Bursting reverberation as a multiscale neuronal network process driven by synaptic depression-facilitation

Astroglial gap junctions strengthen hippocampal network activity by sustaining afterhyperpolarization via KCNQ channels

Throughout the brain, astrocytes form networks mediated by gap junction channels that promote the activity of neuronal ensembles. Although their inputs on neuronal information processing are well established, how molecular gap junction channels shape neuronal network patterns remains unclear. Here, using astroglial connexin-deficient mice, in which astrocytes are disconnected and neuronal bursting patterns are abnormal, we show that astrocyte networks strengthen bursting activity via dynamic regulation of extracellular potassium levels, independently of glutamate homeostasis or metabolic support. Using a facilitation-depression model, we identify neuronal afterhyperpolarization as the key parameter underlying bursting pattern regulation by extracellular potassium in mice with disconnected astrocytes. We confirm this prediction experimentally and reveal that astroglial network control of extracellular potassium sustains neuronal afterhyperpolarization via KCNQ voltage-gated K+ channels. Altogether, these data delineate how astroglial gap junctions mechanistically strengthen neuronal population bursts and point to approaches for controlling aberrant activity in neurological diseases.

Calcium-Dependent Hyperexcitability in Human Stem Cell-Derived Rett Syndrome Neuronal Networks

Background

Mutations in MECP2 predominantly cause Rett syndrome and can be modeled in vitro using human stem cell-derived neurons. Patients with Rett syndrome have signs of cortical hyperexcitability, such as seizures. Human stem cell-derived MECP2 null excitatory neurons have smaller soma size and reduced synaptic connectivity but are also hyperexcitable due to higher input resistance. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of network bursts consistent with a hypoconnectivity phenotype. Here, we examine this issue.

Methods

We reanalyzed multielectrode array data from 3 isogenic MECP2 cell line pairs recorded over 6 weeks (n = 144). We used a custom burst detection algorithm to analyze network events and isolated a phenomenon that we termed reverberating super bursts (RSBs). To probe potential mechanisms of RSBs, we conducted pharmacological manipulations using bicuculline, EGTA-AM, and DMSO on 1 cell line (n = 34).

Results

RSBs, often misidentified as single long-duration bursts, consisted of a large-amplitude initial burst followed by several high-frequency, low-amplitude minibursts. Our analysis revealed that MECP2 null networks exhibited increased frequency of RSBs, which produced increased bursts compared with isogenic controls. Bicuculline or DMSO treatment did not affect RSBs. EGTA-AM selectively eliminated RSBs and rescued network burst dynamics.

Conclusions

During early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hypersynchronic states that potentially translate into seizures. Network hyperexcitability depends on asynchronous neurotransmitter release that is likely driven by presynaptic Ca2+ and can be rescued by EGTA-AM to restore typical network dynamics.

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.

Emergence and fragmentation of the alpha-band driven by neuronal network dynamics

Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the α-band and to the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the α-band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable α-band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing α and δ rhythms during anesthesia, we model the interaction of two excitatory networks with one inhibitory network, showing that this minimal topology underlies the generation of a persistent α-band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the α-band appears when external inputs are suppressed, while fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, α-oscillations could result from the synaptic dynamics of interacting excitatory neuronal networks with and without AHP, a principle that could apply to other rhythms.

Brain oscillations, recorded from electroencephalograms characterize behaviors such as sleep, wakefulness, brain evoked responses, coma or anesthesia. The underlying rhythms for these oscillations are associated at a neuronal population level to fluctuations of the membrane potential between Up (depolarized) and Down (hyperpolarized) states. During anesthesia with propofol, a dominant α-band (8–12Hz) can emerge or disappear, but the underlying mechanism remains unclear. Using modeling, we report that the α-band appears during Up states in neuronal populations driven by short-term synaptic plasticity and synaptic noise. Moreover, we show that three connected neuronal networks representing the thalamocortical loop reproduce the dynamics of the α-band, which emerges following the arrest of excitatory stimulations, but that can disappear by increasing inhibitory inputs. To conclude, short-term plasticity in well connected neuronal networks can explain the emergence and fragmentation of the α-band.

Role of the locus coeruleus in the emergence of power law wake bouts in a model of the brainstem sleep-wake system through early infancy

Infant rats randomly cycle between the sleeping and waking states, which are tightly correlated with the activity of mutually inhibitory brainstem sleep and wake populations. Bouts of sleep and wakefulness are random; from P2-P10, sleep and wake bout lengths are exponentially distributed with increasing means, while during P10-P21, the sleep bout distribution remains exponential while the distribution of wake bouts gradually transforms to power law. The locus coeruleus (LC), via an undeciphered interaction with sleep and wake populations, has been shown experimentally to be responsible for the exponential to power law transition. Concurrently during P10-P21, the LC undergoes striking physiological changes - the LC exhibits strong global 0.3 Hz oscillations up to P10, but the oscillation frequency gradually rises and synchrony diminishes from P10-P21, with oscillations and synchrony vanishing at P21 and beyond. In this work, we construct a biologically plausible Wilson Cowan-style model consisting of the LC along with sleep and wake populations. We show that external noise and strong reciprocal inhibition can lead to switching between sleep and wake populations and exponentially distributed sleep and wake bout durations as during P2-P10, with the parameters of inhibition between the sleep and wake populations controlling mean bout lengths. Furthermore, we show that the changing physiology of the LC from P10-P21, coupled with reciprocal excitation between the LC and wake population, can explain the shift from exponential to power law of the wake bout distribution. To our knowledge, this is the first study that proposes a plausible biological mechanism, which incorporates the known changing physiology of the LC, for tying the developing sleep-wake circuit and its interaction with the LC to the transformation of sleep and wake bout dynamics from P2-P21.

Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states

The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.