GABAergic compensation in connexin36 knock-out mice evident during low-magnesium seizure-like event activity

Gap junctions within the cerebral cortex may facilitate cortical seizure formation by their ability to synchronize electrical activity. To investigate this, one option is to compare wild-type (WT) animals with those lacking the gene for connexin36 (Cx36 KO); the protein that forms neuronal gap junctions between cortical inhibitory cells. However, genetically modified knock-out animals may exhibit compensatory effects; with the risk that observed differences between WT and Cx36 KO animals could be erroneously attributed to Cx36 gap junction effects. In this study we investigated the effect of GABAA-receptor modulation (augmentation with 16 μM etomidate and blockade with 100 μM picrotoxin) on low-magnesium seizure-like events (SLEs) in mouse cortical slices. In WT slices, picrotoxin enhanced both the amplitude (49% increase, p = 0.0006) and frequency (37% increase, p = 0.005) of SLEs; etomidate also enhanced SLE amplitude (18% increase, p = 0.003) but reduced event frequency (25% decrease, p < 0.0001). In Cx36 KO slices, the frequency effects of etomidate and picrotoxin were preserved, but the amplitude responses were abolished. Pre-treatment with the gap junction blocker mefloquin in WT slices did not significantly alter the drug responses, indicating that the reduction in amplitude seen in the Cx36 KO mice was not primarily mediated by their lack of interneuronal gap junctions, but was rather due to pre-existing compensatory changes in these animals. Conclusions from studies comparing seizure characteristics between WT and Cx36 KO mice must be viewed with a degree of caution because of the possible confounding effect of compensatory neurophysiological changes in the genetically modified animals

A continuum model for the dynamics of the phase transition from slow-wave sleep to REM sleep

Previous studies have shown that activated cortical states (awake and rapid eye-movement (REM) sleep), are associated with increased cholinergic input into the cerebral cortex. However, the mechanisms that underlie the detailed dynamics of the cortical transition from slow-wave to REM sleep have not been quantitatively modeled. How does the sequence of abrupt changes in the cortical dynamics (as detected in the electrocorticogram) result from the more gradual change in subcortical cholinergic input? We compare the output from a continuum model of cortical neuronal dynamics with experimentally-derived rat electrocorticogram data. The output from the computer model was consistent with experimental observations. In slow-wave sleep, 0.5–2-Hz oscillations arise from the cortex jumping between “up” and “down” states on the stationary-state manifold. As cholinergic input increases, the upper state undergoes a bifurcation to an 8-Hz oscillation. The coexistence of both oscillations is similar to that found in the intermediate stage of sleep of the rat. Further cholinergic input moves the trajectory to a point where the lower part of the manifold in not available, and thus the slow oscillation abruptly ceases (REM sleep). The model provides a natural basis to explain neuromodulator-induced changes in cortical activity, and indicates that a cortical phase change, rather than a brainstem “flip-flop”, may describe the transition from slow-wave sleep to REM

What can a mean-field model tell us about the dynamics of the cortex?

In this chapter we examine the dynamical behavior of a spatially homogeneous two-dimensional model of the cortex that incorporates membrane potential, synaptic flux rates and long- and short-range synaptic input, in two spatial dimensions, using parameter sets broadly realistic of humans and rats. When synaptic dynamics are included, the steady states may not be stable. The bifurcation structure for the spatially symmetric case is explored, identifying the positions of saddle–node and sub- and supercritical Hopf instabilities. We go beyond consideration of small-amplitude perturbations to look at nonlinear dynamics. Spatially-symmetric (breathing mode) limit cycles are described, as well as the response to spatially-localized impulses. When close to Hopf and saddle–node bifurcations, such impulses can cause traveling waves with similarities to the slow oscillation of slow-wave sleep. Spiral waves can also be induced. We compare model dynamics with the known behavior of the cortex during natural and anesthetic-induced sleep, commenting on the physiological significance of the limit cycles and impulse responses

Stress-Particle Smoothed Particle Hydrodynamics: an application to the failure and post-failure behaviour of slopes

We present a new numerical approach in the framework of Smooth Particle Hydrodynamics (SPH) to solve the zero energy modes and tensile instabilities, without the need for the fine tuning of non-physical artificial parameters. The method uses a combination of stress-points and nodes and includes a new stress-point position updating scheme that also removes the need to implement artificial repulsive forces at the boundary. The model is validated for large deformation geomechanics problems, and is able to simulate strain localisation within soil samples and slopes. In particular, the new model produces stable and accurate results of the failure and post-failure of slopes, consisting of both cohesive and cohesionless materials, for the first time

Entropies of the EEG: The effects of general anaesthesia

The aim of this paper was to compare the performance of different entropy estimators when applied to EEG data taken from patients during routine induction of general anesthesia. The question then arose as to how and why different EEG patterns could affect the different estimators. Therefore we also compared how the different entropy estimators responded to artificially generated signals with predetermined, known, characteristics. This was done by applying the entropy algorithms to pseudoEEG data: (1) computer-generated using a second-order autoregressive (AR2) model, (2) computer-generated white noise added to step signals simulating blink and eyemovement artifacts and, (3) seeing the effect of exogenous (computer-generated) sine-wave oscillations added to the actual clinically-derived EEG data set from patients undergoing induction of anesthesia

Connexin36 knockout mice display increased sensitivity to pentylenetetrazol-induced seizure-like behaviors

Large-scale synchronous firing of neurons during seizures is modulated by electrotonic coupling between neurons via gap junctions. To explore roles for connexin36 (Cx36) gap junctions in seizures, we examined the seizure threshold of connexin36 knockout (Cx36KO) mice using a pentylenetetrazol (PTZ) model

Characteristics of Evoked Potential Multiple EEG Recordings in Patients with Chronic Pain by Means of Parallel Factor Analysis

This paper presents an alternative method, called as parallel factor analysis (PARAFAC) with a continuous wavelet transform, to analyze of brain activity in patients with chronic pain in the time-frequency-channel domain and quantifies differences between chronic pain patients and controls in these domains. The event related multiple EEG recordings of the chronic pain patients and non-pain controls with somatosensory stimuli (pain, random pain, touch, random touch) are analyzed. Multiple linear regression (MLR) is applied to describe the effects of aging on the frequency response differences between patients and controls. The results show that the somatosensory cortical responses occurred around 250 ms in both groups. In the frequency domain, the neural response frequency in the pain group (around 4 Hz) was less than that in the control group (around 5.5 Hz) under the somatosensory stimuli. In the channel domain, cortical activation was predominant in the frontal region for the chronic pain group and in the central region for controls. The indices of active ratios were statistical significant between the two groups in the frontal and central regions. These findings demonstrate that the PARAFAC is an interesting method to understanding the pathophysiological characteristics of chronic pain

The utility of single nucleotide DNA variations as predictors of postoperative pain

Objectives: Genetic variation is an important contributor to postsurgical pain and thereby analgesia requirements. A description of the potential predictive power of genetic variants in pain should instruct improvements in pain management postoperatively. We set out to examine whether a set of genetic variants in pain related genes would show any association with actual pain outcomes in a typical surgical population. Methods: A candidate gene study was carried out in 135 surgical patients with 12 DNA variants (single nucleotide polymorphisms or ‘SNPs’) in known or putative pain pathway genes to detect associations with postoperative pain - measured by a verbal rating score (VRS) and patient-controlled analgesia (PCA) usage rate. Standard PCR based molecular biology approaches were used. Results: At 20-24h after surgery, patients with the 1032G/1032G variant pair for the A1032G variant of the potassium channel KCNJ6 gene had a slightly higher median VRS than those with 1032A/1032A or 1032A/1032G pairs (p=0.04; dominant genetic model). This small difference was most apparent in the orthopaedic surgery patients where the 1032G/1032G pair associated with VRS (median(interquartile range)) of 5(4-6) vs. 3(0.5-4) in 1032A/1032A or 1032A/1032G groups. For PCA, patients with 3435C/3435C or 3435C/3435T pairs for ATPdependent efflux pump gene ABCB1 variant C3435T used PCA at a considerably higher rate of 0.89(0.07-1.66) mg.h-1 compared with just 0.11 (0-0.52) mg.h-1 for the 3435T/3435T pair (p=0.03; dominant model). A significantly higher usage rate was also detected for opioid receptor OPRM1 variant IVS2-691 with usage of 0.77(0.01-1.56) mg.h-1 for the IVS2C/IVS2C or IVS2C/IVS2G group vs. 0.24(0-1.26) mg.h-1 in the IVS2G/IVS2G group (p=0.04; recessive model). Conclusion: While this study has identified some significant statistical associations the potential utility of the studied DNA variants in prediction of postoperative pain and patient-controlled opioid analgesia requirements appears to be quite limited at present