Oscillatory Electrical Activity Research Articles

Changes In Objective Characteristics In Brain Electrical Activity In Newborns As A Function Of Birth Weight

The aim of the present study was to detect characteristic features of oscillatory electrical activity of the brain in the first day of postnatal life depending on the weight of newborns. Material — Eighteen neonates of conditionally normal gestational age (37.7±1.5 weeks) weighing 2500±720 g were included in the study. All neonates were children of first births of mothers aged 18-35 years, all pregnancies were physiologic, conventionally normal, without significant complications. The height of the newborns was 47±4.643 cm and head circumference was33.0±2.908 cm. The Apgar score at delivery was 7-9 points. All newborns were divided among groups 1 (weight: 2850-4000 gr), 2 (weight: 2000-2800 gr) and 3 (1200-2000 gr). Each newborn underwent EEG monitoring (EEG, monopolar recording, channels C3 and C4) for 40 minutes during the first 12 hours after birth. Methods — Automatic processing of EEG was performed without separating the monitoring records into sleep and wakefulness stages. Oscillatory patterns were calculated for each EEG channel based on the continuous wavelet transform method. Statistical estimations of the number and duration of oscillatory patterns developing in different EEG frequency ranges were performed. Results — A strong correlation was found between neonatal birth weight and integral characteristics of the number \ duration of oscillatory patterns in the low-frequency band [4; 6] Hz (r=-0.878\0.920). Practically healthy newborns with different birth weights show statistically different EEG characteristics in the [4; 6] Hz band in the first 12 hours after birth (p-value≤0.005). Conclusion — Electrical activity of the brain varies significantly depending on the weight of newborns immediately after birth. Monitoring of EEG signals according to the proposed algorithm may become the basis for the development of additional tools for early detection of possible disorders of neurological development of the newborn.

Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages

The purpose of this study is to detect the characteristic features of the oscillatory electrical activity of the brain in early postnatal development, depending on the gestational age of newborns. Methods. The study is based on automatic processing of clinical data from electroencephalography of newborns on the third day after birth. Behavioral characteristics assessed periods of sleep and wakefulness, without a precise division into stages of sleep and various states of wakefulness. The processing of multichannel electroencephalography signals was carried out on the basis of the method of modifying the continuous wavelet transform (CWT), which makes it possible to estimate the average characteristics of the number, duration and energy of oscillatory components (patterns) developing in different frequency ranges. Results. A paradoxical picture has been demonstrated describing the state of sleep and wakefulness in weakly preterm infants. For this group of children, the number and average energy of patterns detected in the frequency ranges from 1 to 20 Hz behave in a reflected way during sleep compared to children born at the usual time. At the same time, the average duration of oscillatory patterns remains unchanged. Conclusion. In the first days of a child’s life, it is possible to detect significant differences in the activity of the brain of newborns with slightly different gestational age during sleep/wake behavioral states. Quantitative estimates of the parameters of oscillatory CWT patterns are promising for use as the basis for systems for automatic processing of neonatal brain activity, additional to amplitude electroencephalography estimates. Such systems may be relevant for the search for early signs of anomalies in the development of the central nervous system.

Slow oscillations persist in pancreatic beta cells lacking phosphofructokinase M

Pulsatile insulin secretion by pancreatic beta cells is necessary for tight glucose control in the body. Glycolytic oscillations have been proposed as the mechanism for generating the electrical oscillations underlying pulsatile insulin secretion. The glycolytic enzyme 6-phosphofructokinase-1 (PFK) synthesizes fructose-1,6-bisphosphate (FBP) from fructose-6-phosphate. It has been proposed that the slow electrical and Ca2+ oscillations (periods of 3–5 min) observed in islets result from allosteric feedback activation of PFKM by FBP. Pancreatic beta cells express three PFK isozymes: PFKL, PFKM, and PFKP. A prior study of mice that were engineered to lack PFKM using a gene-trap strategy to delete Pfkm produced a mosaic reduction in global Pfkm expression, but the islets isolated from the mice still exhibited slow Ca2+ oscillations. However, these islets still expressed residual PFKM protein. Thus, to more fully test the hypothesis that beta cell PFKM is responsible for slow islet oscillations, we made a beta-cell-specific knockout mouse that completely lacked PFKM. While PFKM deletion resulted in subtle metabolic changes in vivo, islets that were isolated from these mice continued to exhibit slow oscillations in electrical activity, beta cell Ca2+ concentrations, and glycolysis, as measured using PKAR, an FBP reporter/biosensor. Furthermore, simulations obtained with a mathematical model of beta cell activity shows that slow oscillations can persist despite PFKM loss provided that one of the other PFK isoforms, such as PFKP, is present, even if its level of expression is unchanged. Thus, while we believe that PFKM may be the main regulator of slow oscillations in wild-type islets, PFKP can provide functional redundancy. Our model also suggests that PFKM likely dominates, in vivo, because it outcompetes PFKP with its higher FBP affinity and lower ATP affinity. We thus propose that isoform redundancy may rescue key physiological processes of the beta cell in the absence of certain critical genes.

On the meaning of Hurst entropy applied to EEG data series

The Hurst exponent estimates the degree of self-similarity and predictability of a time series, which, under this nonlinear statistical model, can adopt two opposing tendencies with respect to the way these data series are mobilized over time. Persistent and anti-persistent series are thus described, depending on whether the Hurst value describing them is greater or less than 0.5 in a range of 0 to 1. Those series that show "Hurst effect" (persistent series with H>0.5) are potentially predictable in the short or medium term, which makes them especially interesting for any predictive interest in science or economics. In the brain, when the oscillatory electrical activity of an EEG is analyzed, the time series that make up a complete recording include a variable number of sources, electrodes or channels, which detect the electrical activity of millions of pyramidal neurons at the level of the cerebral cortex. When this signal is decomposed into its frequency components, its spectrum ranges from the slowest waves, or Delta (0.1-4Hz), to the fastest, Gamma (>30 Hz). In the intermediate range, the Theta (4-8Hz); Alpha (8-12Hz); and Beta (13-30Hz) oscillations complete the range of the EEG spectrum. When estimating the Hurst exponent by filtered intervals of the total frequency range, only for the Delta band, the H values exceed H=0.5. The Hurst values for Delta were close to H = 0.85 and all the rest of the spectrum falls to values less than 0.5, in a range from 1.5<H<3.5.In the present work, we extend the interpretation of the Hurst exponent applied to electroencephalographic time series, and we propose to pay special attention to the so-called anti-persistent series (H<0.5) which, overshadowed by the predictive promises of the Hurst effect of persistent series, we believe may hide the very long-term persistence of complex homeostatic processes, stabilized in the human brain over millions of years of evolution.

The great reset: Transcranial pulse stimulation reduces global EEG-entropy in Alzheimer's Disease

Abstract Background: Transcranial Pulse Stimulation (TPS) is a new therapy that uses shockwaves for the treatment of Alzheimer's Disease (AD). In humans, there is first evidence for beneficial clinical effects after a series of six TPS sessions. Magnetic resonance imaging studies suggest up-regulation of memory networks as well as increased cortical thickness after stimulation. Effects on oscillatory electrical brain activity measured with resting state electroencephalography (EEG) have not been reported so far. Methods: A consecutive number of four pilot TPS-treated AD-patients was examined. TPS-naïve resting EEGs were recorded before treatment and compared with EEG-recordings directly after first stimulation. All patients received 4Hz TPS of about 6000 pulses of 0.2 mJ/mm2 (navigated bifrontally, biparietally, bitemporally and praecuneus) using the Neurolith System (Storz Medical). Frontal, parietal, central and temporal channel clusters were analyzed with regard to their alpha, theta delta and gamma power and for a measure of Tsallis entropy. Results: The immediate effect after first TPS on grand-average EEG resting state showed an increase of power in all frequency bands and for total power, although a significant difference between pre- and post-stimulation was not revealed due to power variability. However, the grand-average across patients and channels of Tsallis entropy showed a significant reduction after a single stimulation session. Discussion / Conclusion: Limited due to a high variability (and possibly due to underpower) we found power increase across frequency bands and channels which failed signifance. On the other hand Tsallis entropy was a more sensitive measure for change on EEG activity after stimulation. This might reflect a global reset of uncoordinated and thus pathological neural activity after TPS stimulation. More measurements should be added including longitudinal serial recordings over the treatment course of six sessions with correlations of clinical treatment effects. Keywords: transcranial pulse stimulation, shock wave , Alzheimer, EEG

Geometric analysis of mixed-mode oscillations in a model of electrical activity in human beta-cells

Human pancreatic beta-cells may exhibit complex mixed-mode oscillatory electrical activity, which underlies insulin secretion. A recent biophysical model of human beta-cell electrophysiology can simulate such bursting behavior, but a mathematical understanding of the model’s dynamics is still lacking. Here we exploit time-scale separation to simplify the original model to a simpler three-dimensional model that retains the behavior of the original model and allows us to apply geometric singular perturbation theory to investigate the origin of mixed-mode oscillations. Changing a parameter modeling the maximal conductance of a potassium current, we find that the reduced model possesses a singular Hopf bifurcation that results in small-amplitude oscillations, which go through a period-doubling sequence and chaos until the birth of a large-scale return mechanism and bursting dynamics. The theory of folded node singularities provide insight into the bursting dynamics further away from the singular Hopf bifurcation and the eventual transition to simple spiking activity. Numerical simulations confirm that the insight obtained from the analysis of the reduced model can be lifted back to the original model.

Detection of gastric slow oscillatory contraction using parasagittal cine MR images: Comparison with simultaneously measured electrogastrogram

PurposeTo determine if parasagittal gastric cine magnetic resonance imaging (MRI) is able to measure gastric oscillatory contractions around 0.05 Hz and to determine its relationship with electrical activity as measured by electrogastrography (EGG). MethodsAssessment of the gastric motility is important for the research of the enteric nervous system and for the diagnosis of functional gastric disorders. Electrogastrography is a non-invasive method that can measure gastric oscillatory electrical activity around 0.05 Hz (slow wave) using electrodes on the abdominal skin, but its sensitivity and specificity of the slow wave detection is limited. We used parasagittal gastric cine MRI around the angular incisure to measure gastric oscillatory contraction around 0.05 Hz in 24 healthy volunteers. Cine MRI was acquired with time resolution of 1 s for 10 min while freely breathing participants were lying on the bed. The gastric area of the cross section was measured for each MR image and assessed its change over time. The results were compared with those for simultaneously recorded EGG. ResultsThe main frequency of the gastric area change for each participant ranged from 0.041 to 0.059 Hz (mean ± S.D. = 0.049 ± 0.004), which corresponds to the gastric slow wave frequency (mean ± S.D. = 0.049 ± 0.004) as measured by EGG (p = 7.9585 × 10 −8, Kendall ‘s tau test). Cross correlation analysis showed that 22 of 24 participants’ gastric area changes were significantly (p < 0.05) related to the EGG waveforms. Displacement of the stomach due to respiration did not affect gastric area measurements. ConclusionsParasagittal cine MRI is correlated with EGG recordings and able to detect and quantifying gastric motility abnormalities.

Sleep Spindle Detection and Prediction Using a Mixture of Time Series and Chaotic Features

It is well established that sleep spindles (bursts of oscillatory brain electrical activity) are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features) to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi's, Katz's and Sevcik's Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov's Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Perceptron (MLP) and the K-Nearest Neighbor (KNN) were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a~high discriminative capacity (accuracy = 94.93%, sensitivity = 94.31% and specificity = 95.28%) with significant robustness (accuracy ranging from 91.33% to 94.93%, sensitivity varying from 91.20% to 94.31%, and specificity extending from 89.79% to 95.28%) in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97.96% accuracy.

Chronic Glucose Exposure Systematically Shifts the Oscillatory Threshold of Mouse Islets: Experimental Evidence for an Early Intrinsic Mechanism of Compensation for Hyperglycemia.

Mouse islets exhibit glucose-dependent oscillations in electrical activity, intracellular Ca(2+) and insulin secretion. We developed a mathematical model in which a left shift in glucose threshold helps compensate for insulin resistance. To test this experimentally, we exposed isolated mouse islets to varying glucose concentrations overnight and monitored their glucose sensitivity the next day by measuring intracellular Ca(2+), electrical activity, and insulin secretion. Glucose sensitivity of all oscillation modes was increased when overnight glucose was greater than 2.8mM. To determine whether threshold shifts were a direct effect of glucose or involved secreted insulin, the KATP opener diazoxide (Dz) was coapplied with glucose to inhibit insulin secretion. The addition of Dz or the insulin receptor antagonist s961 increased islet glucose sensitivity, whereas the KATP blocker tolbutamide tended to reduce it. This suggests insulin and glucose have opposing actions on the islet glucose threshold. To test the hypothesis that the threshold shifts were due to changes in plasma membrane KATP channels, we measured cell KATP conductance, which was confirmed to be reduced by high glucose pretreatment and further reduced by Dz. Finally, treatment of INS-1 cells with glucose and Dz overnight reduced high affinity sulfonylurea receptor (SUR1) trafficking to the plasma membrane vs glucose alone, consistent with insulin increasing KATP conductance by altering channel number. The results support a role for metabolically regulated KATP channels in the maintenance of glucose homeostasis.

Direct detection of optogenetically evoked oscillatory neuronal electrical activity in rats using SLOE sequence

The direct detection of neuronal electrical activity is one of the most challenging goals in non-BOLD fMRI research. Previous work has demonstrated its feasibility in phantom and cell culture studies, but attempts in in vivo studies remain few and far between. Most recent in vivo studies used T2*-weighted sequences to directly detect neuronal electrical activity evoked by sensory stimulus. As neuronal electrical signal is usually comprised of a series of spectrally distributed oscillatory waveforms rather than being a direct current, it is most likely to be detected using oscillatory current sensitive sequences. In this study, we explored the potential of using the spin-lock oscillatory excitation (SLOE) sequence with spiral readout to directly detect optogenetically evoked oscillatory neuronal electrical activity, whose main spectral component can be manipulated artificially to match the resonance frequency of spin-lock RF field. In addition, experiments using the stimulus-induced rotary saturation (SIRS) sequence with spiral readout were also performed. Electrophysiological recording and MRI data acquisition were conducted on separate animals. Robust optogenetically evoked oscillatory LFP signals were observed and significant BOLD signals were acquired with the GE-EPI sequence before and after the whole SLOE and SIRS acquisitions, but no significant neuronal current MRI (ncMRI) signal changes were detected. These results indicate that the sensitivity of oscillatory current sensitive sequences needs to be further improved for direct detection of neuronal electrical activity.

Human Islets Exhibit Electrical Activity on Microelectrode Arrays (MEA)

This study demonstrates for the first time that the microelectrode array (MEA) technique allows analysis of electrical activity of islets isolated from human biopsies. We have shown before that this method, i.e., measuring beta cell electrical activity with extracellular electrodes, is a powerful tool to assess glucose responsiveness of isolated murine islets. In the present study, human islets were shown to exhibit glucose-dependent oscillatory electrical activity. The glucose responsiveness could be furthermore demonstrated by an increase of insulin secretion in response to glucose. Electrical activity was increased by tolbutamide and inhibited by diazoxide. In human islets bursts of electrical activity were markedly blunted by the Na(+) channel inhibitor tetrodotoxin which does not affect electrical activity in mouse islets. Thus, the MEA technique emerges as a powerful tool to decipher online the unique features of human islets.Additionally, this technique will enable research with human islets even if only a few islets are available and it will allow a fast and easy test of metabolic integrity of islets destined for transplantation.

Mapping the mechanisms of transcranial alternating current stimulation: a pathway from network effects to cognition.

In recent decades, our appreciation of the complexity of the brain has deepened immensely, as has our understanding of how it performs key functions. In the face of such complexity, and given the rising cost of neuropsychiatric illness (1), an intriguing question is whether we can promote further understanding, and in some cases enhancement, of the typical and atypical brain by targeted modulation of its activity. Notably, transcranial alternating current stimulation (tACS) – which involves transcranial application of weak sinusoidal electrical currents (2) – seems ideally suited to address this question, as it has been demonstrated to modulate endogenous oscillatory electrical activity (3), enhance cognitive functions (4–7), and provide support in neurological disease (8, 9). However, a complete mechanistic pathway between the neuronal and cognitive effects of tACS remains in need of explication, precluding both significant theoretical contribution by tACS studies, and the development of more adaptive neuroenhancement regimes. Therefore, in this Opinion article, we briefly review the role of oscillatory neuronal activity in cognition, before outlining one potential pathway by which the interaction between tACS and endogenous oscillations at a network level may be reconciled with its effects on broader cognitive functions.

Rhythmic ganglion cell activity in bleached and blind adult mouse retinas.

In retinitis pigmentosa – a degenerative disease which often leads to incurable blindness- the loss of photoreceptors deprives the retina from a continuous excitatory input, the so-called dark current. In rodent models of this disease this deprivation leads to oscillatory electrical activity in the remaining circuitry, which is reflected in the rhythmic spiking of retinal ganglion cells (RGCs). It remained unclear, however, if the rhythmic RGC activity is attributed to circuit alterations occurring during photoreceptor degeneration or if rhythmic activity is an intrinsic property of healthy retinal circuitry which is masked by the photoreceptor’s dark current. Here we tested these hypotheses by inducing and analysing oscillatory activity in adult healthy (C57/Bl6) and blind mouse retinas (rd10 and rd1). Rhythmic RGC activity in healthy retinas was detected upon partial photoreceptor bleaching using an extracellular high-density multi-transistor-array. The mean fundamental spiking frequency in bleached retinas was 4.3 Hz; close to the RGC rhythm detected in blind rd10 mouse retinas (6.5 Hz). Crosscorrelation analysis of neighbouring wild-type and rd10 RGCs (separation distance <200 µm) reveals synchrony among homologous RGC types and a constant phase shift (∼70 msec) among heterologous cell types (ON versus OFF). The rhythmic RGC spiking in these retinas is driven by a network of presynaptic neurons. The inhibition of glutamatergic ganglion cell input or the inhibition of gap junctional coupling abolished the rhythmic pattern. In rd10 and rd1 retinas the presynaptic network leads to local field potentials, whereas in bleached retinas additional pharmacological disinhibition is required to achieve detectable field potentials. Our results demonstrate that photoreceptor bleaching unmasks oscillatory activity in healthy retinas which shares many features with the functional phenotype detected in rd10 retinas. The quantitative physiological differences advance the understanding of the degeneration process and may guide future rescue strategies.

Inter-network interactions: impact of connections between oscillatory neuronal networks on oscillation frequency and pattern.

Oscillations in electrical activity are a characteristic feature of many brain networks and display a wide variety of temporal patterns. A network may express a single oscillation frequency, alternate between two or more distinct frequencies, or continually express multiple frequencies. In addition, oscillation amplitude may fluctuate over time. The origin of this complex repertoire of activity remains unclear. Different cortical layers often produce distinct oscillation frequencies. To investigate whether interactions between different networks could contribute to the variety of oscillation patterns, we created two model networks, one generating on its own a relatively slow frequency (20 Hz; slow network) and one generating a fast frequency (32 Hz; fast network). Taking either the slow or the fast network as source network projecting connections to the other, or target, network, we systematically investigated how type and strength of inter-network connections affected target network activity. For high inter-network connection strengths, we found that the slow network was more effective at completely imposing its rhythm on the fast network than the other way around. The strongest entrainment occurred when excitatory cells of the slow network projected to excitatory or inhibitory cells of the fast network. The fast network most strongly imposed its rhythm on the slow network when its excitatory cells projected to excitatory cells of the slow network. Interestingly, for lower inter-network connection strengths, multiple frequencies coexisted in the target network. Just as observed in rat prefrontal cortex, the target network could express multiple frequencies at the same time, alternate between two distinct oscillation frequencies, or express a single frequency with alternating episodes of high and low amplitude. Together, our results suggest that input from other oscillating networks may markedly alter a network's frequency spectrum and may partly be responsible for the rich repertoire of temporal oscillation patterns observed in the brain.

The Relationship between Membrane Potential and Calcium Dynamics in Glucose-Stimulated Beta Cell Syncytium in Acute Mouse Pancreas Tissue Slices

Oscillatory electrical activity is regarded as a hallmark of the pancreatic beta cell glucose-dependent excitability pattern. Electrophysiologically recorded membrane potential oscillations in beta cells are associated with in-phase oscillatory cytosolic calcium activity ([Ca2+]i) measured with fluorescent probes. Recent high spatial and temporal resolution confocal imaging revealed that glucose stimulation of beta cells in intact islets within acute tissue slices produces a [Ca2+]i change with initial transient phase followed by a plateau phase with highly synchronized [Ca2+]i oscillations. Here, we aimed to correlate the plateau [Ca2+]i oscillations with the oscillations of membrane potential using patch-clamp and for the first time high resolution voltage-sensitive dye based confocal imaging. Our results demonstrated that the glucose-evoked membrane potential oscillations spread over the islet in a wave-like manner, their durations and wave velocities being comparable to the ones for [Ca2+]i oscillations and waves. High temporal resolution simultaneous records of membrane potential and [Ca2+]i confirmed tight but nevertheless limited coupling of the two processes, with membrane depolarization preceding the [Ca2+]i increase. The potassium channel blocker tetraethylammonium increased the velocity at which oscillations advanced over the islet by several-fold while, at the same time, emphasized differences in kinetics of the membrane potential and the [Ca2+]i. The combination of both imaging techniques provides a powerful tool that will help us attain deeper knowledge of the beta cell network.

Phase-Coupled Oscillations in the Brain: Nonlinear Phenomena in Cellular Signalling

We report the existence of phase-coupled oscillations in a model neural system. The model consists of a group of excitatory principal cells in interaction with local inhibitory interneurons. The voltages across the membranes of excitatory cells are governed primarily by calcium and potassium ion conductivities. The number of potassium channels open at any given instant changes in accordance with a deterministic law. The time scale of this change is set by a constant which depends on midpoint potentials at which potassium and calcium currents are half-activated. The growth of mean membrane potential of excitatory principal cells is controlled by that of the inhibitory interneurons. Nonlinear oscillatory system associated with these limit cycles starting from two different initial conditions maintain a definite phase relationship. The phase-coupled oscillations in electrical activity of the neuronal cells carry together amplitude, phase, and time information for cellular signaling. This mechanism supports an energy efficient way of information processing in the central nervous system. The information content is encoded as persistent periodic oscillations represented by stable limit cycles in the phase space.

Finding thalamic BOLD correlates to posterior alpha EEG

Oscillatory electrical brain activity in the alpha (8-13 Hz) band is a prominent feature of human electroencephalography (EEG) during alert wakefulness, and is commonly thought to arise primarily from the occipital and parietal parts of the cortex. While the thalamus is considered to play a supportive role in the generation and modulation of cortical alpha rhythms, its precise function remains controversial and incompletely understood. To address this, we evaluated the correlation between the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals in the thalamus and the spontaneous modulation of posterior alpha rhythms based on EEG-fMRI data acquired concurrently during an eyes-closed task-free condition. We observed both negative and positive correlations in the thalamus. The negative correlations were mostly seen within the visual thalamus, with a preference for the pulvinar over lateral geniculate nuclei. The positive correlations were found at the anterior and medial dorsal nuclei. Through functional connectivity analysis of the fMRI data, the pulvinar was found to be functionally associated with the same widespread cortical visual areas where the fMRI signals were negatively correlated with the posterior alpha modulation. In contrast, the dorsal nuclei were part of a distinct functional network that included brain stem, cingulate cortex and cerebellum. These observations are consistent with previous animal electrophysiology studies and the notion that the visual thalamus, and the pulvinar in particular, is intimately involved in the generation and spontaneous modulation of posterior alpha rhythms, facilitated by its reciprocal and widespread interaction with the cortical visual areas. We further postulate that the anterior and medial dorsal nuclei, being part of the ascending neuromodulatory system, may indirectly modulate cortical alpha rhythms by affecting vigilance and arousal levels.

Chaotic dynamics underpins the slow oscillation of general anesthesia and nonREM sleep

Electrical recordings of brain activity show that entry into anesthetic unconsciousness is signposted by the emergence of large, slow oscillations of electrical activity (~1 Hz) that appear very similar to the slow waves observed in natural sleep. In this phase, populations of cortical neurons periodically switch between hyperpolarized inactivity (“down” state), and wake-like depolarized activation (“up” state) [1]. The origin of the slow oscillation has not yet been unambiguously established, and remains an area of intense research and debate [2,3]. Here we suggest a novel mechanism in which the up- and down-states are generated spontaneously by emergent chaotic waves of spatiotemporal activity that sweep the cortex. We present a mean-field model of the cortex in which populations of neurons are densely interlinked by both chemical synapses—including idealized long-range spatially heterogeneous connections—and by direct electrical connections forming a continuous network of interneuronal gap junctions. Anesthetic effect is modeled as a moderate reduction in inhibitory diffusion, paired with an increase in inhibitory postsynaptic potential. We explore model dynamics in the vicinity of a general-anesthetic induced transition from wake to coma. In this region the system is poised at a codimension-2 point where competing Turing (spatial) and Hopf (temporal) instabilities co-exist. We argue that normal functioning of the resting “default-wake” brain requires a delicate balance between these instabilities. Reduction of gap-junction diffusivity disturbs the balance in favor of the Hopf instability, eventually predicting global seizure in the limit of severe imbalance. Our cortical model predicts that introduction of anesthetic to the awake brain will force a subtle rebalancing of dynamic pressures resulting in a coma state that is characterized by emergent low-frequency oscillations whose dynamics is chaotic in time and space: see Fig. ​Fig.1.1. We quantify cortical dynamics in terms of a phase coherence measure and demonstrate that the model-predicted turbulent slow-wave state is characterized by low phase coherence. This prediction is supported by clinical studies of phase synchronization changes in EEG during induction of propofol anesthesia [4]. Figure 1 Spontaneous slow-wave oscillations in cortical firing-rate patterns during 20 s of simulated anesthesia. Traces were recorded from five equally-spaced points lying along the midline of the 25-×25-cm simulated cortical grid. Time-series are chaotic ...

The role of beta-gamma oscillations in unexpected rewards processing

Reward processing in humans is carried out by an extensive fronto-subcortical network that might be coordinated by fast oscillatory electrical activity. Previous studies have identified an increase in beta-gamma oscillatory activity after the processing of positive feedback stimuli but the functional role of this electroencephalographic (EEG) correlate remains unclear. In the present study, we used event-related brain potentials (ERPs) and trial-by-trial wavelet-based time-frequency analysis of the EEG signal to investigate the effects of expectancy and magnitude of positive and negative feedbacks associated with monetary gains and losses in a gambling task. Large increase of beta-gamma oscillatory activity only in unexpected monetary gains was observed,irrespective of its magnitude. Based on recent findings we propose that this increase in beta-gamma oscillatory activity might reflect a general cognitive mechanism in charge of monitoring unexpected positive events based on feedback processing.

Neural dynamics during simultaneous processing of music and language

Event Abstract Back to Event Neural dynamics during simultaneous processing of music and language Elisa Carrus1*, Marcus Pearce1, Geraint Wiggins1 and Joydeep Bhattacharya1 1 Goldsmiths, University of London, United Kingdom Neural mechanisms underlying the processing of music and language have received increasing attention in recent years. Electrophysiological research based on event-related potentials has suggested a possible overlap of resources involved in the processing of syntax in language and music (Koelsch et al., 2005). However, these studies relied exclusively on signal averaging techniques, which do not adequately represent oscillatory electrical brain activity. Aims: the primary aim of the study was to investigate the neural dynamics involved in simultaneous processing of language and melodies. Analyses of oscillatory responses as well as phase synchronization were carried out in order to understand the degree of locally and globally synchronized neuronal assemblies. Multivariate EEG signals were recorded while participants processed visually presented sentences and auditorily presented melodies. Expected or unexpected melodies occurred synchronously with four types of language sentences: correct, incorrect syntax, incongruous semantics, incorrect syntax and incongruous semantics. Music stimuli were built following a computational model developed by Pearce and Wiggins (2006). Melodies were either highly probable or highly improbable in the context of information content. A pattern of neural interactions between music and language processing was revealed suggesting shared neural resources in the processing of music and language. Keywords: Language, Music Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Neural Bases of Language Citation: Carrus E, Pearce M, Wiggins G and Bhattacharya J (2011). Neural dynamics during simultaneous processing of music and language. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00198 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Dr. Elisa Carrus, Goldsmiths, University of London, London, United Kingdom, e.carrus@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Elisa Carrus Marcus Pearce Geraint Wiggins Joydeep Bhattacharya Google Elisa Carrus Marcus Pearce Geraint Wiggins Joydeep Bhattacharya Google Scholar Elisa Carrus Marcus Pearce Geraint Wiggins Joydeep Bhattacharya PubMed Elisa Carrus Marcus Pearce Geraint Wiggins Joydeep Bhattacharya Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.