Spatiotemporal forward solution of the EEG and MEG using network modeling
Spatiotemporal forward solution of the EEG and MEG using network modeling
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DOI：
10.1109/tmi.2002.1009385
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发表时间：
2002-05-01
影响因子：
10.6
通讯作者：
Kelso, JAS
中科院分区：
文献类型：
--
作者：
Jirsa, VK;Jantzen, KJ;Kelso, JAS
动态系统已被证明非常适合于描述广泛的人类协调行为，如与听觉刺激的同步。同时测量脑电图（EEG）和脑磁图（MEG）数据的时空动态表明，大脑信号的动态是高度有序的，也可以通过动态系统理论获得。然而，脑电图和脑磁图的动力学模型通常只在固定电流偶极子或空间脑电图和脑磁图模式等现象学建模方面制定。在本文中，我们的目标是连接三个层次的组织，即协调行为的层次、脑电和脑磁图中观察到的模式的层次和神经网络动力学的层次。为此，我们开发了一种方法框架，该框架定义了三维球面上神经集合的时空动态，即神经场。利用磁共振成像，我们将神经场动力学从球体映射到半球的折叠皮质表面。神经场代表垂直于皮层的电流，因此可以计算颅骨表面的电位和颅骨外的磁场，分别由脑电图和脑磁图测量。为了证明这一动力学，我们展示了由瞬态输入引起的单个皮质部位的激活传播。最后，利用Volterra积分获得手指运动特征与脑电/脑磁图的映射关系。
Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixed-current dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.