scholarly journals Differential Recordings of Local Field Potential:A Genuine Tool to Quantify Functional Connectivity

2018 ◽  
Author(s):  
Meyer Gabriel ◽  
Caponcy Julien ◽  
Paul A. Salin ◽  
Comte Jean-Christophe
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Signal To Noise Ratio ◽  
Field Potential ◽  
Low Frequency ◽  
Large Area ◽  
Signal To Noise ◽  
Cortical Areas ◽  
Electrophysiological Method ◽  
Local Field Potential Lfp

AbstractLocal field potential (LFP) recording is a very useful electrophysiological method to study brain processes. However, this method is criticized for recording low frequency activity in a large area of extracellular space potentially contaminated by distal activity. Here, we theoretically and experimentally compare ground-referenced (RR) with differential recordings (DR). We analyze electrical activity in the rat cortex with these two methods. Compared with RR, DR reveals the importance of local phasic oscillatory activities and their coherence between cortical areas. Finally, we show that DR provides a more faithful assessment of functional connectivity caused by an increase in the signal to noise ratio, and of the delay in the propagation of information between two cortical structures.

Comparison of Direction and Object Selectivity of Local Field Potentials and Single Units in Macaque Posterior Parietal Cortex During Prehension

2007 ◽  
Vol 97 (5) ◽  
pp. 3684-3695 ◽  
Author(s):  
Itay Asher ◽  
Eran Stark ◽  
Moshe Abeles ◽  
Yifat Prut
Keyword(s):  
Parietal Cortex ◽  
Local Field ◽  
Posterior Parietal Cortex ◽  
Signal To Noise Ratio ◽  
Field Potential ◽  
Single Units ◽  
Simple Method ◽  
Related Information ◽  
Posterior Parietal ◽  
Local Field Potential Lfp

Recent studies have shown that the local field potential (LFP) can provide a simple method for obtaining an accurate measure of reaching and saccade behaviors. However, it is not clear whether this signal is equally informative with respect to more complex movements. Here we recorded LFPs and single units (SUs) from different areas in the posterior parietal cortex of macaques during a prehension task and compared LFP selectivity with SU selectivity. We found that parietal LFPs were often selective to target direction or object and that percentages of selective LFPs were similar to percentages of selective SUs. Nevertheless, SUs were more informative than LFPs in several respects. Preferred directions and objects of LFPs usually deviated from a uniform distribution, unlike preferences of SUs. Furthermore, preferences of LFPs did not reflect preferences of SUs even when the two signals were recorded simultaneously via the same electrode. Additionally, selectivity of movement-evoked LFPs appeared only after movement onset, whereas SUs frequently showed premovement selectivity. Spectral analysis revealed a lower signal-to-noise ratio of the LFP signal. Different frequency bands derived from a single LFP site showed inconsistent preferences. Significant relations with target parameters were found for all tested bands of LFP, but effects in the fast (gamma) band exhibited properties that were consistent with contamination of the LFP by residual spiking activity. Taken together, our results suggest that the LFP provides a simple method for extracting ample movement-related information. However, some of its properties make it less adequate for predicting rapidly changing movements.

scholarly journals Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis: Validation and Comparison with Non-Parametric Granger Causality

2019 ◽  
Author(s):  
Timothy O. West ◽  
David M. Halliday ◽  
Steven L. Bressler ◽  
Simon F. Farmer ◽  
Vladimir Litvak
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Local Field ◽  
Autoregressive Model ◽  
Field Potential ◽  
List Type ◽  
Signal To Noise ◽  
Neural Data ◽  
Signal Routing ◽  
Non Parametric

AbstractBackground‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015)MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing.ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.HighlightsNon-parametric directionality (NPD) is a novel directed connectivity metric.NPD estimates are equivalent to Granger causality but more robust to signal confounds.Multivariate extensions of NPD can correctly identify signal routing.AbbreviationsdFCDirected functional connectivityEEGElectroencephalogramLFPLocal field potentialMEGMagnetoencephalogramMVARMultivariate autoregressive (model)NPDNon-parametric directionalityNPGNon-parametric Granger (causality)SMASupplementary motor areaSNRSignal-to-noise ratioSTNSubthalamic Nucleus

PERCEPTUAL SUPPRESSION REVEALED BY ADAPTIVE MULTI-SCALE ENTROPY ANALYSIS OF LOCAL FIELD POTENTIAL IN MONKEY VISUAL CORTEX

2013 ◽  
Vol 23 (02) ◽  
pp. 1350005 ◽  
Author(s):  
MENG HU ◽  
HUALOU LIANG
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Local Field Potential ◽  
Linear Method ◽  
Field Potential ◽  
Cross Entropy ◽  
Neural Dynamic ◽  
Area V4 ◽  
Multi Scale ◽  
Cortical Areas

Generalized flash suppression (GFS), in which a salient visual stimulus can be rendered invisible despite continuous retinal input, provides a rare opportunity to directly study the neural mechanism of visual perception. Previous work based on linear methods, such as spectral analysis, on local field potential (LFP) during GFS has shown that the LFP power at distinctive frequency bands are differentially modulated by perceptual suppression. Yet, the linear method alone may be insufficient for the full assessment of neural dynamic due to the fundamentally nonlinear nature of neural signals. In this study, we set forth to analyze the LFP data collected from multiple visual areas in V1, V2 and V4 of macaque monkeys while performing the GFS task using a nonlinear method — adaptive multi-scale entropy (AME) — to reveal the neural dynamic of perceptual suppression. In addition, we propose a new cross-entropy measure at multiple scales, namely adaptive multi-scale cross-entropy (AMCE), to assess the nonlinear functional connectivity between two cortical areas. We show that: (1) multi-scale entropy exhibits percept-related changes in all three areas, with higher entropy observed during perceptual suppression; (2) the magnitude of the perception-related entropy changes increases systematically over successive hierarchical stages (i.e. from lower areas V1 to V2, up to higher area V4); and (3) cross-entropy between any two cortical areas reveals higher degree of asynchrony or dissimilarity during perceptual suppression, indicating a decreased functional connectivity between cortical areas. These results, taken together, suggest that perceptual suppression is related to a reduced functional connectivity and increased uncertainty of neural responses, and the modulation of perceptual suppression is more effective at higher visual cortical areas. AME is demonstrated to be a useful technique in revealing the underlying dynamic of nonlinear/nonstationary neural signal.

scholarly journals Estimation of neural network model parameters from local field potentials (LFPs)

2019 ◽  
Author(s):  
Jan-Eirik W. Skaar ◽  
Alexander J. Stasik ◽  
Espen Hagen ◽  
Torbjørn V. Ness ◽  
Gaute T. Einevoll
Keyword(s):  
Network Model ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Low Frequency ◽  
Network Models ◽  
Neural Nets ◽  
Model Parameters ◽  
Neuron Network ◽  
Local Field Potential Lfp

AbstractMost modeling in systems neuroscience has beendescriptivewhere neural representations, that is, ‘receptive fields’, have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented bymechanisticmodels based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to infer properties of the neuronal network. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three synaptic weight parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary ‘background’ LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation.Significance statementMost of what we have learned about brain networksin vivohave come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the ground-truth parameters are known. By application of convolutional neural nets (CNNs) we found that the synaptic weights indeed could be accurately estimated from ‘background’ LFP signals, suggesting a future key role for LFP in development of network models.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

Image Acquisition Using Non-Pixeled Amorphous Silicon Based Sensors

2001 ◽  
Vol 685 ◽  
Author(s):  
M. Fernandes ◽  
Yu. Vygranenko ◽  
J. Martins ◽  
M. Vieira
Keyword(s):  
Amorphous Silicon ◽  
Signal To Noise Ratio ◽  
Image Acquisition ◽  
Short Circuit ◽  
Acquisition Time ◽  
Large Area ◽  
Signal To Noise ◽  
Scanning Beam ◽  
Noise Ratio ◽  
Silicon Based

AbstractWe suggest to enhance the performance of image acquisition systems based on large area amorphous silicon based sensors by optimizing the readout parameters such as the intensity and cross-section of scanner beam, acquisition time and bias conditions. The main output device characteristics as image responsivity, signal to noise ratio and spatial resolution were analyzed in open circuit, short circuit and photodiode modes. The result show that the highest signal to noise ratio and best dark to bright ratio can be achieved in short circuit mode.It was shown that the sensor resolution is related to the basic device parameters and, in practice, limited by the acquisition time and scanning beam properties. The scanning beam spot size limits the resolution due to the overlapping of dark and illuminated zones leading to a blurring effect on the final image and a consequent degradation in the resolution.

scholarly journals Signal-to-noise ratio in the membrane potential of the owl's auditory coincidence detectors

2012 ◽  
Vol 108 (10) ◽  
pp. 2837-2845 ◽  
Author(s):  
Go Ashida ◽  
Kazuo Funabiki ◽  
Paula T. Kuokkanen ◽  
Richard Kempter ◽  
Catherine E. Carr
Keyword(s):  
Neural Circuit ◽  
Signal To Noise Ratio ◽  
Phase Locking ◽  
Low Frequency ◽  
Membrane Potentials ◽  
Signal To Noise ◽  
Theoretical Predictions ◽  
Noise Ratio ◽  
Band Limited

Owls use interaural time differences (ITDs) to locate a sound source. They compute ITD in a specialized neural circuit that consists of axonal delay lines from the cochlear nucleus magnocellularis (NM) and coincidence detectors in the nucleus laminaris (NL). Recent physiological recordings have shown that tonal stimuli induce oscillatory membrane potentials in NL neurons (Funabiki K, Ashida G, Konishi M. J Neurosci 31: 15245–15256, 2011). The amplitude of these oscillations varies with ITD and is strongly correlated to the firing rate. The oscillation, termed the sound analog potential, has the same frequency as the stimulus tone and is presumed to originate from phase-locked synaptic inputs from NM fibers. To investigate how these oscillatory membrane potentials are generated, we applied recently developed signal-to-noise ratio (SNR) analysis techniques (Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. J Neurophysiol 104: 2274–2290, 2010) to the intracellular waveforms obtained in vivo. Our theoretical prediction of the band-limited SNRs agreed with experimental data for mid- to high-frequency (>2 kHz) NL neurons. For low-frequency (≤2 kHz) NL neurons, however, measured SNRs were lower than theoretical predictions. These results suggest that the number of independent NM fibers converging onto each NL neuron and/or the population-averaged degree of phase-locking of the NM fibers could be significantly smaller in the low-frequency NL region than estimated for higher best-frequency NL.

DCT Image Steganography Analysis for Privacy Preserving Data Mining

2016 ◽  
Vol 7 (3) ◽  
pp. 1-9 ◽  
Author(s):  
Sahar A. El-Rahman Ismail ◽  
Dalal Al Makhdhub ◽  
Amal A. Al Qahtani ◽  
Ghadah A. Al Shabanat ◽  
Nouf M. Omair ◽  
...  
Keyword(s):  
Data Mining ◽  
Information Hiding ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Signal To Noise ◽  
Privacy Preserving Data Mining ◽  
Frequency Domains ◽  
Privacy And Confidentiality

We live in an information era where sensitive information extracted from data mining systems is vulnerable to exploitation. Privacy preserving data mining aims to prevent the discovery of sensitive information. Information hiding systems provide excellent privacy and confidentiality, where securing confidential communications in public channels can be achieved using steganography. A cover media are exploited using steganography techniques where they hide the payload's existence within appropriate multimedia carriers. This paper aims to study steganography techniques in spatial and frequency domains, and then analyzes the performance of Discrete Cosine Transform (DCT) based steganography using the low frequency and the middle frequency to compare their performance using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The experimental results show that middle frequency has the larger message capacity and best performance.

scholarly journals High-Sensitivity Large-Area Photodiode Read-Out Using a Divide-and-Conquer Technique

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6316
Author(s):  
Guillermo Royo ◽  
Carlos Sánchez-Azqueta ◽  
Concepción Aldea ◽  
Santiago Celma
Keyword(s):  
Traditional Approach ◽  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
Divide And Conquer ◽  
Transimpedance Amplifier ◽  
Large Area ◽  
Signal To Noise ◽  
Remarkable Improvement ◽  
The Core ◽  
Novel Technique

In this letter, we present a novel technique to increase the sensitivity of optical read-out with large integrated photodiodes (PD). It consists of manufacturing the PD in several pieces, instead of a single device, and connecting a dedicated transimpedance amplifier (TIA) to each of these pieces. The output signals of the TIAs are combined, achieving a higher signal-to-noise ratio than with the traditional approach. This work shows a remarkable improvement in the sensitivity and transimpedance without the need for additional modifications or compensation techniques. As a result, an increase in sensitivity of 7.9 dBm and transimpedance of 8.7 dBΩ for the same bandwidth is achieved when dividing the photodiode read-out into 16 parallel paths. The proposed divide-and-conquer technique can be applied to any TIA design, and it is also independent of the core amplifier structure and fabrication process, which means it is compatible with every technology allowing the integration of PDs.

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