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Repeated presentations of sensory stimuli generate transient gamma-frequency (30-80 Hz) responses in neocortex that show plasticity in a task-dependent manner. Complex relationships between individual neuronal outputs and the mean, local field potential (population activity) accompany these changes, but little is known about the underlying mechanisms responsible. Here we show that transient stimulation of input layer 4 sufficient to generate gamma oscillations induced two different, lamina-specific plastic processes that correlated with lamina-specific changes in responses to further, repeated stimulation: Unit rates and recruitment showed overall enhancement in supragranular layers and suppression in infragranular layers associated with excitatory or inhibitory synaptic potentiation onto principal cells, respectively. Both synaptic processes were critically dependent on activation of GABAB receptors and, together, appeared to temporally segregate the cortical representation. These data suggest that adaptation to repetitive sensory input dramatically alters the spatiotemporal properties of the neocortical response in a manner that may both refine and minimize cortical output simultaneously.

Very fast oscillations (VFOs, >80 Hz) are important for physiological brain processes and, in excess, with certain epilepsies. Putative mechanisms for VFO include interneuron spiking and network activity in coupled pyramidal cell axons. It is not known whether either, or both, of these apply in pathophysiological conditions. Spontaneously occurring interictal discharges occur in human tissue in vitro, resected from neocortical epileptic foci. VFO associated with these discharges was manifest in both field potential and, with phase delay, in excitatory synaptic inputs to fast spiking interneurons. Recruitment of somatic pyramidal cell and interneuron spiking was low, with no correlation between VFO power and synaptic inputs to principal cells. Reducing synaptic inhibition failed to affect VFO occurrence, but they were abolished by reduced gap junction conductance. These data suggest a lack of a causal role for interneurons, and favor a nonsynaptic pyramidal cell network origin for VFO in epileptic human neocortex.

We perform a systematic data analysis on high resolution (0.5–12 kHz) multiarray microelectrode recordings from an animal model of spontaneous limbic epilepsy, to investigate the role of high frequency oscillations and the occurrence of early precursors for seizures. Results of spectral analysis confirm the importance of very high frequency oscillations (even greater than 600 Hz) in normal (healthy) and abnormal (epileptic) hippocampus. Furthermore, we show that the measures of Recurrence Quantification Analysis (RQA) and Recurrence Time Statistics (RTS) are successful in indicating, rather uniquely, the onset of ictal state and the occurrence of some warnings/precursors during the pre-ictal state, in contrast to the linear measures investigated.

With the rise in stereoscopic 3D media, there has been concern that viewing stereoscopic 3D (S3D) content could have long-term adverse effects, but little data are available. In the first study to address this, 28 households who did not currently own a 3D TV were given a new TV set, either S3D or 2D. The 116 members of these households all underwent tests of balance, coordination and eyesight, both before they received their new TV set, and after they had owned it for 2 months. We did not detect any changes which appeared to be associated with viewing 3D TV. We conclude that viewing 3D TV does not produce detectable effects on balance, coordination or eyesight over the timescale studied. Practitioner Summary: Concern has been expressed over possible long-term effects of stereoscopic 3D (S3D). We looked for any changes in vision, balance and coordination associated with normal home S3D TV viewing in the 2 months after first acquiring a 3D TV. We find no evidence of any changes over this timescale.

Cardiac ablation is increasingly used to interdict the complex propagation of excitatory waves during atrial fibrillation. While such procedures are useful, they can often fail. Here we use fluorescence imaging to observe the electrical activity of an ablated porcine heart. We find that, while ablation lines do attenuate cardiac waves, the attenuation is incomplete. A remnant of the incident wave survives passage through the ablation barrier, albeit as a subthreshold signal. More importantly, we have found that these subthreshold signals may add constructively and thereby dynamically reduce the effective attenuation, an effect which we call "dynamic transmurality". We suspect this as a factor in the persistence of arrhythmia after ablative.

Manufacturers and the media have raised the possibility that viewing stereoscopic 3D television (S3D TV) may cause temporary disruption to balance and visuomotor coordination. We looked for evidence of such effects in a laboratory-based study. Four hundred and thirty-three people aged 4–82 years old carried out tests of balance and coordination before and after viewing an 80 min movie in either conventional 2D or stereoscopic 3D, while wearing two triaxial accelerometers. Accelerometry produced little evidence of any change in body motion associated with S3D TV. We found no evidence that viewing the movie in S3D causes a detectable impairment in balance or in visuomotor coordination.

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The latter case of large-scale activation is visible in the transition to the epileptic state. After describing the shift to large-scale synchronization we study the role of network topology on this transition. Whereas standard explanations for balanced activity involve populations of inhibitory neurons for limiting activity, we observe how network topology limits activity spreading. A random or small-world topology results in low or high levels of activation. In contrast, a cluster hierarchy based on neuroanatomical knowledge—from cortical clusters such as the visual cortex at the highest level, to individual columns at the lowest level—enables sustained activity in neural systems and prevents large-scale activation as observed during epileptic seizures. The containment of activation critically depends on the ratio of inter-cluster connections. Such topological inhibition by means of a modular hierarchy, in addition to neuronal inhibition, might help to maintain healthy levels of neural activity.

Nervous systems are complex networks par excellence, capable of generating and integrating information from multiple external and internal sources. Within the neuroanatomical network (structural connectivity), the non-linear dynamics of neurons and neuronal populations result in patterns of statistical dependencies (functional connectivity) and causal interactions (effective connectivity), defining three major modalities of complex brain networks [61]. How does the structure of the network relate to its function and what effect do changes of edge or node properties have [37]? Since 1992 [1, 76] tools from graph theory and network analysis [18] were applied to study these questions in neural systems. After describing the properties of neural systems—fiber tracts between brain areas of the mammalian cortex and axons between individual neurons in the nematode C. elegans—we describe dynamics in structure and function. Structural dynamics concern changes in network topology by deleting or adding edges or nodes. We describe the deletion of components in terms of the removal of tissue during strokes or head injuries and the addition of components during the development and growth of neural system. The network topology is robust to random but reacts critically to targeted attacks – similar to a scale-free network. The simulations on network evolution show that spatial growth and time windows during development are sufficient for generating small-world and multi-cluster networks. Functional dynamics concern changes in the activity level of individual neurons or of cortical regions. We observe the spreading of activation first describing spreading in excitable media and then in cardiac tissue before moving to spreading of epileptic seizures in neural networks. In neural networks, the hierarchical and modular topology provides a novel mechanisms of limiting spreading in addition to the known influence of inhibitory nodes. Finally, we

This module represents the formalised opinion of the authors and the CARMEN consortium, which identifies the minimum information required to report the use of electrophysiology in a neuroscience study, for submission to the CARMEN system ("http://www.carmen.org.uk":www.carmen.org.uk).

When calculating correlation networks from multi-electrode array (MEA) data, one works with extensive computations. Unfortunately, as the MEAs grow bigger, the time needed for the computation grows even more: calculating pair-wise correlations for current 60 channel systems can take hours on normal commodity computers whereas for future 1000 channel systems it would take almost 280 times as long, given that the number of pairs increases with the square of the number of channels. Even taking into account the increase of speed in processors, soon it can be unfeasible to compute correlations in a single computer. Parallel computing is a way to sustain reasonable calculation times in the future. We provide a general tool for rapid computation of correlation networks which was tested for: (a) a single computer cluster with 16 cores, (b) the Newcastle Condor System utilizing idle processors of university computers and (c) the inter-cluster, with 192 cores. Our reusable tool provides a simple interface for neuroscientists, automating data partition and job submission, and also allowing coding in any programming language. It is also sufficiently flexible to be used in other high-performance computing environments.

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

A total of 32 microwire electrodes were implanted bilaterally into the hippocampus of Sprague-Dawley rats, which were then stimulated in the manner prescribed for the chronic limbic epilepsy model. After the initial seizure brought on by the stimulation, the animals were recorded at a high sampling rate (approximately 12 kHz) for the entire duration of the latent period. Coherence was calculated across channels in both stimulated (and later seizing) animals and non-stimulated (and thus non-seizing control) animals. Average coherence over time was greatest in intrahemispherical electrode pairs in both stimulated and non-stimulated animals. However, the 200-800 Hz band displays increased coherence interhemispherically and up to 200 Hz band displays decreased coherence interhemispherically: this occurs only in stimulated animals.

Recently a number of groups [1,2] have reported on the existence of pathological High frequency oscillations (HFO's) (oscillations in the frequency range of 80–200 Hz, termed as Ripple band and oscillations in the frequency range of 200 Hz and above, termed as Fast Ripple band) in the epileptic brain both in in-vivo and in-vitro experiments. Our goal in this study is to study the statistical modulation of HFOs during epileptogenesis in order to characterize their function in progression to seizures in the epileptic brain. In this study we define a HFO event as a subset of wave having significant high frequency component with low wave amplitude. HFO are detected from data recorded at a sampling rate of 12000 Hz for the entire duration of epileptogenesis which lasts anywhere from about 3–6 weeks. Statistical analysis on the HFO suggest that occurrence of HFO's occur primarily during the 12 hour dark cycle whereas the HFO's primarily seem to occur during the 12 hour day cycle in the control rat The video recording shows that the rat is primarily in active and exploratory state during the dark cycle. These observations suggest that HFO in epileptic rats are correlated with the state of arousal. Spatial correlation of HFOs in different regions of the brain is also investigated with cross-correlogram. Comparison of cross-correlogram of the post-stimulus HFO in the epileptic rat to the pre stimulus HFO (control) suggests modification in the circuitry in the hippocampus, evidence for which in in-vitro experiments were provided by [3].

High Frequency Oscillations (HFO) in limbic epilepsy represent a marked difference between abnormal and normal brain activity. Faced with the difficult of visually detecting HFOs in large amounts of intracranial EEG data, it is necessary to develop an automated process. This paper presents Teager Energy as a method of finding HFOs. Teager energy is an ideal measure because unlike conventional energy it takes into account the frequency component of the signal as well as signal amplitude. This greatly aids in the dissection of HFOs out of the noise and other signals contained in the EEG. Therein, Teager energy analysis is able to detect high- frequency, low-amplitude components that conventional energy measurements would miss.

Epilepsy is a common neurological disorder that can have damaging effects in the brain including over 50% loss of neuronal activity in the hippocampal regions of the CA1 and CA3. The pre-ictal period was studied in an animal model of limbic epilepsy using Shannon entropy and correlation analysis. The primary aim was to uncover underlying relative changes in signals between the Dentate Gyrus and CA1 areas of the bilateral hippocampus. Preliminary entropy analysis results included dynamical changes between channels in the Dentate Gyrus and channels in the CA1 region at and around the time of the seizure. Epilepsy affects 3-5% of the population worldwide. Epilepsy is a neurological disorder characterized by recurrent and unprovoked seizures. An individual loses awareness when experiencing a complex partial seizure due to the spread of the seizure through both temporal lobes and subsequently impairing memory. (1) Of all cases, approximately 60% respond favorably to anti-epileptic drugs. (2) Regardless of age, sex or race, the harmful effects of limbic Epilepsy can be caused by past infections, vascular malformations, hamartomas and gliomas. Head trauma in the form of hemorrhaging or contusion in the brain often leads to the development of limbic Epilepsy after a number of months to years. This span of time is known as a latent period when cellular and network changes are thought to occur precipitating the onset of seizures. In epileptogenesis over 50% of the neurons in the hippocampal regions of the CA1 and CA3 are lost. Neuronal loss also occurs with the granule cells in the Dentate Gyrus; accompanying these changes is a loss of inhibitory neurons, excitatory neurons and excitatory axonal sprouting. (1) The Chronic Limbic Epilepsy rat model imitates human limbic epilepsy with the initial insult to the brain quiescent period and resultant seizures later in life. The manner in which these seizures develop is thought to be a result of structural changes in the brain such as the strengthening of excitatory networks, loss of inhibitory neurons or suppression of GABA receptors. (3) Since little is known about the time period over which the changes occur, it is proposed that detectable changes occur gradually within the brain over the latent period eventually causing the later hypersynchronous seizure activity. The link between the nature of the pre-ictal period and the electrical changes manifested in the brain are not well characterized. The abnormal mode of communication, a characteristic of seizures, is demonstrated by large- amplitude wave discharges occurring over a large hemisphere of the brain. The preictal period in an animal model of limbic epilepsy is studied using Shannon entropy measurements. The goal of this research is to characterize underlying changes in signals between the Dentate Gyrus and CA1 areas of the bilateral hippocampus.

Selecting signal processing parameters in optical imaging by utilizing the change in Determinism, a measure introduced in Recurrence Quantification Analysis, provides a novel method using the change in residual noise Determinism for improving noise quantification and removal across signals exhibiting disparate underlying tissue pathologies. The method illustrates an improved process for selecting filtering parameters and how using measured signal-to-noise ratio alone can lead to improper parameter selection.

A high-speed video camera and voltage-sensitive dyes were used to acquire high resolution (80times80 pixels) and high-speed (500mus/frame) optical signals of ventricular fibrillation in a Langendorff-perfused porcine heart. The resulting spatiotemporal dynamics were recorded before and after the application of a defibrillation shock in order to study the mechanism of defibrillation failure. We used recurrence plots as a tool to qualify the evolution of ordered behavior on the heart surface before fibrillation was reestablished in defibrillation failure. Such ordered periods may point to robust periods in which the defibrillation attempt has had the most effect and may provide a window in which a smaller, corrective shock may be applied to achieve defibrillation

A high-speed video camera and voltage-sensitive dyes were used to acquire high resolution (80 /spl times/ 80 pixels) and high-speed (1 ms/frame) optical signals of ventricular fibrillation in a Langendorff-perfused porcine heart. The resulting spatiotemporal dynamics were recorded before and after the application of a defibrillation shock in order to study the mechanism of defibrillation failure. We created time space cubes and calculated nonlinear synchronization index measures to qualify the evolution of different types of activity on the heart surface (focal, reentry). We observed changes with time in all types of analysis, thus establishing these techniques as viable methods for detecting defibrillation failure before reentry reestablishes itself.

In recent years radiofrequency energy sources have been utilized in patients with atrial fibrillation to ablate atrial tissue and restore normal sinus rhythm. Radiofrequency ablations significantly minimize time in surgery, and hence postoperative complications, but as yet do not duplicate the ablation transmurality achieved by "cut and sew" techniques. Studies of post-operative fibrillation recurrence attribute treatment failures to the lack of obtaining full lesion transmurality at the time of surgery. Unfortunately, no method or standard exists for validating in-vivo ablation transmurality. Using high-resolution optical mapping studies with Langendorff perfused heart preparations, we are able to correlate spatiotemporal electrical activity with ablation line transmurality, thus providing a means for validating ablation transmurality in order to quantify the type and number of ablation lines used in order to tailor ablation therapies to individual patient needs.

A high‐speed video camera and voltage‐sensitive dyes were used to acquire high resolution (80×80 pixels) and high‐speed (500 μs/frame) optical signals of ventricular fibrillation in a Langendorff‐perfused porcine heart. The resulting spatiotemporal dynamics were recorded before and after the application of a defibrillation shock in order to study the mechanism of defibrillation failure. We calculate nonlinear synchronization index measures to qualify the evolution of different types of activity on the heart surface (focal, reentry). We observe changes with time in the spatial distribution of the first Fourier mode, showing that two main types of activity compete on the heart surface during a failed defibrillation.

We examine data from a stimulated Brillouin scattering experiment with very small feedback (<

Background: During early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 20 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations. Results: We present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into the HDF5 format. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available. Conclusions: 1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.

Of clinical interest are methods and tools to differentiate and localize epileptic foci, for surgical (and possibly other forms of intervention in the future) intervention. Previously, high frequency events, ripples (80-200 Hz) and fast ripples (200-500 Hz), were identified from both in vitro and in vivo recordings of epileptic foci tissue [1], but results were limited in that the data acquired was from single electrodes, and so simultaneous recordings of network activity were not obtained, and thus network dynamics within such regions were not analyzed. Using a 10 x10 grid Utah array electrodes for recording from focal epileptic and control slices of human tissue (control tissue being non-epileptic cortical tissue excised during extraction of a sub-cortical tumor or focus area), we are able to probe the network microstructure and, more critically, how these networks (epileptic focus and control) differ from each other. For analysis, we used standard low-frequency bands (theta, delta, alpha, gamma) as well as higher frequency bands (80-200 Hz, 200-500 Hz) for characteristic signals differentiating the epileptic and control populations. The raw data was filtered at these separate bands and networks were extracted from the filtered data using phase synchronization and cross-correlation as a means to detect phase and amplitude signal similarities, respectively. Once the networks were extracted, we could examine different network measures to determine if network properties were statistically different or not. We measured small-worldness characteristics such as the Clustering Coefficient (which measures how similar a reference node’s neighbour’s connectivity are to the reference node), Characteristic Path Length (which measures how many connections it takes on average to get from any one node to any other node in the network), degree (number of connections each node has), % change of network volatility, Small Worldness coefficient (Normalized Clustering Coefficient/Normalized Characteristic Path Length), modularity Q, and statistical information such as the number and size of (unthresholded) network clusters were extracted and compared across datasets and population.

We employ surrogate data techniques and a new correlation dimension estimation algorithm, the Gaussian kernel algorithm, to uncover non-linearity in human electrocardiogram recordings during normal (sinus) rhythm, ventricular tachycardia (VT) and ventricular fibrillation (VF). We conclude that all three rhythms are not linear (i.e. distinct from a monotonic non-linear transformation of linearly filtered noise) and have significant correlations over a period greater than the inter-beat interval. Furthermore, we observe that sinus rhythm and VT exhibit a correlation dimension of approximately 2.3 and 2.4, respectively. The correlation dimension of VF exceeds 3.2. The entropy of sinus rhythm, VT and VF is approximately 0.69, 0.55, and 0.67 nats/s, respectively. These results indicate that techniques from non-linear dynamical systems theory should help us understand the mechanism underlying ventricular arrhythmia, and that these rhythms are likely to be a combination of low dimensional chaos and noise. 2002 Elsevier Science Ltd. All rights reserved.