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首页> 外文期刊>Journal of Neuroscience Methods >Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index
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Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index

机译：使用相滞后指数从脑电图记录推断脑网络中的体积传导效应

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摘要

In this paper, we test the performance of a synchronicity estimator widely applied in Neuroscience, phase lag index (PLI), for brain network inference in EEG. We implement the four sphere head model to simulate the volume conduction problem present in EEG recordings and measure the activity at the scalp of surrogate sources located at the brain level. Then, networks are estimated under the null hypothesis (independent sources) using PLI, coherence (R) and phase coherence (PC) for the volume conduction and no volume conduction (NVC) cases. It is known that R and PC are highly influenced by volume conduction, leading to the inference of clustered grid networks. PLI was designed to solve this problem. Our simulations show that PLI is partially invariant to volume conduction. The networks found by PLI show small-worldness, with a clustering coefficient higher than random networks. On the contrary, PLI-NVC obtains networks whose distribution is closer to random networks indicating that the high clustering shown by PLI networks are caused by volume conduction. The influence of volume conduction in PLI might lead to biased results in brain network inference from EEG if this behaviour is ignored.
机译：在本文中，我们测试了在神经科学中广泛应用的同步估计器的性能，即相差指数（PLI），用于脑电图的脑网络推断。我们实施四球头模型来模拟脑电图记录中存在的体积传导问题，并在位于大脑水平的替代源头皮上测量活动。然后，针对体积传导和无体积传导（NVC）情况，使用PLI，相干（R）和相位相干（PC）在零假设（独立来源）下估算网络。众所周知，R和PC受体积传导的影响很大，从而导致了集群网格网络的推断。 PLI旨在解决此问题。我们的仿真表明，PLI对于体积传导是部分不变的。 PLI发现的网络显示出小规模世界，其聚类系数高于随机网络。相反，PLI-NVC获得的网络分布更接近随机网络，这表明PLI网络显示的高聚类是由体积传导引起的。如果忽略此行为，则PLI中体积传导的影响可能导致脑电图推断脑网络的结果有偏差。

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