Non-linear auto-regressive models for cross-frequency coupling in neural time series.
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 e...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5739510?pdf=render |
| _version_ | 1819128436920156160 |
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| author | Tom Dupré la Tour Lucille Tallot Laetitia Grabot Valérie Doyère Virginie van Wassenhove Yves Grenier Alexandre Gramfort |
| author_facet | Tom Dupré la Tour Lucille Tallot Laetitia Grabot Valérie Doyère Virginie van Wassenhove Yves Grenier Alexandre Gramfort |
| author_sort | Tom Dupré la Tour |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T08:27:48Z |
| format | Article |
| id | doaj.art-a85ce97944d64ad48aec7b17d64c67e6 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-22T08:27:48Z |
| publishDate | 2017-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-a85ce97944d64ad48aec7b17d64c67e62022-12-21T18:32:35ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-12-011312e100589310.1371/journal.pcbi.1005893Non-linear auto-regressive models for cross-frequency coupling in neural time series.Tom Dupré la TourLucille TallotLaetitia GrabotValérie DoyèreVirginie van WassenhoveYves GrenierAlexandre GramfortWe 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.http://europepmc.org/articles/PMC5739510?pdf=render |
| spellingShingle | Tom Dupré la Tour Lucille Tallot Laetitia Grabot Valérie Doyère Virginie van Wassenhove Yves Grenier Alexandre Gramfort Non-linear auto-regressive models for cross-frequency coupling in neural time series. PLoS Computational Biology |
| title | Non-linear auto-regressive models for cross-frequency coupling in neural time series. |
| title_full | Non-linear auto-regressive models for cross-frequency coupling in neural time series. |
| title_fullStr | Non-linear auto-regressive models for cross-frequency coupling in neural time series. |
| title_full_unstemmed | Non-linear auto-regressive models for cross-frequency coupling in neural time series. |
| title_short | Non-linear auto-regressive models for cross-frequency coupling in neural time series. |
| title_sort | non linear auto regressive models for cross frequency coupling in neural time series |
| url | http://europepmc.org/articles/PMC5739510?pdf=render |
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