Distinct hyperexcitability mechanisms underlie fast ripples and epileptic spikes.

OBJECTIVE: In partial epilepsies, interictal epileptic spikes (IESs) and fast ripples (FRs) represent clinically relevant biomarkers characteristic of epileptogenic networks. However, their specific significance and the pathophysiological changes leading to either FRs or IESs remain elusive. The objective of this study was to analyze the conditions in which hyperexcitable networks can generate either IESs or FRs and to reveal shared or distinct mechanisms that underlie both types of events. METHODS: This study is the first to comparatively analyze mechanisms that induce either IESs or FRs using an approach that combines computational modeling and experimental data (in vivo and in vitro). A detailed CA1 hippocampal network model is introduced. A parameter sensitivity analysis was conducted to determine which model parameters (cell related and network related) allow the most accurate simulation of FRs and IESs. RESULTS: Our model indicates that although FRs and IESs share certain common mechanisms (shifted gamma-aminobutyric acid [GABA]A reversal potential, altered synaptic transmission), there are also critical differences in terms of number of pyramidal cells involved (small vs large), spatial distribution of hyperexcitable pyramidal cells (clustered vs uniform), and firing patterns (weakly vs highly synchronized). In vitro experiments verified that subtle changes in GABAergic and glutamatergic transmission favor either FRs or IESs, as predicted by the model. INTERPRETATION: This study provides insights into the interpretation of 2 interictal markers observed in intracerebral electroencephalographic data. Depending on the degree and spatiotemporal features of hyperexcitability, not only IESs or FRs are generated but also transitions between both types of events.

Time-domain features of epileptic spikes as potential bio-markers of the epileptogenesis process.

Epilepsy is a neurological disorder characterized by recurrent seizures which affects about 1% people worldwide. During the past decades, some mechanisms involved in ictogenesis (generation of seizures) have been identified and, to some extent, partially understood. However, regarding epileptogenesis (process by which a neuronal system becomes epileptic), underlying mechanisms remain elusive. This difficulty is mostly related to the fact that epileptogenesis can only be addressed using experimental models. In this study, we have analyzed the shape of a specific electrophysiological pattern, referred to as "epileptic spike", encountered during the epileptogenesis process in an in vivo model of temporal lobe epilepsy (mouse, kainate). Results show that the features of these transient events (duration and amplitude) change as a function of time as the brain evolves towards the chronic epileptic state characterized by the appearance of spontaneous seizures. Using a detailed computational model of the hippocampus (CA1 sub-field), an interpretation of observed modifications is provided, in relationship with possible alterations that take place in underlying neuronal circuits.

Analysis of intracerebral EEG recordings of epileptic spikes: insights from a neural network model.

The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on "minimal" but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number ( approximately 3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component ("spike'') and the late slow component ("negative wave'') was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network.