Excitation and Inhibition Balance Underlying Epileptiform Activity

Dynamics and conditions for inhibitory synaptic current to induce bursting and spreading depolarization in pyramidal neurons

Enhanced activity of inhibitory neurons, which is often used to suppress behaviors of pyramidal neurons to treat brain diseases, whereas can enhance spiking to a mixed-mode bursting (MMB) in recent experiments on migraine and seizure. The MMB contains a phase with high level of membrane potential/extracellular potassium concentration ([K+]o), which can propagate to form spreading depolarization (SD) wave. Different from the common view that the MMB/SD is often induced by enhanced positive effect or [K+]o, in the present paper, dynamics and conditions for the uncommon MMB/SD evoked by enhanced inhibitory synaptic current are obtained in a theoretical model. Firstly, in addition to the well-known positive threshold across which the common MMB is induced by positive effect, a spiking pyramidal neuron exhibits a novel negative threshold with a low level of [K+]o for the MMB. A long and strong inhibitory stimulation suppresses the spiking to silence phase via a saddle-node bifurcation on an invariant circle at first and then run across the negative threshold, triggering positive feedback to enhance membrane potential and [K+]o to levels high enough, then resulting in the uncommon MMB. Secondly, in a coupling model, enhanced inhibitory effect for enhanced spiking activity of interneuron and conductance of inhibitory synapse, and enhanced spiking activity of pyramidal neuron, are favorable for the uncommon MMB. Then, reducing these activities or parameters present potential measures to prevent the MMB. Finally, in network model, the uncommon MMB of a pyramidal neuron can induce SD wave. The results present a novel theoretical explanation to the uncommon MMB/SD, counterintuitive function of the inhibitory interneuron, and potential measures to treat the diseases.

Robotic Fast Dual-Arm Patch Clamp System for Mechanosensitive Excitability Research of Neurons

OBJECTIVE

A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is proposed in this paper for mechanosensitive excitability research of neurons in brain slice.

METHODS

First, a kinematic model of a dual-arm patch clamp system combined with Monte Carlo method is developed to calculate the workspaces of recording micropipette and stimulation micropipette, and optimize the length of end effector for reducing collision incidences during operation. Then, a quantitative stimulation method to cells using one micropipette is developed based on pressing depth control. Finally, a fast robotic dual-arm patch clamp operation process is proposed based on a three-stage motion control of dual micropipettes to approach target cells and form whole-cell recording with quantitative mechanical stimulation.

RESULTS

Experimental results on 50 pyramidal neurons in the primary visual cortex of mouse brain slices demonstrate that this system achieves a threefold throughput with a 37% improvement in the success rate of the contact process and a 42% improvement in the success rate of whole-cell recording in comparison to manual operation. With these advantages, a mechanical stimulation-regulated increase in neuron excitability is observed in primary visual cortex. The experimental results also show that the sodium ion current may be more sensitive to mechanical stimulation than potassium ion current.

CONCLUSION

Our system significantly improves the efficiency of mechanical stimulation induced excitability research of neurons in brain slices.

SIGNIFICANCE

Our methods have the potential to investigate pathological and pathogenic mechanisms of mechanosensitive ion channel dysfunction-induced diseases in the future.

GABA-Induced Seizure-Like Events Caused by Multi-ionic Interactive Dynamics

Experimental evidence showed that an increase in intracellular chloride concentration [Formula: see text] caused by gamma-aminobutyric acid (GABA) input can promote epileptic firing activity, but the actual mechanisms remain elusive. Here in this theoretical work, we show that influx of chloride and concomitant bicarbonate ion [Formula: see text] efflux upon GABA receptor activation can induce epileptic firing activity by transition of GABA from inhibition to excitation. We analyzed the intrinsic property of neuron firing states as a function of [Formula: see text] We found that as [Formula: see text] increases, the system exhibits a saddle-node bifurcation, above which the neuron exhibits a spectrum of intensive firing, periodic bursting interrupted by depolarization block (DB) state, and eventually a stable DB through a Hopf bifurcation. We demonstrate that only GABA stimuli together with [Formula: see text] efflux can switch GABA's effect to excitation which leads to a series of seizure-like events (SLEs). Exposure to a low [Formula: see text] can drive neurons with high concentrations of [Formula: see text] downward to lower levels of [Formula: see text], during which it could also trigger SLEs depending on the exchange rate with the bath. Our analysis and simulation results show how the competition between GABA stimuli-induced accumulation of [Formula: see text] and [Formula: see text] application-induced decrease of [Formula: see text] regulates the neuron firing activity, which helps to understand the fundamental ionic dynamics of SLE.

Excitation/Inhibition balance relates to cognitive function and gene expression in temporal lobe epilepsy: a high density EEG assessment with aperiodic exponent

Patients with epilepsy are characterized by a dysregulation of excitation/inhibition balance (E/I). The assessment of E/I may inform clinicians during the diagnosis and therapy management, even though it is rarely performed. An accessible measure of the E/I of the brain represents a clinically relevant feature. Here, we exploited the exponent of the aperiodic component of the power spectrum of the electroencephalography (EEG) signal, as a non-invasive and cost-effective proxy of the E/I balance. We recorded resting-state activity with high-density EEG from 67 patients with temporal lobe epilepsy and 35 controls. We extracted the exponent of the aperiodic fit of the power spectrum from source-reconstructed EEG and tested differences between patients with epilepsy and controls. Spearman's correlation was performed between the exponent and clinical variables (age of onset, epilepsy duration and neuropsychology) and cortical expression of epilepsy-related genes derived from the Allen Human Brain Atlas. Patients with temporal lobe epilepsy showed a significantly larger exponent, corresponding to inhibition-directed E/I balance, in bilateral frontal and temporal regions. Lower E/I in the left entorhinal and bilateral dorsolateral prefrontal cortices corresponded to a lower performance of short-term verbal memory. Limited to patients with temporal lobe epilepsy, we detected a significant correlation between the exponent and the cortical expression of GABRA1, GRIN2A, GABRD, GABRG2, KCNA2 and PDYN genes. EEG aperiodic exponent maps the E/I balance non-invasively in patients with epilepsy and reveals a close relationship between altered E/I patterns, cognition and genetics.

Exploration of interictal to ictal transition in epileptic seizures using a neural mass model

An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09976-6.

Using Constrained Square-Root Cubature Kalman Filter for Quantifying the Severity of Epileptic Activities in Mice

(1) Background: Quantification of severity of epileptic activities, especially during electrical stimulation, is an unmet need for seizure control and evaluation of therapeutic efficacy. In this study, a parameter ratio derived from constrained square-root cubature Kalman filter (CSCKF) was formulated to quantify the excitability of local neural network and compared with three commonly used indicators, namely, band power, Teager energy operator, and sample entropy, to objectively determine their effectiveness in quantifying the severity of epileptiform discharges in mice. (2) Methods: A set of one normal and four types of epileptic EEGs was generated by a mathematical model. EEG data of epileptiform discharges during two types of electrical stimulation were recorded in 20 mice. Then, EEG segments of 5 s in length before, during and after the real and sham stimulation were collected. Both simulated and experimental data were used to compare the consistency and differences among the performance indicators. (3) Results: For the experimental data, the results of the four indicators were inconsistent during both types of electrical stimulation, although there was a trend that seizure severity changed with the indicators. For the simulated data, when the simulated EEG segments were used, the results of all four indicators were similar; however, this trend did not match the trend of excitability of the model network. In the model output which retained the DC component, except for the CSCKF parameter ratio, the results of the other three indicators were almost identical to those using the simulated EEG. For CSCKF, the parameter ratio faithfully reflected the excitability of the neural network. (4) Conclusion: For common EEG, CSCKF did not outperform other commonly used performance indicators. However, for EEG with a preserved DC component, CSCKF had the potential to quantify the excitability of the neural network and the associated severity of epileptiform discharges.

Therapeutic potential of cannabidivarin for epilepsy and autism spectrum disorder

Recent years have seen a renewed interest on the possible therapeutic exploitations of specific cannabinoids derived from the Cannabis sativa plant. Thus far, the most studied non-psychotomimetic cannabinoid is cannabidiol (CBD), which has shown promising therapeutic potential for relieving a variety of neurological diseases. However, also its propyl analogue, cannabidivarin (CBDV), has recently gained much attention as a potential therapeutic agent for the management of disabling neurological conditions. This review aims at providing a comprehensive and updated overview of the available animal and human data, which have investigated the possible therapeutic potential of CBDV for the management of epilepsy and autism spectrum disorder.

Glial Modulation of Electrical Rhythms in a Neuroglial Network Model of Epilepsy

OBJECTIVE

An important EEG-based biomarker for epilepsy is the phase-amplitude cross-frequency coupling (PAC) of electrical rhythms; however, the underlying pathways of these pathologic markers are not always clear. Since glial cells have been shown to play an active role in neuroglial networks, it is likely that some of these PAC markers are modulated via glial effects.

METHODS

We developed a 4-unit hybrid model of a neuroglial network, consisting of 16 sub-units, that combines a mechanistic representation of neurons with an oscillator-based Cognitive Rhythm Generator (CRG) representation of glial cells-astrocytes and microglia. The model output was compared with recorded generalized tonic-clonic patient data, both in terms of PAC features, and state classification using an unsupervised hidden Markov model (HMM).

RESULTS

The neuroglial model output showed PAC features similar to those observed in epileptic seizures. These generated PAC features were able to accurately identify spontaneous epileptiform discharges (SEDs) as seizure-like states, as well as a postictal-like state following the long-duration SED, when applied to the HMM machine learning algorithm trained on patient data. The evolution profile of the maximal PAC during the SED compared well with patient data, showing similar association with the duration of the postictal state.

CONCLUSION

The hybrid neuroglial network model was able to generate PAC features similar to those observed in ictal and postictal epileptic states, which has been used for state classification and postictal state duration prediction.

SIGNIFICANCE

Since PAC biomarkers are important for epilepsy research and postictal state duration has been linked with risk of sudden unexplained death in epilepsy, this model suggests glial synaptic effects as potential targets for further analysis and treatment.