scholarly journals Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Yan Chen ◽  
Xinyu Liu ◽  
Shan Li ◽  
Hong Wan
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Nearest Neighbor ◽  
Behavioral Outcomes ◽  
Field Potential ◽  
Principal Component ◽  
Functional Network ◽  
Functional Connections ◽  
Synchronization Likelihood ◽  
Behavior Outcomes

Recent studies indicate that the local field potential (LFP) carries information about an animal’s behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the k-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.

scholarly journals Increased neural correlations in primate auditory cortex during slow-wave sleep

2013 ◽  
Vol 109 (11) ◽  
pp. 2732-2738 ◽  
Author(s):  
Elias B. Issa ◽  
Xiaoqin Wang
Keyword(s):  
Auditory Cortex ◽  
Spontaneous Activity ◽  
Slow Wave ◽  
Local Field ◽  
Local Field Potential ◽  
Slow Wave Sleep ◽  
Field Potential ◽  
Sensory Stimulation ◽  
Acoustic Stimulation ◽  
Functional Connections

During sleep, changes in brain rhythms and neuromodulator levels in cortex modify the properties of individual neurons and the network as a whole. In principle, network-level interactions during sleep can be studied by observing covariation in spontaneous activity between neurons. Spontaneous activity, however, reflects only a portion of the effective functional connectivity that is activated by external and internal inputs (e.g., sensory stimulation, motor behavior, and mental activity), and it has been shown that neural responses are less correlated during external sensory stimulation than during spontaneous activity. Here, we took advantage of the unique property that the auditory cortex continues to respond to sounds during sleep and used external acoustic stimuli to activate cortical networks for studying neural interactions during sleep. We found that during slow-wave sleep (SWS), local (neuron-neuron) correlations are not reduced by acoustic stimulation remaining higher than in wakefulness and rapid eye movement sleep and remaining similar to spontaneous activity correlations. This high level of correlations during SWS complements previous work finding elevated global (local field potential-local field potential) correlations during sleep. Contrary to the prediction that slow oscillations in SWS would increase neural correlations during spontaneous activity, we found little change in neural correlations outside of periods of acoustic stimulation. Rather, these findings suggest that functional connections recruited in sound processing are modified during SWS and that slow rhythms, which in general are suppressed by sensory stimulation, are not the sole mechanism leading to elevated network correlations during sleep.

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.

scholarly journals Differential recordings of local field potential: A genuine tool to quantify functional connectivity

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0209001 ◽  
Author(s):  
Gabriel Meyer ◽  
Julien Carponcy ◽  
Paul Antoine Salin ◽  
Jean-Christophe Comte
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential

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

scholarly journals Primary sensorimotor cortex exhibits complex dependencies of spike-field coherence on neuronal firing rates, field power, and behavior

2018 ◽  
Vol 120 (1) ◽  
pp. 226-238 ◽  
Author(s):  
F. I. Arce-McShane ◽  
B. J. Sessle ◽  
C. F. Ross ◽  
N. G. Hatsopoulos
Keyword(s):  
Functional Connectivity ◽  
Firing Rate ◽  
Local Field ◽  
Sensorimotor Cortex ◽  
Field Potential ◽  
Neuronal Firing ◽  
Firing Rates ◽  
Linear Relationships ◽  
And Behavior ◽  
Field Coherence

Spike-field coherence (SFC) is widely used to assess cortico-cortical interactions during sensorimotor behavioral tasks by measuring the consistency of the relative phases between the spike train of a neuron and the concurrent local field potentials (LFPs). Interpretations of SFC as a measure of functional connectivity are complicated by theoretical work suggesting that estimates of SFC depend on overall neuronal activity. We evaluated the dependence of SFC on neuronal firing rates, LFP power, and behavior in the primary motor (MIo) and primary somatosensory (SIo) areas of the orofacial sensorimotor cortex of monkeys ( Macaca mulatta) during performance of a tongue-protrusion task. Although we occasionally observed monotonically increasing linear relationships between coherence and firing rate, we most often found highly complex, nonmonotonic relationships in both SIo and MIo and sometimes even found that coherence decreased with increasing firing rate. The lack of linear relationships was also true for both LFP power and tongue-protrusive force. Moreover, the ratio between maximal firing rate and the firing rate at peak coherence deviated significantly from unity, indicating that MIo and SIo neurons achieved maximal SFC at a submaximal level of spiking. Overall, these results point to complex relationships of SFC to firing rates, LFP power, and behavior during sensorimotor cortico-cortical interactions: coherence is a measure of functional connectivity whose magnitude is not a mere monotonic reflection of changes in firing rate, LFP power, or the relevantly controlled behavioral parameter. NEW & NOTEWORTHY The concern that estimates of spike-field coherence depend on the firing rates of single neurons has influenced analytical methods employed by experimental studies investigating the functional interactions between cortical areas. Our study shows that the overwhelming majority of the estimated spike-field coherence exhibited complex relations with firing rates of neurons in the orofacial sensorimotor cortex. The lack of monotonic relations was also evident after testing the influence of local field potential power and force on spike-field coherence.

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 Correction: Differential recordings of local field potential: A genuine tool to quantify functional connectivity

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0228147
Author(s):  
Gabriel Meyer ◽  
Julien Carponcy ◽  
Paul Antoine Salin ◽  
Jean-Christophe Comte
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential
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