RT Journal Article
SR Electronic
T1 Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 159731
DO 10.1101/159731
A1 Tom Dupré la Tour
A1 Lucille Tallot
A1 Laetitia Grabot
A1 Valérie Doyère
A1 Virginie van Wassenhove
A1 Yves Grenier
A1 Alexandre Gramfort
YR 2017
UL http://biorxiv.org/content/early/2017/07/06/159731.abstract
AB 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 electrophysiological 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.