Astolfi Laura, Bakardjian Hovagim, Cincotti Febo, Mattia Donatella, Marciani Maria Grazia, De Vico Fallani Fabrizio, Colosimo Alfredo, Salinari Serenella, Miwakeichi Fumikazu, Yamaguchi Yoko, Martinez Pablo, Cichocki Andrzej, Tocci Andrea, Babiloni Fabio
IRCCS Fondazione Santa Lucia, Rome, Italy.
Brain Topogr. 2007 Spring;19(3):107-23. doi: 10.1007/s10548-007-0018-1. Epub 2007 Jun 19.
Static hemodynamic or neuroelectric images of brain regions activated during particular tasks do not convey the information of how these regions communicate to each other. Cortical connectivity estimation aims at describing these interactions as connectivity patterns which hold the direction and strength of the information flow between cortical areas. In this study, we attempted to estimate the causality between distributed cortical systems during a movement volition task in preparation for execution of simple movements by a group of normal healthy subjects and by a group of Spinal Cord Injured (SCI) patients. To estimate the causality between the spatial distributed patterns of cortical activity in the frequency domain, we applied a series of processing steps on the recorded EEG data. From the high-resolution EEG recordings we estimated the cortical waveforms for the regions of interest (ROIs), each representing a selected sensor group population. The solutions of the linear inverse problem returned a series of cortical waveforms for each ROI considered and for each trial analyzed. For each subject, the cortical waveforms were then subjected to Independent Component Analysis (ICA) pre-processing. The independent components obtained by the application of the ThinICA algorithm were further processed by a Partial Directed Coherence algorithm, in order to extract the causality between spatial cortical patterns of the estimated data. The source-target cortical dependencies found in the group of normal subjects were relatively similar in all frequency bands analyzed. For the normal subjects we observed a common source pattern in an ensemble of cortical areas including the right parietal and right lip primary motor areas and bilaterally the primary foot and posterior SMA areas. The target of this cortical network, in the Granger-sense of causality, was shown to be a smaller network composed mostly by the primary foot motor areas and the posterior SMA bilaterally. In the case of the SCI population, both the source and the target cortical patterns had larger sizes than in the normal population. The source cortical areas included always the primary foot and lip motor areas, often bilaterally. In addition, the right parietal area and the bilateral premotor area 6 were also involved. Again, the patterns remained substantially stable across the different frequency bands analyzed. The target cortical patterns observed in the SCI population had larger extensions when compared to the normal ones, since in most cases they involved the bilateral activation of the primary foot movement areas as well as the SMA, the primary lip areas and the parietal cortical areas.
特定任务期间激活的脑区的静态血液动力学或神经电图像并未传达这些区域如何相互通信的信息。皮质连接性估计旨在将这些相互作用描述为连接模式，这些模式包含皮质区域之间信息流的方向和强度。在本研究中，我们试图估计一组正常健康受试者和一组脊髓损伤（SCI）患者在准备执行简单运动的运动意志任务期间分布式皮质系统之间的因果关系。为了估计频域中皮质活动的空间分布模式之间的因果关系，我们对记录的脑电图数据应用了一系列处理步骤。从高分辨率脑电图记录中，我们估计了感兴趣区域（ROI）的皮质波形，每个区域代表一个选定的传感器组群体。线性逆问题的解为每个考虑的ROI和每个分析的试验返回了一系列皮质波形。对于每个受试者，然后对皮质波形进行独立成分分析（ICA）预处理。通过应用ThinICA算法获得的独立成分通过偏定向相干算法进一步处理，以提取估计数据的空间皮质模式之间的因果关系。在分析的所有频带中，正常受试者组中发现的源-目标皮质依赖性相对相似。对于正常受试者，我们在包括右侧顶叶和右侧唇部初级运动区以及双侧初级足部和后辅助运动区（SMA）的一组皮质区域中观察到一种共同的源模式。在格兰杰因果关系意义上，这个皮质网络的目标是一个较小的网络，主要由双侧初级足部运动区和后SMA组成。在SCI人群中情况则不同，源皮质模式和目标皮质模式的范围都比正常人群中的更大。源皮质区域总是包括初级足部和唇部运动区，通常是双侧的。此外，右侧顶叶区域和双侧运动前区6也参与其中。同样，这些模式在分析的不同频带中基本保持稳定。与正常人群相比，在SCI人群中观察到的目标皮质模式范围更大，因为在大多数情况下，它们涉及初级足部运动区以及SMA、初级唇部区域和顶叶皮质区域的双侧激活。
