scholarly journals Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1070
Author(s):  
Liyong Yin ◽  
Fan Tian ◽  
Rui Hu ◽  
Zhaohui Li ◽  
Fuzai Yin
Keyword(s):  
Mutual Information ◽  
Performance Test ◽  
Low Frequency ◽  
Neural Oscillations ◽  
Low Frequency Oscillation ◽  
High Frequency Oscillations ◽  
Phase Amplitude ◽  
Data Length ◽  
Permutation Analysis ◽  
Frequency Phase

Cross-frequency phase–amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.

scholarly journals A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jessica K Nadalin ◽  
Louis-Emmanuel Martinet ◽  
Ethan B Blackwood ◽  
Meng-Chen Lo ◽  
Alik S Widge ◽  
...  
Keyword(s):  
High Frequency ◽  
Statistical Power ◽  
Brain Activity ◽  
Low Frequency ◽  
Modeling Framework ◽  
Frequency Coupling ◽  
Phase Amplitude ◽  
Cross Frequency Coupling ◽  
Frequency Phase

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

scholarly journals Measuring Phase-Amplitude Coupling Between Neuronal Oscillations of Different Frequencies

2010 ◽  
Vol 104 (2) ◽  
pp. 1195-1210 ◽  
Author(s):  
Adriano B. L. Tort ◽  
Robert Komorowski ◽  
Howard Eichenbaum ◽  
Nancy Kopell
Keyword(s):  
Low Frequency ◽  
Amplitude Distribution ◽  
Neuronal Oscillations ◽  
High Frequency Oscillations ◽  
Electrophysiological Signals ◽  
Performance Benchmarks ◽  
Phase Amplitude ◽  
First Time ◽  
Cross Frequency Coupling ◽  
The Brain

Neuronal oscillations of different frequencies can interact in several ways. There has been particular interest in the modulation of the amplitude of high-frequency oscillations by the phase of low-frequency oscillations, since recent evidence suggests a functional role for this type of cross-frequency coupling (CFC). Phase-amplitude coupling has been reported in continuous electrophysiological signals obtained from the brain at both local and macroscopic levels. In the present work, we present a new measure for assessing phase-amplitude CFC. This measure is defined as an adaptation of the Kullback–Leibler distance—a function that is used to infer the distance between two distributions—and calculates how much an empirical amplitude distribution-like function over phase bins deviates from the uniform distribution. We show that a CFC measure defined this way is well suited for assessing the intensity of phase-amplitude coupling. We also review seven other CFC measures; we show that, by some performance benchmarks, our measure is especially attractive for this task. We also discuss some technical aspects related to the measure, such as the length of the epochs used for these analyses and the utility of surrogate control analyses. Finally, we apply the measure and a related CFC tool to actual hippocampal recordings obtained from freely moving rats and show, for the first time, that the CA3 and CA1 regions present different CFC characteristics.

scholarly journals Visualization of Phase-Amplitude Coupling Using Rhythmic High-Frequency Activity

2020 ◽  
Author(s):  
Hiroaki Hashimoto ◽  
Hui Ming Khoo ◽  
Takufumi Yanagisawa ◽  
Naoki Tani ◽  
Satoru Oshino ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
Low Frequency ◽  
Θ Rhythm ◽  
Human Epilepsy ◽  
High Frequency Activity ◽  
Phase Amplitude ◽  
Intracranial Electrode ◽  
Intracranial Electrodes ◽  
Frequency Phase

AbstractObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are gaining attention as key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures.MethodsWe used intracranial electrodes to record seizures of symptomatic focal epilepsy (15 seizures in seven patients). Ripples (80–250 Hz), as representative of HFAs, were evaluated along with PAC. The synchronization index (SI), representing PAC, was used to analyze the coupling between the amplitude of ripples and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were represented by circles that were scaled linearly to the power changes of ripple.ResultsThe main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The video showed that fluctuation of the diameter of these circles indicated the rhythmic changes during significant high values of θ-ripple PAC.ConclusionsWe inferred that ripple activities occurring during seizure evolution were modulated by θ rhythm. In addition, we concluded that rhythmic circles’ fluctuation presented in the video represents the PAC phenomenon. Our video is thus a useful tool for understanding how ripple activity is modulated by the low-frequency phase in relation with PAC.

scholarly journals Study of WAMS Big Data Elastic Store Model in Low-Frequency Oscillation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hua Song ◽  
Yongjun Chen
Keyword(s):  
Big Data ◽  
Performance Test ◽  
High Reliability ◽  
Low Frequency ◽  
Wide Area ◽  
Forced Oscillations ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
New Model ◽  
Oscillation Analysis

Low-frequency oscillation (LFO) is among the key factors that threaten interconnected power grids’ security and stability and restrict transfer capability. In particular, power systems incur now and then weak damping and forced oscillations. To monitor and control LFO, the principles of online calculation and analysis of two types of LFO are studied in this paper. The big data of wide area measurements is an important information source of LFO analysis. Hence, we should make sure it has access to online system continuously, accurately, and reliably. Nevertheless, the conventional linear data store model has difficulty to meet the processing requirements of high rate, multiple concurrency, and high reliability. To deal with it, a new model of double-set elastic store is proposed in this paper. It transforms the storage space linear model to plane model, realizes the management of power system substation group sets in vertical direction and the management of multiple Phase Measurement Units (PMU) uploading data sets in horizontal direction, and hence solves the problems in continuous and reliable access of the wide area measurements data, which is dense and of large scale and has quick update rate, providing technical support of accuracy and robustness of LFO analysis. The performance test and practical application of the proposed new model of double-set elastic store validate its accuracy.

scholarly journals Propofol Anesthesia Increases Long-range Frontoparietal Corticocortical Interaction in the Oculomotor Circuit in Macaque Monkeys

2019 ◽  
Vol 130 (4) ◽  
pp. 560-571 ◽  
Author(s):  
Li Ma ◽  
Wentai Liu ◽  
Andrew E. Hudson
Keyword(s):  
Functional Connectivity ◽  
Long Range ◽  
Low Frequency ◽  
Fold Increase ◽  
Frontal Eye Field ◽  
Lateral Intraparietal Area ◽  
Cortical Areas ◽  
Eye Field ◽  
Phase Amplitude ◽  
Frequency Phase

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Frontoparietal functional connectivity decreases with multiple anesthetics using electrophysiology and functional imaging. This decrease has been proposed as a final common functional pathway to produce anesthesia. Two alternative measures of long-range cortical interaction are coherence and phase-amplitude coupling. Although phase-amplitude coupling within frontal cortex changes with propofol administration, the effects of propofol on phase-amplitude coupling between different cortical areas have not previously been reported. Based on phase-amplitude coupling observed within frontal lobe during the anesthetized period, it was hypothesized that between-lead phase-amplitude coupling analysis should decrease between frontal and parietal leads during propofol anesthesia. Methods A published monkey electrocorticography data set (N = 2 animals) was used to test for interactions in the cortical oculomotor circuit, which is robustly interconnected in primates, and in the visual system during propofol anesthesia using coherence and interarea phase-amplitude coupling. Results Propofol induces coherent slow oscillations in visual and oculomotor networks made up of cortical areas with strong anatomic projections. Frontal eye field within-area phase-amplitude coupling increases with a time course consistent with a bolus response to intravenous propofol (modulation index increase of 12.6-fold). Contrary to the hypothesis, interareal phase-amplitude coupling also increases with propofol, with the largest increase in phase-amplitude coupling in frontal eye field low-frequency phase modulating lateral intraparietal area β-power (27-fold increase) and visual area 2 low-frequency phase altering visual area 1 β-power (19-fold increase). Conclusions Propofol anesthesia induces coherent oscillations and increases certain frontoparietal interactions in oculomotor cortices. Frontal eye field and lateral intraparietal area show increased coherence and phase-amplitude coupling. Visual areas 2 and 1, which have similar anatomic projection patterns, show similar increases in phase-amplitude coupling, suggesting higher order feedback increases in influence during propofol anesthesia relative to wakefulness. This suggests that functional connectivity between frontal and parietal areas is not uniformly decreased by anesthetics.

scholarly journals Multitaper Estimates of Phase-Amplitude Coupling

2021 ◽  
Author(s):  
Kyle Q. Lepage ◽  
Cavan N. Fleming ◽  
Mark Witcher ◽  
Sujith Vijayan
Keyword(s):  
High Frequency ◽  
Broad Band ◽  
Low Frequency ◽  
Partial Coherence ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Statistical Measures ◽  
Frequency Components ◽  
Phase Amplitude

AbstractPhase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing phase-amplitude coupling with existing statistical measures have been well-documented. The limitations of these measures include: (i) response to non-oscillatory, high-frequency, broad-band activity, (ii) response to high-frequency components of the low-frequency oscillation, (iii) adhoc selection of analysis frequency-intervals, and (iv) reliance upon data shuffling to assess statistical significance. In this work, a multitaper phase-amplitude coupling estimator is proposed that addresses issues (i)-(iv) above. Specifically, issue (i) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (ii) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (iii) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (iv).

scholarly journals Phase-based coordination of hippocampal and neocortical oscillations during human sleep

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Roy Cox ◽  
Theodor Rüber ◽  
Bernhard P. Staresina ◽  
Juergen Fell
Keyword(s):  
Eye Movement ◽  
Neural Oscillations ◽  
Rapid Eye Movement ◽  
Physiological Basis ◽  
Slow Oscillations ◽  
Neurosurgical Patients ◽  
High Gamma ◽  
Phase Amplitude ◽  
Frequency Phase

AbstractDuring sleep, new memories undergo a gradual transfer from hippocampal (HPC) to neocortical (NC) sites. Precisely timed neural oscillations are thought to mediate this sleep-dependent memory consolidation, but exactly how sleep oscillations instantiate the HPC-NC dialog remains elusive. Employing overnight invasive electroencephalography in ten neurosurgical patients, we identified three broad classes of phase-based communication between HPC and lateral temporal NC. First, we observed interregional phase synchrony for non-rapid eye movement (NREM) spindles, and N2 and rapid eye movement (REM) theta activity. Second, we found asymmetrical N3 cross-frequency phase-amplitude coupling between HPC slow oscillations (SOs) and NC activity spanning the delta to high-gamma/ripple bands, but not in the opposite direction. Lastly, N2 theta and NREM spindle synchrony were themselves modulated by HPC SOs. These forms of interregional communication emphasize the role of HPC SOs in the HPC-NC dialog, and may offer a physiological basis for the sleep-dependent reorganization of mnemonic content.

ANALYSIS OF STAND RESEARCH RESULTS OF AXIAL DYNAMIC FORCE IN DRILLING BY THREE ROLLER CONE BIT

2019 ◽  
pp. 81-93
Author(s):  
В. М. Мойсишин ◽  
M. V. Lyskanych ◽  
R. A. Zhovniruk ◽  
Ye. P. Majkovych
Keyword(s):  
Low Frequency ◽  
Maximum Energy ◽  
Dynamic Force ◽  
Low Frequency Oscillation ◽  
Drilling Tool ◽  
Rock Destruction ◽  
Frequency Components ◽  
Harmonic Components ◽  
Experimental Values ◽  
Harmful Effects

The purpose of the proposed article is to establish the causes of oscillations of drilling tool and the basic laws of the distribution of the total energy of the process of changing the axial dynamic force over frequencies of spectrum. Variable factors during experiments on the classical plan were the rigidity of drilling tool and the hardness of the rock. According to the results of research, the main power of the process of change of axial dynamic force during drilling of three roller cone bits is in the frequency range 0-32 Hz in which three harmonic frequency components are allocated which correspond to the theoretical values of low-frequency and gear oscillations of the chisel and proper oscillations of the bit. The experimental values of frequencies of harmonic components of energy and normalized spectrum as well as the magnitude of the dispersion of the axial dynamic force and its normalized values at these frequencies are presented. It has been found that with decreasing rigidity of the drilling tool maximum energy of axial dynamic force moves from the low-frequency oscillation region to the tooth oscillation area, intensifying the process of rock destruction and, at the same time, protecting the tool from the harmful effects of the vibrations of the bit. Reducing the rigidity of the drilling tool protects the bit from the harmful effects of the vibrations generated by the stand. The energy reductions in these fluctuations range from 47 to 77%.

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