Role of Neuronal Synchrony in the Generation of Evoked EEG/MEG Responses

Dept. of Neurology, Charité-Universitätsmedizin, Hindenburgdamm 30, 12203 Berlin, Germany.
Journal of Neurophysiology (Impact Factor: 2.89). 10/2010; 104(6):3557-67. DOI: 10.1152/jn.00138.2010
Source: PubMed


Evoked EEG/MEG responses are a primary real-time measure of perceptual and cognitive activity in the human brain, but their neuronal generator mechanisms are not yet fully understood. Arguments have been put forward in favor of either "phase-reset" of ongoing oscillations or "added-energy" models. Instead of advocating for one or the other model, here we show theoretically that the differentiation between these two generation mechanisms might not be possible if based solely on macroscopic EEG/MEG recordings. Using mathematical modeling, we show that a simultaneous phase reset of multiple oscillating neuronal (microscopic) sources contributing to EEG/MEG can produce evoked responses in agreement with both, the "added-energy" and the "phase-reset" model. We observe a smooth transition between the two models by just varying the strength of synchronization between the multiple microscopic sources. Consequently, because precise knowledge about the strength of microscopic ensemble synchronization is commonly not available in noninvasive EEG/MEG studies, they cannot, in principle, differentiate between the two mechanisms for macroscopic-evoked responses.

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Available from: Bartosz Telenczuk
    • "However, the neural mechanism of phase organization at microscopic level may not be visible easily at the macroscopic level of mass activity in EEG and MEG. It might even be that phase organization at microscopic level appears as not distinguishable from an additive model at the macroscopic level, as it has been suggested from a recent simulation study (Telenczuk et al., 2010). The classical example for this point of view is the concept that changes in synchrony within a neural population results in externally measurable signal power changes in EEG and MEG, which led to the term 'event-related synchronization' (Pfurtscheller and Lopes Da Silva, 1999). "
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    ABSTRACT: Background: Synchrony between neuroelectric oscillations in distant brain areas is currently used as an indicator of functional connectivity between the involved neural substrates. Coherence measures, which quantify synchrony, are affected by concurrent brain activities, commonly subsumed as noise. New method: Using Monte-Carlo simulation, we analysed the properties of circular statistics and how those are affected by noise. We considered three different models of neuroelectric signal generation, which are an additive model, phase-reset, and reciprocal phase-interaction. Using the receiver-operating-characteristic method, we compared the performances of currently implemented algorithms for coherence detection such as phase-coherence or phase-locking factor, magnitude-squared coherence, phase-lagging index, all based on circular statistics, and a more general approach to synchrony using measures of mutual information. We compared inter-trial coherence as a method for signal detection with coherence between multiple sources as measure of source interaction and connectivity. Results: Charts of performance characteristics showed that the choice of methods depend on the underlying signal generation model. Detection of coherence requires in general a higher signal-to-noise ratio than detection of the signal itself, and again, the difference in performances depends strongly on the underlying model of signal generation. Comparison with existing methods: Previous comparisons of the performances of different algorithms for signal detection and coherence have not considered systematically the underlying neural generation mechanisms. Conclusion: Detection of coherence generated by additive signals or a phase-reset requires largely higher signal-to-noise ratio compared to signal detection. Only in case of true phase interaction signal detection and coherence measures are similarly sensitive.
    No preview · Article · Jan 2016 · Journal of Neuroscience Methods
    • "Three principal models are still a matter of debate (e.g. Yeung et al., 2004; Sauseng et al., 2007; de Munck & Bijma, 2010; Telenczuk et al., 2010; Turi et al., 2012). In the classical signal-plus-noise (SPN) model, the recorded signal is treated as the superposition of a deterministic stimulus-evoked signal and zero-mean Gaussian noise (e.g. "
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    ABSTRACT: In the analysis of data from magnetoencephalography (MEG) and electroencephalography (EEG), it is common practice to arithmetically average event-related magnetic fields (ERFs) or event-related electric potentials (ERPs) across single trials and subsequently across subjects to obtain the so-called grand mean. Comparisons of grand means, e.g. between conditions, are then often performed by subtraction. These operations, and their statistical evaluation with parametric tests such as anova, tacitly rely on the assumption that the data follow the additive model, have a normal distribution, and have a homogeneous variance. This may be true for single trials, but these conditions are rarely met when ERFs/ERPs are compared between subjects, meaning that the additive model is seldom the correct model for computing grand mean waveforms. Here, we summarize some of our recent work and present new evidence, from auditory-evoked MEG and EEG results, that the non-normal distributions and the heteroscedasticity observed instead result because ERFs/ERPs follow a mixed model with additive and multiplicative components. For peak amplitudes, such as the auditory M100 and N100, the multiplicative component dominates. These findings emphasize that the common practice of simply subtracting arithmetic means of auditory-evoked ERFs or ERPs is problematic without prior adequate transformation of the data. Application of the area sinus hyperbolicus (asinh) transform to data following the mixed model transforms them into the requested additive model with its normal distribution and homogeneous variance. We therefore advise checking the data for compliance with the additive model and using the asinh transform if required. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
    No preview · Article · Mar 2015 · European Journal of Neuroscience
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    • "Previous studies argued that such phase modulations could be either related to additive evoked responses or phase resetting (Sauseng et al., 2007; Becker et al., 2008). Moreover, it is not clear whether the macroscopic phase resetting of EEG oscillations reflects the microscopic phase resetting or additive evoked responses at the single neuron level (Telenczuk et al., 2010). Therefore, it is necessary to examine the issues by combining experimental data at different spatial scales using several indices and mathematical modeling. "
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    ABSTRACT: Electroencephalogram (EEG) phase synchronization analyses can reveal large-scale communication between distant brain areas. However, it is not possible to identify the directional information flow between distant areas using conventional phase synchronization analyses. In the present study, we applied transcranial magnetic stimulation (TMS) to the occipital area in subjects who were resting with their eyes closed, and analyzed the spatial propagation of transient TMS-induced phase resetting by using the transfer entropy (TE), to quantify the causal and directional flow of information. The time-frequency EEG analysis indicated that the theta (5 Hz) phase locking factor (PLF) reached its highest value at the distant area (the motor area in this study), with a time lag that followed the peak of the transient PLF enhancements of the TMS-targeted area at the TMS onset. Phase-preservation index (PPI) analyses demonstrated significant phase resetting at the TMS-targeted area and distant area. Moreover, the TE from the TMS-targeted area to the distant area increased clearly during the delay that followed TMS onset. Interestingly, the time lags were almost coincident between the PLF and TE results (152 vs. 165 ms), which provides strong evidence that the emergence of the delayed PLF reflects the causal information flow. Such tendencies were observed only in the higher-intensity TMS condition, and not in the lower-intensity or sham TMS conditions. Thus, TMS may manipulate large-scale causal relationships between brain areas in an intensity-dependent manner. We demonstrated that single-pulse TMS modulated global phase dynamics and directional information flow among synchronized brain networks. Therefore, our results suggest that single-pulse TMS can manipulate both incoming and outgoing information in the TMS-targeted area associated with functional changes.
    Full-text · Article · Mar 2014 · Frontiers in Human Neuroscience
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