Local Field Potential Data Research Articles

Regional Activity in the Rat Anterior Cingulate Cortex and Insula during Persistence and Quitting in a Physical-Effort Task.

As animals carry out behaviors, particularly costly ones, they must constantly assess whether or not to persist in the behavior or quit. The anterior cingulate cortex (ACC) has been shown to assess the value of behaviors and to be especially sensitive to physical effort costs. Complimentary to these functions, the insula is thought to represent the internal state of the animal including factors such as hunger, thirst, and fatigue. Using a novel weight-lifting task for rats, we characterized the local field potential (LFP) activity of the ACC and anterior insula (AI) during effort expenditure. In the task, male rats are challenged to work for sucrose reward, which costs progressively more effort over time to obtain. Rats are able to quit the task at any point. We found modest shifts in LFP theta (7–9 Hz) activity as the task got progressively more difficult in terms of absolute effort expenditure. However, when the LFP data were analyzed based on the relative progress of the rat toward quitting the task, substantial shifts in LFP power in the theta and gamma (55–100 Hz) frequency bands were observed in ACC and AI. Both ACC and AI theta power decreased as the rats got closer to quitting, while ACC and AI gamma power increased. Furthermore, coherency between ACC and AI in the delta (2–4 Hz) range shifted alongside the performance state of the rat. Overall, we show that ACC and AI LFP activity changes correlate to the relative performance state of rats in an effort-based task.

Low-cost silicon neural probe: fabrication, electrochemical characterization and in vivo validation

This paper presents the fabrication of a silicon neural probe using low-cost microfabrication technologies, such as thin-films deposition, blade dicing, and photolithography. The metal stack that forms the 9 microelectrodes of 50 × 50 µm2 area, the tracks and the pads were made of Ti and Pt, while the passivation stack was SiO2 and Si3N4. The fabricated probe was characterized using electrochemical impedance spectroscopy, before and after deposition of poly(3,4-ethylene-dioxythiophene) (PEDOT) on Pt microelectrodes. The electrochemical deposition of PEDOT, a conductive polymer, reduced the impedance of the Pt microelectrodes. The neural probe with PEDOT was used for in vivo electrophysiological acute recordings in an adult rat. The extracellular recordings were filtered to obtain the spike and local field potential (LFP) data, using Butterworth bandpass filters of 400–6000 Hz and 0.1–300 Hz, respectively. The results obtained with the fabricated neural probe validated its functionality, comparing with the signals acquired with a commercial neural probe, and the viability of the fabrication process, which avoids high–cost and complex etching processes.

Online Mapping With the Deep Brain Stimulation Lead: A Novel Targeting Tool in Parkinson's Disease.

Beta-frequency oscillations (13-30 Hz) are a subthalamic hallmark in patients with Parkinson's disease, and there is increased interest in their utility as an intraoperative marker. The objectives of this study were to assess whether beta activity measured directly from macrocontacts of deep brain stimulation leads could be used (a) as an intraoperative electrophysiological approach for guiding lead placements and (b) for physiologically informed stimulation delivery. Every millimeter along the surgical trajectory, local field-potential data were collected from each macrocontact, and power spectral densities were calculated and visualized (n = 39 patients). This was done for online intraoperative functional mapping and post hoc statistical analyses using 2 methods: generating distributions of spectral activity along surgical trajectories and direct delineation (presence versus lack) of beta peaks. In a subset of patients, this approach was corroborated by microelectrode recordings. Furthermore, the match rate between beta peaks at the final target position and the clinically determined best stimulation site were assessed. Subthalamic recording sites were delineated by both methods of reconstructing functional topographies of spectral activity along surgical trajectories at the group level (P < 0.0001). Beta peaks were detected when any portion of the 1.5 mm macrocontact was within the microelectrode-defined subthalamic border. The highest beta peak at the final implantation site corresponded to the site of active stimulation in 73.3% of hemispheres (P < 0.0001). In 93.3% of hemispheres, active stimulation corresponded to the first-highest or second-highest beta peak. Online measures of beta activity with the deep brain stimulation macroelectrode can be used to inform surgical lead placement and contribute to optimization of stimulation programming procedures. © 2020 International Parkinson and Movement Disorder Society.

Modeling dependence via copula of functionals of Fourier coefficients

The goal of this paper is to develop a measure for characterizing complex dependence between time series that cannot be captured by traditional measures such as correlation and coherence. Our approach is to use copula models of functionals of the Fourier coefficients which is a generalization of coherence. Here, we use standard parametric copula models with a single parameter from both elliptical and Archimedean families. Our approach is to analyze changes in activity in local field potentials in the rat cortex prior to and immediately following the onset of stroke. We present the necessary theoretical background, the multivariate models and an illustration of our methodology on these local field potential data. Simulations with nonlinear dependent data reveal that there is information that is missed by not taking into account dependence on specific frequencies. Moreover, these simulations demonstrate how our proposed method captures more complex nonlinear dependence between time series. Finally, we illustrate our copula-based approach in the analysis of local field potentials of rats.

High Order Spectral Analysis of Local Field Potential Data on a Subgroup of Parkinson's Disease Patients Who Are Carriers of Mutations in the Glucocerebrosidase (GBA) Gene Undergoing DBS Electrode Placement

High Order Spectral Analysis of Local Field Potential Data on a Subgroup of Parkinson's Disease Patients Who Are Carriers of Mutations in the Glucocerebrosidase (GBA) Gene Undergoing DBS Electrode Placement

Adaptive spike-artifact removal from local field potentials uncovers prominent beta and gamma band neuronal synchronization

BackgroundMany neurons synchronize their action potentials to the phase of local field potential (LFP) fluctuations in one or more frequency bands. Analyzing this spike-to-LFP synchronization is challenging, however, when neural spikes and LFP are generated in the same local circuit, because the spike’s action potential waveform leak into the LFP and distort phase synchrony estimates. Existing approaches to address this spike bleed-through artifact relied on removing the average action potential waveforms of neurons, but this leaves artifacts in the LFP and distorts synchrony estimates. New methodWe describe a spike-removal method that surpasses these limitations by decomposing individual action potentials into their frequency components before their removal from the LFP. The adaptively estimated frequency components allow for variable spread, strength and temporal variation of the spike artifact. ResultsThis adaptive approach effectively removes spike bleed-through artifacts in synthetic data with known ground truth, and in single neuron and LFP recordings in nonhuman primate striatum. For a large population of neurons with both narrow and broad action potential waveforms, the use of adaptive artifact removal uncovered 20−35 Hz beta and 35−45 Hz gamma band spike-LFP synchronization that would have remained contaminated otherwise. Comparison with existing methodsWe demonstrate that adaptive spike-artifact removal cleans LFP data that remained contaminated when applying existing Bayesian and non-Bayesian methods of average spike-artifact removal. ConclusionsApplying adaptive spike-removal from field potentials allows to estimate the phase at which neurons synchronize and the consistency of their phase-locked firing for both beta and low gamma frequencies. These metrics may prove essential to understand cell-to-circuit neuronal interactions in multiple brain systems.

Dopamine receptor antagonists effects on low-dimensional attractors of local field potentials in optogenetic mice.

The goal of this study was to investigate the effects of acute cocaine injection or dopamine (DA) receptor antagonists on the medial prefrontal cortex (mPFC) gamma oscillations and their relationship to short term neuroadaptation that may mediate addiction. For this purpose, optogenetically evoked local field potentials (LFPs) in response to a brief 10 ms laser light pulse were recorded from 17 mice. D1-like receptor antagonist SCH 23390 or D2-like receptor antagonist sulpiride, or both, were administered either before or after cocaine. A Euclidian distance-based dendrogram classifier separated the 100 trials for each animal in disjoint clusters. When baseline and DA receptor antagonists trials were combined in a single trial, a minimum of 20% overlap occurred in some dendrogram clusters, which suggests a possible common, invariant, dynamic mechanism shared by both baseline and DA receptor antagonists data. The delay-embedding method of neural activity reconstruction was performed using the correlation time and mutual information to determine the lag/correlation time of LFPs and false nearest neighbors to determine the embedding dimension. We found that DA receptor antagonists applied before cocaine cancels out the effect of cocaine and leaves the lag time distributions at baseline values. On the other hand, cocaine applied after DA receptor antagonists shifts the lag time distributions to longer durations, i.e. increase the correlation time of LFPs. Fourier analysis showed that a reasonable accurate decomposition of the LFP data can be obtained with a relatively small (less than ten) Fourier coefficients.

Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

Tools for characterizing non-linear spectral dependence between spontaneous brain signals are developed, based on the use of parametric copula models (both bivariate and vine models) applied on the magnitude of Fourier coefficients rather than using coherence. The motivation is an experiment on rats that studied the impact of stroke on the connectivity structure (dependence) between local field potentials recorded by various microelectrodes. The following major questions are addressed. The first is to determine changepoints in the regime within a microelectrode for a given frequency band based on a difference between the cumulative distribution functions modeled for each epoch (small window of time). The proposed approach is an iterative algorithm which compares each successive bivariate copulas on all the epochs range, using a bivariate Kolmogorov–Smirnov statistic. The second is to determine if such changes are present only in some microelectrodes versus generalized across the entire network. These issues are addressed by comparing Vine-copulas models fitted for each epoch. The necessary framework is provided and the effectiveness of the methods is shown through the results for the local field potential data analysis of a rat.

Classification of Brainwaves Using Convolutional Neural Network.

Classification of brainwaves in recordings is of considerable interest to neuroscience and medical communities. Classification techniques used presently depend on the extraction of low-level features from the recordings, which in turn affects the classification performance. To alleviate this problem, this paper proposes an end-to-end approach using Convolutional Neural Network (CNN) which has been shown to detect complex patterns in a signal by exploiting its spatiotemporal nature. The present study uses time and frequency axes for the classification using synthesized Local Field Potential (LFP) data. The results are analyzed and compared with the FFT technique. In all the results, the CNN outperforms the FFT by a significant margin especially when the noise level is high. This study also sheds light on certain signal characteristics affecting network performance.

Low-cost solution for rodent home-cage behaviour monitoring.

In the current research on measuring complex behaviours/phenotyping in rodents, most of the experimental design requires the experimenter to remove the animal from its home-cage environment and place it in an unfamiliar apparatus (novel environment). This interaction may influence behaviour, general well-being, and the metabolism of the animal, affecting the phenotypic outcome even if the data collection method is automated. Most of the commercially available solutions for home-cage monitoring are expensive and usually lack the flexibility to be incorporated with existing home-cages. Here we present a low-cost solution for monitoring home-cage behaviour of rodents that can be easily incorporated to practically any available rodent home-cage. To demonstrate the use of our system, we reliably predict the sleep/wake state of mice in their home-cage using only video. We validate these results using hippocampal local field potential (LFP) and electromyography (EMG) data. Our approach provides a low-cost flexible methodology for high-throughput studies of sleep, circadian rhythm and rodent behaviour with minimal experimenter interference.

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data.

Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.

Single-Trial Decoding from Local Field Potential Using Bag of Word Representation

Neural decoding allows us to study the brain functions by investigating the relationship between a stimulus and the corresponding response. Recently, the local field potential (LFP) has been targeted as a hallmark of brain activity for neural decoding. Despite several decoding methods, there is still a lack of a comprehensive framework to decode cognitive functions in an integrated structure. Here, we addressed this issue by developing a dictionary-based method to represent the LFP signals via a bag-of-words (BOW) approach. First, we defined a general dictionary consisting of various Gabor wavelets as the words which enabled us to represent LFPs in word domain. For each trial, the LFP signal was convolved with the dictionary words. The integral of the absolute value and the mean phase of the complex output were considered as histogram weights. In the next step, using cross-validation leave-one-out method, the trials were split into the training and test sets. The weights of each individual word were swapped across trials within a certain category of the training set while the sequential order was maintained. Finally, the test trial was classified using label voting in the k-nearest training trials. We conducted the proposed method on two independent LFP data sets, recorded from the rat primary auditory cortex (A1) and monkey middle temporal area in order to evaluate its efficiency. In addition to the chance level, the proposed method was compared with a standard BOW approach that has been extended recently for biomedical signals classification. Results show a high efficiency (~ 15% improvement in decoding accuracy) of the proposed method. Together, the aforementioned method provides a comprehensive framework for single-trial decoding from short-length electrophysiological signals.

Time-frequency Relevancy Analysis between Local Field Potentials and Lever Pressing Motion of Rats.

Cortical Local field potentials (LFPs) in primary motor cortex (M1) include significant movement information. As a movement decoding source, LFPs have the advantage of being long-lasting and stable. However, LFPs contain activity of neural ensemble from neighborhood or even distant cortical areas. Information fully relevant to the movement task is necessary to be extracted. Existing studies indicate the movement relevant information is embedded in different frequency ranges. The analysis generally assumes linear relationship between band-limited LFPs and movement or specifically bases on the decoding method. The contribution of each frequency band is exhaustedly search for a variety of frequency band combinations, which increases the computation complexity. Considering the distinct frequency ranges may also exhibit relevancy over time, we propose to analyze the time-frequency relevancy between LFP signals and the rat lever-press tasks with a local learning based algorithm method. This approach extracts the important time-frequency features from the nonlinear LFP data structure, and labels the sequence of importance for time-frequency features. Comparing to the greedy search, our method demonstrate more clear time-frequency contribution, especially 50-100Hz signal and 200-400Hz signal for holding and pressing respectively, and are validated by better decoding results.

Compensatory Plasticity in the Lateral Extrastriate Visual Cortex Preserves Audiovisual Temporal Processing following Adult-Onset Hearing Loss

Partial hearing loss can cause neurons in the auditory and audiovisual cortices to increase their responsiveness to visual stimuli; however, behavioral studies in hearing-impaired humans and rats have found that the perceptual ability to accurately judge the relative timing of auditory and visual stimuli is largely unaffected. To investigate the neurophysiological basis of how audiovisual temporal acuity may be preserved in the presence of hearing loss-induced crossmodal plasticity, we exposed adult rats to loud noise and two weeks later performed in vivo electrophysiological recordings in two neighboring regions within the lateral extrastriate visual (V2L) cortex—a multisensory zone known to be responsive to audiovisual stimuli (V2L-Mz) and a predominantly auditory zone (V2L-Az). To examine the cortical layer-specific effects at the level of postsynaptic potentials, a current source density (CSD) analysis was applied to the local field potential (LFP) data recorded in response to auditory and visual stimuli presented at various stimulus onset asynchronies (SOAs). As predicted, differential effects were observed in the neighboring cortical regions' postnoise exposure. Most notably, an analysis of the strength of multisensory response interactions revealed that V2L-Mz lost its sensitivity to the relative timing of the auditory and visual stimuli, due to an increased responsiveness to visual stimulation that produced a prominent audiovisual response irrespective of the SOA. In contrast, not only did the V2L-Az in noise-exposed rats become more responsive to visual stimuli but neurons in this region also inherited the capacity to process audiovisual stimuli with the temporal precision and specificity that was previously restricted to the V2L-Mz. Thus, the present study provides the first demonstration that audiovisual temporal processing can be preserved following moderate hearing loss via compensatory plasticity in the higher-order sensory cortices that is ultimately characterized by a functional transition in the cortical region capable of temporal sensitivity.

Eltoprazine prevents levodopa-induced dyskinesias by reducing causal interactions for theta oscillations in the dorsolateral striatum and substantia nigra pars reticulate

Oscillatory activities within basal ganglia (BG) circuitry in L-DOPA induced dyskinesia (LID), a condition that occurs in patients with Parkinson disease (PD), are not well understood. The aims of this study were firstly to investigate oscillations in main BG input and output structures—the dorsolateral striatum (dStr) and substantia nigra pars reticulata (SNr), respectively— including the direction of oscillation information flow, and secondly to investigate the effects of 5-HT1A/B receptor agonism with eltoprazine on oscillatory activities and abnormal involuntary movements (AIMs) characteristic. To this end, we conducted local field potential (LFP) electrophysiology in the dStr and SNr of LID rats simultaneous with AIM scoring. The LFP data were submitted to power spectral density, coherence, and partial Granger causality analyses. AIM data were analyzed relative to simultaneous oscillatory activities, with and without eltoprazine. We obtained four major findings. 1) Theta band (5–8 Hz) oscillations were enhanced in the dStr and SNr of LID rats. 2) Theta power correlated with AIM scores in the 180-min period after the last LID-inducing L-DOPA injection, but not with daily summed AIM scores during LID development. 3) Oscillatory information flowed from the dStr to the SNr. 4) Chronic eltoprazine reduced BG theta activity in LID rats and normalized information flow directionality, relative to that in LID rats not given eltoprazine. These results indicate that dStr activity plays a determinative role in the causal interactions of theta oscillations and that serotonergic inhibition may suppress dyskinesia by reducing dStr-SNr theta activity and restoring theta network information flow.

Estimation of Vector Autoregressive Parameters and Granger Causality From Noisy Multichannel Data.

The objective of this paper is to estimate the parameters of a multivariate autoregressive process from noisy multichannel data. Using a multivariate generalization of the Cadzow method, we propose a new method for estimating autoregressive parameters from noisy data: the nonlinear Cadzow method. We show that our method outperforms existing multivariate methods such as higher order Yule-Walker method and Kalman EM method on simulated data. We apply our method to estimation of Granger causality from noisy data and again obtain superior results compared to previous methods. Finally, when applied to experimental local field potential data from monkey somatosensory and motor cortical areas, our method produces results consistent with cortical physiology. The proposed nonlinear Cadzow method outperforms existing methods in obtaining denoised estimates of multivariate autoregressive parameters. Since multichannel recordings have become commonplace in biomedical applications ranging from discovering functional connectivity in the brain to speech data analysis and these recordings are inevitably contaminated by measurement noise, we believe our method has the potential for significant impact.

Adaptation and Inhibition Control Pathological Synchronization in a Model of Focal Epileptic Seizure.

Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential (LFP) data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.

Biophysical basis of subthalamic local field potentials recorded from deep brain stimulation electrodes.

Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient's unique brain anatomy and electrode placement impact the recordings. Therefore, we developed a patient-specific computational framework to analyze LFP recordings within a clinical DBS context. We selected a subject with Parkinson's disease implanted with a Medtronic Activa PC+S DBS system and reconstructed their subthalamic nucleus (STN) and DBS electrode location using medical imaging data. The patient-specific STN volume was populated with 235,280 multicompartment STN neuron models, providing a neuron density consistent with histological measurements. Each neuron received time-varying synaptic inputs and generated transmembrane currents that gave rise to the LFP signal recorded at DBS electrode contacts residing in a finite element volume conductor model. We then used the model to study the role of synchronous beta-band inputs to the STN neurons on the recorded power spectrum. Three bipolar pairs of simultaneous clinical LFP recordings were used in combination with an optimization algorithm to customize the neural activity parameters in the model to the patient. The optimized model predicted a 2.4-mm radius of beta-synchronous neurons located in the dorsolateral STN. These theoretical results enable biophysical dissection of the LFP signal at the cellular level with direct comparison to the clinical recordings, and the model system provides a scientific platform to help guide the design of DBS technology focused on the use of subthalamic beta activity in closed-loop algorithms. NEW & NOTEWORTHY The analysis of deep brain stimulation of local field potential (LFP) data is rapidly expanding from scientific curiosity to the basis for clinical biomarkers capable of improving the therapeutic efficacy of stimulation. With this growing clinical importance comes a growing need to understand the underlying electrophysiological fundamentals of the signals and the factors contributing to their modulation. Our model reconstructs the clinical LFP from first principles and highlights the importance of patient-specific factors in dictating the signals recorded.

WaveCSD: A method for estimating transmembrane currents originated from propagating neuronal activity in the neocortex: Application to study cortical spreading depression

BackgroundRecent years have witnessed an upsurge in the development of methods for estimating current source densities (CSDs) in the neocortical tissue from their recorded local field potential (LFP) reflections using microelectrode arrays. Among these, methods utilizing linear arrays work under the assumption that CSDs vary as a function of cortical depth; whereas they are constant in the tangential direction, infinitely or in a confined cylinder. This assumption is violated in the analysis of neuronal activity propagating along the neocortical sheet, e.g. propagation of alpha waves or cortical spreading depression. New methodHere, we developed a novel mathematical method (waveCSD) for CSD analysis of LFPs associated with a planar wave of neocortical neuronal activity propagating at a constant velocity towards a linear probe. ResultsResults show that the algorithm is robust to the presence of noise in LFP data and uncertainties in knowledge of propagation velocity. Also, results show high level of accuracy of the method in a wide range of electrode resolutions. Using in vivo experimental recordings from the rat neocortex, we employed waveCSD to characterize transmembrane currents associated with cortical spreading depressions. Comparison with existing method(s)Simulation results indicate that waveCSD has a significantly higher reconstruction accuracy compared to the widely-used inverse CSD method (iCSD), and the regularized kernel CSD method (kCSD), in the analysis of CSDs originating from propagating neuronal activity. ConclusionsThe waveCSD method provides a theoretical platform for estimation of transmembrane currents from their LFPs in experimental paradigms involving wave propagation.

On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation.

Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed-loop control. In particular, optogenetics-the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface application-specified integrated circuit (ASIC) for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08 μV) recording channels optimized for recording local field potentials (LFPs) (0.1-300Hz bandwidth, 5mV range, sampled 10-bit@4kHz), which are more stable for chronic applications. For stimulation, it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1mA range) and pulse-width modulation for high-radiance light emitting diodes (LEDs). The system includes a fully digital interface using a serial peripheral interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a finite state machine (FSM), which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially available 0.35 μm CMOS technology occupying a 1.95mm 1.10mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, in vitro and in vivo experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.