Nonlinear Causal Analysis of Neural Signals

神经信号的非线性因果分析

• 批准号: 9789882
• 负责人: TERRENCE J SEJNOWSKI
• 金额: $ 34.71万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-22 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789882
• 关键词: Affect; Algorithmic Analysis; Algorithms; Area; Brain; Calcium; Cells; Clinical assessments; Collaborations; Communication; Complement; Computer software; Coupling; Data; Data Analyses; Data Set; Dendrites; Dependence; Diagnosis; Electrocorticogram; Electrodes; Epilepsy; Epileptogenesis; Etiology; Frequencies; General Hospitals; Goals; Hodgkin-Huxley model; Hour; Human; Implant; Massachusetts; Measurement; Measures; Methods; Modeling; Modification; Morphologic artifacts; Neurons; Non-linear Models; Nonlinear Dynamics; Patients; Polynomial Models; Property; Research; Resolution; Sampling; Seizures; Series; Site; Sleep; Structure; Synapses; System; Techniques; Testing; Time; Time Series Analysis; analytical method; awake; base; dynamic system; fluorescence imaging; graphical user interface; improved; insight; network architecture; network models; neurotransmission; novel strategies; open source; programs; relating to nervous system; simulation; temporal measurement; voltage sensitive dye

Abstract The goal of this research is to develop new multivariate data analysis techniques for neural recordings that reveal causal dependencies between recording sites. Delay Differential Analysis (DDA) is a robust and efficient nonlinear time-domain algorithm for time series data that complements linear spectral methods. DDA combines delay and differential embeddings in nonlinear dynamical systems to discriminate between different normal and abnormal cortical states with high temporal resolution and insensitivity to artifacts. The proposed research generalizes Granger causality for linear systems by developing a cross-dynamical version of DDA (CD-DDA) to measure the flow of information between brain areas. This is an important problem for which existing approaches are inadequate. CD-DDA will be applied first to simulations of cortical network models with Hodgkin-Huxley neurons, where causal influence can be controlled and the efficacy of CD-DDA can be validated. In collaboration with Sydney Cash at the Massachusetts General Hospital, CD-DDA will then be applied to electrocorticography (ECoG) recordings from human epilepsy patients with implanted grids of electrodes. We previously analyzed these recordings with DDA, which revealed differences between cortical states leading up to seizures, abrupt shifts at the onsets of the seizures and altered cortical states long after the seizures. These ECoG recordings will be re-analyzed using CD-DDA, which should reveal how communication between cortical areas reconfigures before seizures. We also have access to many hours of interictal recordings, which will give us the opportunity to establish a baseline for how information flows in cortical circuits during more normal cortical activity. We will make the software for all of the DDA algorithms we have developed openly available. These new algorithms will have many other applications for analyzing neural signals online in other brain areas and from other neural time series, including calcium fluorescence imaging from single cells, dendrites and synapses and recordings using voltage-sensitive dyes.

摘要 这项研究的目标是开发用于神经记录的新的多变量数据分析技术 揭示记录站点之间的因果关系。时延差分分析是一种稳健而有效的fi 时间序列数据的非线性时域算法，它是线性谱方法的补充。DDA联合收割机 非线性动力系统中区分正态与非正态的时滞与微分嵌入 异常的皮质状态，时间分辨率高，对伪影不敏感。拟议的研究 通过开发交叉动力版本的DDA(CD-DDA)来推广线性系统的Granger因果关系 测量大脑区域之间信息的flow。这是一个重要的问题，现有的方法 是不够的。CD-dda将被用于用fi-Huxley模拟大脑皮层网络模型 神经元，在fl的原因是可以控制的，并可以验证CD-fi的影响。在协作中 随着西德尼·卡什在马萨诸塞州总医院的工作，CD-DDA将被应用于皮层脑电图术 (ECOG)植入电极栅格的人类癫痫患者的记录。我们之前分析过 这些DDA的记录，揭示了导致癫痫发作的皮质状态之间的差异，突然 癫痫发作开始时的变化和癫痫发作后很久的皮质状态改变。这些ECoG记录 将使用CD-DDA重新分析，这将揭示大脑皮层区域之间的通信是如何重新检测fi的 在癫痫发作前。我们还可以接触到长达数小时的间歇期录音，这将使我们有机会 为在更正常的大脑皮层活动期间，信息fl如何在大脑皮层回路中传播建立一个基线。我们会 使我们开发的所有DDA算法的软件都公开可用。这些新算法将 有许多其他应用程序，用于在线分析其他大脑区域和来自其他神经时间的神经信号 系列，包括单细胞、树突和突触的钙fl荧光成像和使用 电压敏感染料。