A time-series model of phase amplitude cross frequency coupling and comparison of spectral characteristics with neural data.

Content Provider	Europe PMC
Author	Lepage, Kyle Q. Vijayan, Sujith
Copyright Year	2015
Abstract	Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposed models, based on multiplying a pulsatile function of a low-frequency oscillation (θ) with an unobserved and high-frequency component, yields a process with a spectrum that is consistent with observation. Other models, such as those employing a biphasic pulsatile function of a low-frequency oscillation, are demonstrated to be less suitable. We introduce the full stochastic process time series model as a summation of three component weak-sense stationary (WSS) processes, namely, θ, γ, and η, with η a 1/f α noise process. The γ process is constructed as a product of a latent and unobserved high-frequency process x with a function of the lagged, low-frequency oscillatory component (θ). After demonstrating that the model process is WSS, an appropriate method of simulation is introduced based upon the WSS property. This work may be of interest to researchers seeking to connect inhibitory and excitatory dynamics directly to observation in a model that accounts for known temporal dependence or to researchers seeking to examine what can occur in a multiplicative time-domain CFC mechanism.
ISSN	23146133
Journal	Biomed Research International
Volume Number	2015
PubMed Central reference number	PMC4383361
PubMed reference number	25866758
e-ISSN	23146141
DOI	10.1155/2015/140837
Language	English
Publisher	Hindawi
Publisher Date	2015-03-19
Access Restriction	Open
Rights License	This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2015 K. Q. Lepage and S. Vijayan.
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology