Zavada Andrei, Strijkstra Arjen M, Boerema Ate S, Daan Serge, Beersma Domien G M
Department of Informatics, Sussex University, Falmer, Brighton, UK.
J Sleep Res. 2009 Mar;18(1):3-10. doi: 10.1111/j.1365-2869.2008.00696.x. Epub 2008 Oct 13.
The regulation of the timing of sleep is thought to be linked to the temporal dynamics of slow-wave activity [SWA, electroencephalogram (EEG) spectral power in the approximately 0.75-4.5 Hz range] in the cortical non-rapid eye movement (NREM) sleep EEG. In the two-process model of sleep regulation, SWA was used as a direct indication of sleep debt, or Process S. Originally, estimation of the latter was performed in a gross way, by measuring average SWA across NREM-REM sleep cycles, fitting an exponential curve to the values thus obtained and estimating its time constant. In later studies, SWA was assumed to be proportional to the instantaneous decay rate of Process S, rather than taken as a direct reflection of S. Following up on this, we extended the existing model of SWA dynamics in which the effects of intrusions of REM sleep and wakefulness were incorporated. For each subject, a 'gain constant' can be estimated that quantifies the efficiency of SWA in dissipating S. As the course of SWA is variable across cortical locations, local differences are likely to exist in the rate of discharge of S, eventually leading to different levels of S in different cortical regions. In this study, we estimate the extent of local differences of SWA regulation on the basis of the extended model of SWA dynamics, for 26 locations on the scalp. We observed higher efficiency of SWA in dissipation of S in frontal EEG derivations, suggesting that SWA regulation has a clear local aspect. This result further suggests that the process involved in (local) SWA regulation cannot be identical to the Process S involved (with Process C) in effectual determination of sleep timing - a single behaviour that cannot vary between locations on the scalp. We therefore propose to distinguish these two representations and characterize the former, purely SWA-related, as 'Process Z', which then is different for different locations on the scalp. To demonstrate those differences, we compare the gain constants derived for the medial EEG derivations (Fz, Cz, Pz, Oz) with each other and with the decay rate derived from SWA values per NREM-REM sleep cycle.
睡眠时机的调节被认为与皮质非快速眼动(NREM)睡眠脑电图中慢波活动[SWA,脑电图(EEG)在约0.75 - 4.5 Hz范围内的频谱功率]的时间动态有关。在睡眠调节的双过程模型中,SWA被用作睡眠债或过程S的直接指标。最初,对后者的估计是以一种粗略的方式进行的,即通过测量整个NREM - REM睡眠周期的平均SWA,对由此获得的值拟合一条指数曲线并估计其时间常数。在后来的研究中,SWA被假定与过程S的瞬时衰减率成正比,而不是被视为S的直接反映。在此基础上,我们扩展了现有的SWA动态模型,其中纳入了快速眼动睡眠和清醒状态侵入的影响。对于每个受试者,可以估计一个“增益常数”,该常数量化了SWA在消散S方面的效率。由于SWA的过程在不同皮质位置是可变的,S的释放速率可能存在局部差异,最终导致不同皮质区域的S水平不同。在本研究中,我们基于SWA动态扩展模型,估计头皮上26个位置的SWA调节局部差异程度。我们观察到额叶脑电图导联中SWA在消散S方面效率更高,这表明SWA调节具有明显的局部特征。这一结果进一步表明,参与(局部)SWA调节的过程与有效确定睡眠时间(与过程C一起)所涉及的过程S不同——睡眠时间这一单一行为在头皮不同位置之间不会变化。因此,我们建议区分这两种表现形式,并将前者,即纯粹与SWA相关的,表征为“过程Z”,它在头皮不同位置是不同的。为了证明这些差异,我们将从内侧脑电图导联(Fz、Cz、Pz、Oz)得出的增益常数相互比较,并与每个NREM - REM睡眠周期从SWA值得出的衰减率进行比较。