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Topological approaches for seizure abatement have received scarce attention. The ability to reset the phase of biological oscillations has been widely exploited in cardiology, as evidenced in part by the usefulness of implantable of defibrillators, but not in epileptology. The aim of this work is to investigate the feasibility of seizure blockage using single or brief monophasic (DC) pulse trains.

Single DC or brief (0.1 s) pulse trains were delivered manually or automatically to generalized seizures, induced in rats with the convulsant 3-mercaptoprionic acid, a GABA inhibitor. Treatment outcome (blocked vs. not blocked seizures) was ascertained visually and correlated with the "rhythmicity index", an indirect estimate of neuronal synchrony level.

Blockage using single or brief (0.1 s) DC pulses was consistently achieved for seizures with a rhythmicity index > 0.6, while seizures with levels <0.6 were not, although transient phase changes in their oscillations were effected.

This work reveals that level of neuronal synchronization may be an important factor in determining the probability of seizure blockage. Seizure blockage using single or brief DC pulse trains and its effects on neural tissue merit further investigation. The clinical applicability of this therapeutic modality and means to enhance it are discussed.

Transcranial magnetic stimulation was used to study the effect of recurrent seizures on cortical excitability over time in epilepsy. 77 patients with firm diagnoses of idiopathic generalized epilepsy (IGE) or focal epilepsy were repeatedly evaluated over three years. At onset, all groups had increased cortical excitability. At the end of follow-up the refractory group was associated with a broad increase in cortical excitability. Conversely, cortical excitability decreased in all seizure free groups after introduction of an effective medication.

High frequency oscillations (HFO) appear to be a promising marker for delineating the seizure onset zone (SOZ) in patients with localization related epilepsy. It remains, however, a purely observational phenomenon and no common mechanism has been proposed to relate HFOs and seizure generation. In this work we show that a cascade of two computational models, one on detailed compartmental scale and a second one on neural mass scale can explain both the autonomous generation of HFOs and the presence of epileptic seizures as emergent properties. To this end we introduce axonal–axonal gap junctions on a microscopic level and explore their impact on the higher level neural mass model (NMM). We show that the addition of gap junctions can generate HFOs and simultaneously shift the operational point of the NMM from a steady state network into bistable behavior that can autonomously generate epileptic seizures. The epileptic properties of the system, or the probability to generate epileptic type of activity, increases gradually with the increase of the density of axonal–axonal gap junctions. We further demonstrate that ad hoc HFO detectors used in previous studies are applicable to our simulated data.

Seizure activity leads to increases in extracellular potassium concentration ([K${}^{+}$]o), which can result in changes in neuronal passive and active membrane properties as well as in population activities. In this study, we examined how extracellular potassium modulates seizure activities using an acute 4-AP induced seizure model in the neocortex, both in vivo and in vitro. Moderately elevated [K${}^{+}$]o up to 9mM prolonged seizure durations and shortened interictal intervals as well as depolarized the neuronal resting membrane potential (RMP). However, when [K${}^{+}$]o reached higher than 9mM, seizure like events (SLEs) were blocked and neurons went into a depolarization-blocked state. Spreading depression was never observed as the blockade of ictal events could be reversed within 1–2min after the raised [K${}^{+}$]o was changed back to control levels. This concentration-dependent dual effect of [K${}^{+}$]o was observed using in vivo and in vitro mouse brain preparations as well as in human neocortical tissue resected during epilepsy surgery. Blocking the Ih current, mediated by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, modulated the elevated [K${}^{+}$]o influence on SLEs by promoting the high [K${}^{+}$]o inhibitory actions. These results demonstrate biphasic actions of raised [K${}^{+}$]o on neuronal excitability and seizure activity.

Complex dynamical systems may exhibit sudden autonomous changes from one state to another. Such changes that occur rapidly in comparison to the regular dynamics have been termed critical transitions. Examples of such phenomena can be found in many complex systems: changes in climate and ocean circulation, changes in wildlife populations, financial crashes, as well as in medical conditions like asthma attacks and depression. It has been recognized that critical transitions, even if they arise in completely different contexts and situations, share several common attributes and also generic early-warning signals that indicate that a critical transition is approaching. In the present study, we review briefly the general characteristics that have been observed in systems prior to critical transitions and apply these general indicators to nearly 300 epileptic seizures collected from human subjects using invasive EEG. Only in about 8% of the patients was evidence of critical transitions found. In the remaining majority of cases no early warning signals that behaved consistently prior to seizures were observed. These results do not rule out the possibility of critical transitions to seizure but point to limited relevance of their early warning signals in the context of human epilepsy observed using intracranial EEG recordings.

Traditionally, it is considered that neuronal synchronization in epilepsy is caused by a chain reaction of synaptic excitation. However, it has been shown that synchronous epileptiform activity may also arise without synaptic transmission. In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of ${\text{Na}}^{+}$, ${\text{K}}^{+}$, ${\text{Ca}}^{2+}$ and ${\text{Cl}}^{-}$ ions, glial uptake and extracellular diffusion mechanisms. We show that the network behavior with fixed ionic concentrations may be quite different from the neurons’ behavior when more detailed modeling of ionic dynamics is included. In particular, we show that in the extended model strong discharge of inhibitory interneurons may result in long lasting accumulation of extracellular ${\text{K}}^{+}$, which sustains the depolarization of the principal cells and causes their pathological discharges. This effect is not present in a reduced, purely synaptic network. These results point to the importance of nonsynaptic mechanisms in the transition to seizure.

This study examined hyperbaric oxygen seizures (seizures induced by breathing pure oxygen under pressure). In addition to the subject's importance to military diving, this preparation is of more generic interest because it provides an experimental model of chemically induced seizures that are generalized at onset. The object of this preliminary study was to find noise-robust numerical procedures that can identify the time of seizure onset. Several candidate methods were compared. They included a high order FIR filter, wavelet denoising and computation of the Hurst exponent. In these calculations, the original signal was corrupted with progressively larger amplitude additive noise. All three methods successfully identified seizure onset to an SNR of -10 dB. Of the methods considered, only the Hurst exponent was able to find the seizure when the SNR dropped to -20 dB.

How seizures are initiated remains far from clear. Understanding the dynamics of neuronal networks, which lead to transition to seizure, and identifying reliable markers for this process remains an area of active research. Recent studies demonstrated that seizures can be preceded by detectable changes in cellular and network dynamics. These dynamical features resemble a process of critical slowing which is characterized by an increase variance in time series, shift to lower frequencies, increased skewness etc. Additionally, it is accompanied by a progressive decrease of neuronal network resilience, which manifests as enhanced sensitivity to external or internal perturbations. Transition to seizure occurs during a very unstable state of network dynamics when even very weak perturbations are capable of tipping the dynamical regime of the network to seizure. Demonstrating that the dynamics of epileptic neuronal networks are governed by similar principles to other dynamical systems opens new ways to design better methods to examine network dynamical state and to control and/or reverse the transition to seizure.

The evaluation, standardization, and reproducibility of studies in the field of seizure prediction has always been hampered by the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the European Epilepsy database EPILEPSIAE with long-term EEG recordings of 275 patients, annotated by EEG experts and supplemented with extensive meta-data. Since the first 60 datasets have been made available in 2012, we illustrate the content and structure of the database that will affect current standards in the field of prediction and facilitate reproducibility and comparison of studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and related topics including computational neuroscience.