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.

抽象的 这项研究的目标是开发新的神经记录多变量数据分析技术 揭示记录站点之间的因果关系。延迟微分分析 (DDA) 是一种稳健且高效的方法 用于时间序列数据的非线性时域算法，补充了线性谱方法。 DDA 结合 非线性动力系统中的延迟和差分嵌入来区分不同的正常和 具有高时间分辨率和对伪影不敏感的异常皮质状态。拟议的研究 通过开发 DDA 的交叉动力学版本 (CD-DDA) 来推广线性系统的格兰杰因果关系 测量大脑区域之间的信息流。这是现有方法解决的一个重要问题 是不够的。 CD-DDA 将首先应用于 Hodgkin-Huxley 皮质网络模型的模拟 神经元，其中因果影响可以被控制并且 CD-DDA 的功效可以被验证。合作中 与马萨诸塞州总医院的 Sydney Cash 合作，CD-DDA 将应用于皮层电图描记术 （ECoG）来自植入电极网格的人类癫痫患者的记录。我们之前分析过 这些 DDA 记录揭示了导致癫痫发作的皮质状态之间的差异，突然 癫痫发作时的变化以及癫痫发作后很长时间内皮质状态的改变。这些 ECoG 记录 将使用 CD-DDA 重新分析，这应该揭示皮层区域之间的通信如何重新配置 癫痫发作前。我们还可以获得许多小时的发作间期录音，这将为我们提供机会 为更正常的皮层活动期间信息如何在皮层回路中流动建立基线。我们将 公开我们开发的所有 DDA 算法的软件。这些新算法将 有许多其他应用程序可以在线分析其他大脑区域和其他神经时间的神经信号 系列，包括单细胞、树突和突触的钙荧光成像以及使用 电压敏感染料。