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 这项研究的目的是为神经记录开发新的多元数据分析技术 揭示记录站点之间的因果关系。延迟差分分析（DDA）是强大而有效的 用于完成线性光谱方法的时间序列数据的非线性时间域算法。 DDA组合 非线性动态系统中的延迟和差异嵌入，以区分不同的正常和 具有高临时分辨率和对伪影的不敏感的皮质状态异常。拟议的研究 通过开发DDA（CD-DDA）的跨动力版本来概括线性系统的Granger因果关系 测量大脑区域之间的信息流。这是现有方法的重要问题 不足。 CD-DDA将首先应用于使用Hodgkin-Huxley的皮质网络模型的模拟 神经元，可以控制因果影响并可以验证CD-DDA的效率。合作 在马萨诸塞州综合医院的悉尼现金，CD-DDA将应用于电视学 （ECOG）植入电极网格的人癫痫患者的记录。我们以前进行了分析 这些与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 癫痫发作后很长一段时间，在癫痫发作和皮质状态改变的侵害上发生了变化。这些ECOG录音 将使用CD-DDA重新分析，这应该揭示皮质区域之间的沟通方式 癫痫发作之前。我们还可以访问许多小时的发作录音，这将使我们有机会 建立一个基线，以使信息如何在更正常的皮质活性过程中流动。我们将 为我们公开开发的所有DDA算法制作软件。这些新算法将 还有许多其他应用程序在其他大脑区域和其他神经时间在线分析神经信号 系列，包括来自单个细胞，树突以及突触和记录的钙荧光成像 电压敏感染料。