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Time-Variant Modeling of Brain Processes

机译：脑过程的时变模型

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摘要

In science and engineering mathematical modeling serves as a tool for the understanding of processes and systems and as a testing bed for several hypotheses, e.g., concerning the testing (prediction) of functional limits by simulations. A brief overview of current modeling strategies in brain research is given, spatial scales ranging from single neuron to large scale activity of and between brain regions are considered. The models are mainly time-invariant. Three time-variant modeling strategies, which enable a model-based signal analysis, are described and applied to large scale signals. The first is derived from adaptive filter theory and covers linear system and linear as well as nonlinear process models. The second is based on modeled brain source signals, i.e., the inverse problem must be solved. The third strategy consists of a generalization of Dynamic Causal Modeling (DCM); DCM is frequently used for analysis of directed interactions between brain structures. Examples are derived from neonatal electroencephalography (EEG) monitoring of preterm and fullterm newborns. A further example is based on high-density recordings of event-related potentials (ERPs) and shows the combination of a time-variant ERP-based source model, as a part of a realistic head model, with a multivariate process model to analyze the time evolution of interactions between source processes before and during the execution of a complex motoric task. In two other examples hemodynamic signals (functional magnetic resonance imaging—fMRI) are utilized for analysis of interactions between brain regions, where nonlinear, multivariate models are used.
机译：在科学和工程中，数学建模可作为了解过程和系统的工具，并且可作为多种假设的测试平台，例如，有关通过仿真测试功能极限的假设。简要概述了当前脑研究中的建模策略，考虑了从单个神经元到大脑区域之间以及大脑区域之间的大规模活动的空间尺度。这些模型主要是时不变的。描述了三种时变建模策略，这些策略可实现基于模型的信号分析，并将其应用于大规模信号。第一个是从自适应滤波器理论衍生而来的，涉及线性系统，线性以及非线性过程模型。第二种是基于建模的脑源信号，即必须解决反问题。第三种策略包括动态因果模型（DCM）的概括； DCM通常用于分析大脑结构之间的定向相互作用。实例来自对早产和足月新生儿的新生儿脑电图（EEG）监测。另一个示例基于事件相关电位（ERP）的高密度记录，并显示了基于时变ERP的源模型（作为实际头部模型的一部分）与多变量过程模型的组合，以分析在执行复杂的汽车任务之前和期间，源过程之间的交互作用的时间演化。在另外两个示例中，血液动力学信号（功能磁共振成像-fMRI）用于分析大脑区域之间的相互作用，其中使用了非线性多元模型。

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