Multivariate cross-frequency coupling via generalized eigendecomposition

Null Hypothesis Testing ◽

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.

Multivariate cross-frequency coupling via generalized eigendecomposition

eLife ◽

10.7554/elife.21792 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 24

Author(s):

Michael X Cohen

Keyword(s):

Null Hypothesis ◽

Brain Activity ◽

Field Potential ◽

Simulated Data ◽

Null Hypothesis Testing ◽

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.

An oscillator ensemble model of sequence learning

10.1101/645085 ◽

2019 ◽

Author(s):

Alexander Maye ◽

Peng Wang ◽

Jonathan Daume ◽

Xiaolin Hu ◽

Andreas K. Engel

Keyword(s):

Brain Activity ◽

Phase Locking ◽

Sequence Elements ◽

Synchronization Phase ◽

Frequency Coupling ◽

Human Participants ◽

The Individual ◽

Good Agreement

AbstractLearning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral ‘fingerprint’ which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this ‘fingerprint’ by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.

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

10.1101/2020.11.25.398628 ◽

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.

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

eLife ◽

10.7554/elife.44287 ◽

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 ◽

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.

Cross-frequency coupling in real and simulated data

International Journal of Psychophysiology ◽

10.1016/j.ijpsycho.2012.07.054 ◽

2012 ◽

Vol 85 (3) ◽

pp. 382-383

Author(s):

V. Müller ◽

V. Jirsa ◽

U. Lindenberger

Keyword(s):

Simulated Data ◽

Frequency Coupling ◽

Cross Frequency Coupling

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

10.1101/519470 ◽

2019 ◽

Cited By ~ 2

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 ◽

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.

Local Perturbations of Cortical Excitability Propagate Differentially Through Large-Scale Functional Networks

Cerebral Cortex ◽

10.1093/cercor/bhz314 ◽

2020 ◽

Vol 30 (5) ◽

pp. 3352-3369 ◽

Cited By ~ 3

Author(s):

Zachary P Rosenthal ◽

Ryan V Raut ◽

Ping Yan ◽

Deima Koko ◽

Andrew W Kraft ◽

...

Keyword(s):

Large Scale ◽

Cortical Excitability ◽

Brain Activity ◽

Field Potential ◽

Brain Network ◽

Network Activity ◽

Wide Field ◽

Activity Levels ◽

Parvalbumin Interneuron

Abstract Electrophysiological recordings have established that GABAergic interneurons regulate excitability, plasticity, and computational function within local neural circuits. Importantly, GABAergic inhibition is focally disrupted around sites of brain injury. However, it remains unclear whether focal imbalances in inhibition/excitation lead to widespread changes in brain activity. Here, we test the hypothesis that focal perturbations in excitability disrupt large-scale brain network dynamics. We used viral chemogenetics in mice to reversibly manipulate parvalbumin interneuron (PV-IN) activity levels in whisker barrel somatosensory cortex. We then assessed how this imbalance affects cortical network activity in awake mice using wide-field optical neuroimaging of pyramidal neuron GCaMP dynamics as well as local field potential recordings. We report 1) that local changes in excitability can cause remote, network-wide effects, 2) that these effects propagate differentially through intra- and interhemispheric connections, and 3) that chemogenetic constructs can induce plasticity in cortical excitability and functional connectivity. These findings may help to explain how focal activity changes following injury lead to widespread network dysfunction.