scholarly journals An oscillator ensemble model of sequence learning

2019 ◽  
Author(s):  
Alexander Maye ◽  
Peng Wang ◽  
Jonathan Daume ◽  
Xiaolin Hu ◽  
Andreas K. Engel
Keyword(s):  
Brain Activity ◽  
Phase Locking ◽  
Electrophysiological Recordings ◽  
Sequence Elements ◽  
Synchronization Phase ◽  
Frequency Coupling ◽  
Human Participants ◽  
The Individual ◽  
Cross Frequency Coupling ◽  
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.

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 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 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 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 BDNF Val66Met polymorphism as putative genetic substrate of music-induced plasticity in auditory prediction

2021 ◽  
Author(s):  
Silvia EP Bruzzone ◽  
Leonardo Bonetti ◽  
Tiina Paunio ◽  
Katri Kantojarvi ◽  
Marina Kliuchko ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Auditory Processing ◽  
Brain Activity ◽  
Musical Training ◽  
Predictive Processing ◽  
Brain Anatomy ◽  
Prediction Errors ◽  
Functional Changes ◽  
Human Participants ◽  
The Individual

Predictive processing of sounds depends on the constant updating of priors based on exposure to posteriors, which through repeated exposure mediates learning. The result of such corrections to the model is seen in musicians, whose lifelong training results in measurable plasticity of audio-motor brain anatomy and functionality. It has been suggested that the plasticity of auditory predictive processes depends on the interaction between the environment and the individual genetic substrate. However, empirical evidence to this is still missing. BDNF is a critical genetic factor affecting learning and plasticity, and its widely studied functional variant Val66Met single-nucleotide polymorphism offers a unique opportunity to investigate neuroplastic functional changes occurring upon a years-long training. We hypothesised that BDNF gene variations would be driving neuroplasticity of the auditory cortex in musically trained human participants. To this goal, musicians and non-musicians were recruited and divided in Val/Val and Met carriers and their brain activity measured with magnetoencephalography (MEG) while they listened to a regular auditory sequence containing different types of prediction errors. The auditory cortex responses to prediction errors was enhanced in Val/Val carriers who underwent intensive musical training, compared to Met and non-musicians. Our results point at a role of gene-regulated neurotrophic factors in the neural adaptations of auditory processing after long-term training.

Fluid Intelligence and the Cross-Frequency Coupling of Neuronal Oscillations

2016 ◽  
Vol 19 ◽  
Author(s):  
Adam Chuderski
Keyword(s):  
Fluid Intelligence ◽  
Theoretical Models ◽  
Optimal Level ◽  
Neuronal Oscillations ◽  
Frequency Coupling ◽  
Individual Capacity ◽  
Oscillatory Coupling ◽  
The Individual ◽  
Promising Line ◽  
Cross Frequency Coupling

AbstractSeveral existing theoretical models predict that the individual capacity of working memory and abstract reasoning (fluid intelligence) strongly depends on certain features of neuronal oscillations, especially their cross-frequency coupling. Empirical evidence supporting these predictions is still scarce, but it makes the future studies on oscillatory coupling a promising line of research that can uncover the physiological underpinnings of fluid intelligence. Cross-frequency coupling may serve as the optimal level of description of neurocognitive processes, integrating their genetic, structural, neurochemical, and bioelectrical underlying factors with explanations in terms of cognitive operations driven by neuronal oscillations.

scholarly journals 40 Hz acoustic stimulation decreases amyloid beta and modulates brain rhythms in a mouse model of Alzheimer’s disease

2018 ◽  
Author(s):  
Juho Lee ◽  
Seungjun Ryu ◽  
Hyun-Ju Kim ◽  
Jieun Jung ◽  
Boreom Lee ◽  
...  
Keyword(s):  
Mouse Model ◽  
Therapeutic Potential ◽  
Phase Locking ◽  
Acoustic Stimulation ◽  
Gamma Power ◽  
Frequency Coupling ◽  
Phase Locking Value ◽  
Gamma Band Oscillations ◽  
Cross Frequency Coupling ◽  
The Brain

AbstractIntroductionThe accumulation of amyloid-beta (Aβ) is one of the neuropathologic hallmarks of Alzheimer’s disease (AD) and abnormal gamma band oscillations and brain connectivity have been observed. Recently, a therapeutic potential of gamma entrainment of the brain was reported by Iaccarino et al. However, the affected areas were limited to hippocampus and visual cortex. Therefore, we sought to test the effects of acoustic stimulation in a mouse model of AD.MethodsFreely moving 6-month-old 5XFAD mice with electroencephalogram (EEG) electrodes were treated with daily two-hour acoustic stimulation at 40Hz for 2 weeks. Aβ and microglia were evaluated by immunohistochemistry and ELISA. Evoked and spontaneous gamma power were analyzed by wavelet analysis. Coherence, phase locking value (PLV), and cross-frequency coupling were analyzed.ResultsThe number of Aβ plaques decreased in the pre-and infralimbic (PIL) and hippocampus regions and soluble Aβ-40 and Aβ-42 peptides in PIL in the acoustic stimulation group. We also found that the number of microglia increased in PIL and hippocampus. In EEG analysis, evoked gamma power was decreased and spontaneous gamma power was increased. Gamma coherence and phase locking value did not show significant changes. Cross-frequency coupling was shifted from gamma-delta to gamma-theta rhythm.ConclusionIn summary, we found that acoustic stimulation at 40Hz can reduce Aβ in the brain and restore the gamma band oscillations and the frontoparietal connectivity. Our data suggest that acoustic stimulation might alter the natural deterioration processes of AD and have a therapeutic potential in AD.

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 Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

PLoS Biology ◽  
2020 ◽  
Vol 18 (5) ◽  
pp. e3000685 ◽  
Author(s):  
Felix Siebenhühner ◽  
Sheng H. Wang ◽  
Gabriele Arnulfo ◽  
Anna Lampinen ◽  
Lino Nobili ◽  
...  
Keyword(s):  
Resting State ◽  
Electrophysiological Recordings ◽  
Frequency Coupling ◽  
Cross Frequency Coupling
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