H-current modulation of cortical Up and Down states.

Hippocampal sharp wave-ripple complexes (SWRs) involve the synchronous discharge of thousands of cells throughout the CA3-CA1-subiculum-entorhinal cortex axis. Their strong transient output affects cortical targets, rendering SWRs a possible means for memory transfer from the hippocampus to the neocortex for long-term storage. Neurophysiological observations of hippocampal activity modulation by the cortical slow oscillation (SO) during deep sleep and anesthesia, and correlations between ripples and UP states, support the role of SWRs in memory consolidation through a cortico-hippocampal feedback loop. We couple a cortical network exhibiting SO with a hippocampal CA3-CA1 computational network model exhibiting SWRs, in order to model such cortico-hippocampal correlations and uncover important parameters and coupling mechanisms controlling them. The cortical oscillatory output entrains the CA3 network via connections representing the mossy fiber input, and the CA1 network via the temporoammonic pathway (TA). The spiking activity in CA3 and CA1 is shown to depend on the excitation-to-inhibition ratio, induced by combining the two hippocampal inputs, with mossy fiber input controlling the UP-state correlation of CA3 population bursts and corresponding SWRs, whereas the temporoammonic input affects the overall CA1 spiking activity. Ripple characteristics and pyramidal spiking participation to SWRs are shaped by the strength of the Schaffer collateral drive. A set of in vivo recordings from the rat hippocampus confirms a model-predicted segregation of pyramidal cells into subgroups according to the SO state where they preferentially fire and their response to SWRs. These groups can potentially play distinct functional roles in the replay of spike sequences.

Sleep is known to be important for memory consolidation [1], and memories are thought to be stored in the hippocampus during the wakefulness and “transferred” to cortex during sleep [2]. Recently, memory replay – repeatable sequences of pyramidal cell firing – has been demonstrated during sleep, and associated with characteristic brain oscillations, giving rise to the hypothesis that these may form the critical neural substrate of memory consolidation. Tampering with replay can disrupt memory formation and consolidation [3], and the mechanisms underlying sequence replay are still unknown. During sleep, replay events are associated with specific patterns of neuronal activity. Replay is seen in cortex during sleep oscillation – a rhythmic ( 150 Hz) firing [4]. It is believed that hippocampal ripples may contribute to the cortical active state generation, however specific interactions between ripples and slow oscillation remain unknown. In the present study, build on our previous research [5], we develop a model of ripple generation to explore interaction between cortical slow oscillation and hippocampal ripples during sleep. Data suggest that during ripple events only a few pyramidal (excitatory) cells are recruited in CA1, and they spike at the peak of the event, while perisomatic interneurons (a kind of inhibitory cells) spike across the duration of the event [6]. The distribution of spike timing with respect to ripple peak is different for different types of interneurons. In hippocampal area CA1, axo-axonic cells tend to spike at ripple initiation and be suppressed in the later part of a ripple [7]. Since axo-axonic cells are in a crucial position to suppress the spikes of the pyramidal cell they impinge upon, we hypothesize that they can regulate the initiation of specific cell activity replay. In this study we designed a computational model of ripple generation that emphasizes the role of axo-axonic cells in selecting which pyramidal cells are participating in ripple activity, hence what sequence is replayed during a given ripple. In our model, high-frequency firing of the perisomatic interneurons mediate high-frequency LFP oscillations in pyramidal neurons while axo-axonic interneurons define specific sequence of pyramidal cell firing. Using such model, we investigate the timing relationship between cortical slow-wave oscillations and hippocampal ripples. Our study proposes a novel mechanism of hippocampal ripple generation and predicts how interactions between different electrographic events during sleep may contribute to coordinated replay in cortical and hippocampal networks.

Depending on brain's current levels of relaxation, focus, and other mental states, brain will produced variable proportions of brain wave types. Each category of EEG(electroencephalography). Generally people's brain waves will respond to lights and sounds thuding at brain wave frequencies, and it will impact the change in state of brain — this activity called “entrainment.” We can schedule our brain to observe any brain wave experience as we like, by toggling number of pulses into eyes and ears. So here we are going to program various frequencies which are correlated to brain modes like Alfa, Beta, Gamma, Theta, & Delta by implementing Brain wave generator (BWG) using dsPIC DSC. This will be very useful in medical field for psychiatrist and many more.

Growing evidence suggests that brain activity during sleep, as well as sleep regulation, are tightly linked with synaptic function and network excitability at the local and global levels. We previously reported that a mutation in synaptobrevin 2 (Vamp2) in restless (rlss) mice results in a marked increase of wakefulness and suppression of sleep, in particular REM sleep (REMS), as well as increased consolidation of sleep and wakefulness. In this study, using finer-scale invivo electrophysiology recordings, we report that spontaneous cortical activity in rlss mice during NREM sleep (NREMS) is characterized by an occurrence of abnormally prolonged periods of complete neuronal silence (OFF-periods), often lasting several seconds, similar to the burst suppression pattern typically seen under deep anesthesia. Increased incidence of prolonged network OFF-periods was not specific to NREMS but also present in REMS and wake in rlss mice. Slow-wave activity (SWA) was generally increased in rlss mice relative to controls, while higher frequencies, including theta-frequency activity, were decreased, further resulting in diminished differences between vigilance states. The relative increase in SWA after sleep deprivation was attenuated in rlss mice, suggesting either that rlss mice experience persistently elevated sleep pressure or, alternatively, that the intrusion of sleep-like patterns of activity into the wake state attenuates the accumulation of sleep drive. We propose that a deficit in global synaptic neurotransmitter release leads to "state inertia," reflected in an abnormal propensity of brain networks to enter and remain in a persistent "default state" resembling coma or deep anesthesia.

Burst firing in subthalamic nucleus (STN) has been suggested to be highly associated with the motor symptoms in Parkinson’s disease, which result from a loss of dopamine. Although it is clinically very important to clarify the mechanism underlying the bursting dynamics, complex interactions between STN and other brain areas make it difficult to understand. In anesthetized rats, STN neurons exhibit low-frequency (1 Hz) bursts, which are synchronous with cortical slow oscillations and are significantly strengthened by dopamine depletion. To reproduce these low-frequency bursts, we examine a conductance-based model of an STN neuron that includes NMDA-type glutamatergic inputs reflecting cortical oscillations. In addition, the neuron model contains GABAergic inhibitory inputs, which are assumed to result from the activities of globus pallidus (GP). We show that the STN neuron model can reproduce low-frequency bursts synchronized with cortical activity, in the presence of GABAergic inhibition. In addition, we demonstrate that increased GABA activity leads to enhanced burstiness whereas increased NMDA conductances mainly augment STN firing rate. The induction of burst firing additionally decreases the coherence between STN and cortical activities. These results may give insights into how the complicated interactions between the STN, cortex, and GP can modulate the dynamics of bursting oscillations in the basal ganglia.

Physiologically based arousal state estimation and dynamics

Chapter 13 - Disorders of Consciousness

In the first electrocorticograms, Richard [Caton (1877)][1] connected a mirror galvanometer to exposed rabbit brains and observed slow sleep-related fluctuations. Several decades later, [Loomis et al. (1937)][2] applied the then-fledgling technique of scalp EEG to classify sleep stages in humans,

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

The existence of normal sleep in patients in a vegetative state is still a matter of debate. Previous electrophysiological sleep studies in patients with disorders of consciousness did not differentiate patients in a vegetative state from patients in a minimally conscious state. Using high-density electroencephalographic sleep recordings, 11 patients with disorders of consciousness (six in a minimally conscious state, five in a vegetative state) were studied to correlate the electrophysiological changes associated with sleep to behavioural changes in vigilance (sustained eye closure and muscle inactivity). All minimally conscious patients showed clear electroencephalographic changes associated with decreases in behavioural vigilance. In the five minimally conscious patients showing sustained behavioural sleep periods, we identified several electrophysiological characteristics typical of normal sleep. In particular, all minimally conscious patients showed an alternating non-rapid eye movement/rapid eye movement sleep pattern and a homoeostatic decline of electroencephalographic slow wave activity through the night. In contrast, for most patients in a vegetative state, while preserved behavioural sleep was observed, the electroencephalographic patterns remained virtually unchanged during periods with the eyes closed compared to periods of behavioural wakefulness (eyes open and muscle activity). No slow wave sleep or rapid eye movement sleep stages could be identified and no homoeostatic regulation of sleep-related slow wave activity was observed over the night-time period. In conclusion, we observed behavioural, but no electrophysiological, sleep wake patterns in patients in a vegetative state, while there were near-to-normal patterns of sleep in patients in a minimally conscious state. These results shed light on the relationship between sleep electrophysiology and the level of consciousness in severely brain-damaged patients. We suggest that the study of sleep and homoeostatic regulation of slow wave activity may provide a complementary tool for the assessment of brain function in minimally conscious state and vegetative state patients.

Information flow in neocortical circuits is regulated by two key parameters: intrinsic neuronal properties and the short-term activity-dependent plasticity of synaptic transmission. Using multineuronal whole-cell voltage recordings, we characterized the postnatal maturation of the electrophysiological properties and short-term plasticity of excitatory synaptic transmission between pairs of layer 5 (L5) pyramidal neurons (n = 158) in acute slices of rat visual cortex over the first postnatal month. We found that the intrinsic and synaptic properties of L5 pyramidal neurons develop in parallel. Before postnatal day 15 (P15), intrinsic electrophysiological properties were tuned to low-frequency operation, characterized by high apparent input resistance, a long membrane time constant, and prolonged somatic action potentials. Unitary excitatory synaptic potentials were of large amplitude (P11-P15; median, 514 μV), but showed pronounced use-dependent depression during prolonged regular and physiologically relevant presynaptic action potential firing patterns. In contrast, in mature animals we observed a developmental decline of the peak amplitude of unitary EPSPs (P25-P29; median, 175 μV) paralleled by a decrease in apparent input resistance, membrane time constant, and somatic action potential duration. Notably, synaptic signaling of complex action potential firing patterns was also transformed, with P25-P29 connections faithfully signaling action potential trains at frequencies up to 40 Hz (1st to 50th action potential ratio, 0.91 ± 0.12). Postnatal refinement of intrinsic properties and short-term plasticity therefore transforms the capacity of the L5 excitatory neural network of the visual cortex to generate and process patterns of action potential firing and contribute to network activity.

During slow-wave sleep and deep anesthesia, the rat hippocampus displays a slow oscillation (SO) that follows "up-and-down" state transitions in the neocortex. There has been recent debate as to whether this local field potential (LFP) rhythm reflects internal processing or entrains with respiratory inputs. To solve this issue, here we have concomitantly recorded respiration along with hippocampal, neocortical, and olfactory bulb (OB) LFPs in rats anesthetized with urethane. During the course of anesthesia, LFPs transitioned between activity states characterized by the emergence of different oscillations. By jointly analyzing multisite LFPs and respiratory cycles, we could distinguish three types of low-frequency hippocampal oscillations: (1) SO, which coupled to neocortical up-and-down transitions; (2) theta, which phase-reversed across hippocampal layers and was largest at the fissure; and (3) a low-frequency rhythm with largest amplitude in the dentate gyrus, which coupled to respiration-entrained oscillations in OB and to respiration itself. In contrast, neither theta nor SO coupled to respiration. The hippocampal respiration-coupled rhythm and SO had frequency <1.5 Hz, whereas theta tended to be faster (>3 Hz). Tracheotomy abolished hippocampal respiration-coupled rhythm, which was restored by rhythmic delivery of air puffs into the nasal cavity. These results solve the apparent contradictions among previous studies by demonstrating that the rat hippocampus produces multiple types of low-frequency oscillations. Because they synchronize with different brain circuits, however, we postulate that each activity pattern plays a unique role in information processing. The rat hippocampus exhibits a large-amplitude slow oscillation (<1.5 Hz) during deep sleep and anesthesia. It is currently debated whether this rhythm reflects internal processing with the neocortex or entrainment by external inputs from rhythmic nasal respiration, which has similar frequency. Here we reconcile previous studies by showing that the hippocampus can actually produce two low-frequency rhythms at nearby frequencies: one that indeed couples to respiration and another that is coupled to the neocortex. We further show that the respiration-coupled rhythm differs from theta oscillations. The results support a role for brain oscillations in connecting distant brain regions, and posit the respiratory cycle as an important reference for neuronal communication between olfactory and memory networks.

As most afferent axons to the thalamus originate in the cerebral cortex, we assumed that the slow (< 1 Hz) cortical oscillation described in the two companion articles is reflected in reticular (RE) thalamic and thalamocortical cells. We hypothesized that the cortically generated slow rhythm would appear in the thalamus in conjunction with delta and spindle oscillations arising from intrinsic and network properties of thalamic neurons. Intracellular recordings have been obtained in anesthetized cats from RE (n = 51) and cortically projecting (n = 240) thalamic neurons. RE cells were physiologically identified by cortically evoked high-frequency spike bursts and depolarizing spindle oscillations. Thalamocortical cells were recognized by backfiring from appropriate neocortical areas, spindle-related cyclic IPSPs, and hyperpolarization-activated delta oscillation consisting of rhythmic low-threshold spikes (LTSs) alternating with afterhyperpolarizing potentials (AHPs). The slow rhythm (0.3-0.5 Hz) was recorded in 65% of RE neurons. In approximately 90% of oscillating cells, the rhythm consisted of prolonged depolarizations giving rise to trains of single action potentials. DC hyperpolarization increased the synaptic noise and, in a few cells, suppressed the long-lasting depolarizing phase of the slow rhythm, without blocking the fast EPSPs. In approximately 10% of oscillating neurons, the hyperpolarizing phase of the oscillation was much more pronounced, thus suggesting that the slow rhythm was produced by inhibitory sculpturing of the background firing. The slow oscillation was associated with faster rhythms (4-8 Hz) in the same RE neuron. The slow rhythm of RE neurons was closely related to EEG wave complexes recurring with the same frequency, and its strong dependency upon a synchronized state of cortical EEG was observed during shifts in EEG patterns at different levels of anesthesia. In 44% of thalamocortical cells the slow rhythm of depolarizing sequences was apparent and it could coexist with delta or spindle oscillations in the same neuron. The occurrence of the slowly recurring depolarizing envelopes was delayed by the hyperpolarizing spindle sequences or by the LTS-AHP sequences of delta oscillation. The hyperpolarization-activated delta potentials that tended to dampen after a few cycles were grouped in sequences recurring with the slow rhythm. We finally propose a unified scenario of the genesis of the three major sleep rhythms: slow, delta, and spindle oscillations.

7. Thalamic mechanisms in the generation and modulation of the cortical slow oscillation in mice

Slow wave sleep (SWS) is characterized by the predominance of delta waves and slow oscillations, reflecting the synchronized activity of large cortical neuronal populations. Amongst other functions, SWS plays a crucial role in the restorative capacity of sleep. Rhythmic acoustic stimulation (RAS) during SWS has been shown a cost-effective method to enhance slow wave activity. Slow wave activity can be expressed in a region-specific manner as a function of previous waking activity. However, it is unclear whether slow waves can be enhanced in a region-specific manner using RAS. We investigated the effects of unilaterally presented rhythmic acoustic sound patterns on sleep electroencephalographic (EEG) oscillations. Thirty-five participants received during SWS 12-second long rhythmic bursts of pink noise (at a rate of 1 Hz) that alternated with non-stimulated, silent periods, unilaterally delivered into one of the ears of the participants. As expected, RAS enhanced delta power, especially in its low-frequency components between 0.75 and 2.25 Hz. However, increased slow oscillatory activity was apparent in both hemispheres regardless of the side of the stimulation. The most robust increases in slow oscillatory activity appeared during the first 3-4 seconds of the stimulation period. Furthermore, a short-lasting increase in theta and sigma power was evidenced immediately after the first pulse of the stimulation sequences. Our findings indicate that lateralized RAS has a strong potential to globally enhance slow waves during daytime naps. The lack of localized effects suggests that slow waves are triggered by the ascending reticular system and not directly by specific auditory pathways.