scholarly journals Revealing the Dynamic Nature of Amplitude Modulated Neural Entrainment With Holo-Hilbert Spectral Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Chi-Hung Juan ◽  
Kien Trong Nguyen ◽  
Wei-Kuang Liang ◽  
Andrew J. Quinn ◽  
Yen-Hsun Chen ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Temporal Dynamics ◽  
A Priori ◽  
Sensory Stimuli ◽  
Electrophysiological Data ◽  
Study Results ◽  
Non Linear ◽  
Neural Entrainment ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

Patterns in external sensory stimuli can rapidly entrain neuronally generated oscillations observed in electrophysiological data. Here, we manipulated the temporal dynamics of visual stimuli with cross-frequency coupling (CFC) characteristics to generate steady-state visual evoked potentials (SSVEPs). Although CFC plays a pivotal role in neural communication, some cases reporting CFC may be false positives due to non-sinusoidal oscillations that can generate artificially inflated coupling values. Additionally, temporal characteristics of dynamic and non-linear neural oscillations cannot be fully derived with conventional Fourier-based analyses mainly due to trade off of temporal resolution for frequency precision. In an attempt to resolve these limitations of linear analytical methods, Holo-Hilbert Spectral Analysis (HHSA) was investigated as a potential approach for examination of non-linear and non-stationary CFC dynamics in this study. Results from both simulation and SSVEPs demonstrated that temporal dynamic and non-linear CFC features can be revealed with HHSA. Specifically, the results of simulation showed that the HHSA is less affected by the non-sinusoidal oscillation and showed possible cross frequency interactions embedded in the simulation without any a priori assumptions. In the SSVEPs, we found that the time-varying cross-frequency interaction and the bidirectional coupling between delta and alpha/beta bands can be observed using HHSA, confirming dynamic physiological signatures of neural entrainment related to cross-frequency coupling. These findings not only validate the efficacy of the HHSA in revealing the natural characteristics of signals, but also shed new light on further applications in analysis of brain electrophysiological data with the aim of understanding the functional roles of neuronal oscillation in various cognitive functions.

scholarly journals An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Pierpaolo Sorrentino ◽  
Michele Ambrosanio ◽  
Rosaria Rucco ◽  
Fabio Baselice
Keyword(s):  
A Priori ◽  
Behavioral Responses ◽  
Real Data ◽  
Frequency Synchronization ◽  
Brain Areas ◽  
Neural Ensembles ◽  
Frequency Coupling ◽  
Cross Frequency Coupling ◽  
Reconstructed Data

Abstract Background Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization. Methods In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals. Results The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment. Conclusions In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data.

scholarly journals Dynamic modulation of theta–gamma coupling during rapid eye movement sleep

SLEEP ◽  
2019 ◽  
Vol 42 (12) ◽  
Author(s):  
Mojtaba Bandarabadi ◽  
Richard Boyce ◽  
Carolina Gutierrez Herrera ◽  
Claudio L Bassetti ◽  
Sylvain Williams ◽  
...  
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Temporal Dynamics ◽  
Brain Structures ◽  
Rapid Eye Movement ◽  
Sleep Episode ◽  
Frequency Coupling ◽  
Theta Phase ◽  
Hippocampal Networks ◽  
Cross Frequency Coupling

Abstract Theta phase modulates gamma amplitude in hippocampal networks during spatial navigation and rapid eye movement (REM) sleep. This cross-frequency coupling has been linked to working memory and spatial memory consolidation; however, its spatial and temporal dynamics remains unclear. Here, we first investigate the dynamics of theta–gamma interactions using multiple frequency and temporal scales in simultaneous recordings from hippocampal CA3, CA1, subiculum, and parietal cortex in freely moving mice. We found that theta phase dynamically modulates distinct gamma bands during REM sleep. Interestingly, we further show that theta–gamma coupling switches between recorded brain structures during REM sleep and progressively increases over a single REM sleep episode. Finally, we show that optogenetic silencing of septohippocampal GABAergic projections significantly impedes both theta–gamma coupling and theta phase coherence. Collectively, our study shows that phase-space (i.e. cross-frequency coupling) coding of information during REM sleep is orchestrated across time and space consistent with region-specific processing of information during REM sleep including learning and memory.

scholarly journals Detection of cross-frequency coupling between brain areas: an extension of phase-linearity measurement

2020 ◽  
Author(s):  
Pierpaolo Sorrentino ◽  
Michele Ambrosanio ◽  
Rosaria Rucco ◽  
Joana Cabral ◽  
Leonardo L. Gollo ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
A Priori ◽  
Broad Band ◽  
Main Idea ◽  
Theoretical Explanation ◽  
Frequency Synchronization ◽  
Brain Areas ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

AbstractThe current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited sample sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.

scholarly journals Non-linear auto-regressive models for cross-frequency coupling in neural time series

2017 ◽  
Vol 13 (12) ◽  
pp. e1005893 ◽  
Author(s):  
Tom Dupré la Tour ◽  
Lucille Tallot ◽  
Laetitia Grabot ◽  
Valérie Doyère ◽  
Virginie van Wassenhove ◽  
...  
Keyword(s):  
Time Series ◽  
Non Linear ◽  
Auto Regressive ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

scholarly journals Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

2017 ◽  
Author(s):  
Tom Dupré la Tour ◽  
Lucille Tallot ◽  
Laetitia Grabot ◽  
Valérie Doyère ◽  
Virginie van Wassenhove ◽  
...  
Keyword(s):  
Time Series ◽  
Model Selection ◽  
High Frequency ◽  
Goodness Of Fit ◽  
Model Based ◽  
Non Linear ◽  
Auto Regressive ◽  
Frequency Coupling ◽  
Cross Frequency Coupling ◽  
Driving Signal

AbstractWe address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.Author SummaryNeural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation.

scholarly journals Mutual Information of Multiple Rhythms for EEG Signals

2020 ◽  
Vol 14 ◽  
Author(s):  
Antonio José Ibáñez-Molina ◽  
María Felipa Soriano ◽  
Sergio Iglesias-Parro
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
A Priori ◽  
Basic Mechanism ◽  
Frequency Component ◽  
Brain Functioning ◽  
Single Method ◽  
Spatiotemporal Scales ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

Electroencephalograms (EEG) are one of the most commonly used measures to study brain functioning at a macroscopic level. The structure of the EEG time series is composed of many neural rhythms interacting at different spatiotemporal scales. This interaction is often named as cross frequency coupling, and consists of transient couplings between various parameters of different rhythms. This coupling has been hypothesized to be a basic mechanism involved in cognitive functions. There are several methods to measure cross frequency coupling between two rhythms but no single method has been selected as the gold standard. Current methods only serve to explore two rhythms at a time, are computationally demanding, and impose assumptions about the nature of the signal. Here we present a new approach based on Information Theory in which we can characterize the interaction of more than two rhythms in a given EEG time series. It estimates the mutual information of multiple rhythms (MIMR) extracted from the original signal. We tested this measure using simulated and real empirical data. We simulated signals composed of three frequencies and background noise. When the coupling between each frequency component was manipulated, we found a significant variation in the MIMR. In addition, we found that MIMR was sensitive to real EEG time series collected with open vs. closed eyes, and intra-cortical recordings from epileptic and non-epileptic signals registered at different regions of the brain. MIMR is presented as a tool to explore multiple rhythms, easy to compute and without a priori assumptions.

scholarly journals Influence of Slope Direction on the Soil Seed Bank and Seedling Regeneration of Castanopsis hystrix Seed Rain

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 500
Author(s):  
Zong Zhao ◽  
Yong Liu ◽  
Hongyan Jia ◽  
Wensheng Sun ◽  
Angang Ming ◽  
...  
Keyword(s):  
Seed Bank ◽  
Temporal Dynamics ◽  
Soil Seed Bank ◽  
Soil Layer ◽  
Seed Rain ◽  
Seed Vigor ◽  
Study Results ◽  
The Impact ◽  
Mature Seeds

Objective: To investigate the impact of different slope directions on the quantity and quality of the soil seed bank and seedling germination process of Castanopsis hystrix plantations. Method: Fixed sample plots in forest stands of Castanopsis hystrix were established on different slope directions (sunny slope, semi-sunny slope, semi-shady slope, and shady slope). The characteristics of the forest stand were investigated, and per-wood scaling was carried out. The temporal dynamics of the seed rain and seed bank were quantified using seed rain collectors and by collecting soil samples from different depths. The quantity and quality of the seeds were determined, and the vigor of mature seeds was measured throughout the study. Results: (1) The diffusion of Castanopsis hystrix seed rain started in mid-September, reached its peak from late October to early November, and ended in mid-December. (2) The dissemination process, occurrence time, and composition of the seed rain varied between the different slope directions. The seed rain intensity on the semi-sunny slope was the highest (572.75 ± 9.50 grains∙m−2), followed by the sunny slope (515.60 ± 10.28 grains∙m−2), the semi-shady slope (382.13 ± 12.11 grains∙m−2), and finally the shady slope (208.00 ± 11.35 grains∙m−2). The seed rain on the sunny slope diffused earliest and lasted the longest, while the seed rain on the shady slope diffused latest and lasted the shortest time. Seed vigor and the proportion of mature seeds within the seed rain were greatest on the semi-sunny slope, followed by the sunny slope, semi-shady slope, and the shady slope. (3) From the end of the seed rain to August of the following year, the amount of total reserves of the soil seed banks was highest on the semi-sunny slope, followed by the sunny slope then the semi-shady slope, and it was the lowest on the shady slope. The amount of mature, immature, gnawed seeds and seed vigor of the soil seed bank in various slope directions showed a decreasing trend with time. The seeds of the seed bank in all slope directions were mainly distributed in the litter layer, followed by the 0–2 cm humus layer, and only a few seeds were present in the 2–5 cm soil layer. (4) The seedling density of Castanopsis hystrix differed significantly on the different slope directions. The semi-sunny slope had the most seedlings, followed by the sunny slope, semi-shady slope, and the shady slope. Conclusions: The environmental conditions of the semi-sunny slope were found to be most suitable for the seed germination and seedling growth of Castanopsis hystrix, and more conducive to the regeneration and restoration of its population.

scholarly journals Delta-mediated cross-frequency coupling organizes oscillatory activity across the rat cortico-basal ganglia network

2013 ◽  
Vol 7 ◽  
Author(s):  
Jon López-Azcárate ◽  
María Jesús Nicolás ◽  
Ivan Cordon ◽  
Manuel Alegre ◽  
Miguel Valencia ◽  
...  
Keyword(s):  
Basal Ganglia ◽  
Oscillatory Activity ◽  
Frequency Coupling ◽  
Cross Frequency Coupling
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