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 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.

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 Thalamocortical dysrhythmia in intraoperative recordings of focal epilepsy

2019 ◽  
Vol 121 (6) ◽  
pp. 2020-2027 ◽  
Author(s):  
Daniel J. Martire ◽  
Simeon Wong ◽  
Mirriam Mikhail ◽  
Ayako Ochi ◽  
Hiroshi Otsubo ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Large Scale ◽  
Focal Epilepsy ◽  
Critical Role ◽  
Epileptogenic Zone ◽  
Thalamocortical Dysrhythmia ◽  
High Frequency Activity ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

Resonant interactions between the thalamus and cortex subserve a critical role for maintenance of consciousness as well as cognitive functions. In states of abnormal thalamic inhibition, thalamocortical dysrhythmia (TCD) has been described. The characteristics of TCD include a slowing of resting oscillations, ectopic high-frequency activity, and increased cross-frequency coupling. Here, we demonstrate the presence of TCD in four patients who underwent resective epilepsy surgery with chronically implanted electrodes under anesthesia, continuously recording activity from brain regions at the periphery of the epileptogenic zone before and after resection. Following resection, we report an acceleration of the large-scale network resting frequency coincident with decreases in cross-frequency phase-amplitude coupling. Interregional functional connectivity in the surrounding cortex was also increased following resection of the epileptogenic focus. These findings provide evidence for the presence of TCD in focal epilepsy and highlight the importance of reciprocal thalamocortical oscillatory interactions in defining novel biomarkers for resective surgeries. NEW & NOTEWORTHY Thalamocortical dysrhythmia (TCD) occurs in the context of thalamic dysfacilitation and is characterized by slowing of resting oscillations, ectopic high-frequency activity, and cross-frequency coupling. We provide evidence for TCD in focal epilepsy by studying electrophysiological changes occurring at the periphery of the resection margin. We report acceleration of resting activity coincident with decreased cross-frequency coupling and increased functional connectivity. The study of TCD in epilepsy has implications as a biomarker and therapeutic target.

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 Multivariate cross-frequency coupling via generalized eigendecomposition

2017 ◽  
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

AbstractThis 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, with validations in simulated data and applications in several empirical datasets. gedCFC accurately recovers physiologically plausible CFC patterns embedded in noise where traditional CFC methods perform poorly. It is also demonstrated that spike-field coherence in multichannel local field potential data can be analyzed using the gedCFC framework, with significant advantages over traditional spike-field coherence analyses. Null-hypothesis testing is also discussed.

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 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 Ten-Hour Exposure to Low-Dose Ketamine Enhances Corticostriatal Cross-Frequency Coupling and Hippocampal Broad-Band Gamma Oscillations

2018 ◽  
Vol 12 ◽  
Author(s):  
Tony Ye ◽  
Mitchell J. Bartlett ◽  
Matthew B. Schmit ◽  
Scott J. Sherman ◽  
Torsten Falk ◽  
...  
Keyword(s):  
Low Dose ◽  
Broad Band ◽  
Gamma Oscillations ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

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.

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