Department of Psychology and Center for Neural Science, New York University, New York, New York, United States of America.
Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka, Japan.
PLoS One. 2018 Mar 12;13(3):e0193107. doi: 10.1371/journal.pone.0193107. eCollection 2018.
Currently, non-invasive methods for studying the human brain do not routinely and reliably measure spike-rate-dependent signals, independent of responses such as hemodynamic coupling (fMRI) and subthreshold neuronal synchrony (oscillations and event-related potentials). In contrast, invasive methods-microelectrode recordings and electrocorticography (ECoG)-have recently measured broadband power elevation in field potentials (50-200 Hz) as a proxy for locally averaged spike rates. Here, we sought to detect and quantify stimulus-related broadband responses using magnetoencephalography (MEG). Extracranial measurements like MEG and EEG have multiple global noise sources and relatively low signal-to-noise ratios; moreover high frequency artifacts from eye movements can be confounded with stimulus design and mistaken for signals originating from brain activity. For these reasons, we developed an automated denoising technique that helps reveal the broadband signal of interest. Subjects viewed 12-Hz contrast-reversing patterns in the left, right, or bilateral visual field. Sensor time series were separated into evoked (12-Hz amplitude) and broadband components (60-150 Hz). In all subjects, denoised broadband responses were reliably measured in sensors over occipital cortex, even in trials without microsaccades. The broadband pattern was stimulus-dependent, with greater power contralateral to the stimulus. Because we obtain reliable broadband estimates with short experiments (20 minutes), and with sufficient signal-to-noise to distinguish responses to different stimuli, we conclude that MEG broadband signals, denoised with our method, offer a practical, non-invasive means for characterizing spike-rate-dependent neural activity for addressing scientific questions about human brain function.
目前，研究人类大脑的非侵入性方法通常无法可靠地测量依赖于尖峰率的信号，而这些信号与诸如血流耦合（fMRI）和亚阈值神经元同步性（振荡和事件相关电位）等反应无关。相比之下，侵入性方法 - 微电极记录和皮层电图（ECoG） - 最近已经测量了场电位中的宽带功率升高（50-200 Hz），作为局部平均尖峰率的代理。在这里，我们试图使用脑磁图（MEG）检测和量化与刺激相关的宽带响应。像 MEG 和 EEG 这样的颅外测量有多个全局噪声源和相对较低的信噪比；此外，来自眼动的高频伪影可能与刺激设计混淆，并被误认为是源自大脑活动的信号。出于这些原因，我们开发了一种自动降噪技术，可以帮助揭示感兴趣的宽带信号。受试者在左、右或双侧视野中观察 12 Hz 对比度反转模式。传感器时间序列被分为诱发（12 Hz 幅度）和宽带成分（60-150 Hz）。在所有受试者中，即使在没有微扫视的试验中，在枕叶皮层上的传感器中也可靠地测量到了去噪宽带响应。宽带模式是刺激依赖性的，与刺激相对侧的功率更大。由于我们可以用短实验（20 分钟）获得可靠的宽带估计，并且具有足够的信噪比来区分对不同刺激的反应，因此我们得出结论，用我们的方法去噪的 MEG 宽带信号为描述人类大脑功能科学问题的依赖于尖峰率的神经活动提供了一种实用的、非侵入性的方法。
