Modular Patterns of Phase Desynchronization Networks During a Simple Visuomotor Task (original) (raw)
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Physica A: Statistical Mechanics and its Applications, 2018
h i g h l i g h t s • Here, chemical and electrical synapses are considered to describe a neural network. • The network exhibits phase synchronization depending on the synaptic strength. • Different functional structures are observed as synaptic strength is varied. • The observed synchronization can be suppressed by appropriated feedback signals. • The suppression of synchronization depends on feedback intensity and time delay.
Measuring temporal dynamics of functional networks using phase spectrum of fMRI data
NeuroImage, 2005
We present a novel method to measure relative latencies between functionally connected regions using phase-delay of functional magnetic resonance imaging data. Derived from the phase component of coherency, this quantity estimates the linear delay between two time-series. In conjunction with coherence, derived from the magnitude component of coherency, phase-delay can be used to examine the temporal properties of functional networks. In this paper, we apply coherence and phase-delay methods to fMRI data in order to investigate dynamics of the motor network during task and rest periods. Using the supplementary motor area (SMA) as a reference region, we calculated relative latencies between the SMA and other regions within the motor network including the dorsal premotor cortex (PMd), primary motor cortex (M1), and posterior parietal cortex (PPC). During both the task and rest periods, we measured significant delays that were consistent across subjects. Specifically, we found significant delays between the SMA and the bilateral PMd, bilateral M1, and bilateral PPC during the task condition. During the rest condition, we found that the temporal dynamics of the network changed relative to the task period. No significant delays were measured between the SMA and the left PM and left M1; however, the right PM, right M1, and bilateral PPC were significantly delayed with respect to the SMA. Additionally, we observed significant map-wise differences in the dynamics of the network at task compared to the network at rest. These differences were observed in the interaction between the SMA and the left M1, left superior frontal gyrus, and left middle frontal gyrus. These temporal measurements are important in determining how regions within a network interact and provide valuable information about the sequence of cognitive processes within a network.
Time-Delayed Mutual Information of the Phase as a Measure of Functional Connectivity
PLoS ONE, 2012
We propose a time-delayed mutual information of the phase for detecting nonlinear synchronization in electrophysiological data such as MEG. Palus already introduced the mutual information as a measure of synchronization [1]. To obtain estimates on small data-sets as reliably as possible, we adopt the numerical implementation as proposed by Kraskov and colleagues [2]. An embedding with a parametric time-delay allows a reconstruction of arbitrary nonstationary connective structuresso-called connectivity patterns-in a wide class of systems such as coupled oscillatory or even purely stochastic driven processes [3]. By using this method we do not need to make any assumptions about coupling directions, delay times, temporal dynamics, nonlinearities or underlying mechanisms. For verifying and refining the methods we generate synthetic data-sets by a mutual amplitude coupled network of Rö ssler oscillators with an a-priori known connective structure. This network is modified in such a way, that the power-spectrum forms a 1=f power law, which is also observed in electrophysiological recordings. The functional connectivity measure is tested on robustness to additive uncorrelated noise and in discrimination of linear mixed input data. For the latter issue a suitable de-correlation technique is applied. Furthermore, the compatibility to inverse methods for a source reconstruction in MEG such as beamforming techniques is controlled by dedicated dipole simulations. Finally, the method is applied on an experimental MEG recording.
Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity
PLoS computational biology, 2015
Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, ...
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.
2000
This chapter summarizes currently available techniques for measuring synchronization between neural sources identified through EEG and MEG recordings. First the evidence for the involvement of neural synchronization in the implementation of cognitive processes is described. This involvement is mainly through the provision of high-quality communication between active brain regions, allowing integration of processing activities through the exchange of information and control signals. Second, we describe several useful techniques for obtaining phase information from time series of EEG and MEG records, and measuring phase locking or phase coherence using these methods. These include wavelet analysis and the analytic signal using the Hilbert transform for obtaining phase information, and phase-locking value and coherence for obtaining useful indices of synchronization. Finally, we summarize several available techniques for locating neural sources of EEG and MEG records and describe the use of the phase-locking measurements in ascertaining synchronization between sources located with these techniques. The techniques include those involving blind separation of sources, such as independent component analysis or principle component analysis, and those involving use of brain anatomy to constrain source locations, such as beamformer or LORETA. We also provide a few examples of published or forthcoming research that has used these approaches. All of the techniques described are available either in commercial software (such as BESA and MATLAB) or in freeware that runs in MATLAB (such as EEGLAB, Fieldtrip, Brainstorm). Some custom programming might be required (e.g., in MATLAB using the Signal Processing Toolbox) to implement some of the measurements.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
Cross-frequency phase-amplitude coupling (PAC) within large neuronal populations is hypothesized to play a functional role in information processing in a range of cognitive tasks. The goal of our study was to examine the putative role of PAC in the brain networks that mediate continuous visuomotor control. We estimated the cortical activity that mediates visuomotor control via magnetoencephalography (MEG) recordings in 15 healthy volunteers. We extracted the cortical signal amplitudes and phases at the frequencies of interest by means of band-pass filtering followed by Hilbert transforms. To quantify task-related changes of PAC, we implemented a technique based on the Kullback-Leibler divergence. The choice of this technique among others was based on the results of comparisons performed on simulations of coupled sources in various noise conditions. The application of PAC to the MEG data revealed a significant task-related increase in coupling between the phase of delta (2-5 Hz) and ...
Characterizing instantaneous phase relationships in whole-brain fMRI activation data
Human Brain Mapping, 2002
Typically, fMRI data is processed in the time domain with linear methods such as regression and correlation analysis. We propose that the theory of phase synchronization may be used to more completely understand the dynamics of interacting systems, and can be applied to fMRI data as a novel method of detecting activation. Generalized synchronization is a phenomenon that occurs when there is a nonlinear functional relationship present between two or more coupled, oscillatory systems, whereas phase synchronization is defined as the locking of the phases while the amplitudes may vary. In this study, we developed an application of phase synchronization analysis that is appropriate for fMRI data, in which the phase locking condition is investigated between a voxel time series and the reference function of the task performed. A synchronization index is calculated to quantify the level of phase locking, and a nonparametric permutation test is used to determine the statistical significance of the results. We performed the phase synchronization analysis on the data from five volunteers for an event-related finger-tapping task. Functional maps were created that provide information on the interrelations between the instantaneous phases of the reference function and the voxel time series in a whole-brain fMRI activation data set. We conclude that this method of analysis is useful for revealing additional information on the complex nature of the fMRI time series. Hum. Brain Mapping 16:71-80, 2002.
Phase synchronization between LFP and spiking activity in motor cortex during movement preparation
Neurocomputing, 2007
A common approach to measure and assess cortical dynamics focuses on the analysis of mass signals, such as the local field potential (LFP), as an indicator for the underlying network activity. To improve our understanding of how such field potentials and cortical spiking dynamics are related, we analyzed the phase and amplitude relationships between extracellular recordings from motor cortex of monkey in a delayed pointing task. We applied methods from phase synchronization analysis to extract the instantaneous phase of the LFP time series and to characterize the degree of phase coupling between the spike train and oscillation cycles in a frequencyindependent manner. In particular, we investigated the dependence of observed phase preferences on the different periods of a behavioral trial. Furthermore, we present evidence to support the hypothesis that increased LFP oscillation amplitudes are related to a stronger degree of synchronization between the LFP and spike signals. However, neurons tend to keep a fixed phase relationship to the LFP independent of the amplitude or the choice of the electrode used to record the LFP.