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

DDALAB：通过非线性因果分析从神经记录中识别潜在状态

• 批准号: 10643212
• 负责人: TERRENCE J SEJNOWSKI
• 金额: $ 124.07万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643212
• 关键词: Acceleration; Archives; Area; BRAIN initiative; Behavior; Brain; Brain Diseases; Collaborations; Communication; Communities; Complement; Computer software; Computers; Data; Data Analyses; Data Set; Databases; Dependence; Disease; Early Diagnosis; Ecosystem; Educational workshop; Electroencephalography; Epilepsy; Event-Related Potentials; Eye; Feedback; Fourier Analysis; Frequencies; Funding; General Hospitals; Goals; High Performance Computing; Letters; Libraries; Licensing; Light; Link; Machine Learning; Massachusetts; Methods; Modeling; Morphologic artifacts; Neurons; Neurosciences; Noise; Operating System; Output; Patients; Physics; Polynomial Models; Population; Process; Property; Psychiatry; Psychoses; Research; Research Personnel; Resolution; Resources; Running; Scalp structure; Schizophrenia; Seizures; Series; Signal Transduction; Site; Specific qualifier value; Speech; Systems Theory; Techniques; Testing; Time; Training; Visualization; Visualization software; Writing; data format; data repository; dynamic system; early detection biomarkers; experience; graphical user interface; improved; insight; large scale data; multiple data sources; neural; open source; programs; rapid detection; response; sensory input; sensory stimulus; software development; speech recognition; supercomputer; theories; user-friendly

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.

概括 该建议的目的是开发Ddalab，这是一个软件平台，将使研究人员成为可能 识别潜在皮质状态并使用延迟分析大量神经元中的信息流 差分分析（DDA）。尽管可以使用DDA来分析任何时间序列数据，但我们最初将集中精力 关于头皮和IEEG数据记录的EEG记录，直接来自大脑。除了发展 软件使调查员可以轻松分析自己​​的录音，我们还将开发与 存储在OpenNeuro Archive中的录音，该数据存储库资助了大脑计划。这些数据可以是 通过在本地计算机上运行或直接从OpenNeuro导入到Openneuro中进行分析和可视化 Nemar资源并通过圣地亚哥超级计算机的Neuroscience Gateway（NSG）处理 高性能计算（HPC）的中心（SDSC）。我们建议将ddalab集成到现有 大脑倡议支持的生态系统。 延迟差分分析（DDA）是一种适合时间序列波形的非线性时间域技术， 基于线性傅立叶分析的完整频域技术通常。 DDA有一个数字 分析大脑记录的优势： •DDA能够在线性技术看不见的记录中提取非线性特征。 •神经记录和其他时间序列可以与一些低阶时间延迟的多项式准确拟合 术语，通常只有3个参数。这减少了过度填充，使DDA对大多数 工件，允许DDA用于在线分析原始录音，而无需进行预处理。 •DDA的输出是时间序列的高度压缩版本，因为噪声和文物是 被忽略。 DDA提取物和从原始数据中提取脑信号，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。 •同一组DDA模型适合跨主题的录音，这表明DDA正在捕获基本 皮质动力学的特性。 •与光谱窗口相比，在移动窗口中需要更少的时间点，从而改善了时间 解决。 DDALAB将提供数据分析，以识别皮质状态的潜在变化和可视化工具的潜在变化 用于提取大脑区域之间信息流的估计值。这些方法可以是 由整个研究界应用于整个大脑记录并更好地分析大脑记录 治疗脑疾病患者。该提案中开发的软件将公开可用 通过GITHUB带有开源软件许可证。用户不必购买商业软件或 取决于专有数据格式。