间序列可以用几个低阶时延多项式精确拟合 术语，通常只有3个参数。这减少了过度填充，并使DDA对大多数 人工制品，允许DDA用于在线分析原始记录而无需预处理。 ·DDA的输出是时间序列的高度压缩版本，因为噪声和伪影是 忽视DDA从原始数据中提取和提炼大脑信号，以供后续分析。 ·与机器学习相比，指定模型所需的数据要少得多。 ·同一组DDA模型适合不同受试者的记录，表明DDA正在捕捉基本的 皮质动力学的特性 ·与频谱窗口相比，移动窗口中需要更少的时间点，从而改善了时间 分辨率 DDALAB将提供数据分析，以识别皮层状态的潜在变化，并提供可视化工具， 用于提取大脑区域之间信息定向流动的估计值。这些方法可以 被研究界广泛应用于分析广泛的大脑记录， 治疗脑部疾病的方法。本提案中开发的软件将公开提供 通过GitHub获得开源软件许可证。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.