入睡K-综合波产生的生理机制模型仿真研究

在确定人类睡眠脑电客观分期的国际标准中,有两类脑电特征波可以用来确定入睡状态（睡眠第二期）,即纺锤波和K－综合波。在前文中已提出了产生纺锤波的生理机制模型。按照1998年后对K－综合波形成的生理机制的看法,建立了微观神经元环路模型,其放电节律与实验中入睡时神经元放电的振荡节律相一致。而由大量这种相同环路组成的网络模型则在皮层处可产生符合K－综合波的波形。这一结果再次启示了脑信息处理中如何由微观神经元群放电特征整合为脑的宏观功能状态的过程。     

基于生理解剖知识的入睡机制神经元群网络模型研究

以生理解剖知识为基础,在已有的丘脑网状核细胞和丘脑皮质细胞间组成的入睡机制的两细胞环路模型［１］和由此两细胞环路组成的网络模型［２］的基础上,提出了增加皮层细胞在内的三种细胞组成的环路模型和网络模型,以使模型更符合近来认为睡眠机制是皮层和丘脑环路中出现特定的同步振荡的看法［３］。并能使模型的仿真结果可以和规定人体睡眠分期的脑电特征波相对应。这一网络模型的仿真结果,在一定条件下,确能在皮层细胞处出现符合睡眠分期中规定的标志入睡的纺锤波,这一初步结果,启示我们用模型仿真方法来进一步探讨睡眠机制和用模型仿真方法来进一步探讨人脑的微观神经元的电活动是如何通过同步振荡整合到宏观功能状态的某些信息处理过程的可能性。     

海马神经振荡的产生机制和功能

神经元集群(neuronal ensemble)的节律性活动往往能诱导产生清晰可见的神经振荡,反映着该群神经元规则化和同步化的活动。通常依据频率可将神经振荡分为delta振荡(0.5~3 Hz)、theta振荡(4~12 Hz)、beta振荡(12~30 Hz)、gamma振荡(30~100 Hz)和尖波涟漪(sharp-wave ripples,SWR)(100 Hz的纹波叠加在0.01~3 Hz的尖波上)。这些神经振荡在人和动物的许多脑区中出现,常伴随着感觉、运动、睡眠等行为产生,在认知、学习和记忆巩固过程中发挥着至关重要的作用。本文简要回顾海马脑区神经振荡的研究历程,对其中的最重要的三种神经振荡——theta振荡、gamma振荡和SWR的产生机制、主要功能及各频率神经振荡的相互作用作出概述,并对今后的研究方向作出展望。     

海马CA1区ripple节律相关高频放电中间神经元

通过在清醒小（Mus musculus）大脑同步记录海马区单神经元放电和场电位,发现在海马CAl区存在两类-9慢波睡眠时海马特征场电位“ripple”高频振荡（100-250Hz）相关的高频放电中间神经元．这两类神经元在慢波睡眠时的放电与ripple在时间上有高度同步性,对应每个ripple振荡波,它们都有一串高频放电．其中一类中间神经元（类型Ⅰ）在一个ripple振荡波的每个子振荡周期基本有1个放电,而另一类中间神经元（类型Ⅱ）则有1-2个放电．在ripple振荡波时段,这两类中间神经元的峰值放电频率分别高达310±33．17Hz（类型Ⅰ）和410±47．61Hz（类型Ⅱ）．动物清醒活动时,这两类中间神经元的放电与海马场电位的theta节律有锁相关系,它们的最大放电概率在theta节律的波谷段．给予动物摇晃刺激时,这两类中间神经元的放电频率均会短促增加．这些研究结果显示,海马CAl区的这种高频放电中间神经元参与调节海马神经元网络的整体活动状态．     

多通道在体记录技术——动作电位与场电位信号处理

多通道在体记录可以同时记录到多个神经元的胞外放电信号以及对应的局部场电位的活动信号。如何对记录到的这两种电信号进行合适的处理,以确保实验结果的准确性,是运用好多通道在体记录技术的关键之一。本文旨在针对多通道在体记录的原始数据,介绍动作电位及场电位信号的常用数据处理方法。动作电位信号属于高频信号,一般用40 kHz的高速采样频率进行采集和记录。根据记录到的神经元胞外动作电位波形,运用主成分分析技术,再结合四电极记录技术的优势,可对来自记录电极周围不同空间位置的神经元放电信号进行良好的甄别,从而获得较精确的单神经元放电时间序列。而局部场电位信号属低频信号(300 Hz),一般用1 kHz的采样频率进行采集和记录。记录到的场电位原始信号需要进行数字滤波,从而分离出场电位信号中不同频率段的节律性振荡。啮齿类动物海马结构中常见的节律性振荡有动物清醒活动及快速眼动睡眠时的theta节律(4~12 Hz);清醒认知活动过程中,伴随着theta节律一起出现的gamma节律(30~80 Hz);以及清醒静止及慢波睡眠时的ripple高频振荡(100~250 Hz)。针对以上处理获得的数据,常用的后续数据分析方法有:神经元放电间隔分析、神经元放电自相关与互相关分析、以及信号的频谱分析等。     

强直电刺激嗅内皮质-海马环路诱发CA1神经元的癫痫同步性膜电位振荡行为

目的和方法 :4 0 0～ 5 0 0 μm大鼠水平脑切片含有封闭的EC 海马环路。强直电刺激 (60Hz ,2s)海马Schaeffer侧支诱发癫痫放电 ,全细胞记录CA1胞体层单个神经元电活动 ,同步记录相应树突区细胞外场电位 ,探讨单个神经元膜电位振荡特性与细胞外癫痫电活动之间的关系。结果 :①强直电刺激诱发CA1神经元膜电位后放性振荡行为呈宽频特征 (3～ 10 0Hz)。以θ节律多见 ,跟随在刺激引起的膜电位去极化或超极化偏移 (paroxysmaldepolarizingorhyperpolaringshift,PDSorPHS)之后 ,振荡波的上升支和下降支分别由膜电位去极化 超极化或超极化 去极化成分构成 ;②逐渐增强的IPSP构成了膜电位振荡的起搏成分 ,继而反弹形成锋电位和阈下振荡 ,与细胞外癫痫样电活动同步 ,并促成癫痫放电由紧张性向阵挛性形式转变 ;③发现了电偶联电位 (spikelets)以及细胞之间的染料偶联现象。结论 :单个神经元作为振荡器可以启动群体神经元超同步化癫痫样电活动 ;缝隙连接可能参与了膜电位振荡的启动与场电位癫痫样电活动的同步作用。     

6-OHDA 帕金森病大鼠快动眼睡眠状态下 皮层脑电及基底节场电位的异常变化

目的:了解帕金森病(PD)模型大鼠在快动眼睡眠状态下皮层脑电和基底节场电位的异常变化。方法:用6-羟基多巴胺(6-OHDA)脑内两点注射法建立PD大鼠模型,并经阿扑吗啡注射诱发旋转对模型进行评价。通过多导宏电极在体电生理记录技术结合视频录像,对正常大鼠和6-OHDA大鼠PD模型进行苍白球场电位和皮层M1、M2区脑电的多部位24小时同时记录。功率谱分析和相干分析用于揭示快动眼睡眠状态下各记录位点信号的频率成分以及不同记录位点神经元集群之间的变化。结果:与正常大鼠相比,6-OHDA帕金森病模型大鼠在REM期间的皮层脑电在θ和γ频段上都有变化:初级运动皮质M1区的θ频段成分消失,辅助运动区M2的θ频段成分略有增加,患侧苍白球的θ频段成分增大显著;M1区的γ频段成分增大,而γ频段成分在苍白球基本没有变化。结论:6-OHDA对中脑多巴胺能神经元的损害可造成大鼠双侧皮层M1区θ节律的消失和γ节律的增强,以及对侧M1-M2区之间在γ节律上的同步被显著增强,而γ节律在苍白球没有变化。这些异常电活动可能是由于VTA受损引起从而与帕金森病的快动眼睡眠行为障碍有关。     

神经递质在睡醒转换中作用的数学模型仿真研究

根据已有的生理解剖知识［１ ］ 提出了关于入睡机制的三细胞环路模型及其网络模型［２ ］ 。在文献［２］ 模型的基础上,根据神经递质在睡醒转换中的重要作用及其作用方式主要是通过影响丘脑中的两种离子流－ 钾的漏电流（ＩＫＬ） 和超极化激活的阳离子流（Ｉｈ） ,以及丘脑皮质系统中突触传递强度的变化而起作用的生理知识,修改了［２］ 中三细胞环路模型及其网络模型。模型仿真结果显示,在适当调节模型参数的条件下,确能使细胞环路产生入睡纺锤节律与觉醒快速放电之间的双向转换,其网络模型也能通过同步振荡在皮层处出现人脑电中规定入睡标志的特征纺锤波与规定觉醒期的低幅快波之间的双向转换。此结果又一次启示了脑信息处理中如何通过同步振荡机制将表示微观特性的神经元群放电特征整合为脑的宏观功能状态的过程。     

6-OHDA帕金森病大鼠快动眼睡眠状态下皮层脑电及基底节场电位的异常变化

目的：了解帕金森病（PD）模型大鼠在快动眼睡眠状态下皮层脑电和基底节场电位的异常变化。方法：用6-羟基多巴胺（6-OHDA）脑内两点注射法建立PD大鼠模型,并经阿扑吗啡注射诱发旋转对模型进行评价。通过多导宏电极在体电生理记录技术结合视频录像,对正常大鼠和6-OHDA大鼠PD模型进行苍白球场电位和皮层M1、M2区脑电的多部位24小时同时记录。功率谱分析和相干分析用于揭示快动眼睡眠状态下各记录位点信号的频率成分以及不N记录位点神经元集群之间的变化。结果：与正常大鼠相比,6-OHDA帕金森病模型大鼠在REM期间的皮层脑电在臼和y频段上都有变化：初级运动皮质M1区的θ频段成分消失,辅助运动区M2的θ频段成分略有增加,患侧苍白球的θ频段成分增大显著;M1区的γ频段成分增大,而γ频段成分在苍白球基本没有变化。结论：6-OHDA对中脑多巴胺能神经元的损害可造成大鼠双侧皮层M1区θ节律的消失和γ节律的增强,以及对侧M1-M2区之间在γ节律上的同步被显著增强,而γ节律在苍白球没有变化。这些异常电活动可能是由于VTA受损引起从而与帕金森病的快动眼睡眠行为障碍有关。     

微弱电刺激对失眠者睡眠状况及睡眠脑电影响的初步研究

根据睡眠是由脑内亿万神经元同步振荡所刻划的观点［１］,及各种电刺激对动物睡眠影响的实验［２,３］,设计了用特定θ频率的正弦波微弱电流,刺激失眠病人颈部安眠２穴,以观察其对受试者脑电频率的客观影响。其结果是刺激后失眠病人由醒到２期的脑电记录中,θ波逐渐增加,增加了病人的总睡眠时间。这启示我们这种脑部的特殊频率微弱电流刺激,可能有引起脑部神经元群的共振现象,改变了受试者脑电中频率成分的分布特征,从而有助于失眠的治疗。这一现象是值得进一步研究的。     

Cellular mechanisms of the slow (<1 Hz) oscillation in thalamocortical neurons in vitro

The slow (<1 Hz) rhythm is a defining feature of the electroencephalogram during sleep. Since cortical circuits can generate this rhythm in isolation, it is assumed that the accompanying slow oscillation in thalamocortical (TC) neurons is largely a passive reflection of neocortical activity. Here we show, however, that by activating the metabotropic glutamate receptor (mGluR), mGluR1a, cortical inputs can recruit intricate cellular mechanisms that enable the generation of an intrinsic slow oscillation in TC neurons in vitro with identical properties to those observed in vivo. These mechanisms rely on the "window" component of the T-type Ca(2+) current and a Ca(2+)-activated, nonselective cation current. These results suggest an active role for the thalamus in shaping the slow (<1 Hz) sleep rhythm.     

Hippocampal memory consolidation during sleep: a comparison of mammals and birds

The transition from wakefulness to sleep is marked by pronounced changes in brain activity. The brain rhythms that characterize the two main types of mammalian sleep, slow‐wave sleep (SWS) and rapid eye movement (REM) sleep, are thought to be involved in the functions of sleep. In particular, recent theories suggest that the synchronous slow‐oscillation of neocortical neuronal membrane potentials, the defining feature of SWS, is involved in processing information acquired during wakefulness. According to the Standard Model of memory consolidation, during wakefulness the hippocampus receives input from neocortical regions involved in the initial encoding of an experience and binds this information into a coherent memory trace that is then transferred to the neocortex during SWS where it is stored and integrated within preexisting memory traces. Evidence suggests that this process selectively involves direct connections from the hippocampus to the prefrontal cortex (PFC), a multimodal, high‐order association region implicated in coordinating the storage and recall of remote memories in the neocortex. The slow‐oscillation is thought to orchestrate the transfer of information from the hippocampus by temporally coupling hippocampal sharp‐wave/ripples (SWRs) and thalamocortical spindles. SWRs are synchronous bursts of hippocampal activity, during which waking neuronal firing patterns are reactivated in the hippocampus and neocortex in a coordinated manner. Thalamocortical spindles are brief 7–14 Hz oscillations that may facilitate the encoding of information reactivated during SWRs. By temporally coupling the readout of information from the hippocampus with conditions conducive to encoding in the neocortex, the slow‐oscillation is thought to mediate the transfer of information from the hippocampus to the neocortex. Although several lines of evidence are consistent with this function for mammalian SWS, it is unclear whether SWS serves a similar function in birds, the only taxonomic group other than mammals to exhibit SWS and REM sleep. Based on our review of research on avian sleep, neuroanatomy, and memory, although involved in some forms of memory consolidation, avian sleep does not appear to be involved in transferring hippocampal memories to other brain regions. Despite exhibiting the slow‐oscillation, SWRs and spindles have not been found in birds. Moreover, although birds independently evolved a brain region—the caudolateral nidopallium (NCL)—involved in performing high‐order cognitive functions similar to those performed by the PFC, direct connections between the NCL and hippocampus have not been found in birds, and evidence for the transfer of information from the hippocampus to the NCL or other extra‐hippocampal regions is lacking. Although based on the absence of evidence for various traits, collectively, these findings suggest that unlike mammalian SWS, avian SWS may not be involved in transferring memories from the hippocampus. Furthermore, it suggests that the slow‐oscillation, the defining feature of mammalian and avian SWS, may serve a more general function independent of that related to coordinating the transfer of information from the hippocampus to the PFC in mammals. Given that SWS is homeostatically regulated (a process intimately related to the slow‐oscillation) in mammals and birds, functional hypotheses linked to this process may apply to both taxonomic groups.     

Transcranial electrical currents to probe EEG brain rhythms and memory consolidation during sleep in humans

Previously the application of a weak electric anodal current oscillating with a frequency of the sleep slow oscillation (～0.75 Hz) during non-rapid eye movement sleep (NonREM) sleep boosted endogenous slow oscillation activity and enhanced sleep-associated memory consolidation. The slow oscillations occurring during NonREM sleep and theta oscillations present during REM sleep have been considered of critical relevance for memory formation. Here transcranial direct current stimulation (tDCS) oscillating at 5 Hz, i.e., within the theta frequency range (theta-tDCS) is applied during NonREM and REM sleep. Theta-tDCS during NonREM sleep produced a global decrease in slow oscillatory activity conjoint with a local reduction of frontal slow EEG spindle power (8-12 Hz) and a decrement in consolidation of declarative memory, underlining the relevance of these cortical oscillations for sleep-dependent memory consolidation. In contrast, during REM sleep theta-tDCS appears to increase global gamma (25-45 Hz) activity, indicating a clear brain state-dependency of theta-tDCS. More generally, results demonstrate the suitability of oscillating-tDCS as a tool to analyze functions of endogenous EEG rhythms and underlying endogenous electric fields as well as the interactions between EEG rhythms of different frequencies.     

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

During slow-wave sleep, brain electrical activity is dominated by the slow (< 1 Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons. In this new study, we developed a novel class of reduced discrete time spiking neuron models for large-scale network simulations of wake and sleep dynamics. In addition to the spiking mechanism, the new model implemented nonlinearities capturing effects of the leak current, the Ca²⁺ dependent K⁺ current and the persistent Na⁺ current that were found to be critical for transitions between Up and Down states of the slow oscillation. We applied the new model to study large-scale two-dimensional cortical network activity during slow-wave sleep. Our study explained traveling wave dynamics and characteristic synchronization properties of transitions between Up and Down states of the slow oscillation as observed in vivo in recordings from cats. We further predict a critical role of synaptic noise and slow adaptive currents for spike sequence replay as found during sleep related memory consolidation.     

In vivo electrophysiological evidences for cortical neuron-glia interactions during slow (<1 Hz) and paroxysmal sleep oscillations.

The cortical activity results from complex interactions within networks of neurons and glial cells. The dialogue signals consist of neurotransmitters and various ions, which cross through the extracellular space. Slow (<1 Hz) sleep oscillations were first disclosed and investigated at the neuronal level where they consist of an alternation of the membrane potential between a depolarized and a hyperpolarized state. However, neuronal properties alone could not account for the mechanisms underlying the oscillatory nature of the sleeping cortex. Here I will show the behavior of glial cells during the slow sleep oscillation and its relationship with the variation of the neuronal membrane potential (pairs of neurons and glia recorded simultaneously and intracellularly) suggesting that, in contrast with previous assumptions, glial cells are not idle followers of neuronal activity. I will equally present measurements of the extracellular concentration of K(+) and Ca(2+), ions known to modulate the neuronal excitability. They are also part of the ionic flux that is spatially buffered by glial cells. The timing of the spatial buffering during the slow oscillation suggests that, during normal oscillatory activity, K(+) ions are cleared from active spots and released in the near vicinity, where they modulate the excitability of the neuronal membrane and contribute to maintain the depolarizing phase of the oscillation. Ca(2+) ions undergo a periodic variation of their extracellular concentration, which modulates the synaptic efficacy. The depolarizing phase of the slow oscillation is associated with a gradual depletion of the extracellular Ca(2+) promoting a progressive disfacilitation in the network. This functional synaptic neuronal disconnection is responsible for the ending of the depolarizing phase of the slow oscillation and the onset of a phasic hyperpolarization during which the neuronal network is silent and the intra- and extracellular ionic concentrations return to normal values. Spike-wave seizures often develop during sleep from the slow oscillation. Here I will show how the increased gap junction communication substantiates the facility of the glial syncytium to spatially buffer K(+) ions that were uptaken during the spike-wave seizures, and therefore contributing to the long-range recruitment of cortical territories. Similar mechanisms as those described during the slow oscillation promote the periodic (2-3 Hz) recurrence of spike-wave complexes.     

Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency

Sleep problems are commonly reported in Rett syndrome (RTT); however the electroencephalographic (EEG) biomarkers underlying sleep dysfunction are poorly understood. The aim of this study was to analyze the temporal evolution of quantitative EEG (qEEG) biomarkers in overnight EEGs recorded from girls (2–9 yrs. old) diagnosed with RTT using a non-traditional automated protocol. In this study, EEG spectral analysis identified high delta power cycles representing slow wave sleep (SWS) in 8–9h overnight sleep EEGs from the frontal, central and occipital leads (AP axis), comparing age-matched girls with and without RTT. Automated algorithms quantitated the area under the curve (AUC) within identified SWS cycles for each spectral frequency wave form. Both age-matched RTT and control EEGs showed similar increasing trends for recorded delta wave power in the EEG leads along the antero-posterior (AP). RTT EEGs had significantly fewer numbers of SWS sleep cycles; therefore, the overall time spent in SWS was also significantly lower in RTT. In contrast, the AUC for delta power within each SWS cycle was significantly heightened in RTT and remained heightened over consecutive cycles unlike control EEGs that showed an overnight decrement of delta power in consecutive cycles. Gamma wave power associated with these SWS cycles was similar to controls. However, the negative correlation of gamma power with age (r = -.59; p<0.01) detected in controls (2–5 yrs. vs. 6–9 yrs.) was lost in RTT. Poor % SWS (i.e., time spent in SWS overnight) in RTT was also driven by the younger age-group. Incidence of seizures in RTT was associated with significantly lower number of SWS cycles. Therefore, qEEG biomarkers of SWS in RTT evolved temporally and correlated significantly with clinical severity.     

Low-frequency rhythmic electrocutaneous hand stimulation during slow-wave night sleep: Physiological and therapeutic effects

Neocortical EEG slow wave activity (SWA) in the delta frequency band (0.5–4.0 Hz) is a hallmark of slow wave sleep (SWS) and its power is a function of prior wake duration and an indicator of a sleep need. SWS is considered the most important stage for realization of recovery functions of sleep. Possibility of impact on characteristics of a night sleep by rhythmic (0.8–1.2 Hz) subthreshold electocutaneous stimulation of a hand during SWS is shown: 1st night—adaptation, 2nd night—control, 3d and 4th nights—with stimulation during SWA stages of a SWS. Stimulation caused significant increase in average duration of SWS and EEG SWA power (in 11 of 16 subjects), and also well-being and mood improvement in subjects with lowered emotional tone. It is supposed that the received result is caused by functioning of a hypothetical mechanism directed on maintenance and deepening of SWS and counteracting activating, awakening influences of the afferent stimulation. The results can be of value both for understanding the physiological mechanisms of sleep homeostasis and for development of non-pharmacological therapy of sleep disorders.     

Analysis of the electroencephalographic activity associated with thalamic tumors

A physiologically based model of corticothalamic dynamics is used to investigate the electroencephalographic (EEG) activity associated with tumors of the thalamus. Tumor activity is modeled by introducing localized two-dimensional spatial non-uniformities into the model parameters, and calculating the resulting activity via the coupling of spatial eigenmodes. The model is able to reproduce various qualitative features typical of waking eyes-closed EEGs in the presence of a thalamic tumor, such as the appearance of abnormal peaks at theta ( approximately 3Hz) and spindle ( approximately 12Hz) frequencies, the attenuation of normal eyes-closed background rhythms, and the onset of epileptic activity, as well as the relatively normal EEGs often observed. The results indicate that the abnormal activity at theta and spindle frequencies arises when a small portion of the brain is forced into an over-inhibited state due to the tumor, in which there is an increase in the firing of (inhibitory) thalamic reticular neurons. The effect is heightened when there is a concurrent decrease in the firing of (excitatory) thalamic relay neurons, which are in any case inhibited by the reticular ones. This is likely due to a decrease in the responsiveness of the peritumoral region to cholinergic inputs from the brainstem, and a corresponding depolarization of thalamic reticular neurons, and hyperpolarization of thalamic relay neurons, similar to the mechanism active during slow-wave sleep. The results indicate that disruption of normal thalamic activity is essential to generate these spectral peaks. Furthermore, the present work indicates that high-voltage and epileptiform EEGs are caused by a tumor-induced local over-excitation of the thalamus, which propagates to the cortex. Experimental findings relating to local over-inhibition and over-excitation are discussed. It is also confirmed that increasing the size of the tumor leads to greater abnormalities in the observable EEG. The usefulness of EEG for localizing the tumor is investigated.