Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213.
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
eNeuro. 2022 Jun 8;9(3). doi: 10.1523/ENEURO.0012-22.2022. Print 2022 May-Jun.
Electroencephalography (EEG) has long been used to index brain states, from early studies describing activity in the presence and absence of visual stimulation to modern work employing complex perceptual tasks. These studies have shed light on brain-wide signals but often lack explanatory power at the single neuron level. Similarly, single neuron recordings can suffer from an inability to measure brain-wide signals accessible using EEG. Here, we combined these techniques while monkeys performed a change detection task and discovered a novel link between spontaneous EEG activity and a neural signal embedded in the spiking responses of neuronal populations. This "slow drift" was associated with fluctuations in the subjects' arousal levels over time: decreases in prestimulus α power were accompanied by increases in pupil size and decreases in microsaccade rate. These results show that brain-wide EEG signals can be used to index modes of activity present in single neuron recordings, that in turn reflect global changes in brain state that influence perception and behavior.
脑电图(EEG)长期以来一直被用于标记脑状态,从早期描述视觉刺激存在和缺失时的活动的研究,到现代采用复杂感知任务的研究。这些研究揭示了全脑信号,但往往缺乏在单个神经元水平上的解释能力。同样,单个神经元记录可能无法测量使用 EEG 可获得的全脑信号。在这里,当猴子执行变化检测任务时,我们结合了这些技术,并发现了自发 EEG 活动与神经元群体的尖峰反应中嵌入的神经信号之间的新联系。这种“缓慢漂移”与被试随时间变化的觉醒水平波动有关:刺激前α功率的降低伴随着瞳孔大小的增加和微扫视率的降低。这些结果表明,全脑 EEG 信号可用于标记单个神经元记录中存在的活动模式,而这些模式反过来又反映了影响感知和行为的大脑状态的全局变化。