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 本研究的目标是开发用于神经记录的新的多元数据分析技术， 揭示记录位点之间的因果依赖关系。延迟微分分析（DDA）是一种稳健而有效的 时间序列数据的非线性时域算法，补充线性谱方法。DDA组合 延迟和微分嵌入在非线性动力系统，以区分不同的正常和 异常皮质状态，具有高时间分辨率和对伪影不敏感。拟议研究 通过开发DDA的交叉动力学版本（CD-DDA），将线性系统的格兰杰因果关系推广到 测量大脑区域之间的信息流。这是一个重要的问题，现有的方法 是不够的。CD-DDA将首先应用于Hodgkin-Huxley皮层网络模型的模拟 神经元，其中因果关系可以控制，CD-DDA的有效性可以得到验证。合作 与马萨诸塞州总医院的Sydney Cash一起，CD-DDA将被应用于皮层电描记术 这是来自植入电极网格的人类癫痫患者的皮层脑电图（ECoG）记录。我们之前分析过 这些记录与DDA，揭示了导致癫痫发作的皮质状态之间的差异，突然 在癫痫发作时发生变化，并在癫痫发作后很长时间内改变皮质状态。这些皮层脑电图记录 将使用CD-DDA进行重新分析，这将揭示皮层区域之间的通信是如何相互作用的。 在癫痫发作之前。我们还能拿到几个小时的发作间期录音，这让我们有机会 以建立一个基线，了解在更正常的皮层活动中，信息如何在皮层回路中传播。我们将 使我们开发的所有DDA算法的软件公开可用。这些新算法将 还有许多其他的应用程序，用于分析其他大脑区域和其他神经时间的在线神经信号 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。