Differential contribution of excitatory and inhibitory neurons in shaping neurovascular coupling in different epileptic neural states.

Understanding the neurovascular coupling (NVC) underlying hemodynamic changes in epilepsy is crucial to properly interpreting functional brain imaging signals associated with epileptic events. However, how excitatory and inhibitory neurons affect vascular responses in different epileptic states remains unknown. We conducted real-time in vivo measurements of cerebral blood flow (CBF), vessel diameter, and excitatory and inhibitory neuronal calcium signals during recurrent focal seizures. During preictal states, decreases in CBF and arteriole diameter were closely related to decreased γ-band local field potential (LFP) power, which was linked to relatively elevated excitatory and reduced inhibitory neuronal activity levels. Notably, this preictal condition was followed by a strengthened ictal event. In particular, the preictal inhibitory activity level was positively correlated with coherent oscillating activity specific to inhibitory neurons. In contrast, ictal states were characterized by elevated synchrony in excitatory neurons. Given these findings, we suggest that excitatory and inhibitory neurons differentially contribute to shaping the ictal and preictal neural states, respectively. Moreover, the preictal vascular activity, alongside with the γ-band, may reflect the relative levels of excitatory and inhibitory neuronal activity, and upcoming ictal activity. Our findings provide useful insights into how perfusion signals of different epileptic states are related in terms of NVC.

Impact of COVID-19 pandemic on the clinical activities related to arrhythmias and electrophysiology in Italy: results of a survey promoted by AIAC (Italian Association of Arrhythmology and Cardiac Pacing).

COVID-19 outbreak had a major impact on the organization of care in Italy, and a survey to evaluate provision of for arrhythmia during COVID-19 outbreak (March-April 2020) was launched. A total of 104 physicians from 84 Italian arrhythmia centres took part in the survey. The vast majority of participating centres (95.2%) reported a significant reduction in the number of elective pacemaker implantations during the outbreak period compared to the corresponding two months of year 2019 (50.0% of centres reported a reduction of > 50%). Similarly, 92.9% of participating centres reported a significant reduction in the number of implantable cardioverter-defibrillator (ICD) implantations for primary prevention, and 72.6% a significant reduction of ICD implantations for secondary prevention (> 50% in 65.5 and 44.0% of the centres, respectively). The majority of participating centres (77.4%) reported a significant reduction in the number of elective ablations (> 50% in 65.5% of the centres). Also the interventional procedures performed in an emergency setting, as well as acute management of atrial fibrillation had a marked reduction, thus leading to the conclusion that the impact of COVID-19 was disrupting the entire organization of health care, with a massive impact on the activities and procedures related to arrhythmia management in Italy.

Phase of firing coding of learning variables across the fronto-striatal network during feature-based learning.

The prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that populations of neurons encoding choice outcomes, outcome prediction errors, and outcome history in their firing rates also carry significant information in their phase-of-firing at a 10-25 Hz band-limited beta frequency at which they synchronize across lateral prefrontal cortex, anterior cingulate cortex and anterior striatum when outcomes were processed. The phase-of-firing code exceeds information that can be obtained from firing rates alone and is evident for inter-areal connections between anterior cingulate cortex, lateral prefrontal cortex and anterior striatum. For the majority of connections, the phase-of-firing information gain is maximal at phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document enhanced information of three important learning variables at specific phases of firing in the beta cycle at an inter-areally shared beta oscillation frequency during goal-directed behavior.

Exploring differences between self-report and electrophysiological indices of drowsy driving: A usability examination of a personal brain-computer interface device.

INTRODUCTION: Impaired driving has resulted in numerous accidents, fatalities, and costly damage. One particularly concerning type of impairment is driver drowsiness. Despite advancements, modern vehicle safety systems remain ineffective at keeping drowsy drivers alert and aware of their state, even temporarily. Until recently the use of user-centric brain-computer interface (BCI) devices to capture electrophysiological data relating to driver drowsiness has been limited. METHOD: In this study, 25 participants drove on a simulated roadway under drowsy conditions. RESULTS: Neither subjective nor electrophysiological measures differed between individuals who showed overt signs of drowsiness (prolonged eye closure) during the drive. However, the directionality and effect size estimates provided by the BCI device suggested the practicality and feasibility of its future implementation in vehicle safety systems. Practical applications: This research highlights opportunities for future BCI device research for use to assess the state of drowsy drivers in a real-world context.

An evaluation of some assumptions underpinning the bidomain equations of electrophysiology.

Tissue level cardiac electrophysiology is usually modelled by the bidomain equations, or the monodomain simplification of the bidomain equations. One assumption made when deriving the bidomain equations is that both the intracellular and extracellular spaces are in electrical equilibrium. This assumption neglects the disturbance of this equilibrium in thin regions close to the cell membrane known as Debye layers. We first demonstrate that the governing equations at the cell, or microscale, level may be adapted to take account of these Debye layers with little additional complexity, provided the permittivity within the Debye layers satisfies certain conditions that are believed to be satisfied for biological cells. We then homogenize the microscale equations using a technique developed for an almost periodic microstructure. Cardiac tissue is usually modelled as sheets of cardiac fibres stacked on top of one another. A common assumption is that an orthogonal coordinate system can be defined at each point of cardiac tissue, where the first axis is in the fibre direction, the second axis is orthogonal to the first axis but lies in the sheet of cardiac fibres and the third axis is orthogonal to the cardiac sheet. It is assumed further that both the intracellular and extracellular conductivity tensors are diagonal with respect to these axes and that the diagonal entries of these tensors are constant across the whole tissue. Using the homogenization technique we find that this assumption is usually valid for cardiac tissue, but highlight situations where the assumption may not be valid.

Spanish Implantable Cardioverter-defibrillator Registry. 15th Official Report of the Spanish Society of Cardiology Electrophysiology and Arrhythmias Section (2018).

INTRODUCTION AND OBJECTIVES: This article presents the data corresponding to automated implantable cardioverter-defibrillator (ICD) implants in Spain reported to the Spanish Registry in 2018. METHODS: The data in this registry include both primary implants and generator replacements and were gathered from a data collection sheet voluntarily completed by implantation centers. RESULTS: In 2018, 6421 implant sheets were received compared with 7077 reported by Eucomed (European Confederation of Medical Suppliers Associations). This represents data on 90.7% of the devices implanted in Spain. Compliance ranged between 99.6% for the field "name of the implanting hospital" and 12.4% for "population of residence". A total of 173 hospitals reported their data to the registry, representing a slight decrease compared with hospitals participating in 2017 (n=181). CONCLUSIONS: After the reduction in ICD implants in 2017, the number of implants increased in 2018, with the highest number of ICDs implanted in Spain. The total number of implants remains much lower than the European Union average, with substantial differences between autonomous communities.

The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository.

Our understanding of the complex interplay between structural and functional organisation of brain networks is being advanced by the development of novel multi-modal analyses approaches. The Age-ility Project (Phase 1) data repository offers open access to structural MRI, diffusion MRI, and resting-state fMRI scans, as well as resting-state EEG recorded from the same community participants (n=131, 15-35 y, 66 male). Raw imaging and electrophysiological data as well as essential demographics are made available via the NITRC website. All data have been reviewed for artifacts using a rigorous quality control protocol and detailed case notes are provided.

Machine learning for automatic prediction of the quality of electrophysiological recordings.

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select "good" recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80-90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.

Registro Español de Desfibrilador Automático Implantable. VIII Informe Oficial del Grupo de Trabajo de Desfibrilador Automático Implantable de la Sociedad Española de Cardiología (2011) / Spanish implantable cardioverter-defibrillator registry. Eighth official report of the Spanish Society of Cardiology Working Group on Implantable Cardioverter-Defibrillators (2011)

Introducción y objetivos. Se presentan los resultados del Registro Español de Desfibrilador Automático Implantable de 2011 elaborado por la Sección de Electrofisiología y Arritmias de la Sociedad Española de Cardiología. Métodos. Se envió de forma prospectiva a la Sociedad Española de Cardiología la hoja de recogida de datos cumplimentada voluntariamente por cada equipo implantador. Resultados. El número de implantes comunicado fue 4.481 (el 83,6% del total de implantes estimado). El número de implantes por millón de habitantes fue 97 y el estimado, 116,2. Los primoimplantes fueron el 70,2%. Se obtuvieron datos de 167 hospitales (22 más que en 2010). La mayoría de los implantes (82,1%) se realizaron en varones. La media de edad fue 62,4 ± 14,1 años. La mayoría de los pacientes presentaban disfunción ventricular severa o moderada-severa y clase funcional II de la New York Heart Association. La cardiopatía más frecuente fue la isquémica, seguida de la dilatada. Las indicaciones por prevención primaria han seguido aumentando respecto a años anteriores y son ya el 70,6% de los primoimplantes. Los implantes realizados por electrofisiólogos también han seguido aumentado y ahora son el 78,4%. Conclusiones. El Registro de Desfibrilador Automático Implantable recoge información de casi el 84% de los implantes que se realizan en España. Es el primer año de nuestra serie en que el número de implantes ha disminuido ligeramente respecto al año anterior, como también ha ocurrido en el resto de Europa, aunque el número de implantes por prevención primaria ha seguido incrementándose (AU)

Introduction and objectives. To summarize the findings of the Spanish Implantable Cardioverter-Defibrillator Registry for 2011 compiled by the Electrophysiology and Arrhythmia Section of the Spanish Society of Cardiology. Methods. Each implantation team voluntarily and prospectively recorded data on a data collection form, which was then sent to the Spanish Society of Cardiology. Results. Overall, 4481 device implantations were notified, representing 83.6% of the estimated total number of implantations. The notified implantation rate was 97 per million population and the estimated total implantation rate was 116.2 per million. First implantations accounted for 70.2% of the total notified. Data were collected from 167 hospitals (22 more than in 2010). Most implantable cardioverter-defibrillator implantations took place in men (82.1%). The mean age was 62.4 (14.1) years. Most patients had severe or moderate-to-severe ventricular dysfunction and were in New York Heart Association functional class II. The most frequent underlying cardiac condition was ischemic heart disease, followed by dilated cardiomyopathy. The number of indications for primary prevention increased over the previous year and accounted for 70.6% of first implantations. Overall, 78.4% of implantable cardioverter-defibrillators were implanted by cardiac electrophysiologists. Conclusions. The 2011 Spanish Implantable Cardioverter-Defibrillator Registry includes data on almost 84% of all implantations of these devices performed in Spain. This was the first year in which the number of implants decreased slightly from the previous year, as also occurred in the rest of Europe. The percentage of implants for primary prevention continued to increase (AU)

Statistical testing in electrophysiological studies.

This article describes the mechanics and rationale of four different approaches to the statistical testing of electrophysiological data: (1) the Neyman-Pearson approach, (2) the permutation-based approach, (3), the bootstrap-based approach, and (4) the Bayesian approach. These approaches are evaluated from the perspective of electrophysiological studies, which involve multivariate (i.e., spatiotemporal) observations in which source-level signals are picked up to a certain extent by all sensors. Besides formal statistical techniques, there are also techniques that do not involve probability calculations but are very useful in dealing with multivariate data (i.e., verification of data-based predictions, cross-validation, and localizers). Moreover, data-based decision making can also be informed by mechanistic evidence that is provided by the structure in the data.

A data-driven framework for neural field modeling.

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch-Tung-Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.

Time-frequency analysis using damped-oscillator pseudo-wavelets: Application to electrophysiological recordings.

The damped-oscillator pseudo-wavelet is presented as a method of time-frequency analysis along with a new spectral density measure, the data power. An instantaneous phase can be defined for this pseudo-wavelet, and it is easily inverted. The data power measure is tested on both computer generated data and in vivo intrahippocampal electrophysiological recordings from a rat. The data power spectral density is found to give better time and frequency resolution than the more conventional total energy measure, and additionally shows intricate time-frequency structure in the rat that is altered in association with the emergence of epilepsy. With epileptogenesis, the baseline theta oscillation is severely degraded and is absorbed into a broader gamma band. There are also broad 600 Hz and 2000 Hz bands which localize to hippocampal layers that are distinct from those of the theta and gamma bands. The 600 Hz band decreases in prominence with epileptogenesis while the 2000 Hz band increases in prominence. The origins of these high frequency bands await further study. In general, we find that the damped-oscillator pseudo-wavelet is easy to use and is particularly suitable for problems where a wide range of oscillator frequencies is expected.

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis).

Statistical analysis of large-scale neuronal recording data.

Relating stimulus properties to the response properties of individual neurons and neuronal networks is a major goal of sensory research. Many investigators implant electrode arrays in multiple brain areas and record from chronically implanted electrodes over time to answer a variety of questions. Technical challenges related to analyzing large-scale neuronal recording data are not trivial. Several analysis methods traditionally used by neurophysiologists do not account for dependencies in the data that are inherent in multi-electrode recordings. In addition, when neurophysiological data are not best modeled by the normal distribution and when the variables of interest may not be linearly related, extensions of the linear modeling techniques are recommended. A variety of methods exist to analyze correlated data, even when the data are not normally distributed and the relationships are nonlinear. Here we review expansions of the Generalized Linear Model designed to address these data properties. Such methods are used in other research fields, and the application to large-scale neuronal recording data will enable investigators to determine the variable properties that convincingly contribute to the variances in the observed neuronal measures. Standard measures of neuron properties such as response magnitudes can be analyzed using these methods, and measures of neuronal network activity such as spike timing correlations can be analyzed as well. We have done just that in recordings from 100-electrode arrays implanted in the primary somatosensory cortex of owl monkeys. Here we illustrate how one example method, Generalized Estimating Equations analysis, is a useful method to apply to large-scale neuronal recordings.

Taquicardia originada en las venas pulmonares: ¿es útil la onda P durante ritmo sinusal? / Pulmonary vein tachycardia: is the sinus rhythm p-wave useful?

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In vivo performance of a microelectrode neural probe with integrated drug delivery.

OBJECT: The availability of sophisticated neural probes is a key prerequisite in the development of future brain-machine interfaces (BMIs). In this study, the authors developed and validated a neural probe design capable of simultaneous drug delivery and electrophysiology recordings in vivo. Focal drug delivery promises to extend dramatically the recording lives of neural probes, a limiting factor to clinical adoption of BMI technology. METHODS: To form the multifunctional neural probe, the authors affixed a 16-channel microfabricated silicon electrode array to a fused silica catheter. Three experiments were conducted in rats to characterize the performance of the device. Experiment 1 examined cellular damage from probe insertion and the drug distribution in tissue. Experiment 2 measured the effects of saline infusions delivered through the probe on concurrent electrophysiological measurements. Experiment 3 demonstrated that a physiologically relevant amount of drug can be delivered in a controlled fashion. For these experiments, Hoechst and propidium iodide stains were used to assess insertion trauma and the tissue distribution of the infusate. Artificial CSF (aCSF) and tetrodotoxin (TTX) were injected to determine the efficacy of drug delivery. RESULTS: The newly developed multifunctional neural probes were successfully inserted into rat cortex and were able to deliver fluids and drugs that resulted in the expected electrophysiological and histological responses. The damage from insertion of the device into brain tissue was substantially less than the volume of drug dispersion in tissue. Electrophysiological activity, including both individual spikes as well as local field potentials, was successfully recorded with this device during real-time drug delivery. No significant changes were seen in response to delivery of aCSF as a control experiment, whereas delivery of TTX produced the expected result of suppressing all spiking activity in the vicinity of the catheter outlet. CONCLUSIONS: Multifunctional neural probes such as the ones developed and validated within this study have great potential to help further understand the design space and criteria for the next generation of neural probe technology. By incorporating integrated drug delivery functionality into the probes, new treatment options for neurological disorders and regenerative neural interfaces using localized and feedback-controlled delivery of drugs can be realized in the near future.

Causes of transient instabilities in the dynamic clamp.

The dynamic clamp is a widely used method for integrating mathematical models with electrophysiological experiments. This method involves measuring the membrane voltage of a cell, using it to solve computational models of ion channel dynamics in real-time, and injecting the calculated current(s) back into the cell. Limitations of this technique include those associated with single electrode current clamping and the sampling effects caused by the dynamic clamp. In this study, we show that the combination of these limitations causes transient instabilities under certain conditions. Through physical experiments and simulations, we show that dynamic clamp instability is directly related to the sampling delay and the maximum simulated conductance being injected. It is exaggerated by insufficient electrode series resistance and capacitance compensation. Increasing the sampling rate of the dynamic clamp system increases dynamic clamp stability; however, this improvement, is constrained by how well the electrode series resistance and capacitance are compensated. At present, dynamic clamp sampling rates are justified solely on the temporal dynamics of the models being simulated; here we show that faster rates increase the stable range of operation for the dynamic clamp system. In addition, we show that commonly accepted levels of resistance compensation nevertheless significantly compromise the stability of a dynamic clamp system.

Analysis of between-trial and within-trial neural spiking dynamics.

Recording single-neuron activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-trial and within-trial neural spiking dynamics. Our model has the PSTH as a special case. We provide a framework for model estimation, model selection, goodness-of-fit analysis, and inference. In an analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we demonstrate how the SS-GLM may be used to answer frequently posed neurophysiological questions including, What is the nature of the between-trial and within-trial task-specific modulation of the neural spiking activity? How can we characterize learning-related neural dynamics? What are the timescales and characteristics of the neuron's biophysical properties? Our results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and within-trial neural dynamics readily visible in raster plots, as well as the less apparent fast (1-10 ms), intermediate (11-20 ms), and longer (>20 ms) timescale features of the neuron's biophysical properties.

Making quantal analysis more convenient, fast, and accurate: user-friendly software QUANTAN.

Quantal analysis of synaptic transmission is an important tool for understanding the mechanisms of synaptic plasticity and synaptic regulation. Although several custom-made and commercial algorithms have been created for the analysis of spontaneous synaptic activity, software for the analysis of action potential evoked release remains very limited. The present paper describes a user-friendly software package QUANTAN which has been created to analyze electrical recordings of postsynaptic responses. The program package is written using Borland C++ under Windows platform. QUANTAN employs and compares several algorithms to extract the average quantal content of synaptic responses, including direct quantal counts, the analysis of synaptic amplitudes, and the analysis of integrated current traces. The integration of several methods in one user-friendly program package makes quantal analysis of action potential evoked release more reliable and accurate. To evaluate the variability in quantal content, QUANTAN performs deconvolution of the distributions of amplitudes or areas of synaptic responses employing a ridge regression method. Other capabilities of QUANTAN include the analysis of the time-course and stationarity of quantal release. In summary, QUANTAN uses digital records of synaptic responses as an input and computes the distribution of quantal content and synaptic parameters. QUANTAN is freely available to other scholars over the internet.