Gabriella Panuccio

Neurosteroids and epilepsy.

Purpose of reviewNeurosteroids are a family of compounds synthesized directly in the brain by transforming cholesterol into pregnenolone, which is then converted to compounds such as allopregnanolone and allotetrahydrodeoxycorticosterone. In view of their ability to modulate neurotransmission, neurosteroids may influence the clinical course of epileptic disorders. In this review, we highlight two emerging properties of neurosteroids, that is, their anticonvulsant and antiepileptogenic activities. Recent findingsIt has been shown that fluctuations in neurosteroid synthesis, such as those seen in response to stress or during the ovarian cycle, determine an increase in seizure threshold. Moreover, increased neurosteroid synthesis, presumably occurring in glial cells during epileptogenesis, delays the appearance of recurrent spontaneous seizures in an animal model of temporal lobe epilepsy; such an effect may be due to augmented tonic γ-aminobutyric acid type A receptor-mediated inhibition. Finally, clinical trials with ganaxolone, an allopregnanolone analogue, have demonstrated beneficial effects in pharmacoresistant epileptic patients, whereas finasteride – which interferes with neurosteroid synthesis – facilitates seizures in catamenial epilepsy. SummaryThe overall evidence suggests that neurosteroids may represent a novel therapeutic strategy in epileptic disorders and a future perspective to control epileptogenicity.

TREATING EPILEPSY VIA ADAPTIVE NEUROSTIMULATION: A REINFORCEMENT LEARNING APPROACH

This paper presents a new methodology for automatically learning an optimal neurostimulation strategy for the treatment of epilepsy. The technical challenge is to automatically modulate neurostimulation parameters, as a function of the observed EEG signal, so as to minimize the frequency and duration of seizures. The methodology leverages recent techniques from the machine learning literature, in particular the reinforcement learning paradigm, to formalize this optimization problem. We present an algorithm which is able to automatically learn an adaptive neurostimulation strategy directly from labeled training data acquired from animal brain tissues. Our results suggest that this methodology can be used to automatically find a stimulation strategy which effectively reduces the incidence of seizures, while also minimizing the amount of stimulation applied. This work highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders such as epilepsy.

Epileptiform synchronization in the cingulate cortex

Purpose:  The anterior cingulate cortex (ACC)—which plays a role in pain, emotions and behavior—can generate epileptic seizures. To date, little is known on the neuronal mechanisms leading to epileptiform synchronization in this structure. Therefore, we investigated the role of excitatory and inhibitory synaptic transmission in epileptiform activity in this cortical area. In addition, since the ACC presents with a high density of opioid receptors, we studied the effect of opioid agonism on epileptiform synchronization in this brain region.

On the ictogenic properties of the piriform cortex in vitro

Purpose:  The piriform cortex (PC) is known to be epileptic‐prone and it may be involved in the manifestation of limbic seizures. Herein, we have characterized some electrophysiologic and pharmacologic properties of the spontaneous epileptiform activity generated by PC networks maintained in vitro.

In vitro ictogenesis and parahippocampal networks in a rodent model of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a chronic epileptic disorder involving the hippocampal formation. Details on the interactions between the hippocampus proper and parahippocampal networks during ictogenesis remain, however, unclear. In addition, recent findings have shown that epileptic limbic networks maintained in vitro are paradoxically less responsive than non-epileptic control (NEC) tissue to application of the convulsant drug 4-aminopyridine (4AP). Field potential recordings allowed us to establish here the effects of 4AP in brain slices obtained from NEC and pilocarpine-treated epileptic rats; these slices included the hippocampus and parahippocampal areas such as entorhinal and perirhinal cortices and the amygdala. First, we found that both types of tissue generate epileptiform discharges with similar electrographic characteristics. Further investigation showed that generation of robust ictal-like discharges in the epileptic rat tissue is (i) favored by decreased hippocampal output (ii) reinforced by EC-subiculum interactions and (iii) predominantly driven by amygdala networks. We propose that a functional switch to alternative synaptic routes may promote network hyperexcitability in the epileptic limbic system.

Neurogenic Radial Glia-like Cells in Meninges Migrate and Differentiate into Functionally Integrated Neurons in the Neonatal Cortex

Whether new neurons are added in the postnatal cerebral cortex is still debated. Here, we report that the meninges of perinatal mice contain a population of neurogenic progenitors formed during embryonic development that migrate to the caudal cortex and differentiate into Satb2+ neurons in cortical layers II-IV. The resulting neurons are electrically functional and integrated into local microcircuits. Single-cell RNA sequencing identified meningeal cells with distinct transcriptome signatures characteristic of (1) neurogenic radial glia-like cells (resembling neural stem cells in the SVZ), (2) neuronal cells, and (3) a cell type with an intermediate phenotype, possibly representing radial glia-like meningeal cells differentiating to neuronal cells. Thus, we have identified a pool of embryonically derived radial glia-like cells present in the meninges that migrate and differentiate into functional neurons in the neonatal cerebral cortex.

Adaptive control of epileptiform excitability in an in vitro model of limbic seizures

Deep brain stimulation (DBS) is a promising tool for treating drug-resistant epileptic patients. Currently, the most common approach is fixed-frequency stimulation (periodic pacing) by means of stimulating devices that operate under open-loop control. However, a drawback of this DBS strategy is the impossibility of tailoring a personalized treatment, which also limits the optimization of the stimulating apparatus. Here, we propose a novel DBS methodology based on a closed-loop control strategy, developed by exploiting statistical machine learning techniques, in which stimulation parameters are adapted to the current neural activity thus allowing for seizure suppression that is fine-tuned on the individual scale (adaptive stimulation). By means of field potential recording from adult rat hippocampus-entorhinal cortex (EC) slices treated with the convulsant drug 4-aminopyridine we determined the effectiveness of this approach compared to low-frequency periodic pacing, and found that the closed-loop stimulation strategy: (i) has similar efficacy as low-frequency periodic pacing in suppressing ictal-like events but (ii) is more efficient than periodic pacing in that it requires less electrical pulses. We also provide evidence that the closed-loop stimulation strategy can alternatively be employed to tune the frequency of a periodic pacing strategy. Our findings indicate that the adaptive stimulation strategy may represent a novel, promising approach to DBS for individually-tailored epilepsy treatment.

Cell Type-Specific Properties of Subicular GABAergic Currents Shape Hippocampal Output Firing Mode

GABAergic function of the subiculum is central to the regulation of hippocampal output activity. Subicular neuronal networks are indeed under potent control by local inhibition. However, information about the properties of GABAergic currents generated by neurons of this parahippocampal area in normal tissue is still missing. Here, we describe GABAA receptor (GABAAR)-mediated phasic and tonic currents generated by principal cells (PCs) and interneurons (INs) of the rat subiculum. We show that in spite of similar synaptic current densities, INs generate spontaneous IPSCs (sIPSCs) that occur less frequently and exhibit smaller charge transfer, thus receiving less synaptic total current than PCs. Further distinction of PCs between intrinsically bursting (IB) and regular-spiking (RS) neurons suggested that sIPSCs generated by the two PC sub-types are likely to be similar. PCs and INs are also controlled by a similar tonic inhibition. However, whereas a comparable tonic current density is found in RS cells and INs, IB neurons are constrained by a greater inhibitory tone. Finally, pharmacological blockade of GABAAR did not promote functional switch of RS neurons to IB mode, but influenced the bursting propensity of IB cells and released fast spiking activity in INs. Our findings reveal differences in GABAergic currents between PCs and INs as well as within PC sub-types. We propose that GABAergic inhibition may shape hippocampal output activity by providing cell type-specific fine-tuning of subicular excitatory and inhibitory drives.

Involvement of inward rectifier and M-type currents in carbachol-induced epileptiform synchronization

Exposure to cholinergic agonists is a widely used paradigm to induce epileptogenesis in vivo and synchronous activity in brain slices maintained in vitro. However, the mechanisms underlying these effects remain unclear. Here, we used field potential recordings from the lateral entorhinal cortex in horizontal rat brain slices to explore whether two different K(+) currents regulated by muscarinic receptor activation, the inward rectifier (K(IR)) and the M-type (K(M)) currents, have a role in carbachol (CCh)-induced field activity, a prototypical model of cholinergic-dependent epileptiform synchronization. To establish whether K(IR) or K(M) blockade could replicate CCh effects, we exposed slices to blockers of these currents in the absence of CCh. K(IR) channel blockade with micromolar Ba(2+) concentrations induced interictal-like events with duration and frequency that were lower than those observed with CCh; by contrast, the K(M) blocker linopirdine was ineffective. Pre-treatment with Ba(2+) or linopirdine increased the duration of epileptiform discharges induced by subsequent application of CCh. Baclofen, a GABA(B) receptor agonist that activates K(IR), abolished CCh-induced field oscillations, an effect that was abrogated by the GABA(B) receptor antagonist CGP 55845, and prevented by Ba(2+). Finally, when applied after CCh, the K(M) activators flupirtine and retigabine shifted leftward the cumulative distribution of CCh-induced event duration; this effect was opposite to what seen during linopirdine application under similar experimental conditions. Overall, our findings suggest that K(IR) rather than K(M) plays a major regulatory role in controlling CCh-induced epileptiform synchronization.

Evidence-based modeling of network discharge dynamics during periodic pacing to control epileptiform activity

Deep brain stimulation (DBS) is a promising therapeutic approach for epilepsy treatment. Recently, research has focused on the implementation of stimulation protocols that would adapt to the patients need (adaptive stimulation) and deliver electrical stimuli only when it is most useful. A formal mathematical description of the effects of electrical stimulation on neuronal networks is a prerequisite for the development of adaptive DBS algorithms. Using tools from non-linear dynamic analysis, we describe an evidence-based, mathematical modeling approach that (1) accurately simulates epileptiform activity at time-scales of single and multiple ictal discharges, (2) simulates modulation of neural dynamics during epileptiform activity in response to fixed, low-frequency electrical stimulation, (3) defines a mapping from real-world observations to model state, and (4) defines a mapping from model state to real-world observations. We validate the real-world utility of the models properties by statistical comparison between the number, duration, and interval of ictal-like discharges observed in vitro and those simulated in silica under conditions of repeated stimuli at fixed-frequency. These validation results confirm that the evidence-based modeling approach captures robust, informative features of neural network dynamics of in vitro epileptiform activity under periodic pacing and support its use for further implementation of adaptive DBS protocols for epilepsy treatment.