Facilitating neuronal connectivity analysis of evoked responses by exposing local activity with principal component analysis preprocessing: simulation of evoked MEG.
Facilitating neuronal connectivity analysis of evoked responses by exposing local activity with principal component analysis preprocessing: simulation of evoked MEG.
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DOI：
10.1007/s10548-012-0250-1
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发表时间：
2013-04
期刊：
影响因子：
2.7
通讯作者：
Stephen, Julia
中科院分区：
文献类型：
--
作者：
Gao, Lin;Zhang, Tongsheng;Wang, Jue;Stephen, Julia
当对事件相关脑电和脑磁图进行连接性分析时，背景中自发活动的强空间相关性的存在可能会掩盖局部神经元的诱发活动，从而导致虚假连接。在本文中，我们假设在与事件相关的实验中，可以使用PCA分解来减少背景活动，从而进一步提高连通性分析的性能。通过仿真验证了这一思想，发现对于306通道的Elekta Neuromag系统，前4台PC代表了主要的背景活动，经过预处理后的源连接模式与模拟中设计的真实连接模式一致。通过丢弃前几个PC来提高诱发响应的信噪比，表明当移除前几个PC时，主要生理频段的相干性增加。此外，经过主成分分析的预处理后，诱发信息得以保留。总而言之，前几个PC代表背景活动，可以使用PCA分解将其移除，以揭示所研究通道的诱发活动。因此，主成分分析可以作为一种预处理方法来改进对事件相关数据的神经元连通性分析。
When connectivity analysis is carried out for event related EEG and MEG, the presence of strong spatial correlations from spontaneous activity in background may mask the local neuronal evoked activity and lead to spurious connections. In this paper, we hypothesized PCA decomposition could be used to diminish the background activity and further improve the performance of connectivity analysis in event related experiments. The idea was tested using simulation, where we found that for the 306-channel Elekta Neuromag system, the first 4 PCs represent the dominant background activity, and the source connectivity pattern after preprocessing is consistent with the true connectivity pattern designed in the simulation. Improving signal to noise of the evoked responses by discarding the first few PCs demonstrates increased coherences at major physiological frequency bands when removing the first few PCs. Furthermore, the evoked information was maintained after PCA preprocessing. In conclusion, it is demonstrated that the first few PCs represent background activity, and PCA decomposition can be employed to remove it to expose the evoked activity for the channels under investigation. Therefore, PCA can be applied as a preprocessing approach to improve neuronal connectivity analysis for event related data.
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影响因子：
4.6
作者：
Kobayashi, T;Kuriki, S
通讯作者：
Kuriki, S
影响因子：
5.7
作者：
Horwitz, B
通讯作者：
Horwitz, B
影响因子：
3.2
作者：
Gourevitch, B;Le Bouquin-Jeannes, R
通讯作者：
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DOI：
10.1016/s0013-4694(97)00066-7
发表时间：
1997-11-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
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作者：
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通讯作者：
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影响因子：
3
作者：
Aine, C. J.;Sanfratello, L.;Stephen, J. M.
通讯作者：
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