SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters
SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters
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DOI：
10.1016/j.neuroimage.2013.07.079
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发表时间：
2014-02-01
期刊：
影响因子：
5.7
通讯作者：
Nikulin, Vadim V.
中科院分区：
文献类型：
--
作者：
Daehne, Sven;Meinecke, Frank C.;Nikulin, Vadim V.
以前，神经元振荡功率的调节在功能上与感觉、运动和认知操作有关。这种联系通常是通过将功率调制与特定的目标变量（如反应时间或任务等级）联系起来建立的。因此，由此产生的空间光谱表征受到神经生理学的解释。作为一种替代方法，可以应用独立成分分析（ICA）或替代分解方法，并且组件的功率可能与目标变量相关。在本文中，我们表明这些标准方法是次优的，因为第一种方法没有考虑到由于体积传导而产生的许多源的叠加，而第二种方法无法利用有关目标变量的可用信息。为了改进这些方法，我们引入了一种新的（监督的）源分离框架，称为源功率调制（SPoC）。SPoC在分解过程中使用目标变量，以便优先考虑功率与目标变量相调节的组件。我们提出了两种实现SPoC方法的算法。通过对真实头部模型的仿真，我们证明了SPoC算法能够提取出与目标变量具有高度相关性的神经元成分。在这项任务中，SPoC算法优于其他基于传感器数据或ICA方法的常用技术。此外，利用听觉稳态范式下的真实脑电图（EEG）记录，我们通过提取与听觉输入强度高度相关的神经元成分，证明了SPoC算法的实用性。考虑到模拟和真实脑电图记录的结果，我们得出结论，SPoC代表了最佳提取神经元成分的适当方法，显示了功率与连续变化的行为相关参数的耦合。(C) 2013年作者。Elsevier Inc版权所有。
Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters. (C) 2013 The Authors. Published by Elsevier Inc All rights reserved.