Non-linear auto-regressive models for cross-frequency coupling in neural time series
| Autor: | Virginie van Wassenhove, Yves Grenier, Alexandre Gramfort, Laetitia Grabot, Lucille Tallot, Tom Dupré la Tour, Valérie Doyère |
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| Přispěvatelé: | Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Neuroscience Paris Seine (NPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Neurosciences de Paris-Saclay (Neuro-PSI), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie cognitive (LCogn), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Neuroimagerie cognitive (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Sud - Paris 11 (UP11), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Signal, Statistique et Apprentissage (S2A), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Département Images, Données, Signal (IDS), Télécom ParisTech, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut des Neurosciences Paris-Saclay (NeuroPSI), Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, ANR-16-CE37-0004,AutoTime,Du codage automatique à la perception consciente du temps dans le système nerveux central: un déficit fondamental dans la schizophrénie?(2016), Neurosciences Paris Seine (NPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Saclay (COmUE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Physiology Computer science [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology MESH: Neurons Action Potentials computer.software_genre Mathematical and Statistical Techniques 0302 clinical medicine Goodness of fit [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Medicine and Health Sciences Feature (machine learning) Bandwidth (Signal Processing) lcsh:QH301-705.5 MESH: Action Potentials Parametric statistics Neurons Mammals 0303 health sciences [STAT.AP]Statistics [stat]/Applications [stat.AP] Ecology [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior Physics Brain Eukaryota [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences Signal Filtering Electrophysiology Computational Theory and Mathematics Autoregressive model Modeling and Simulation Vertebrates Physical Sciences Parametric model symbols Engineering and Technology Algorithm Research Article Models Neurological Neurophysiology Research and Analysis Methods Machine learning Rodents 03 medical and health sciences Cellular and Molecular Neuroscience symbols.namesake MESH: Brain MESH: Models Neurological Acoustic Signals Sine Waves Genetics Animals Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology business.industry Model selection [SCCO.NEUR]Cognitive science/Neuroscience Organisms Probabilistic logic Biology and Life Sciences Acoustics Filter (signal processing) Bandpass Filters Nonlinear system 030104 developmental biology lcsh:Biology (General) Speech Signal Processing Signal Processing Amniotes Artificial intelligence Hilbert transform business Mathematical Functions computer 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Public Library of Science, 2017, 13 (12), pp.e1005893. ⟨10.1371/journal.pcbi.1005893⟩ PLoS Computational Biology, Public Library of Science, 2017 PLoS Computational Biology, 2017, 13 (12), pp.e1005893. ⟨10.1371/journal.pcbi.1005893⟩ PLoS Computational Biology, Vol 13, Iss 12, p e1005893 (2017) PLOS Computational Biology |
| ISSN: | 1553-734X 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1005893⟩ |
| Popis: | 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. Author summary Neural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation. |
| Databáze: | OpenAIRE |
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