Activity in neocortex is often characterized by synchronized oscillations of neurons and networks, resulting in the generation of a local field potential and electroencephalogram. Do the neuronal networks of the cerebellum also generate synchronized oscillations and are they under the influence of those in the neocortex? Here we show that in the absence of any overt external stimulus, the cerebellar cortex generates a slow oscillation that is correlated with that of the neocortex. Disruption of the neocortical slow oscillation abolishes the cerebellar slow oscillation, whereas blocking cerebellar activity has no overt effect on the neocortex. We provide evidence that the cerebellar slow oscillation results in part from the activation of granule, Golgi, and Purkinje neurons. In particular, we show that granule and Golgi cells discharge trains of single spikes, and Purkinje cells generate complex spikes, during the Up state of the slow oscillation. Purkinje cell simple spiking is weakly related to the cerebellar and neocortical slow oscillation in a minority of cells. Our results indicate that the cerebellum generates rhythmic network activity that can be recorded as an LFP in the anesthetized animal, which is driven by synchronized oscillations of the neocortex. Furthermore, we show that correlations between neocortical and cerebellar LFPs persist in the awake animal, indicating that neocortical circuits modulate cerebellar neurons in a similar fashion in natural behavioral states. Thus, the projection neurons of the neocortex collectively exert a driving and modulatory influence on cerebellar network activity.
somatosensory cortex; corticospinal tract; local field potential; Purkinje cell; granule cell; Crus II
Slow waves are the most prominent electroencephalographic (EEG) feature of non-rapid eye movement (NREM) sleep. During NREM sleep, cortical neurons oscillate approximately once every second between a depolarized upstate, when cortical neurons are actively firing, and a hyperpolarized downstate, when cortical neurons are virtually silent (Steriade et al., 1993a; Destexhe et al., 1999; Steriade et al., 2001). Intracellular recordings indicate that the origins of the slow oscillation are cortical and that cortico-cortical connections are necessary for their synchronization (Steriade et al. 1993b; Amzica and Steriade, 1995; Timofeev and Steriade, 1996; Timofeev et al., 2000). The currents produced by the near-synchronous slow oscillation of large populations of neurons appear on the scalp as EEG slow waves (Amzica and Steriade, 1997).
Despite this cellular understanding, questions remain about the role of specific cortical structures in individual slow waves. Early EEG studies of slow waves in humans were limited by the small number of derivations employed and by the difficulty of relating scalp potentials to underlying brain activity (Brazier 1949; Roth et al 1956). Functional neuroimaging methods offer exceptional spatial resolution but lack the temporal resolution to track individual slow waves (Maquet, 2000; Dang-Vu et al., 2008). Intracranial recordings in patient populations are limited by the availability of medically necessary electrode placements and can be confounded by pathology and medications (Nir et al., 2010; Cash et al., 2009; Wenneberg 2010).
Source modeling of high-density EEG recordings offers a unique opportunity for neuroimaging sleep slow waves. So far, the results have challenged several of the influential topographic observations about slow waves that had persisted since the original EEG recordings of sleep. These recent analyses revealed that individual slow waves are idiosyncratic cortical events and that the negative peak of the EEG slow wave often involves cortical structures not necessarily apparent from the scalp, like the inferior frontal gyrus, anterior cingulate, posterior cingulate and precuneus (Murphy et al., 2009). In addition, not only do slow waves travel (Massimini et al., 2004), but they often do so preferentially through the areas comprising the major connectional backbone of the human cortex (Hagmann et al., 2008). In this chapter we will review the cellular, intracranial recording and neuroimaging results concerning EEG slow waves. We will also confront a long held belief about peripherally evoked slow waves, also known as K-complexes, namely that they are modality-independent and do not involve cortical sensory pathways. The analysis included here is the first to directly compare K-complexes evoked with three different stimulation modalities within the same subject on the same night using high-density EEG.
slow oscillation; source modeling; K-complex; neuroimaging; electroencephalography
In slow neocortical paroxysmal oscillations, the de- and hyperpolarizing envelopes in neocortical neurons are large compared with slow sleep oscillations. Increased local synchrony of membrane potential oscillations during seizure is reflected in larger electroencephalographic oscillations and the appearance of spike- or polyspike-wave complex recruitment at 2- to 3-Hz frequencies. The oscillatory mechanisms underlying this paroxysmal activity were investigated in computational models of cortical networks. The extracellular K+ concentration ([K+]o) was continuously computed based on neuronal K+ currents and K+ pumps as well as glial buffering. An increase of [K+]o triggered a transition from normal awake-like oscillations to 2- to 3-Hz seizure-like activity. In this mode, the cells fired periodic bursts and nearby neurons oscillated highly synchronously; in some cells depolarization led to spike inactivation lasting 50–100 ms. A [K+]o increase, sufficient to produce oscillations could result from excessive firing (e.g., induced by external stimulation) or inability of K+ regulatory system (e.g., when glial buffering was blocked). A combination of currents including high-threshold Ca2+, persistent Na+ and hyperpolarization-activated depolarizing (Ih) currents was sufficient to maintain 2- to 3-Hz activity. In a network model that included lateral K+ diffusion between cells, increase of [K+]o in a small region was generally sufficient to maintain paroxysmal oscillations in the whole network. Slow changes of [K+]o modulated the frequency of bursting and, in some case, led to fast oscillations in the 10- to 15-Hz frequency range, similar to the fast runs observed during seizures in vivo. These results suggest that modifications of the intrinsic currents mediated by increase of [K+]o can explain the range of neocortical paroxysmal oscillations in vivo.
During non-rapid eye movement (NREM) sleep and certain types of anaesthesia, neurons in the neocortex and thalamus exhibit a distinctive slow (<1 Hz) oscillation that consists of alternating UP and DOWN membrane potential states and which correlates with a pronounced slow (<1 Hz) rhythm in the EEG. Whilst several studies have claimed that the slow oscillation is generated exclusively in neocortical networks and then transmitted to other brain areas, substantial evidence exists to suggest that the full expression of the slow oscillation in an intact thalamocortical network requires the balanced interaction of oscillator systems in both the neocortex and thalamus. Within such a scenario, we have previously argued that the powerful low-threshold Ca2+ potential (LTCP)-mediated burst of action potentials that initiates the UP states in individual thalamocortical neurons may be a vital signal for instigating UP states in related cortical areas. To investigate these issues we constructed a computational model of the thalamocortical network which encompasses the important known aspects of the slow oscillation that have been garnered from earlier in vivo and in vitro experiments. By using this model we confirm that the overall expression of the slow oscillation is intricately reliant on intact connections between thalamus and cortex. In particular, we demonstrate that UP state-related LTCP-mediated bursts in thalamocortical neurons are proficient in triggering synchronous UP states in cortical networks, thereby bringing about a synchronous slow oscillation in the whole network. The importance of LTCP-mediated action potential bursts in the slow oscillation is also underlined by the observation that their associated dendritic Ca2+ signals are the only ones that inform corticothalamic synapses of the thalamocortical neuron output, since they, but not those elicited by tonic action potential firing, reach the distal dendritic sites where these synapses are located.
thalamic neurons; cortical neurons; probabilistic network model; dendrites; intrinsic calcium signalling
During non-rapid eye movement sleep and certain types of anaesthesia, neurons in the neocortex and thalamus exhibit a distinctive slow (<1 Hz) oscillation that consists of alternating UP and DOWN membrane potential states and which correlates with a pronounced slow (<1 Hz) rhythm in the electroencephalogram. While several studies have claimed that the slow oscillation is generated exclusively in neocortical networks and then transmitted to other brain areas, substantial evidence exists to suggest that the full expression of the slow oscillation in an intact thalamocortical (TC) network requires the balanced interaction of oscillator systems in both the neocortex and thalamus. Within such a scenario, we have previously argued that the powerful low-threshold Ca2+ potential (LTCP)-mediated burst of action potentials that initiates the UP states in individual TC neurons may be a vital signal for instigating UP states in related cortical areas. To investigate these issues we constructed a computational model of the TC network which encompasses the important known aspects of the slow oscillation that have been garnered from earlier in vivo and in vitro experiments. Using this model we confirm that the overall expression of the slow oscillation is intricately reliant on intact connections between the thalamus and the cortex. In particular, we demonstrate that UP state-related LTCP-mediated bursts in TC neurons are proficient in triggering synchronous UP states in cortical networks, thereby bringing about a synchronous slow oscillation in the whole network. The importance of LTCP-mediated action potential bursts in the slow oscillation is also underlined by the observation that their associated dendritic Ca2+ signals are the only ones that inform corticothalamic synapses of the TC neuron output, since they, but not those elicited by tonic action potential firing, reach the distal dendritic sites where these synapses are located.
thalamic neurons; cortical neurons; probabilistic network model; dendrites; intrinsic calcium signalling
During NREM sleep and under certain types of anaesthesia, the mammalian brain exhibits a distinctive slow (<1 Hz) rhythm. At the cellular level, this rhythm correlates with so-called UP and DOWN membrane potential states. In the neocortex, these UP and DOWN states correspond to periods of intense network activity and widespread neuronal silence, respectively, whereas in thalamocortical (TC) neurons, UP/DOWN states take on a more stereotypical oscillatory form, with UP states commencing with a low-threshold Ca2+ potential (LTCP). Whilst these properties are now well recognised for neurons in cats and rats, whether or not they are also shared by neurons in the mouse is not fully known. To address this issue, we obtained intracellular recordings from neocortical and TC neurons during the slow (<1 Hz) rhythm in anaesthetised mice. We show that UP/DOWN states in this species are broadly similar to those observed in cats and rats, with UP states in neocortical neurons being characterised by a combination of action potential output and intense synaptic activity, whereas UP states in TC neurons always commence with an LTCP. In some neocortical and TC neurons, we observed ‘spikelets’ during UP states, supporting the possible presence of electrical coupling. Lastly, we show that, upon tonic depolarisation, UP/DOWN states in TC neurons are replaced by rhythmic high-threshold bursting at ~5 Hz, as predicted by in vitro studies. Thus, UP/DOWN state generation appears to be an elemental and conserved process in mammals that underlies the slow (<1 Hz) rhythm in several species, including humans.
EEG; Oscillations; Sleep; Neocortex; Thalamocortical; Electroencephalogram; T-type calcium channel; Thalamus; Neocortical neurons
During NREM sleep and under certain types of anaesthesia the mammalian brain exhibits a distinctive slow (<1 Hz) rhythm. At the cellular level this rhythm correlates with so-called UP and DOWN membrane potential states. In the neocortex these UP and DOWN states correspond to periods of intense network activity and widespread neuronal silence, respectively, whereas in thalamocortical (TC) neurons, UP/DOWN states take on a more stereotypical oscillatory form with UP states commencing with a low-threshold Ca2+ potential (LTCP). Whilst these properties are now well recognized for neurons in cats and rats, whether or not they are also shared by neurons in the mouse is not fully known. To address this issue we obtained intracellular recordings from neocortical and TC neurons during the slow (<1 Hz) rhythm in anaesthetized mice. We show that UP/DOWN states in this species are broadly similar to those observed in cats and rats, with UP states in neocortical neurons being characterized by a combination of action potential output and intense synaptic activity whereas UP states in TC neurons always commence with an LTCP. In some neocortical and TC neurons we observed ‘spikelets’ during UP states, supporting the possible presence of electrical coupling. Lastly, we show that upon tonic depolarization, UP/DOWN states in TC neurons are replaced by rhythmic high-threshold (HT) bursting at ~5 Hz, as predicted by in vitro studies. Thus, UP/DOWN state generation appears to be an elemental and conserved process in mammals that underlies the slow (<1 Hz) rhythm in several species, including humans.
EEG; oscillations; sleep; neocortex; thalamocortical
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.
slow-wave sleep; rapid eye movement sleep; homeostasis; sharp-wave ripple; theta; spindle; neostriatum caudolaterale; prefrontal cortex; hippocampus; long-term memory
The most prominent EEG events in sleep are slow waves, reflecting a slow (<1 Hz) oscillation between up and down states in cortical neurons. It is unknown whether slow oscillations are synchronous across the majority or the minority of brain regions—are they a global or local phenomenon? To examine this, we recorded simultaneously scalp EEG, intracerebral EEG, and unit firing in multiple brain regions of neurosurgical patients. We find that most sleep slow waves and the underlying active and inactive neuronal states occur locally. Thus, especially in late sleep, some regions can be active while others are silent. We also find that slow waves can propagate, usually from medial prefrontal cortex to the medial temporal lobe and hippocampus. Sleep spindles, the other hallmark of NREM sleep EEG, are likewise predominantly local. Thus, intracerebral communication during sleep is constrained because slow and spindle oscillations often occur out-of-phase in different brain regions.
Deep anesthesia is commonly used as a model of slow-wave sleep (SWS). Ketamine-xylazine anesthesia reproduces the main features of sleep slow oscillation: slow, large amplitude waves in field potential, which are generated by the alternation of hyperpolarized and depolarized states of cortical neurons. However, direct quantitative comparison of field potential and membrane potential fluctuations during natural sleep and anesthesia is lacking, so it remains unclear how well the properties of sleep slow oscillation are reproduced by the ketamine-xylazine anesthesia model. Here, we used field potential and intracellular recordings in different cortical areas in the cat, to directly compare properties of slow oscillation during natural sleep and ketamine-xylazine anesthesia. During SWS cortical activity showed higher power in the slow/delta (0.1-4 Hz) and spindle (8-14 Hz) frequency range, while under anesthesia the power in the gamma band (30-100 Hz) was higher. During anesthesia, slow waves were more rhythmic and more synchronous across the cortex. Intracellular recordings revealed that silent states were longer and the amplitude of membrane potential around transition between active and silent states was bigger under anesthesia. Slow waves were largely uniform across cortical areas under anesthesia, but in SWS they were most pronounced in associative and visual areas, but smaller and less regular in somatosensory and motor cortices. We conclude that although the main features of the slow oscillation in sleep and anesthesia appear similar, multiple cellular and network features are differently expressed during natural SWS as compared to ketamine-xylazine anesthesia.
Sleep; oscillations; synchrony; intracellular; anesthesia; ketamine-Xylazine
Cerebral cortical slow-wave activity (SWA) is prominent during sleep and also during ketamine-induced anesthesia. SWA in EEG recordings is closely linked to prominent fluctuations between up- and down-states in the membrane potential of pyramidal neurons. However, little is known about how the cerebellum is linked into SWA and whether slow oscillations influence sensory cerebellar responses. To examine these issues, we simultaneously recorded EEG from the cerebral cortex (SI, MI, and SMA), local field potentials at the input stage of cerebellar processing in the cerebellar granule cell layer (GCL) and inferior olive (IO), and single unit activity at the output stage of the cerebellum in the deep cerebellar nuclei (DCN). We found that in ketamine-anesthetized rats, SWA was synchronized between all recorded cortical areas and was phase locked with local field potentials of the GCL, IO, and single unit activity in the DCN. We found that cortical up-states are linked to activation of GCL neurons but to inhibition of cerebellar output from the DCN, with the latter an effect likely mediated by Purkinje cells. A partial coherence analysis showed further that a large portion of SWA shared between GCL and DCN was transmitted from the cortex, since the coherence shared between GCL and DCN was diminished when the effect of cortical activity was subtracted. To determine the causal flow of information between structures, a directed transfer function was calculated between the simultaneous activities of SI, MI, SMA, GCL and DCN. This analysis showed that the primary direction of information flow was from cortex to the cerebellum, and that SI had a stronger influence than other cortical areas on DCN activity. The strong functional connectivity with SI in particular is in agreement with previous findings of a strong cortical component in cerebellar sensory responses.
Rat; Deep Cerebellar Nuclei; Cerebral Cortex; Single Unit Activity; Local Field Potential; Directed Transfer Function
Neocortical gamma (30–80 Hz) rhythms correlate with attention, movement and perception and are often disrupted in neurological and psychiatric disorders. Gamma primarily occurs during alert brain states characterized by the so-called “desynchronized” EEG. Is this because gamma rhythms are devoid of synchrony? In this review we take a historical approach to answering this question. Richard Caton and Adolf Beck were the first to report the rhythmic voltage fluctuations in the animal brain. They were limited by the poor amplification of their early galvanometers. Thus when they presented light or other stimuli, they observed a disappearance of the large resting oscillations. Several groups have since shown that visual stimuli lead to low amplitude gamma rhythms and that groups of neurons in the visual cortices fire together during individual gamma cycles. This synchronous firing can more strongly drive downstream neurons. We discuss how gamma-band synchrony can support ongoing communication between brain regions, and highlight an important fact: there is at least local neuronal synchrony during gamma rhythms. Thus, it is best to refer to the low amplitude, high frequency EEG as an “activated”, not “desynchronized”, EEG.
desynchronized EEG; gamma oscillations; synchrony; history of gamma rhythms
During neocortical development, neurons exhibit highly synchronized patterns of spontaneous activity, with correlated bursts of action potential firing dominating network activity. This early activity is eventually replaced by more sparse and decorrelated firing of cortical neurons, which modeling studies predict is a network state that is better suited for efficient neural coding. The precise time course and mechanisms of this crucial transition in cortical network activity have not been characterized in vivo. We used in vivo two-photon calcium imaging in combination with whole-cell recordings in both unanesthetized and anesthetized mice to monitor how spontaneous activity patterns in ensembles of layer 2/3 neurons of barrel cortex mature during postnatal development. We find that as early as postnatal day 4, activity is highly synchronous within local clusters of neurons. At the end of the second postnatal week, neocortical networks undergo a transition to a much more desynchronized state that lacks a clear spatial structure. Strikingly, deprivation of sensory input from the periphery had no effect on the time course of this transition. Therefore, developmental desynchronization of spontaneous neuronal activity is a fundamental network transition in the neocortex that appears to be intrinsically generated.
two-photon; 2-photon; calcium; barrel cortex; plasticity; whole-cell; in vivo; whisker; inhibition; layer 2/3; deconvolution; UP state; DOWN state
The mammalian brain expresses a wide range of state-dependent network oscillations which vary in frequency and spatial extension. Such rhythms can entrain multiple neurons into coherent patterns of activity, consistent with a role in behaviour, cognition and memory formation. Recent evidence suggests that locally generated fast network oscillations can be systematically aligned to long-range slow oscillations. It is likely that such cross-frequency coupling supports specific tasks including behavioural choice and working memory.
We analyzed temporal coupling between high-frequency oscillations and EEG theta activity (4–12 Hz) in recordings from mouse parietal neocortex. Theta was exclusively present during active wakefulness and REM-sleep. Fast oscillations occurred in two separate frequency bands: gamma (40–100 Hz) and fast gamma (120–160 Hz). Theta, gamma and fast gamma were more prominent during active wakefulness as compared to REM-sleep. Coupling between theta and the two types of fast oscillations, however, was more pronounced during REM-sleep. This state-dependent cross-frequency coupling was particularly strong for theta-fast gamma interaction which increased 9-fold during REM as compared to active wakefulness. Theta-gamma coupling increased only by 1.5-fold.
State-dependent cross-frequency-coupling provides a new functional characteristic of REM-sleep and establishes a unique property of neocortical fast gamma oscillations. Interactions between defined patterns of slow and fast network oscillations may serve selective functions in sleep-dependent information processing.
In the neocortex, neurons participate in epochs of elevated activity, or Up states, during periods of quiescent wakefulness, slow-wave sleep, and general anesthesia. The regulation of firing during and between Up states is of great interest because it can reflect the underlying connectivity and excitability of neurons within the network. Automated analysis of the onset and characteristics of Up state firing across different experiments and conditions requires a robust and accurate method for Up state detection. Using measurements of membrane potential mean and variance calculated from whole-cell recordings of neurons from control and postseizure tissue, the authors have developed such a method. This quantitative and automated method is independent of cell- or condition-dependent variability in underlying noise or tonic firing activity. Using this approach, the authors show that Up state frequency and firing rates are significantly increased in layer 2/3 neocortical neurons 24 hours after chemo-convulsant-induced seizure. Down states in postseizure tissue show greater membrane-potential variance characterized by increased synaptic activity. Previously, the authors have found that postseizure increase in excitability is linked to a gain-of-function in BK channels, and blocking BK channels in vitro and in vivo can decrease excitability and eliminate seizures. Thus, the authors also assessed the effect of BK-channel antagonists on Up state properties in control and postseizure neurons. These data establish a robust and broadly applicable algorithm for Up state detection and analysis, provide a quantitative description of how prior seizures increase spontaneous firing activity in cortical networks, and show how BK-channel antagonists reduce this abnormal activity.
epilepsy; seizure; Up state; BK channels; classification
The neuronal cortical network generates slow (<1 Hz) spontaneous rhythmic activity that emerges from the recurrent connectivity. This activity occurs during slow wave sleep or anesthesia and also in cortical slices, consisting of alternating up (active, depolarized) and down (silent, hyperpolarized) states. The search for the underlying mechanisms and the possibility of analyzing network dynamics in vitro has been subject of numerous studies. This exposes the need for a detailed quantitative analysis of the membrane fluctuating behavior and computerized tools to automatically characterize the occurrence of up and down states.
Intracellular recordings from different areas of the cerebral cortex were obtained from both in vitro and in vivo preparations during slow oscillations. A method that separates up and down states recorded intracellularly is defined and analyzed here. The method exploits the crossover of moving averages, such that transitions between up and down membrane regimes can be anticipated based on recent and past voltage dynamics. We demonstrate experimentally the utility and performance of this method both offline and online, the online use allowing to trigger stimulation or other events in the desired period of the rhythm. This technique is compared with a histogram-based approach that separates the states by establishing one or two discriminating membrane potential levels. The robustness of the method presented here is tested on data that departs from highly regular alternating up and down states.
We define a simple method to detect cortical states that can be applied in real time for offline processing of large amounts of recorded data on conventional computers. Also, the online detection of up and down states will facilitate the study of cortical dynamics. An open-source MATLAB® toolbox, and Spike 2®-compatible version are made freely available.
The thalamic reticular (RE) nucleus is a major source of inhibition in the thalamus. It plays a crucial role in regulating the excitability of thalamocortical networks and in generating some sleep rhythms. Current-clamp intracellular recordings of RE neurons in cats under barbiturate anesthesia revealed the presence of membrane bistability in ~20% of neurons. Bistability consisted of two alternate membrane potentials, separated by ~17–20 mV. While non-bistable (common) RE neurons fired rhythmic spike-bursts during spindles, bistable RE neurons fired tonically, with burst modulation, throughout spindle sequences. Bistability was strongly voltage dependent and only expressed under resting conditions (i.e. no current injection). The transition from the silent to the active state was a regenerative event that could be activated by brief depolarization, whereas brief hyperpolarizations could switch the membrane potential from the active to the silent state. These effects outlasted the current pulses. Corticothalamic stimulation could also switch the membrane potential from silent to active states. Addition of QX-314 in the recording micropipette either abolished or disrupted membrane bistability, suggesting INa(p) to be responsible for its generation. Thalamocortical cells presented various patterns of spindling that reflected the membrane bistability in RE neurons. Finally, experimental data and computer simulations predicted a role for RE neurons’ membrane bistability in inducing various patterns of spindling in target thalamocortical cells. We conclude that membrane bistability of RE neurons is an intrinsic property, likely generated by INa(p) and modulated by cortical influences, as well as a factor that determines different patterns of spindle rhythms in thalamocortical neurons.
Brain cortex activity, as variously recorded by scalp or cortical electrodes in the electroencephalography (EEG) frequency range, probably reflects the basic strategy of brain information processing. Various hypotheses have been advanced to interpret this phenomenon, the most popular of which is that suitable combinations of excitatory and inhibitory neurons behave as assemblies of oscillators susceptible to synchronization and desynchronization. Implicit in this view is the assumption that EEG potentials are epiphenomena of action potentials, which is consistent with the argument that voltage variations in dendritic membranes reproduce the postsynaptic effects of targeting neurons. However, this classic argument does not really fit the discovery that firing synchronization over extended brain areas often appears to be established in about 1 ms, which is a small fraction of any EEG frequency component period. This is in contrast with the fact that all computational models of dynamic systems formed by more or less weakly interacting oscillators of near frequencies take more than one period to reach synchronization. The discovery that the somatodendritic membranes of specialized populations of neurons exhibit intrinsic subthreshold oscillations (ISOs) in the EEG frequency range, together with experimental evidence that short inhibitory stimuli are capable of resetting ISO phases, radically changes the scheme described above and paves the way to a novel view. This paper aims to elucidate the nature of ISO generation mechanisms, to explain the reasons for their reliability in starting and stopping synchronized firing, and to indicate their potential in brain information processing. The need for a repertoire of extraneuronal regulation mechanisms, putatively mediated by astrocytes, is also inferred. Lastly, the importance of ISOs for the brain as a parallel recursive machine is briefly discussed.
Subthreshold oscillations; Subthreshold oscillation control; Neuron-firing synchronization; EEG; Brainweb; Phasic and tonic inhibitions
Studies of animal models of Huntington's disease (HD) have revealed that neocortical and neostriatal neurons of these animals in vitro exhibit a number of morphological and physiological changes, including increased input resistance and changes in neocortical synaptic inputs. We measured the functional effects of polyglutamate accumulation in neocortical neurons in R6/2 mice (8–14 weeks of age) and their age-matched non-transgenic littermates using in vivo intracellular recordings. All neurons showed spontaneous membrane potential fluctuations. The current/voltage and the firing properties of the HD neocortical neurons were significantly altered, especially in the physiologically relevant current range around and below threshold. As a result, membrane potential transitions from the Down state to Up state were evoked with smaller currents in HD neocortical neurons than in controls. The excitation-to-frequency curves of the HD mice were significantly steeper than those of controls, indicating a smaller input–output dynamic range for these neurons. Increased likelihood of Down to Up state transitions could cause pathological recruitment of corticostriatal assemblies by increasing correlated neuronal activity. We measured coherence of the in vivo intracellular recordings with simultaneously recorded electrocorticograms. We found that the peak of the coherence at <5 Hz was significantly smaller in the HD animals, indicating that the amount of coherence in the state transitions of single neurons is less correlated with global activity than non-transgenic controls. We propose that decreased correlation of neocortical inputs may be a major physiological cause underlying the errors in sensorimotor pattern generation in HD.
Huntington's disease; cortex; striatum; R6/2; intracellular recording; in vivo
Oscillatory and synchronized activities in the mammalian brain have been correlated with the execution of complex cognitive tasks. Similar oscillations have been observed in local field potentials (LFPs) in flies, in this case correlated with different attentional states. To further test the significance of these oscillations we recorded LFPs from the brain of Drosophila melanogaster as it responded to the presentation of olfactory stimuli. We find that responses in the 70–80 Hz range increase during olfactory stimulation. Recurrent stimulation specifically decreased the power of LFPs in this frequency range. Delivery of electric shocks before olfactory stimulation modulated LFPs in the 70–80 Hz range by evoking a transient increase. These results suggest that these signals are a simple neuronal correlate of higher-order olfactory processing in flies.
Objectives: Gamma oscillations (30–100 Hz gamma electroencephalographic (EEG) activity) correlate with high frequency synchronous rhythmic bursting in assemblies of cerebral neurons participating in aspects of consciousness. Previous studies in a kainic acid animal model of epilepsy revealed increased intensity of gamma rhythms in background EEG preceding epileptiform discharges, leading the authors to test for intensified gamma EEG in humans with epilepsy.
Methods: 64 channel cortical EEG were recorded from 10 people with primary generalised epilepsy, 11 with partial epilepsy, and 20 controls during a quiescent mental state. Using standard methods of EEG analysis the strength of EEG rhythms (fast Fourier transformation) was quantified and the strengths of rhythms in the patient groups compared with with controls by unpaired t test at 1 Hz intervals from 1 Hz to 100 Hz.
Results: In patients with generalised epilepsy, there was a threefold to sevenfold increase in power of gamma EEG between 30 Hz and 100 Hz (p<0.01). Analysis of three unmedicated patients with primary generalised epilepsies revealed an additional 10-fold narrow band increase of power around 35 Hz–40 Hz (p<0.0001). There were no corresponding changes in patients with partial epilepsy.
Conclusions: Increased gamma EEG is probably a marker of the underlying ion channel or neurotransmitter receptor dysfunction in primary generalised epilepsies and may also be a pathophysiological prerequisite for the development of seizures. The finding provides a new diagnostic approach and also links the pathophysiology of generalised epilepsies to emerging concepts of neuronal correlates of consciousness.
Local field potentials and the underlying endogenous electric fields (EFs) are traditionally considered to be epiphenomena of structured neuronal network activity. Recently, however, externally applied EFs have been shown to modulate pharmacologically evoked network activity in rodent hippocampus. In contrast, very little is known about the role of endogenous EFs during physiological activity states in neocortex. Here we used the neocortical slow oscillation in vitro as a model system to show that weak sinusoidal and naturalistic EFs enhance and entrain physiological neocortical network activity with an amplitude threshold within the range of in vivo endogenous field strengths. Modulation of network activity by positive and negative feedback fields based on the network activity in real-time provide direct evidence for a feedback loop between neuronal activity and endogenous EF. This significant susceptibility of active networks to EFs that only cause small changes in membrane potential in individual neurons suggests that endogenous EFs could guide neocortical network activity.
neocortex; electric field; local field potential; positive feedback; modulation; cortical slow oscillation; network activity; acute slice preparation; network dynamics
Interregional interactions of oscillatory activity are crucial for the integrated processing of multiple brain regions. However, while the EEG in virtually all brain structures passes through substantial modifications during sleep, it is still an open question whether interactions between neocortical and medial temporal EEG oscillations also depend on the state of alertness. Several previous studies in animals and humans suggest that hippocampal-neocortical interactions crucially depend on the state of alertness (i.e., waking state or sleep). Here, we analyzed scalp and intracranial EEG recordings during sleep and waking state in epilepsy patients undergoing presurgical evaluation. We found that the amplitudes of oscillations within the medial temporal lobe and the neocortex were more closely correlated during sleep, in particular during non-REM sleep, than during waking state. Possibly, the encoding of novel sensory inputs, which mainly occurs during waking state, requires that medial temporal dynamics are rather independent from neocortical dynamics, while the consolidation of memories during sleep may demand closer interactions between MTL and neocortex.
Memories are thought to be encoded as a distributed representation in the neocortex. The medial prefrontal cortex (mPFC) has been shown to support the expression of memories that initially depend on the hippocampus (HPC), yet the mechanisms by which the HPC and mPFC access the distributed representations in the neocortex are unknown. By measuring phase synchronization of local field potential (LFP) oscillations, we found that learning initiated changes in neuronal communication of the HPC and mPFC with the lateral entorhinal cortex (LEC), an area that is connected with many other neocortical regions. LFPs were recorded simultaneously from the three brain regions while rats formed an association between an auditory stimulus (CS) and eyelid stimulation (US) in a trace eyeblink conditioning paradigm, as well as during retention 1 month following learning. Over the course of learning, theta oscillations in the LEC and mPFC became strongly synchronized following presentation of the CS on trials in which rats exhibited a conditioned response (CR), and this strengthened synchronization was also observed during remote retention. In contrast, CS-evoked theta synchronization between the LEC and HPC decreased with learning. Our results suggest that communication between the LEC and mPFC are strengthened with learning whereas the communication between the LEC and HPC are concomitantly weakened, suggesting that enhanced LEC–mPFC communication may be a neuronal correlate for theoretically proposed neocortical reorganization accompanying encoding and consolidation of a memory.
consolidation; episodic memory; trace conditioning; EEG; rats
GnRH neurones fire spontaneous bursts of action potentials, but little is understood about the underlying mechanisms. Here we show evidence for two types of bursting/oscillation driven by different mechanisms. Properties of these different types are clarified using mathematical modeling and a recently developed active-phase/silent-phase correlation technique. The first type of GnRH neurone (1–2%) exhibits slow (~0.05Hz) spontaneous oscillations in membrane potential. Action potential bursts are often observed during oscillation depolarization, but some oscillations were entirely subthreshold. Oscillations persist after blockade of fast sodium channels with TTX and blocking receptors for ionotropic fast synaptic transmission, indicating they are intrinsically generated. In the second type of GnRH neurone, bursts were irregular and TTX caused a stable membrane potential. The two types of bursting cells exhibited distinct active-phase/silent-phase correlation patterns, which is suggestive of distinct mechanisms underlying the rhythms. Further studies of type 1 oscillating cells revealed that the oscillation period was not affected by current or voltage steps, although amplitude was sometimes damped. Oestradiol, an important feedback regulator of GnRH neuronal activity, acutely and markedly altered oscillations, specifically depolarizing the oscillation nadir and initiating or increasing firing. Blocking calcium-activated potassium channels, which are rapidly reduced by oestradiol, had a similar effect on oscillations. Kisspeptin, a potent activator of GnRH neurones, translated the oscillation to more depolarised potentials, without altering period or amplitude. These data show that there are at least two distinct types of GnRH neurone bursting patterns with different underlying mechanisms.
burst; oscillation; hypothalamus; neuroendocrine; parabolic