ArticleFrontiers in computational neuroscience2013
Cross-frequency coupling in real and virtual brain networks.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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94 citing papers in PubMed.
- The influence of nonlinear resonance on human cortical oscillations.Communications biology · 2026Article
- Emergence of multifrequency activity in a laminar neural mass model.PLoS computational biology · 2026Article
- A proposal for realizing cognitive functions using traveling waves, phase-locked and synchronized patterns, holography, neural mixing, and single sideband communications.Frontiers in computational neuroscience · 2026Article
- Self-organization and circular causation as synergetic foundations of social interaction: a multilayer network perspective.Frontiers in network physiology · 2026Article
- Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis.Neuroscience informatics · 2025Article
- Enhancing working memory in MCI: Modulating alpha-gamma coupling and gamma oscillations via rTMS.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Article
- Pumping up your predictive power for cognitive state detection with the proper GAINS.NeuroImage · 2025Article
- A mechanism for the emergence of low-dimensional structures in brain dynamics.NPJ systems biology and applications · 2025Article
- Non-invasive brain stimulation to modulate neural activity in Parkinson's disease.NPJ Parkinson's disease · 2025Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Deciphering temporal scales of visual awareness: insights from flicker frequency modulation in continuous flash suppression.Neuroscience of consciousness · 2025Article
- Theta-gamma coupling: nonlinearity as a universal cross-frequency coupling mechanism.Frontiers in behavioral neuroscience · 2025Article
- What can neurofeedback and transcranial alternating current stimulation reveal about cross-frequency coupling?Frontiers in neuroscience · 2025Article
- The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network.Frontiers in neuroinformatics · 2025Article
- Node-layer duality in networked systems.Nature communications · 2024Article
- Measuring functional connectivity in frequency-domain helps to better characterize brain function.Human brain mapping · 2024Article
- Mutual information of multiple rhythms in schizophrenia.Brain structure & function · 2024Article
- A machine learning approach for real-time cortical state estimation.Journal of neural engineering · 2024Article
- Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks.Frontiers in neuroscience · 2024Article
- Dynamic Changes in Local Activity and Network Interactions among the Anterior Cingulate, Amygdala, and Cerebellum during Associative Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
34 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed.
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