ArticleFrontiers in systems neuroscience2017
Neural Cross-Frequency Coupling Functions.
Article in Frontiers in systems neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 76 citations in OpenAlex.
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- A principal component entropy metric for assessing global synchronicity in EEG signals.Scientific reports · 2026Article
- High-Pass Filtering through Short-Term Synaptic Facilitation Amplifies Low-Frequency Modulation of Bursting Input.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Physiological Noise in Cardiorespiratory Time-Varying Interactions.Entropy (Basel, Switzerland) · 2026Article
- What can neurofeedback and transcranial alternating current stimulation reveal about cross-frequency coupling?Frontiers in neuroscience · 2025Article
- Neural correlates underlying state anxiety alterations following sleep deprivation: insights from frontal alpha asymmetry and phase-amplitude coupling.Frontiers in psychology · 2025Article
- The phase coherence of the neurovascular unit is reduced in Huntington's disease.Brain communications · 2024Article
- Functional connectivity between the amygdala and prefrontal cortex underlies processing of emotion ambiguity.Translational psychiatry · 2023Article
- White-matter degradation and dynamical compensation support age-related functional alterations in human brain.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Cross-frequency coupling between slow harmonics via the real brainstem oscillators: An in vivo animal study.PloS one · 2023Article
- Ensemble of coupling forms and networks among brain rhythms as function of states and cognition.Communications biology · 2022Article
- A Peri-Ictal EEG-Based Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP) Derived From Brain Network Analysis.Frontiers in network physiology · 2022Article
- Identifying Cognitive Impairment in Elderly Using Coupling Functions Between Cerebral Oxyhemoglobin and Arterial Blood Pressure.Frontiers in aging neuroscience · 2022Article
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- Addressing indirect frequency coupling via partial generalized coherence.Scientific reports · 2021Article
- Coupling between Blood Pressure and Subarachnoid Space Width Oscillations during Slow Breathing.Entropy (Basel, Switzerland) · 2021Article
- Article
- Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG.Sensors (Basel, Switzerland) · 2020Article
- Dynamic network interactions among distinct brain rhythms as a hallmark of physiologic state and function.Communications biology · 2020Article
- EEG state-trajectory instability and speed reveal global rules of intrinsic spatiotemporal neural dynamics.PloS one · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics-strength and form-are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG) data of the human resting state, and the differences that arise when the eyes are either open (EO) or closed (EC) are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here.
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