Finding brain oscillations with power dependencies in neuroimaging data
Finding brain oscillations with power dependencies in neuroimaging data
复制标题
DOI：
10.1016/j.neuroimage.2014.03.075
复制
发表时间：
2014-08-01
期刊：
影响因子：
5.7
通讯作者：
Haufe, Stefan
中科院分区：
文献类型：
--
作者：
Daehne, Sven;Nikulin, Vadim V.;Haufe, Stefan
同一频段内神经元振荡的相位同步被认为是不同脑区之间通信的主要机制。另一方面，跨频率通信更灵活，允许不同频率的振荡之间的相互作用。在这样的交叉频率相互作用中，振幅对振幅的相互作用是一个特别的兴趣，因为它们显示了在给定任务中不同神经元群体的空间同步强度如何相互关联。在此之前，振幅与振幅之间的相关性主要是在传感器水平上进行研究，而我们提出了一种使用空间滤波器的源分离方法，该方法可以最大限度地提高脑电/脑磁图（EEG/MEG）或颅内多通道记录的脑振荡包络之间的相关性。因此，我们的方法被称为典型源功率相关分析（cSPoC），即使在信号的信噪比很低的情况下，也能够仅基于假设的耦合行为提取真实的大脑振荡。除了使用cSPoC分析同一受试者的交叉频率相互作用外，我们还表明它可以用于研究受试者间神经元振荡的振幅动力学。我们评估了cSPoC在模拟中的性能，以及在三种不同的真实脑电图数据分析场景下的性能，每种场景涉及几个受试者。在模拟中，cSPoC优于无监督的最先进方法。在对真实脑电图记录的分析中，我们展示了在主体和频段内以及跨主体和频段的有意义的功率-功率耦合的出色的无监督发现。(C) 2014爱思唯尔公司版权所有。
Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands. (C) 2014 Elsevier Inc. All rights reserved.