Zavaglia Melissa, Astolfi Laura, Babiloni Fabio, Ursino Mauro
Department of Electronics, Computer Science, and Systems, University of Bologna, viale Venezia 52, I-47026 Cesena, Italy.
J Neurosci Methods. 2006 Oct 30;157(2):317-29. doi: 10.1016/j.jneumeth.2006.04.022. Epub 2006 Jun 6.
Neural mass models have been used for many years to study the macroscopic dynamics of neural populations in a simple and computationally inexpensive way. In this paper, we modified a model proposed by Wendling et al. [Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci 2002;15:1499-508] to simulate EEG power spectral density (PSD) in some regions of interest (ROIs) during simple tasks (finger movement or working memory tests). The work consists of two subsequent stages: (1) in the first we evaluated the role of some model parameters (i.e., average gain of synapses and their time constants) in affecting power spectral density. This analysis confirmed the possibility to simulate various EEG rhythms (in the alpha, beta and gamma frequency ranges) by modifying just the time constants of the synapses. The position of the individual rhythms (i.e., the corresponding peaks in the PSD) can be finely tuned acting on the average gain of fast inhibitory synapses. This analysis suggested that a single neural mass model produces a unimodal spectrum, which can be finely adjusted, but cannot mimic the overall complexity of EEG in an entire cortical area. (2) Hence, in the second stage we built a model of a ROI by combining three neural mass models arranged in parallel. With this model, and using an automatic fitting procedure, we carefully reproduced the PSD of cortical EEG in several ROIs during finger movement, and their temporal changes during a working memory task, by estimating nine parameters. The estimated parameters represent the excitation of each population (mean value and variance of exogenous input noise) and the average gain of fast inhibitory synapses. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with a high number of electrodes on the scalp (46-96). Results show that the proposed model is able to mimic PSD of cortical activity acting on a few parameters, which represent external activation and short-time synaptic changes. This information may be exploited to reach a quantitative summary of electrical activity in ROIs during a task, and to derive information on connectivity, starting from non-invasive EEG measurements.
神经团模型多年来一直被用于以简单且计算成本低的方式研究神经群体的宏观动力学。在本文中，我们修改了Wendling等人提出的一个模型[Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci 2002;15:1499 - 508]，以模拟简单任务（手指运动或工作记忆测试）期间某些感兴趣区域（ROI）的脑电图功率谱密度（PSD）。这项工作包括两个后续阶段：（1）在第一阶段，我们评估了一些模型参数（即突触的平均增益及其时间常数）对功率谱密度的影响。该分析证实了仅通过修改突触的时间常数就有可能模拟各种脑电图节律（在α、β和γ频率范围内）。各个节律的位置（即PSD中的相应峰值）可以通过作用于快速抑制性突触的平均增益进行微调。该分析表明，单个神经团模型产生单峰频谱，虽然可以精细调整，但无法模拟整个皮质区域脑电图的整体复杂性。（2）因此，在第二阶段，我们通过组合三个并行排列的神经团模型构建了一个ROI模型。使用这个模型，并通过自动拟合程序，我们通过估计九个参数，仔细再现了手指运动期间几个ROI中皮质脑电图的PSD及其在工作记忆任务期间的时间变化。估计的参数代表每个群体的兴奋性（外源输入噪声的平均值和方差）以及快速抑制性突触的平均增益。皮质脑电图是使用反向传播算法计算的，从头皮上大量电极（46 - 96个）进行的测量开始。结果表明，所提出的模型能够通过作用于几个代表外部激活和短时突触变化的参数来模拟皮质活动的PSD。这些信息可用于在任务期间获得ROI中电活动的定量总结，并从无创脑电图测量中得出关于连接性的信息。
