The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization (original) (raw)
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PloS one, 2016
Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for...
Phase Synchrony among Neuronal Oscillations in the Human Cortex
Journal of Neuroscience, 2005
Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among ␣ (ϳ10 Hz),  (ϳ20 Hz), and ␥ (ϳ30-40 Hz) oscillations. These tasks also enhanced the "classical" within-frequency synchrony in these frequency bands, but the spatial patterns of ␣, , and ␥ synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between ␥and ␣-band oscillations. These data indicate that crossfrequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.
Phase-amplitude coupling is a promising construct to study cognitive processes in electroencephalography (EEG) and magnetencephalography (MEG). Due to the novelty of the concept, various measures are used in the literature to calculate phase-amplitude coupling. Here, performance of the three most widely used phase-amplitude coupling measures – phase-locking value (PLV), mean vector length (MVL), and modulation index (MI) – is thoroughly compared with the help of simulated data. We combine advantages of previous reviews and use a realistic data simulation, examine moderators and provide inferential statistics for the comparison of all three indices of phase-amplitude coupling. Our analyses show that all three indices successfully differentiate coupling strength and coupling width when monophasic coupling is present. While the mean vector length was most sensitive to modulations in coupling strengths and width, biphasic coupling can solely be detected by the modulation index. Coupling...
Power and phase properties of oscillatory neural responses in the presence of background activity
Journal of Computational Neuroscience, 2012
Natural sensory inputs, such as speech and music, are often rhythmic. Recent studies have consistently demonstrated that these rhythmic stimuli cause the phase of oscillatory, i.e. rhythmic, neural activity, recorded as local field potential (LFP), electroencephalography (EEG) or magnetoencephalography (MEG), to synchronize with the stimulus. This phase synchronization, when not accompanied by any increase of response power, has been hypothesized to be the result of phase resetting of ongoing, spontaneous, neural oscillations measurable by LFP, EEG, or MEG. In this article, however, we argue that this same phenomenon can be easily explained without any phase resetting, and where the stimulus-synchronized activity is generated independently of background neural oscillations. It is demonstrated with a simple (but general) stochastic model that, purely due to statistical properties, phase synchronization, as measured by 'inter-trial phase coherence', is much more sensitive to stimulus-synchronized neural activity than is power. These results question the usefulness of analyzing the power and phase of stimulus-synchronized activity as separate and complementary measures; particularly in the case of attempting to demonstrate whether stimulus-synchronized neural activity is generated by phase resetting of ongoing neural oscillations.
NeuroImage, 2013
Groups of neurons tend to synchronize in distinct frequency bands. Within a given frequency band, synchronization is defined as the consistency of phase relations between site pairs, over time. This synchronization has been investigated in numerous studies and has been found to be modulated by sensory stimulation or cognitive conditions. Here, we investigate local field potentials (LFPs) and multi-unit activity (MUA) recorded from area V4 of two monkeys performing a selective visual attention task. We show that phase relations, that are consistent over time, are typically diverse across site pairs. That is, across site pairs, mean phase relations differ substantially and this across-site-pair phase-relation diversity (SPHARED, for Spatial PHAse RElation Diversity) is highly reliable. Furthermore, we show that visual stimulation and selective attention can shift the pattern of phase relations across site pairs. These shifts are again diverse and this across-site-pair phase-relation-shift diversity (SPHARESD) is again highly reliable. We find SPHARED for LFP-LFP, LFP-MUA and MUA-MUA pairs, stimulus-induced SPHARESD for LFP-LFP and LFP-MUA pairs, and attention-induced SPHARESD for LFP-LFP pairs. SPHARESD is a highly interesting signal from the perspective of impact on downstream neuronal activity. We provide several pieces of evidence for such a role.
The Relationship Between Synchronization Among Neuronal Populations and Their Mean Activity Levels
Neural Computation, 1999
In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population ring rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynam ics. (In a companion article, Chawla (forthcoming), we address the same issue using stimulus-evoked transients.) By manipulating parameters such as extrinsic input, intrinsic noise, synaptic ef cacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local eld potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of speci c mechanisms decoupling changes in synchronization from ring levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.