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

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

AbstractCross 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 how CFC evolves during seizures and is affected by electrical stimuli.

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 A Time-Series Model of Phase Amplitude Cross Frequency Coupling and Comparison of Spectral Characteristics with Neural Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Kyle Q. Lepage ◽  
Sujith Vijayan
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Time Series Model ◽  
High Frequency Component ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposed models, based on multiplying a pulsatile function of a low-frequency oscillation (θ) with an unobserved and high-frequency component, yields a process with a spectrum that is consistent with observation. Other models, such as those employing a biphasic pulsatile function of a low-frequency oscillation, are demonstrated to be less suitable. We introduce the full stochastic process time series model as a summation of three component weak-sense stationary (WSS) processes, namely,θ,γ, andη, withηa1/fαnoise process. Theγprocess is constructed as a product of a latent and unobserved high-frequency processxwith a function of the lagged, low-frequency oscillatory component (θ). After demonstrating that the model process is WSS, an appropriate method of simulation is introduced based upon the WSS property. This work may be of interest to researchers seeking to connect inhibitory and excitatory dynamics directly to observation in a model that accounts for known temporal dependence or to researchers seeking to examine what can occur in a multiplicative time-domain CFC mechanism.

scholarly journals Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex

2018 ◽  
Author(s):  
Christian D. Márton ◽  
Makoto Fukushima ◽  
Corrie R. Camalier ◽  
Simon R. Schultz ◽  
Bruno B. Averbeck
Keyword(s):  
Information Processing ◽  
Auditory Cortex ◽  
Predictive Coding ◽  
Low Frequency ◽  
Information Need ◽  
Top Down ◽  
Bottom Up ◽  
Low Frequencies ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

AbstractPredictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom up interactions in sensory processing. The theory has suggested that specific frequency bands relay bottom-up and top-down information (e.g. “γ up, β down”). But it remains unclear whether this notion generalizes to cross-frequency interactions. Furthermore, most of the evidence so far comes from visual pathways. Here we examined cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up made prominent use of low frequencies: low-to-low frequency (θ, α, β) and low frequency-to-high γ couplings were predominant top-down, while low frequency-to-low γ couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting they are a general feature of information processing in auditory cortex. Moreover, our findings showed that low-frequency PAC alternated between predominantly top-down or bottom-up over time. Altogether, this suggests sensory information need not be propagated along separate frequencies upwards and downwards. Rather, information can be unmixed by having low frequencies couple to distinct frequency ranges in the target region, and by alternating top-down and bottom-up processing over time.1SignificanceThe brain consists of highly interconnected cortical areas, yet the patterns in directional cortical communication are not fully understood, in particular with regards to interactions between different signal components across frequencies. We employed a a unified, computationally advantageous Granger-causal framework to examine bi-directional cross-frequency interactions across four sectors of the auditory cortical hierarchy in macaques. Our findings extend the view of cross-frequency interactions in auditory cortex, suggesting they also play a prominent role in top-down processing. Our findings also suggest information need not be propagated along separate channels up and down the cortical hierarchy, with important implications for theories of information processing in the brain such as predictive coding.

scholarly journals Impact of Catheter Ablation on Sleep Quality and Relationship Between Sleep Stability and Recurrence of Paroxysmal Atrial Fibrillation After Successful Ablation: 24‐Hour Holter‐Based Cardiopulmonary Coupling Analysis

2020 ◽  
Vol 9 (23) ◽  
Author(s):  
Woohyeun Kim ◽  
Jin Oh Na ◽  
Robert J. Thomas ◽  
Won Young Jang ◽  
Dong Oh Kang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Sleep Quality ◽  
High Frequency ◽  
Low Frequency ◽  
Coupling Analysis ◽  
Very Low Frequency ◽  
Cardiopulmonary Coupling ◽  
Frequency Coupling ◽  
Af Recurrence

Background Sleep fragmentation and sleep apnea are common in patients with atrial fibrillation (AF). We investigated the impact of radio‐frequency catheter ablation (RFCA) on sleep quality in patients with paroxysmal AF and the effect of a change in sleep quality on recurrence of AF. Methods and Results Of 445 patients who underwent RFCA for paroxysmal AF between October 2007 and January 2017, we analyzed 225 patients who had a 24‐hour Holter test within 6 months before RFCA. Sleep quality was assessed by cardiopulmonary coupling analysis using 24‐hour Holter data. We compared cardiopulmonary coupling parameters (high‐frequency coupling, low‐frequency coupling, very‐low‐frequency coupling) before and after RFCA. Six months after RFCA, the high‐frequency coupling (marker of stable sleep) and very‐low‐frequency coupling (rapid eye movement/wake marker) was significantly increased (29.84%–36.15%; P <0.001; and 26.20%–28.76%; P =0.002, respectively) while low‐frequency coupling (unstable sleep marker) was decreased (41.25%–32.13%; P <0.001). We divided patients into 3 tertiles according to sleep quality before RFCA, and the risk of AF recurrence in each group was compared. The second tertile was used as a reference; patients with unstable sleep (Tertile 3) had a significantly lower risk of AF recurrence (hazard ratio [HR], 0.32; 95% CI, 0.12–0.83 for high‐frequency coupling; and HR, 0.22; 95% CI, 0.09–0.58 for low‐frequency coupling). Conclusions Sleep quality improved after RFCA in patients with paroxysmal AF. The recurrence rate was significantly lower in patients who had unstable sleep before RFCA. These results suggest that RFCA can influence sleep quality, and sleep quality assessment before RFCA may provide a risk marker for recurrence after RFCA in patients with paroxysmal AF.

scholarly journals Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes

2020 ◽  
Author(s):  
Felix Bröhl ◽  
Christoph Kayser
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Low Frequency ◽  
List Type ◽  
Theta Band ◽  
Acoustic Fluctuations ◽  
Eeg Recordings ◽  
Band Limited ◽  
Speech Envelope ◽  
Speech Tracking

AbstractThe representation of speech in the brain is often examined by measuring the alignment of rhythmic brain activity to the speech envelope. To conveniently quantify this alignment (termed ‘speech tracking’) many studies consider the overall speech envelope, which combines acoustic fluctuations across the spectral range. Using EEG recordings, we show that using this overall envelope can provide a distorted picture on speech encoding. We systematically investigated the encoding of spectrally-limited speech-derived envelopes presented by individual and multiple noise carriers in the human brain. Tracking in the 1 to 6 Hz EEG bands differentially reflected low (0.2 – 0.83 kHz) and high (2.66 – 8 kHz) frequency speech-derived envelopes. This was independent of the specific carrier frequency but sensitive to attentional manipulations, and reflects the context-dependent emphasis of information from distinct spectral ranges of the speech envelope in low frequency brain activity. As low and high frequency speech envelopes relate to distinct phonemic features, our results suggest that functionally distinct processes contribute to speech tracking in the same EEG bands, and are easily confounded when considering the overall speech envelope.HighlightsDelta/theta band EEG tracks band-limited speech-derived envelopes similar to real speechLow and high frequency speech-derived envelopes are represented differentiallyHigh-frequency derived envelopes are more susceptible to attentional and contextual manipulationsDelta band tracking shifts towards low frequency derived envelopes with more acoustic detail

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.

High-Frequency Oscillations and Epileptogenic Network

2021 ◽  
Vol 19 ◽  
Author(s):  
Xiaonan Li ◽  
Herui Zhang ◽  
Huanling Lai ◽  
Jiaoyang Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
High Frequency Oscillation ◽  
Epileptogenic Zone ◽  
Network Development ◽  
High Frequency Oscillations ◽  
Frequency Coupling ◽  
Different Characteristics ◽  
The Relationship ◽  
Cross Frequency Coupling

: Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80–600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological high-frequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.

scholarly journals Multivariate cross-frequency coupling via generalized eigendecomposition

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Michael X Cohen
Keyword(s):  
Null Hypothesis ◽  
Brain Activity ◽  
Field Potential ◽  
Simulated Data ◽  
Electrophysiological Recordings ◽  
Null Hypothesis Testing ◽  
Frequency Coupling ◽  
Cross Frequency Coupling ◽  
Field Coherence ◽  
New Framework

This paper presents a new framework for analyzing cross-frequency coupling in multichannel electrophysiological recordings. The generalized eigendecomposition-based cross-frequency coupling framework (gedCFC) is inspired by source-separation algorithms combined with dynamics of mesoscopic neurophysiological processes. It is unaffected by factors that confound traditional CFC methods—such as non-stationarities, non-sinusoidality, and non-uniform phase angle distributions—attractive properties considering that brain activity is neither stationary nor perfectly sinusoidal. The gedCFC framework opens new opportunities for conceptualizing CFC as network interactions with diverse spatial/topographical distributions. Five specific methods within the gedCFC framework are detailed, these are validated in simulated data and applied in several empirical datasets. gedCFC accurately recovers physiologically plausible CFC patterns embedded in noise that causes traditional CFC methods to perform poorly. The paper also demonstrates that spike-field coherence in multichannel local field potential data can be analyzed using the gedCFC framework, which provides significant advantages over traditional spike-field coherence analyses. Null-hypothesis testing is also discussed.

scholarly journals Low-frequency phase measurement with high-frequency squeezing

2012 ◽  
Vol 20 (16) ◽  
pp. 18173 ◽  
Author(s):  
Zehui Zhai ◽  
Jiangrui Gao
Keyword(s):  
High Frequency ◽  
Phase Measurement ◽  
Low Frequency ◽  
Frequency Phase
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