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 该提案的目标是开发 DDALAB，一个软件平台，使研究人员能够 使用 Delay 识别潜在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，但我们首先将重点关注。 除了开发之外，还可以使用头皮的脑电图记录和直接来自大脑的 iEEG 数据记录。 软件使调查人员可以轻松分析自己​​的录音，我们还将开发与 存储在 OpenNeuro 档案中的记录，该档案是由 BRAIN Initiative 资助的数据存储库。 使用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 我们建议将 DDALAB 集成到现有的高性能计算中心 (SDSC)。 BRAIN Initiative 支持的生态系统。 延迟微分分析 (DDA) 是一种拟合时间序列波形的非线性时域技术， 补充了基于线性傅立叶分析的常用频域技术。 分析大脑记录的优点： • DDA 能够提取录音中线性技术无法看到的非线性特征。 • 神经记录和其他时间序列可以用一些低阶延时多项式精确拟合 术语，通常只有 3 个参数，这可以减少过度填充并使 DDA 对大多数人不敏感。 工件，允许 DDA 用于在线分析原始录音而无需预处理。 • DDA 的输出是时间序列的高度压缩版本，因为噪声和伪影都被忽略了。 DDA 从原始数据中提取并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 模型适合不同受试者的记录，表明 DDA 正在捕捉基本原理 皮质动力学的特性。 • 与光谱窗口相比，移动窗口中需要的时间点更少，从而提高了时间 解决。 DDALAB 将提供用于识别皮质状态潜在变化的数据分析和可视化工具，这些工具可以 这些方法可用于提取大脑区域之间定向信息流的估计。 被整个研究界广泛应用来分析广泛的大脑记录并更好地开发 该提案中开发的软件将公开提供给脑部疾病患者的治疗。 通过 GitHub 获得开源软件许可证的用户无需购买商业软件或。 取决于专有数据格式。