Up-down States Research Articles

UP-DOWN states and ripples differentially modulate membrane potential dynamics across DG, CA3, and CA1 in awake mice.

Hippocampal ripples are transient population bursts that structure cortico-hippocampal communication and play a central role in memory processing. However, the mechanisms controlling ripple initiation in behaving animals remain poorly understood. Here we combine multisite extracellular and whole-cell recordings in awake mice to contrast the brain state and ripple modulation of subthreshold dynamics across hippocampal subfields. We find that entorhinal input to the dentate gyrus (DG) exhibits UP and DOWN dynamics with ripples occurring exclusively in UP states. While elevated cortical input in UP states generates depolarization in DG and CA1, it produces persistent hyperpolarization in CA3 neurons. Furthermore, growing inhibition is evident in CA3 throughout the course of the ripple buildup, while DG and CA1 neurons exhibit depolarization transients 100 ms before and during ripples. These observations highlight the importance of CA3 inhibition for ripple generation, while pre-ripple responses indicate a long and orchestrated ripple initiation process in the awake state.

Thalamus mediates neocortical Down state transition via GABAB-receptor-targeting interneurons

Slow-wave sleep is characterized by near-synchronous alternation of active Up states and quiescent Down states in the neocortex. Although the cortex itself can maintain these oscillations, the full expression of Up-Down states requires intact thalamocortical circuits. Sensory thalamic input can drive the cortex into an Up state. Here we show that midline thalamic neurons terminate Up states synchronously across cortical areas. Combining local field potential, single-unit, and patch-clamp recordings in conjunction with optogenetic stimulation and silencing in mice invivo, we report that thalamic input mediates Down transition via activation of layer 1 neurogliaform inhibitory neurons acting on GABAB receptors. These results strengthen the evidence that thalamocortical interactions are essential for the full expression of slow-wave sleep, show that Down transition is an active process mediated by cortical GABAB receptors, and demonstrate that thalamus synchronizes Down transitions across cortical areas during natural slow-wave sleep.

Structural, thermodynamic, electronic, and magnetic properties of superconducting FeSe–CsCl type: Ab initio searching technique with van der Waals corrections

One of the key development factors for advanced functional solid-state materials is the high-pressure effect. Structural searches of iron selenide (FeSe) under pressures have been investigated using the ab initio random structure searching technique. The DFT–D2 method of Grimme with van der Waals corrections is used to calculate the physical properties such as structural, lattice-vibrational, thermodynamic, electronic, magnetic, and superconducting properties. The lowest enthalpy systems at pressures above 110 GPa appear closely in two structures which are orthorhombic-Pmmn and cubic-CsCl space groups. Besides, the temperature effect on thermodynamic properties is also analyzed using quasi-harmonic vibrational approximation. Demagnetized states of the FeSe–CsCl system can be determined from electronic structure and spin up-down states. The discovery of a new metallic CsCl-phase encourages us to investigate the superconducting transition temperature (Tc) at pressures greater than 110 GPa. This indicates that the Tc value in the CsCl-type FeSe under high pressure is related to the Fermi surface size.

Dynamic Configuration of Coactive Micropatterns in the Default Mode Network During Wakefulness and Sleep.

Background: The default mode network (DMN) is a prominent intrinsic network that is observable in many mammalian brains. However, a few studies have investigated the temporal dynamics of this network based on direct physiological recordings. Methods: Herein, we addressed this issue by characterizing the dynamics of local field potentials from the rat DMN during wakefulness and sleep with an exploratory analysis. We constructed a novel coactive micropattern (CAMP) algorithm to evaluate the configurations of rat DMN dynamics, and further revealed the relationship between DMN dynamics with different wakefulness and alertness levels. Results: From the gamma activity (40-80 Hz) in the DMN across wakefulness and sleep, three spatially stable CAMPs were detected: a common low-activity level micropattern (cDMN), an anterior high-activity level micropattern (aDMN), and a posterior high-activity level micropattern (pDMN). A dynamic balance across CAMPs emerged during wakefulness and was disrupted in sleep stages. In the slow-wave sleep (SWS) stage, cDMN became the primary activity pattern, whereas aDMN and pDMN were the major activity patterns in the rapid eye movement sleep stage. In addition, further investigation revealed phasic relationships between CAMPs and the up-down states of the slow DMN activity in the SWS stage. Conclusion: Our study revealed that the dynamic configurations of CAMPs were highly associated with different stages of wakefulness, and provided a potential three-state model to describe the DMN dynamics for wakefulness and alertness. Impact statement In the current study, a novel coactive micropattern (CAMP) method was developed to elucidate fast default mode network (DMN) dynamics during wakefulness and sleep. Our findings demonstrated that the dynamic configurations of DMN activity are specific to different wakefulness stages and provided a three-state DMN CAMP model to depict wakefulness levels, thus revealing a potentially new neurophysiological representation of alertness levels. This work could elucidate the DMN dynamics underlying different stages of wakefulness and have important implications for the theoretical understanding of the neural mechanism of wakefulness and alertness.

Cholinergic modulation of Up-Down states in the mouse medial entorhinal cortex in vitro.

Cholinergic tone is high during wake and rapid eye movement sleep and lower during slow wave sleep (SWS). Nevertheless, the low tone of acetylcholine during SWS modulates sharp wave ripple incidence in the hippocampus and slow wave activity in the neocortex. Linking the hippocampus and neocortex, the medial entorhinal cortex (mEC) regulates the coupling between these structures during SWS, alternating between silent Down states and active Up states, which outlast neocortical ones. Here, we investigated how low physiological concentrations of acetylcholine (ACh; 100-500nM) modulate Up and Down states in a mEC slice preparation. We find that ACh has a dual effect on mEC activity: it prolongs apparent Up state duration as recorded in individual cells and decreases the total synaptic charge transfer, without affecting the duration of detectable synaptic activity. The overall outcome of ACh application is excitatory and we show that ACh increases Up state incidence via muscarinic receptor activation. The mean firing rate of principal neurons increased in around half of the cells while the other half showed a decrease in firing rate. Using two-photon calcium imaging of population activity, we found that population-wide network events are more frequent and rhythmic during ACh and confirmed that ACh modulates cell participation in these network events, consistent with a role for cholinergic modulation in regulating information flow between the hippocampus and neocortex during SWS.

Layer 3 Pyramidal Cells in the Medial Entorhinal Cortex Orchestrate Up-Down States and Entrain the Deep Layers Differentially

Up-down states (UDS) are synchronous cortical events of neuronal activity during non-REM sleep. The medial entorhinal cortex (MEC) exhibits robust UDS during natural sleep and under anesthesia. However, little is known about the generation and propagation of UDS-related activity in the MEC. Here, we dissect the circuitry underlying UDS generation and propagation across layers in the MEC using both invivo and invitro approaches. We provide evidence that layer 3 (L3) MEC is crucial in the generation and maintenance of UDS in the MEC. Furthermore, we find that the two sublayers of the L5 MEC participate differentially during UDS. Our findings show that L5b, which receives hippocampal output, is strongly innervated by UDS activity originating in L3 MEC. Our data suggest that L5b acts as a coincidence detector during information transfer between the hippocampus and the cortex and thereby plays an important role in memory encoding and consolidation.

Ethanol Alters Variability, But Not Rate, of Firing in Medial Prefrontal Cortex Neurons of Awake-Behaving Rats.

The medial prefrontal cortex (mPFC) is a brain region involved in the evaluation and selection of motivationally relevant outcomes. Neural activity in mPFC is altered following acute ethanol (EtOH) use and, in rodent models, doses as low as 0.75g/kg yield cognitive deficits. Deficits in decision making following acute EtOH are thought to be mediated, at least in part, by decreases in mPFC firing rates (FRs). However, the data leading to this conclusion have been generated exclusively in anesthetized rodents. The present study characterizes the effects of acute EtOH injections on mPFC neural activity in awake-behaving rodents. Awake-behaving and anesthetized in vivo electrophysiological recordings were performed. We utilized 3 groups: the first received 2 saline injections, the second received a saline injection followed by 1.0g/kg EtOH, and the last received saline followed by 2g/kg EtOH. One week later, an anesthetized recording occurred where a saline injection was followed by an injection of 1.0g/kg EtOH. The anesthetized condition showed robust decreases in neural activity and differences in up-down states (UDS) dynamics. In the awake-behaving condition, FRs were grouped according to behavioral state: moving, not-moving, and sleep. The differences in median FRs were found for each treatment and behavioral state combination. A FR decrease was only found in the 2.0g/kg EtOH treatment during not-moving states. However, robust decreases in FR variability were found across behavioral state in both the 1.0 and 2.0g/kg EtOH treatment. Sleep was separately analyzed. EtOH modulated the UDS during sleep producing decreases in FRs. In conclusion, the changes in neural activity following EtOH administration in anesthetized animals are not conserved in awake-behaving animals. The most prominent difference following EtOH was a decrease in FR variability suggesting that acute EtOH may be affecting decision making via this mechanism.

Higher-Order Cumulants Drive Neuronal Activity Patterns, Inducing UP-DOWN States in Neural Populations.

A major challenge in neuroscience is to understand the role of the higher-order correlations structure of neuronal populations. The dichotomized Gaussian model (DG) generates spike trains by means of thresholding a multivariate Gaussian random variable. The DG inputs are Gaussian distributed, and thus have no interactions beyond the second order in their inputs; however, they can induce higher-order correlations in the outputs. We propose a combination of analytical and numerical techniques to estimate higher-order, above the second, cumulants of the firing probability distributions. Our findings show that a large amount of pairwise interactions in the inputs can induce the system into two possible regimes, one with low activity (“DOWN state”) and another one with high activity (“UP state”), and the appearance of these states is due to a combination between the third- and fourth-order cumulant. This could be part of a mechanism that would help the neural code to upgrade specific information about the stimuli, motivating us to examine the behavior of the critical fluctuations through the Binder cumulant close to the critical point. We show, using the Binder cumulant, that higher-order correlations in the outputs generate a critical neural system that portrays a second-order phase transition.

Stimulus-induced transitions between spike-wave discharges and spindles with the modulation of thalamic reticular nucleus.

It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations applied in the cortical neuronal populations can also initiate the SWD and slow-wave oscillation from the resting states under the typical inhibitory intensity from TRN to SRN. Then, we expand into a 3-compartment coupled thalamocortical model network in linear and circular structures, respectively, to explore the spatiotemporal evolutions of wave states in different compartments. The main results are: (i) for the open-ended model network, SWD induced by stimulus in the first compartment can be transformed into sleep-like slow UP-DOWN and spindle states as it propagates into the downstream compartments; (ii) for the close-ended model network, weak stimulations performed in the first compartment can result in the consistent experimentally observed spindle oscillations in all three compartments; in contrast, stronger periodic single-pulse stimulations applied in the first compartment can induce periodic transitions between SWD and spindle oscillations. Detailed investigations reveal that multi-attractor coexistence mechanism composed of SWD, spindles and background state underlies these state evolutions. What's more, in order to demonstrate the state evolution stability with respect to the topological structures of neural network, we further expand the 3-compartment coupled network into 10-compartment coupled one, with linear and circular structures, and nearest-neighbor (NN) coupled network as well as its realization of small-world (SW) topology via random rewiring, respectively. Interestingly, for the cases of linear and circular connetivities, qualitatively similar results were obtained in addition to the more irregularity of firings. However, SWD can be eventually transformed into the consistent low-amplitude oscillations for both NN and SW networks. In particular, SWD evolves into the slow spindling oscillations and background tonic oscillations within the NN and SW network, respectively. Our modeling and simulation studies highlight the effect of network topology in the evolutions of SWD and spindling oscillations, which provides new insights into the mechanisms of cortical seizures development.

Statistics of a neuron model driven by asymmetric colored noise.

Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

High-frequency Oscillations and Other Electrophysiological Biomarkers of Epilepsy: Underlying Mechanisms

In the normal mammalian brain, neuronal synchrony occurs on a spatial scale of submillimeters to centimeters and temporal scale of submilliseconds to seconds that is reflected in the occurrence of high-frequency oscillations, physiological sharp waves and slow wave sleep oscillations referred to as Up-Down states. In the epileptic brain, the well-studied pathologic counterparts to these physiological events are pathological high-frequency oscillations and interictal spikes that could be electrophysiological biomarkers of epilepsy. Establishing these abnormal events as biomarkers of epilepsy will largely depend on a better understanding of the mechanisms underlying their generation, which will not only help distinguish pathological from physiological events, but will also determine what roles these pathological events play in epileptogenesis and epileptogenicity. This article focuses on the properties and neuronal mechanisms supporting the generation of high-frequency oscillations and interictal spikes, and introduces a new phenomenon called Up-spikes.

Switching of cortical Up and Down states: reproduction of the Shu-Hasenstaub-McCormick experiment from a conductance-based model

Cortical Up- and Down states are a salient feature of mammalian slow wave sleep and contribute predominantely to the low frequency (1Hz and below) delta power of the scalp electroencephalogram (EEG).The role of slow wave oscillations recently raised considerable attention from the observation that slow waves can be electrically stimulated [1,2], and that after such a stimulation, a significant increase in memory consolidation can be observed [1]. Cortical slow-waves are comprised of collective depolarzation (Up) and polarization (Down) phases whereby Up phases show an increased firing rate reminescent of wakefulness [3] and Down states are characterized by comparatively silent levels of neural activity. A recent experimental demonstration of Up- and Down state switching under electrical stimulation was presented by Shu, Hasenstaub and McCormick [4]. Their experiment showed that in ferret brain slices an Up state could be triggered electrically, as well as that by stimulation during the Up state by an impulse of same polarity, the Up state could be terminated, and the dependence of the Up state duration depending on the impulse amplitude and the time difference between the two impulses was studied quantitatively. In a previous study, we have proposed a minimal model mimicking the interplay between recurrent excitation and inhibition controlled by slow adaptive currents [5]. By this quite generic model the experimental time-dependence already can be explained. While the model needs only few parameters, it does not explicitely model neural spiking and it is desired to computationally confirm the approach also from a conductance-based model. To study stimulation at this level of description, we follow the established conductance-based cortex model by Compte et al. [6] which incorporates a two-compartment (soma and dendrite) membrane potential within a Hodgkin-Huxley type formalism. We use a one-dimensional distance-dependent randomly connected betwork of 256 regular spiking (RS) and 64 fast-spiking (FS) interneurons, an increase of the size did not significantly affect the qualitative behavior. We observe self-generated Up-Down states in the absence of stimulation in accordance with the detailed study of [6]. If we apply low stimulus intensities, the Up state duration is trivially affected marginally, as in the experiment, and or high stimulus intensities a termination of Up states is enforced, giving two delimiting lines as in [4]. For intermediate stimulus intensites the termination of the Up state depends on the stimulus intensity such that the experiment is confirmed within the measurement precision in [4].

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals.

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.

Noradrenergic Neurons of the Locus Coeruleus Are Phase Locked to Cortical Up-Down States during Sleep

Nonrapid eye movement (NREM) sleep is characterized by periodic changes in cortical excitability that are reflected in the electroencephalography (EEG) as high-amplitude slow oscillations, indicative of cortical Up/Down states. These slow oscillations are thought to be involved in NREM sleep-dependent memory consolidation. Although the locus coeruleus (LC) noradrenergic system is known to play a role in off-line memory consolidation (that may occur during NREM sleep), cortico-coerulear interactions during NREM sleep have not yet been studied in detail. Here, we investigated the timing of LC spikes as a function of sleep-associated slow oscillations. Cortical EEG was monitored, along with activity of LC neurons recorded extracellularly, in nonanesthetized naturally sleeping rats. LC spike-triggered averaging of EEG, together with phase-locking analysis, revealed preferential firing of LC neurons along the ascending edge of the EEG slow oscillation, correlating with Down-to-Up state transition. LC neurons were locked best when spikes were shifted forward ∼50 ms in time with respect to the EEG slow oscillation. These results suggest that during NREM sleep, firing of LC neurons may contribute to the rising phase of the EEG slow wave by providing a neuromodulatory input that increases cortical excitability, thereby promoting plasticity within these circuits.

Investigating up-down states in a 2d network of pyramidal and interneurons.

Event Abstract Back to Event Investigating up-down states in a 2d network of pyramidal and interneurons. David C. Higgins1, 2*, Jens C. Claussen1, 3, Thomas Martinetz1 and Arne Weigenand1, 3 1 Univesität zu Lübeck, Institut für Neuro- und Bioinformatik, Germany 2 University of Lübeck, Graduate School for Computing in Medicine and Life Sciences, Germany 3 Universität zu Lübeck, SFB654 – “Plasticity and Sleep”, Germany A previous result from our group, based on an experiment by Shu, et al. [1], investigated generating up-states in a 1-dimensional conductance-based model of pyramidal and interneurons in ferret brain slices. Here we investigate simplifying the neuronal model and transferring it to a 2-dimensional network architecture with special emphasis on network effects which lead to up-down states.

Distinct Roles of GABAA and GABAB Receptors in Balancing and Terminating Persistent Cortical Activity

Cortical networks spontaneously fluctuate between persistently active Up states and quiescent Down states. The Up states are maintained by recurrent excitation within local circuits, and can be turned on and off by synaptic input. GABAergic inhibition is believed to be important for stabilizing such persistent activity by balancing the excitation, and could have an additional role in terminating the Up state. Here, we report that GABA(A) and GABA(B) receptor-mediated inhibition have distinct and complementary roles in balancing and terminating persistent activity. In a model of Up-Down states expressed in slices of rat entorhinal cortex, the GABA(A) receptor antagonist, gabazine (50-500 nM), concentration-dependently decreased Up state duration, eventually leading to epileptiform bursts. In contrast, the GABA(B) receptor antagonist, CGP55845 (50 nM to 1 microM), increased the duration of persistent network activity, and prevented stimulus-induced Down state transitions. These results suggest that while GABA(A) receptor-mediated inhibition is necessary for balancing persistent activity, activation of GABA(B) receptors contributes to terminating Up states.

Neuronal Shot Noise and Brownian 1/f2 Behavior in the Local Field Potential

We demonstrate that human electrophysiological recordings of the local field potential (LFP) from intracranial electrodes, acquired from a variety of cerebral regions, show a ubiquitous 1/f2 scaling within the power spectrum. We develop a quantitative model that treats the generation of these fields in an analogous way to that of electronic shot noise, and use this model to specifically address the cause of this 1/f2 Brownian noise. The model gives way to two analytically tractable solutions, both displaying Brownian noise: 1) uncorrelated cells that display sharp initial activity, whose extracellular fields slowly decay in time and 2) rapidly firing, temporally correlated cells that generate UP-DOWN states.

Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model.

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP–DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.

Differential responses of hippocampal subfields to cortical up–down states

The connectivity of the hippocampal trisynaptic circuit, formed by the dentate gyrus, the CA3 and the CA1 region, is well characterized anatomically and functionally in vitro. The functional connectivity of this circuit in vivo remains to be understood. Toward this goal, we investigated the influence of the spontaneous, synchronized oscillations in the neocortical local field potential, reflecting up-down states (UDS) of cortical neurons, on the hippocampus. We simultaneously measured the extracellular local field potential in association cortex and the membrane potential of identified hippocampal excitatory neurons in anesthetized mice. Dentate gyrus granule cells showed clear UDS modulation that was phase locked to cortical UDS with a short delay. In contrast, CA3 pyramidal neurons showed mixed UDS modulation, such that some cells were depolarized during the cortical up state and others were hyperpolarized. CA1 pyramidal neurons, located farther downstream, showed consistent UDS modulation, such that when the cortical and dentate gyrus neurons were depolarized, the CA1 pyramidal cells were hyperpolarized. These results demonstrate the differential functional connectivity between neocortex and hippocampal subfields during UDS oscillations.

Cortico-hippocampal interaction during up-down states and memory consolidation

Sleep may promote the transfer of memories from hippocampus to cortex. New work shows that experiences are replayed during sleep in both brain regions and that replay in these two areas occurs synchronously.