Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.
Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.
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DOI：
10.1109/tbme.2016.2616474
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
He B
中科院分区：
文献类型：
--
作者：
Sohrabpour A;Ye S;Worrell GA;Zhang W;He B
结合源成像技术和定向连接分析可以以非侵入性方式提供有关底层大脑网络的有用信息。此前，源成像技术已成功用于确定活动源或提取源时间进程以进行格兰杰因果关系分析。在这项工作中，我们利用源成像算法来查找网络节点（感兴趣区域），然后提取激活时间序列以进行进一步的格兰杰因果关系分析。这项工作的目的是从非侵入性电磁信号中客观地找到网络节点，提取激活时间过程并对提取的序列应用格兰杰分析来研究现实条件下的大脑网络。使用源成像方法来识别网络节点并提取时间进程，然后应用格兰杰因果关系分析来描绘底层大脑网络的定向功能连接。在已知底层网络（节点和连接模式）的情况下进行计算机模拟研究；此外，这种方法已在部分癫痫患者中进行了评估，以根据脑电图和/或脑磁图记录的发作间期和发作期信号研究癫痫网络。在模拟研究中估计底层大脑网络时，网络节点的定位误差小于 5 毫米，标准化连接误差约为 20%。此外，还对两名局灶性癫痫患者进行了研究，发现驱动癫痫网络的节点与颅内记录或手术切除的临床结果一致。我们的研究表明，将源成像算法与格兰杰因果关系分析相结合可以精确识别底层网络（无论是在网络节点位置还是节点间连通性方面）。源成像和格兰杰分析相结合的技术是研究正常或病理性大脑状况的有效工具。
Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.