Data-driven modeling of phase interactions between spontaneous MEG oscillations
Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is r...
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| Veröffentlicht in: | Human brain mapping Jg. 32; H. 7; S. 1161 - 1178 |
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| Abstract | Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase‐interaction dynamics. Methods: We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. Results: The methodology is tested using simulations and is applied to ongoing occipital–frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital–frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. Conclusion: When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Hum Brain Mapp, 2011. © 2011 Wiley‐Liss, Inc. |
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| AbstractList | Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics. Methods: We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. Results: The methodology is tested using simulations and is applied to ongoing occipital-frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital-frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. Conclusion: When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Hum Brain Mapp, 2011. ? 2011 Wiley-Liss, Inc. Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics. We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. The methodology is tested using simulations and is applied to ongoing occipital-frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital-frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics. Methods: We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. Results: The methodology is tested using simulations and is applied to ongoing occipital-frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital-frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. Conclusion: When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc. [PUBLICATION ABSTRACT] Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics.OBJECTIVESynchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics.We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived.METHODSWe propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived.The methodology is tested using simulations and is applied to ongoing occipital-frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital-frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms.RESULTSThe methodology is tested using simulations and is applied to ongoing occipital-frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital-frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms.When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking.CONCLUSIONWhen the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase‐interaction dynamics. Methods: We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. Results: The methodology is tested using simulations and is applied to ongoing occipital–frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital–frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. Conclusion: When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Hum Brain Mapp, 2011. © 2011 Wiley‐Liss, Inc. |
| Author | van Someren, Eus J.W. van Dijk, Bob W. Stam, Cornelis J. de Munck, Jan C. Bijma, Fetsje van der Vaart, Aad W. van der Werf, Ysbrand Y. Hindriks, Rikkert |
| AuthorAffiliation | 2 Department of Medical Physics and Technology, VU University Medical Centre, Amsterdam, The Netherlands 3 Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands 4 Department of Sleep and Cognition, Netherlands Institute for Neurosciences (An Institute of the Royal Netherlands Academy of Arts and Sciences), Amsterdam, The Netherlands 1 Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands |
| AuthorAffiliation_xml | – name: 2 Department of Medical Physics and Technology, VU University Medical Centre, Amsterdam, The Netherlands – name: 4 Department of Sleep and Cognition, Netherlands Institute for Neurosciences (An Institute of the Royal Netherlands Academy of Arts and Sciences), Amsterdam, The Netherlands – name: 1 Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands – name: 3 Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands |
| Author_xml | – sequence: 1 givenname: Rikkert surname: Hindriks fullname: Hindriks, Rikkert email: hindriks@few.vu.nl organization: Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands – sequence: 2 givenname: Fetsje surname: Bijma fullname: Bijma, Fetsje organization: Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands – sequence: 3 givenname: Bob W. surname: van Dijk fullname: van Dijk, Bob W. organization: Department of Medical Physics and Technology, VU University Medical Centre, Amsterdam, The Netherlands – sequence: 4 givenname: Cornelis J. surname: Stam fullname: Stam, Cornelis J. organization: Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands – sequence: 5 givenname: Ysbrand Y. surname: van der Werf fullname: van der Werf, Ysbrand Y. organization: Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands – sequence: 6 givenname: Eus J.W. surname: van Someren fullname: van Someren, Eus J.W. organization: Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands – sequence: 7 givenname: Jan C. surname: de Munck fullname: de Munck, Jan C. organization: Department of Medical Physics and Technology, VU University Medical Centre, Amsterdam, The Netherlands – sequence: 8 givenname: Aad W. surname: van der Vaart fullname: van der Vaart, Aad W. organization: Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands |
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| Keywords | Nervous system diseases Radiodiagnosis Magnetoencephalography Interaction EEG Electrophysiology Electroencephalography Phase locking oscillatory activity Synchronization Modeling Volume conduction MEG functional interaction Oscillator Oscillation coupled oscillators |
| Language | English |
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| SubjectTerms | Biological and medical sciences Brain - physiology Brain mapping Conduction Cortical Synchronization - physiology coupled oscillators Data processing EEG Frequency dependence functional interaction Investigative techniques, diagnostic techniques (general aspects) Magnetoencephalography Mathematical models Medical sciences MEG Models, Neurological Nervous system Nervous system (semeiology, syndromes) Nervous system as a whole Neurology Oscillators oscillatory activity phase locking Radiodiagnosis. Nmr imagery. Nmr spectrometry Rhythms Synchronization volume conduction |
| Title | Data-driven modeling of phase interactions between spontaneous MEG oscillations |
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