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Disentangling coupled brain systems from EEG and MEG data
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IOP 2016
57 Symposium C1 How useful are nonlinear methods for EEG and MEG data analysis? Organizer: Guido Nolte (Germany) Most methods to analyze EEG or MEG data are linear, e.g. when studying ERPs, ERFs, spectral power or coherence. Many other methods, like 1:1 phase locking or mutual information are formally nonlinear but in practice the results depend largely on linear properties of the data. In many cases it is therefore not clear whether a nonlinear method is just a complicated way to look at well-known phenomena. Nonlinear features of EEG and MEG data, which cannot be explained as a trivial reflection of linear ones, are typically weaker than linear features, but may contain valuable information about brain states or brain pathologies which are not observable using linear methods. A prominent example of a nonlinear phenomenon is cross-frequency coupling, which has become of major interest in the research community lately. One often attempts to distinguish this from ‘trivial’ higher harmonics e.g. of the well-known alpha rhythm, but one could also argue that these higher harmonics are valuable nonlinear phenomena which are interesting in its own rights. The goal of this symposium is to discuss what kind of nonlinear phenomena are observable in electrophysiological data and what kind of additional information about the human brain we get when studying those. Benefits of those methods could be improved biomarkers for the diagnosis of brain diseases or simply a better understanding of brain dynamics. doi:10.1016/j.ijpsycho.2016.07.023
375 Disentangling coupled brain systems from EEG and MEG data Laura Marzettia, Federico Chellaa, Vittorio Pizzellaa, Guido Nolteb Insitute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy b Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany a
Brain cognitive functions arise through the coordinated activity of several brain regions, which form complex dynamical systems operating at multiple temporal scales. The spatio-temporal characterization of these systems is of fundamental importance for a better understanding of brain processes as well as for characterizing the signatures of healthy and diseased cognition. To this aim, it is crucial to develop methods able to disclose functional connections within and between brain systems and to disentangle functional systems. Among these methods, linear and nonlinear techniques can be used for detecting interactions occurring at the same frequency or at different frequencies (i.e., cross frequency coupling) from multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data. In this framework, a particular interest is deserved by methods characterized by the desirable property of being robust to spurious interactions arising from mixing artifacts, i.e. volume conduction or source leakage. Namely, we present a novel approach to the third order spectral analysis of EEG and MEG data for studying cross frequency functional brain connectivity that generalizes the properties of the imaginary part of coherency from the linear to the nonlinear case. Specifically, we demonstrate in simulations that the method is robust to mixing artifacts. Moreover, our simulations show that the method can
be reliably be used to: i) detect the coupled systems; ii) estimate the phase difference between the interacting sources. The method performance are affected by the increasing level of noise rather than by the complexity (i.e., number of sources) in the interacting systems. The method is then applied to analyze spontaneous EEG and MEG data. Our results reveal a cross frequency coupling between brain sources at 10 Hz and 20 Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal space to source level by using a fit-based procedure, thus highlighting that the 10 to 20 Hz dominant interaction localized in an occipito-motor network. doi:10.1016/j.ijpsycho.2016.07.024
385 Phase-amplitude coupling – how to achieve proper statistics Andreas Daffertshofera, Bernadette van Wijkb a Vrije Universiteit Amsterdam MOVE Research Institute Department of Human Movement Science, Amsterdam, Netherlands b Department of Neurology Charité-University Medicine Berlin, Berlin, Germany Background: Cross-frequency coupling has been proposed to be the mechanism for interaction of local and global neural processes integrating neural information across timescales. Electrophysiological recordings of brain activity reveal cross-frequency coupling between low and high frequency signal components in the form of phase-amplitude coupling (PAC). We test the reliability of PAC estimates and their robustness against different confounding biases. Methods: We derived, where possible analytically, appropriate correction and normalization terms so that PAC estimates are bounded within the interval between 0 and 1, are insensitive to the mean and variance of amplitude fluctuations in the signal, and that they can be used to judge the effect size of coupling. We illustrate this using numerically simulated signals and data taken from local field potential recordings of the subthalamic nucleus in Parkinson’s disease patients. Results: The quality of phase definition crucially affects the value of PAC. We recommend using an algorithm proposed by Kralemann et al. to transform time-dependent phases under study to be uniformly distributed. After correction a variant of the conventional phase-locking value can provide reliable estimates of PAC. Alternative statistical evaluation can be realized using a rank statistics introduced by Mardia, Liddell, and Ord, or through generalized linear modeling. Discussion & Conclusion: For meaningful comparisons between conditions or subjects, it is paramount that estimates are unbiased. And, the evaluation of effect size relies on appropriate quantification of PAC. We addressed these issues in detail calling for great care when interpreting PAC. doi:10.1016/j.ijpsycho.2016.07.025
434 Bicoherence. The higher harmonics strike back Guido Nolte Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany While bicoherence and cross-bicoherence are well-known measures of functional relations across frequencies reflecting nonlinear
IOP 2016
57 Symposium C1 How useful are nonlinear methods for EEG and MEG data analysis? Organizer: Guido Nolte (Germany) Most methods to analyze EEG or MEG data are linear, e.g. when studying ERPs, ERFs, spectral power or coherence. Many other methods, like 1:1 phase locking or mutual information are formally nonlinear but in practice the results depend largely on linear properties of the data. In many cases it is therefore not clear whether a nonlinear method is just a complicated way to look at well-known phenomena. Nonlinear features of EEG and MEG data, which cannot be explained as a trivial reflection of linear ones, are typically weaker than linear features, but may contain valuable information about brain states or brain pathologies which are not observable using linear methods. A prominent example of a nonlinear phenomenon is cross-frequency coupling, which has become of major interest in the research community lately. One often attempts to distinguish this from ‘trivial’ higher harmonics e.g. of the well-known alpha rhythm, but one could also argue that these higher harmonics are valuable nonlinear phenomena which are interesting in its own rights. The goal of this symposium is to discuss what kind of nonlinear phenomena are observable in electrophysiological data and what kind of additional information about the human brain we get when studying those. Benefits of those methods could be improved biomarkers for the diagnosis of brain diseases or simply a better understanding of brain dynamics. doi:10.1016/j.ijpsycho.2016.07.023
375 Disentangling coupled brain systems from EEG and MEG data Laura Marzettia, Federico Chellaa, Vittorio Pizzellaa, Guido Nolteb Insitute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy b Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany a
Brain cognitive functions arise through the coordinated activity of several brain regions, which form complex dynamical systems operating at multiple temporal scales. The spatio-temporal characterization of these systems is of fundamental importance for a better understanding of brain processes as well as for characterizing the signatures of healthy and diseased cognition. To this aim, it is crucial to develop methods able to disclose functional connections within and between brain systems and to disentangle functional systems. Among these methods, linear and nonlinear techniques can be used for detecting interactions occurring at the same frequency or at different frequencies (i.e., cross frequency coupling) from multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data. In this framework, a particular interest is deserved by methods characterized by the desirable property of being robust to spurious interactions arising from mixing artifacts, i.e. volume conduction or source leakage. Namely, we present a novel approach to the third order spectral analysis of EEG and MEG data for studying cross frequency functional brain connectivity that generalizes the properties of the imaginary part of coherency from the linear to the nonlinear case. Specifically, we demonstrate in simulations that the method is robust to mixing artifacts. Moreover, our simulations show that the method can
be reliably be used to: i) detect the coupled systems; ii) estimate the phase difference between the interacting sources. The method performance are affected by the increasing level of noise rather than by the complexity (i.e., number of sources) in the interacting systems. The method is then applied to analyze spontaneous EEG and MEG data. Our results reveal a cross frequency coupling between brain sources at 10 Hz and 20 Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal space to source level by using a fit-based procedure, thus highlighting that the 10 to 20 Hz dominant interaction localized in an occipito-motor network. doi:10.1016/j.ijpsycho.2016.07.024
385 Phase-amplitude coupling – how to achieve proper statistics Andreas Daffertshofera, Bernadette van Wijkb a Vrije Universiteit Amsterdam MOVE Research Institute Department of Human Movement Science, Amsterdam, Netherlands b Department of Neurology Charité-University Medicine Berlin, Berlin, Germany Background: Cross-frequency coupling has been proposed to be the mechanism for interaction of local and global neural processes integrating neural information across timescales. Electrophysiological recordings of brain activity reveal cross-frequency coupling between low and high frequency signal components in the form of phase-amplitude coupling (PAC). We test the reliability of PAC estimates and their robustness against different confounding biases. Methods: We derived, where possible analytically, appropriate correction and normalization terms so that PAC estimates are bounded within the interval between 0 and 1, are insensitive to the mean and variance of amplitude fluctuations in the signal, and that they can be used to judge the effect size of coupling. We illustrate this using numerically simulated signals and data taken from local field potential recordings of the subthalamic nucleus in Parkinson’s disease patients. Results: The quality of phase definition crucially affects the value of PAC. We recommend using an algorithm proposed by Kralemann et al. to transform time-dependent phases under study to be uniformly distributed. After correction a variant of the conventional phase-locking value can provide reliable estimates of PAC. Alternative statistical evaluation can be realized using a rank statistics introduced by Mardia, Liddell, and Ord, or through generalized linear modeling. Discussion & Conclusion: For meaningful comparisons between conditions or subjects, it is paramount that estimates are unbiased. And, the evaluation of effect size relies on appropriate quantification of PAC. We addressed these issues in detail calling for great care when interpreting PAC. doi:10.1016/j.ijpsycho.2016.07.025
434 Bicoherence. The higher harmonics strike back Guido Nolte Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany While bicoherence and cross-bicoherence are well-known measures of functional relations across frequencies reflecting nonlinear