Dynamic decomposition of spatiotemporal neural signals.
Dynamic decomposition of spatiotemporal neural signals.
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DOI：
10.1371/journal.pcbi.1005540
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发表时间：
2017-05
影响因子：
4.3
通讯作者：
Maris E
中科院分区：
文献类型：
--
作者：
Ambrogioni L;van Gerven MAJ;Maris E
神经信号的特点是丰富的时间和时空动态，反映了组织的皮层网络。理论研究表明，神经网络可以在不同的动态范围内运行，这些动态范围对应于特定类型的信息处理。在这里，我们提出了一个数据分析框架，使用这些动态的线性化模型，以便将测量的神经信号分解成一系列的组件，捕获有节奏和非节奏的神经活动。该方法是基于随机微分方程和高斯过程回归。通过计算机模拟和脑磁图数据的分析，我们证明了该方法的有效性，在识别有意义的调制振荡信号破坏结构化的时间和时空噪声。这些结果表明，该方法是特别适合于复杂的时间和时空神经信号的分析和解释。在神经科学中，研究人员通常对特定信号成分的调制感兴趣（例如，特定频带中的振荡），其必须从有节奏和无节奏活动的背景中提取。由于干扰背景信号通常具有比感兴趣的分量更高的幅度，因此开发能够执行某种信号分解的方法至关重要。在本文中，我们介绍了一种贝叶斯分解方法，该方法利用神经时间动态的先验动力学模型，以提取具有明确定义的动态特征的信号分量。该方法基于高斯过程回归，先验分布由线性随机微分方程的协方差函数确定。使用模拟和分析的真实的脑磁图数据，我们表明，这些知情的先验分布允许可解释的动态分量的提取和相关的信号调制的估计。我们推广的时空皮层活动的分析方法，并表明，该框架是密切相关的良好的源重建技术。
Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. In neuroscience, researchers are often interested in the modulations of specific signal components (e.g., oscillations in a particular frequency band), that have to be extracted from a background of both rhythmic and non-rhythmic activity. As the interfering background signals often have higher amplitude than the component of interest, it is crucial to develop methods that are able to perform some sort of signal decomposition. In this paper, we introduce a Bayesian decomposition method that exploits a prior dynamical model of the neural temporal dynamics in order to extract signal components with well-defined dynamic features. The method is based on Gaussian process regression with prior distributions determined by the covariance functions of linear stochastic differential equations. Using simulations and analysis of real MEG data, we show that these informed prior distributions allow for the extraction of interpretable dynamic components and the estimation of relevant signal modulations. We generalize the method to the analysis of spatiotemporal cortical activity and show that the framework is intimately related to well-established source-reconstruction techniques.