Stiripentol, a putative antiepileptic drug, enhances the duration of opening of GABAa-receptor channels

Summary: Purpose: Stiripentol (STP) is currently an efficient drug for add-on therapy in infantile epilepsies because it improves the efficacy of antiepileptic drugs (AEDs) through its potent inhibition of liver cytochromes P450. In addition, STP directly reduces seizures in several animal models of epilepsy, suggesting that it might also have anticonvulsive effects of its own. However, its underlying mechanisms of action are unknown. Methods: We examined the interactions of STP with ?-aminobutyric acid (GABA) transmission by using patch-clamp methods in CA3 pyramidal neurons in the neonatal rat. Results: STP markedly increased miniature inhibitory postsynaptic current (mIPSC) decay-time constant in a concentration-dependent manner. The prolongation of mIPSC duration does not result from an interaction with GABA transporters because it persisted in the presence of GAT-1 inhibitors (SKF-89976A and NO-711). An interaction with benzodiazepine or neurosteroid binding sites also was excluded because STP-mediated increase of decay time was still observed when these sites were initially saturated (by clobazam, zolpidem, or pregnanolone) or blocked (by flumazenil or dehydroepiandrosterone sulfate), respectively. In contrast, saturating barbiturate sites with pentobarbital clearly occluded this effect of STP, suggesting that STP and barbiturates interact at the same locus. This was directly confirmed by using outside-out patches, because STP increased the duration and not the frequency of opening of GABAA channels. Conclusions: At clinically relevant concentrations, STP enhances central GABA transmission through a barbiturate-like effect, suggesting that STP should possess an antiepileptic effect by itself

Perturbed Information Processing Complexity in Experimental Epilepsy

International audienceComorbidities, such as cognitive deficits, which often accompany epilepsies, constitute a basal state, while seizures are rare and transient events. This suggests that neural dynamics, in particular those supporting cognitive function, are altered in a permanent manner in epilepsy. Here, we test the hypothesis that primitive processes of information processing at the core of cognitive function (i.e., storage and sharing of information) are altered in the hippocampus and the entorhinal cortex in experimental epilepsy in adult, male Wistar rats. We find that information storage and sharing are organized into substates across the stereotypic states of slow and theta oscillations in both epilepsy and control conditions. However, their internal composition and organization through time are disrupted in epilepsy, partially losing brain state selectivity compared with controls, and shifting toward a regimen of disorder. We propose that the alteration of information processing at this algorithmic level of computation, the theoretical intermediate level between structure and function, may be a mechanism behind the emergent and widespread comorbidities associated with epilepsy, and perhaps other disorders. SIGNIFICANCE STATEMENT Comorbidities, such as cognitive deficits, which often accompany epilepsies, constitute a basal state, while seizures are rare and transient events. This suggests that neural dynamics, in particular those supporting cognitive function, are altered in a permanent manner in epilepsy. Here, we show that basic processes of information processing at the core of cognitive function (i.e., storage and sharing of information) are altered in the hippocampus and the entorhinal cortex (two regions involved in memory processes) in experimental epilepsy. Such disruption of information processing at the algorithmic level itself could underlie the general performance impairments in epilepsy

A systematic framework for functional connectivity measures

International audienceVarious methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures—based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets

A systematic framework for functional connectivity measures

Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures – based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets

Interneurons contribute to the hemodynamic/metabolic response to epileptiform discharges

International audienceInterpretation of hemodynamic responses in epilepsy is hampered by an incomplete understanding of the underlying neurovascular coupling, especially the contributions of excitation and inhibition. We made simultaneous multimodal recordings of local field potentials (LFPs), firing of individual neurons, blood flow, and oxygen level in the somatosensory cortex of anesthetized rats. Epileptiform discharges induced by bicuculline injections were used to trigger large local events. LFP and blood flow were robustly coupled, as were LFP and tissue oxygen. In a parametric linear model, LFP and the baseline activities of cerebral blood flow and tissue partial oxygen tension contributed significantly to blood flow and oxygen responses. In an analysis of recordings from 402 neurons, blood flow/tissue oxygen correlated with the discharge of putative interneurons but not of principal cells. Our results show that interneuron activity is important in the vascular and metabolic responses during epileptiform discharges