Soto Juan L P, Lachaux Jean-Philippe, Baillet Sylvain, Jerbi Karim
Department of Telecommunications and Control Engineering, University of São Paulo, São Paulo, Brazil.
Brain Dynamics and Cognition Team, Lyon Neuroscience Research Center, INSERM U1028 - CNRS UMR5292 - Lyon University, Lyon, France.
J Neurosci Methods. 2016 Sep 15;271:169-81. doi: 10.1016/j.jneumeth.2016.07.017. Epub 2016 Jul 26.
Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources.
In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices.
Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling.
Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices.
Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test.
在各种认知任务中已观察到不同脑区之间的跨频相互作用。利用脑电图和脑磁图（EEG/MEG）数据，两个脑区之间的典型连通性测量方法会在特定频带内分析每个区域的单个量；鉴于EEG/MEG信号的宽带性质，可能需要进行许多统计测试来识别真正的耦合。此外，由于从EEG/MEG重建的活动空间分辨率较差，一些相互作用实际上可能是由于脑源的线性混合所致。
在本研究中，描述了一种使用典型相关分析（CCA）检测MEG数据中跨频功能连通性的方法。我们证明CCA能够识别相关信号以及导致相关性的频率。我们还基于交叉协方差矩阵的对称性实现了一种处理线性混合的程序。
我们对模拟和真实MEG数据的测试表明，CCA能够检测相互作用的位置以及导致这些相互作用的频率，同时准确地排除虚假耦合。
近期技术通过观察两个位置之间活动的时间延迟来排除虚假相互作用，而我们提出了一种线性混合模型，并展示了其与交叉协方差矩阵对称性方面的关系。
我们的测试表明CCA方法在连通性研究中的优势，因为它允许在单一统计测试中同时评估跨频相互作用的几种可能组合。
