Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG
Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG
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DOI：
10.1016/j.mri.2004.10.006
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发表时间：
2004-12-01
影响因子：
2.5
通讯作者：
Babiloni, F
中科院分区：
文献类型：
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作者：
Astolfi, L;Cincotti, F;Babiloni, F
目前有不同的脑成像设备可用于提供基于血流动力学、代谢或电磁测量的人类功能性皮质活动的图像。然而，在特定任务期间激活的大脑区域的静态图像并不传达这些区域是如何相互连接的信息。大脑连通性的概念在神经科学中起着核心作用，文献中采用了不同的连通性定义，功能性的和有效性的。虽然功能连通性被定义为不同脑区活动之间的时间一致性，但有效连通性被定义为最简单的大脑回路，它将产生与实验观察到的大脑皮质部位之间相同的时间关系。结构方程模型(SEM)是神经科学中最常用的有效连通性估计方法，其典型应用是通过功能磁共振成像(FMRI)测试与脑血流动力学行为相关的数据，而直接传递函数(DTF)方法是一种基于时间序列的多变量自回归(MVAR)建模和Granger因果概念的频域方法。为了正确估计皮质信号，我们使用了由个体MRI构建的受试者的多隔室头部模型(头皮、颅骨、硬脑膜、皮质)、分布源模型和皮层电流密度的正则化线性逆源估计。在将扫描电子显微镜和DTF方法应用于从高分辨率脑电数据估计的皮层波形之前，我们进行了一项仿真研究，在测试信号的产生过程中系统地处理了不同的主要因素(信噪比、信噪比和模拟皮层活动持续时间、长度)，并用方差分析(ANOVA)来评估估计连通性的误差。统计分析表明，在模拟过程中，在合理的操作条件下，即当数据显示在赫兹的采样率下的非连续脑电记录的信噪比至少为3且长度至少为75 S时，扫描电子显微镜和离散余弦估计器都能够正确地估计强加的连通性模式。因此，通过将高分辨率脑电技术和线性逆估计与扫描电子显微镜或DTF方法相结合，可以在任何实际脑电记录中满足的一般条件下有效地估计大脑皮质活动的有效和功能连接模式。我们的结论是，大脑皮层连接性的估计不仅可以通过血流动力学测量来完成，还可以通过先进的计算技术处理的脑电信号来完成。(C)2004 Elsevier Inc.保留所有权利。
Different brain imaging devices are presently available to provide images of the human functional cortical activity, based on hemodynamic, metabolic or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. While the functional connectivity is defined as the temporal coherence among the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally among cortical sites. The structural equation modeling (SEM) is the most used method to estimate effective connectivity in neuroscience, and its typical application is on data related to brain hemodynarnic behavior tested by functional magnetic resonance imaging (fMRI), whereas the directed transfer function (DTF) method is a frequency-domain approach based on both a multivariate autoregressive (MVAR) modeling of time series and on the concept of Granger causality.This study presents advanced methods for the estimation of cortical connectivity by applying SEM and DTF on the cortical signals estimated from high-resolution electroencephalography (EEG) recordings, since these signals exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. To estimate correctly the cortical signals, we used a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual MRI, a distributed source model and a regularized linear inverse source estimates of cortical current density. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high-esolution EEG data, we performed a simulation study, in which different main factors (signal-to-noise ratio, SNR, and simulated cortical activity duration, LENGTH) were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity pattems under reasonable operative conditions, that is, when data exhibit an SNR of at least 3 and a LENGTH of at least 75 s of nonconsecutive EEG recordings at 64 Hz of sampling rate. Hence, effective and functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high-resolution EEG techniques and linear inverse estimation with SEM or DTF methods. We conclude that the estimation of cortical connectivity can be perfomed not only with hemodynamic measurements, but also with EEG signals treated with advanced computational techniques. (C) 2004 Elsevier Inc. All rights reserved.