Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, 1081 HV, Amsterdam, The Netherlands.
NBT Analytics BV, Amsterdam, The Netherlands.
Eur J Neurosci. 2018 Oct;48(8):2674-2683. doi: 10.1111/ejn.13672. Epub 2017 Oct 6.
Neuronal oscillations exhibit complex amplitude fluctuations with autocorrelations that persist over thousands of oscillatory cycles. Such long-range temporal correlations (LRTC) are thought to reflect neuronal systems poised near a critical state, which would render them capable of quick reorganization and responsive to changing processing demands. When we concentrate, however, the influence of internal and external sources of distraction is better reduced, suggesting that neuronal systems involved with sustained attention could benefit from a shift toward the less volatile sub-critical state. To test these ideas, we recorded electroencephalography (EEG) from healthy volunteers during eyes-closed rest and during a sustained attention task requiring a speeded response to images deviating in their presentation duration. We show that for oscillations recorded during rest, high levels of alpha-band LRTC in the sensorimotor region predicted good reaction-time performance in the attention task. During task execution, however, fast reaction times were associated with high-amplitude beta and gamma oscillations with low LRTC. Finally, we show that reduced LRTC during the attention task compared to the rest condition correlates with better performance, while increased LRTC of oscillations from rest to attention is associated with reduced performance. To our knowledge, this is the first empirical evidence that 'resting-state criticality' of neuronal networks predicts swift behavioral responses in a sensorimotor task, and that steady attentive processing of visual stimuli requires brain dynamics with suppressed temporal complexity.
神经元振荡表现出复杂的振幅波动，具有持续数千个振荡周期的自相关。这种长程时间相关性（LRTC）被认为反映了神经元系统处于临界状态附近，这使得它们能够快速重组并对不断变化的处理需求做出反应。然而，当我们集中注意力时，内部和外部干扰源的影响会更好地减少，这表明与持续注意力相关的神经元系统可能受益于向较少波动的亚临界状态转变。为了验证这些想法，我们在健康志愿者闭眼休息和执行需要快速响应呈现持续时间不同的图像的持续注意力任务期间记录了脑电图（EEG）。我们表明，对于在休息期间记录的振荡，感觉运动区域中高水平的 alpha 波段 LRTC 预测了注意力任务中的良好反应时间表现。然而，在任务执行期间，快速反应时间与具有低 LRTC 的高振幅 beta 和 gamma 振荡相关。最后，我们表明，与休息条件相比，注意力任务期间的 LRTC 减少与更好的表现相关，而从休息到注意力的振荡的 LRTC 增加与表现降低相关。据我们所知，这是第一个经验证据表明，神经元网络的“静息状态临界性”预测了在感觉运动任务中的快速行为反应，并且视觉刺激的稳定注意力处理需要具有抑制时间复杂性的大脑动力学。
