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It is generally considered that epilepsy and seizures are related to alteration in neuronal excitation/ inhibition balance. On the other hand, using an in vi‑ tro isolated guinea pig brain model of focal seizures, it has been shown that seizures start with strong firing of inhibitory interneurons, silence of principal cells, and a massive increase of extracellular potassium concentration. In order to investigate the link between ionic dynamics and experimentally observed seizure pattern, we developed a computational model of hippocampal network embedded in the extracellular space with realistic Na+ , K+ , Cl- , and Ca2+ dynamics, glial cells, and a diffusion mechanism. The model exhibits seizure‑like activity that is qualitatively similar to experimentally observed seizures in the isolated guinea pig brain. We show that, in the model, strong discharge of inhibitory interneurons leads to long lasting accumulation of extracellular potassium, which triggers and sustains abnormal discharges of the neuronal network, including ictal bursting. Using computational modeling, we also suggest novel antiepileptic therapies targeting potassium regulation systems.
Steady State Visual Evoked Potentials are the steady‑state responses elicited by flicker stimulation. Frequency of oscillation of these responses corresponds to the stimulus frequency and its harmonics. Since the very first reports on SSVEP in 1966, they have been commonly assumed to be stationary (i.e. steady-state) signals which power and other properties are stable over time. In our study we submitted human subjects to long term (i.e. 60 seconds) visual periodic stimulation. In most cases, the instantaneous power of SSVEP significantly evolved over time. Furthermore the temporal behavior of the response was strongly dependent on the stimulus frequency. The possible explanation and potential impact of these results is discussed.
BACKGROUND AND AIMS: Enhanced beta frequency activity (16–24 Hz) serves as a carrier for distributing attentional activation across the visual system. This work aims to characterize beta activity and its generators in the primary visual cortices (V1/2) by measuring the correlations between signals from different cortical locations. In general, the degree of synchronization between two neuronal sites results from interplay of driving sensory inputs, neuronal connectivity and the arousal state. In order to test the mechanisms that influence the high amplitude synchronized beta activity we compared cortical recordings of cats performing visual attentional task and the relevant computational model. METHODS: We recorded local field potentials from several sites of the cats’ V1/2 during stimulus-driven attentional task and measured their correlation strengths. We hypothesized that higher correlation indicated closer functional relation between given signal pair. In parallel we used network model comprising 16 domains representing cortical patches that included mutual lateral inhibitory connections. The model consisted of single compartment excitatory and inhibitory cells with extended Hodgkin-Huxley dynamics. These cells received two kinds of Poisson inputs, representing the bottom-up sensory input and top-down cortical modulation. RESULTS: The physiological recordings showed that correlation strength mostly decreased with higher amplitude beta signals except of few recording pairs, which increased their correlation coefficients close to one. Similar results could be obtained with the modeled network of cortical neurons receiving common sensory input via lateral inhibitory interneurons. CONCLUSIONS: Our modeling study explains the appearance of heterogeneous organization of cortical beta activity obtained in physiological experiments. The synchronized signals activated by common sensory input form patches of cortical mosaic, which are spatially contrasted by lateral inhibitory connections.
K-complexes - phenomena occurring in sleep EEG - pose severe challenges in terms of detection as well as finding their physiological origin. In this study, K-complexes (KCs) were evoked by auditory stimuli delivered during sleep. The use of evoked KCs enables testing the sleeping nervous system under good experimental control. This paradigm allowed us to adopt into the KC studies a method of signal analysis that provides time-frequency maps of statistically significant changes in signal energy density. Our results indicate that KCs and sleep spindles may be organized by a slow oscillation. Accordingly, KCs might be evoked only if the stimulus occurs in a certain phase of the slow oscillation. We also observed middle-latency evoked responses following auditory stimulation in the last sleep cycle. This effect was revealed only by the time-frequency maps and was not visible in standard averages.
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