IEEE Trans Med Imaging. 2019 Apr;38(4):873-882. doi: 10.1109/TMI.2018.2873423. Epub 2018 Nov 5.
The problem of describing how different brain areas interact between each other has been granted a great deal of attention in the last years. The idea that neuronal ensembles behave as oscillators and that they communicate through synchronization is now widely accepted. To this regard, EEG and MEG provide the signals that allow the estimation of such communication in vivo. Hence, phase-based metrics are essential. However, the application of phased-based metrics for measuring brain connectivity has proved problematic so far, since they appear to be less resilient to noise as compared to amplitude-based ones. In this paper, we address the problem of designing a purely phase-based brain connectivity metric, insensitive to volume conduction and resilient to noise. The proposed metric, named phase linearity measurement (PLM), is based on the analysis of similar behaviors in the phases of the recorded signals. The PLM is tested in two simulated datasets as well as in real MEG data acquired at the Naples MEG center. Due to its intrinsic characteristics, the PLM shows considerable noise rejection properties as compared to other widely adopted connectivity metrics. We conclude that the PLM might be valuable in order to allow better estimation of phase-based brain connectivity.
近年来，描述大脑不同区域之间相互作用的问题受到了极大关注。神经元集合作为振荡器的观点以及它们通过同步进行通信的观点现在已被广泛接受。在这方面，EEG 和 MEG 提供了允许在体内估计这种通信的信号。因此，基于相位的度量至关重要。然而，迄今为止，基于相位的度量在测量脑连接性方面的应用已被证明存在问题，因为与基于幅度的度量相比，它们似乎对噪声的抵抗力较弱。在本文中，我们解决了设计一种对体积传导不敏感且对噪声具有抵抗力的纯基于相位的脑连接性度量的问题。所提出的度量称为相位线性度测量（PLM），它基于对记录信号相位中类似行为的分析。在两个模拟数据集以及在那不勒斯 MEG 中心采集的实际 MEG 数据中对 PLM 进行了测试。由于其内在特性，与其他广泛采用的连通性度量相比，PLM 显示出相当强的噪声抑制特性。我们得出结论，PLM 可能具有价值，以便能够更好地估计基于相位的脑连接性。
