Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure
Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.
Special issue of Epilepsia dedicated to the 31stInternational Epilepsy Congress Istanbul, Turkey 5th–9th September, 2015
33rd international Epilepsy Congress Bangkok, Thailand | 22–26 June 2019 Satellite Symposia Abstracts
In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30-100 Hz) range, coupled to a delta/theta frequency (1-8 Hz) neural oscillator. Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes.
Working memory (WM) requires selective information gating, active information maintenance, and rapid active updating. Hence performing a WM task needs rapid and controlled transitions between neural persistent activity and the resting state. We propose that changes in correlations in neural activity provides a mechanism for the required WM operations. As a proof of principle, we implement sustained activity and WM in recurrently coupled spiking networks with neurons receiving excitatory random background activity where background correlations are induced by a common noise source. We first characterize how the level of background correlations controls the stability of the persistent state. With sufficiently high correlations, the sustained state becomes practically unstable, so it cannot be initiated by a transient stimulus. We exploit this in WM models implementing the delay match to sample task by modulating flexibly in time the correlation level at different phases of the task. The modulation sets the network in different working regimes: more prompt to gate in a signal or clear the memory. We examine how the correlations affect the ability of the network to perform the task when distractors are present. We show that in a winner-take-all version of the model, where two populations cross-inhibit, correlations make the distractor blocking robust. In a version of the mode where no cross inhibition is present, we show that appropriate modulation of correlation levels is sufficient to also block the distractor access while leaving the relevant memory trace in tact. The findings presented in this manuscript can form the basis for a new paradigm about how correlations are flexibly controlled by the cortical circuits to execute WM operations.
Purpose: High frequency oscillations (HFO 80–500 Hz) are recognized as biomarkers for epileptogenic brain tissue to be resected in epilepsy surgery. While several studies describe group level statistics, assessment in the individual patient still needs further validation. Here we compare automated HFO analysis with routine pre- surgical evaluation.
Method: We analyzed long- term stereo EEG recordings of 9 epilepsy patients. For each patient, we identified at least 30 minutes of NREM sleep. HFO were detected by a fully automated algorithm and EEG contacts with the highest rate designated the HFO area. We quantified the stability of the HFO area over time and compared it with the seizure onset zone (SOZ).
Results: Focal SOZ was identified in 8/9 patients. The HFO area was included in the SOZ in 7/8 patients with mesial temporal, temporal and insular SOZ. In the one patient with multifocal seizures, the HFO analysis showed poor stability over time.
Conclusion: HFO could reliably identify focal SOZ in our cohort. Therefore, high stability of the HFO pattern is clinically relevant for epilepsy surgery.
Abstracts of the 12th European Congress on Epileptology
Cognitive effort leads to a seeming cacophony of brain oscillations. For example, during tasks engaging working memory (WM), specific oscillatory frequency bands modulate in space and time. Despite ample data correlating such modulation to task performance, a mechanistic explanation remains elusive. We propose that flexible control of neural oscillations provides a unified mechanism for the rapid and controlled transitions between the computational operations required by WM. We show in a spiking network model that modulating the input oscillation frequency sets the network in different operating modes: rapid memory access and load is enabled by the beta-gamma oscillations, maintaining a memory while ignoring distractors by the theta, rapid memory clearance by the alpha. The various frequency bands determine the dynamic gating regimes enabling the necessary operations for WM, whose succession explains the need for the complex oscillatory brain dynamics during effortful cognition.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.
This article describes the expierence of studying factors influencing the social well-being of educational migrants as mesured by means of a psychological well-being scale (A. Perrudet-Badoux, G.A. Mendelsohn, J.Chiche, 1988) previously adapted for Russian by M.V. Sokolova. A statistical analysis of the scale's reliability is performed. Trends in dynamics of subjective well-being are indentified on the basis the correlations analysis between the condbtbions of adaptation and its success rate, and potential mechanisms for developing subjective well-being among student migrants living in student hostels are described. Particular attention is paid to commuting as a factor of adaptation.