ArticleScientific reports2022
State space methods for phase amplitude coupling analysis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A State-Space Framework for Causal Detection of Hippocampal Ripple-Replay Events.IEEE transactions on bio-medical engineering · 2026Article
- An Adaptable Phase-Tracking System for Parkinsonian Rest Tremor: Design and In-Clinic Feasibility.IEEE journal of translational engineering in health and medicine · 2025Article
- Article
- A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings.bioRxiv : the preprint server for biology · 2024Article
- A mutual information measure of phase-amplitude coupling using gamma generalized linear models.Frontiers in computational neuroscience · 2024Article
- Different Methods to Estimate the Phase of Neural Rhythms Agree But Only During Times of Low Uncertainty.eNeuro · 2023Article
- Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty.bioRxiv : the preprint server for biology · 2023Article
- Switching state-space modeling of neural signal dynamics.PLoS computational biology · 2023Article
- Article
- A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms.PLoS computational biology · 2021Article
- Association Between Lower Preoperative Cognition With Intraoperative Electroencephalographic Features Consistent With Deep States of Anesthesia in Older Patients: An Observational Cohort Study.Anesthesia and analgesia · 2021Observational
- Addressing Pitfalls in Phase-Amplitude Coupling Analysis with an Extended Modulation Index Toolbox.Neuroinformatics · 2021Article
- Multilevel State-Space Models Enable High Precision Event Related Potential Analysis.Conference record. Asilomar Conference on Signals, Systems & ComputersArticle
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Authors and funding
4 authors.
Funding
Abstract
Phase amplitude coupling (PAC) is thought to play a fundamental role in the dynamic coordination of brain circuits and systems. There are however growing concerns that existing methods for PAC analysis are prone to error and misinterpretation. Improper frequency band selection can render true PAC undetectable, while non-linearities or abrupt changes in the signal can produce spurious PAC. Current methods require large amounts of data and lack formal statistical inference tools. We describe here a novel approach for PAC analysis that substantially addresses these problems. We use a state space model to estimate the component oscillations, avoiding problems with frequency band selection, nonlinearities, and sharp signal transitions. We represent cross-frequency coupling in parametric and time-varying forms to further improve statistical efficiency and estimate the posterior distribution of the coupling parameters to derive their credible intervals. We demonstrate the method using simulated data, rat local field potentials (LFP) data, and human EEG data.
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