ArticlePLoS computational biology2017
Non-linear auto-regressive models for cross-frequency coupling in neural time series.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Trial
- Thalamocortical orchestration of human theory of mind.bioRxiv : the preprint server for biology · 2026Article
- Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors.bioRxiv : the preprint server for biology · 2026Article
- Effects of pharmacological modulation of cortical excitability on resting-state EEG PAC in humans.Scientific reports · 2026Article
- Frontocingulate-parietal-limbic circuits associated with both ruminative brooding and self-regulatory processes.Frontiers in human neuroscience · 2026Article
- Panx1a modulates metabolic stress signaling and synaptic composition in the developing zebrafish brain.Cell and tissue research · 2025Article
- Altered oscillatory coupling reflects possible inhibitory interneuron dysfunction in Rett syndrome.medRxiv : the preprint server for health sciences · 2025Article
- Spatiotemporal Dynamics of Language Information Representation within the Left Inferior Parietal Lobule Network: An Intracranial SEEG Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- The role of feedforward and feedback inhibition in modulating theta-gamma cross-frequency interactions in neural circuits.PLoS computational biology · 2025Article
- Event-related dynamic phase-amplitude coupling analysis reveals facial emotional processing deficits in patients with major depressive disorder: a cross-sectional study.BMC psychiatry · 2025Article
- Concurrent processing of the prosodic hierarchy is supported by cortical entrainment and phase-amplitude coupling.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Elevated phase amplitude coupling as a depression biomarker in epilepsy.Epilepsy & behavior : E&B · 2024Article
- The applied principles of EEG analysis methods in neuroscience and clinical neurology.Military Medical Research · 2023Review
- Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis.Frontiers in neuroscience · 2023Article
- Neurocognitive analyses reveal that video game players exhibit enhanced implicit temporal processing.Communications biology · 2022Article
- State space methods for phase amplitude coupling analysis.Scientific reports · 2022Article
- Using linear parameter varying autoregressive models to measure cross frequency couplings in EEG signals.Frontiers in human neuroscience · 2022Article
- Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.Human brain mapping · 2021Article
- Multitaper estimates of phase-amplitude coupling.Journal of neural engineering · 2021Article
- Shifted phase of EEG cross-frequency coupling in individuals with Phelan-McDermid syndrome.Molecular autism · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model "goodness of fit" via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.