Manual Basic Mechanisms of the EEG (Brain Dynamics)

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Moreover, based on fMRI data different levels of insights concerning the interaction of brain regions can be reached starting from a more descriptive determination of a correlational relationship functional connectivity to more sophisticated specifications of the directed influence from one region to another.

In fact, functional coupling in the brain is carried out by oscillation patterns in different frequency bands which can be directly assessed using methods such as electroencephalography EEG and magnetoencephalography MEG. Gamma-band oscillations are dependent on intact function of the N-methyl-D-aspartate NMDA receptor 12 and improvement of glutamatergic neurotransmission at the NMDA receptor has been suggested to be a promising new strategy for the treatment of patients with schizophrenia.

EEG like MEG is well known to basically represent synaptic activity although there are some exceptions eg, high-frequency bursts representing action potentials. On the cellular level, both excitatory and inhibitory postsynaptic potentials EPSPs and IPSPs produce current sinks and sources in the extracellular medium next to the apical dendrite and the soma of a pyramidal cell, resulting in a dipolar source-sink configuration. Local field potentials LFPs are currents established by activity of a number of surrounding neurons and measured together in vivo.

LFPs represent a summation of synaptic events, including afferent inputs and synaptic inputs originating from local neurons. Measured at the cortical surface, we can detect the electrocorticogram, or at an even longer distance from the scalp, the EEG. Given the strong representation of synaptic activity in the EEG, the question arises as to whether fMRI signals are related to similar or different aspects of neuronal activity, such as neuronal spiking.

In fact, in many experimental situations, synaptic activity is highly correlated with the firing rate of the neuron to which the synapses under consideration belong. Accordingly, it is not surprising that in many cases the fMRI signal correlates equally well with LFPs and spiking activity. However, in a few studies, there has been successful differentiation between synaptic activity and spiking activity with regard to the related hemodynamic changes. Radiofrequency-related heating of electrodes or brain tissue has to be considered, and there are several factors that are relevant such as the scanning sequence, the number of EEG electrodes, or the field strength of the MRI scanner.

Here, two main artefacts have to be considered: the cardiac pulse-related artefact and the image acquisition artefact. For both types of artefacts, today there are post-processing artefact removal strategies available with sufficient efficacy. The pulse-related artefact, which is often also referred to as a ballistocardiogram, or BCG artefact, is complex in its origin with a role of pulsatile movements of scalp vessels on adjacent electrodes and head rotation.

EEG and Action Potentials

Post-processing strategies are based either on waveform removal approaches such as the average artefact subtraction algorithm 24 or on pattern removal approaches such as independent component analysis. Based on the spatial patterns of correlated time series that are quite reliably identified in resting state BOLD signals, several intrinsic brain networks have been identified such as the default-mode network DMN , the dorsal attention network DAN or the salience network SN.

Within these networks, brain regions show increased functional connectivity on time-scales of seconds to minutes. Alterations in resting state networks are found in several neuropsychiatric conditions such as Alzheimer s disease. For example, the topographic representation of the EEG remains stable over periods of around ms. Microstates reflect the summation of concomitant neuronal activity across brain regions rather than activity specific to any frequency band.

Alterations in microstates have been demonstrated in several psychiatric disorders such as schizophrenia. Another very interesting approach will be the investigation of the relationship of EEG coherence patterns and fMRI connectivity. Recently, in monkeys using the combination of fMRI and invasive electrophysiological measurements, the relationship between interareal BOLD correlations and neural oscillations was investigated. An appealing aspect of the combination of these methods is the fact that both EEG and fMRI are overlapping in their sensitivity to synaptic processing and accordingly brain function can be assessed by means of simultaneous EEG-fMRI with high temporal and high spatial resolution.

One of the most promising applications for EEG-fMRI today is the characterization of brain network structure and dynamics. National Center for Biotechnology Information , U. Journal List Dialogues Clin Neurosci v. Dialogues Clin Neurosci. Author information Copyright and License information Disclaimer. This article has been cited by other articles in PMC. Abstract Progress in the understanding of normal and disturbed brain function is critically dependent on the methodological approach that is applied. Introduction Since functional segregation and integration are key principles in the organization of brain function, 1 characterization of connectivity mechanisms in brain networks is a major goal in human neuroscience today.

Open in a separate window. Figure 1. A EEG single-trial data of a typical data set at electrode Cz. In many trials, a GBR between 30 and ms post-stimulus is present. However, the amplitudes of the GBR are variable over time. This variability can be used for specific predictions of the related BOLD signal. In the lower part of the figure, the corresponding averaged GBR is shown. In the lower right corner, a glass brain view is provided, demonstrating a 3D view of the three abovementioned clusters. Single-trial coupling of the gamma-band response and the corresponding BOLD signal.

Characterization of brain networks Based on the spatial patterns of correlated time series that are quite reliably identified in resting state BOLD signals, several intrinsic brain networks have been identified such as the default-mode network DMN , the dorsal attention network DAN or the salience network SN.

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Friston KJ. Modalities, modes, and models in functional neuroimaging. Fox PT. Distributed processing; distributed functions? Schmitt A. Schizophrenia as a disorder of disconnectivity. Eur Arch Psychiatry Clin Neurosci. Network discovery with DCM. Buzsaki G. Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.

Fisch L. Herrmann CS.

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Human gamma-band activity: a review on cognitive and behavioral correlates and network models. Neurosci Biobehav Rev. Fujisawa S. A 4 Hz oscillation adaptively synchronizes prefrontal, VTA, and hippocampal activities. Colgin LL. Oscillations and hippocampal-prefrontal synchrony. Curr Opin Neurobiol. Spencer KM. Abnormal neural synchrony in schizophrenia. J Neurosci. In such a way, the dynamics of brain activity within a given macrostate can be considered as a sequence of relatively stable brain microstates which are reflected in EEG as piecewise stationary segments [ 47 ].

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Consecutive macrostates in its turn comprise a new sequence in another time-scale. Such functional EEG structure comprises hierarchical multivariability which reflects the poly-operational structure of brain activity [ 48 , 49 ]. Spectral decomposition, to this day, still remains the main analytical paradigm for analysis of EEG oscillations due to the importance of oscillations as a general phenomenon of neuronal activity. Additionally, a power spectrum is a compact and natural representation of steady state neural activity [ 50 ].

The comparison of absolute and relative changes in frequency bands of the power spectrum has revealed important information about the electrical activity of the brain and its relationship to human behaviour [ 51 ]. In fact, and as explored in our early work [ 5 , 55 ] the total power spectrum does not characterize each of the individual power-spectra for each EEG segment Figs. Example of mean power spectra averaged out over one-minute EEG in the insertions , their variability grey areas and correspondent relative presence of each individual power spectra vertical bars for each EEG channel separately.

EEG registered during eyes closed resting condition. Such variability is due to the fact that piecewise stationary EEG segments are described by different classes of spectral patterns SPs [ 2 , 3 , 56 ].

Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks

This suggests that ongoing brain activity occurs in discontinuous steps and confirms that the cerebral cortex is continuously active even in wakefulness. The frequency of each SP type occurrence reflects the probability for the occurrence of particular neuronal dynamics which altogether constitute a dynamic repertoire of brain activity in particular functional state [ 6 ]. Average power spectrum effect and piecewise stationary organization of EEG scheme. Alternative interpretations of the average power spectrum effect changes from state I to state II are illustrated.

The average characteristics of a signal predominantly reflect an influence of high-amplitude segments of the long EEG epochs, thereby totally obscuring the low-amplitude desynchronized segments [ 57 ] Fig. Additionally, it is impossible to derive information on temporal dynamics of brain activity from average power spectrum Fig. Moreover, while analysing an average power spectrum there may be difficulties in its meaningful interpretation if the spectrum is not matched to the EEG nonstationary structure Fig. From the Fig. Unfortunately, it is impossible to give privilege to any of them based solely on the average power spectrum and without the information on piecewise stationary organization of the EEG.

From these examples it follows that the activity of some brain processes may be reflected in the EEG as changes of its structure without changes in the average spectral characteristics of the signal.

Resting state brain dynamics and its transients: a combined TMS-EEG study | Scientific Reports

Additionally, the frequency bands are predefined and taken in isolation from each other in the vast majority of EEG studies. At the same time, brain functioning is represented by multiple oscillations [ 27 ]. According to the superposition principle introduced by Basar et al. A relatively new promising area in the study of EEG during rest and cognitive processing is based on the reduction of the signal to the elementary spectra SPs of various types in accordance with the number of types of EEG stationary segments instead of the usage of averaged power spectrum for the same EEG [ 2 , 3 , 5 , 6 , 56 , 58 ].

It has been suggested that the operational elements of behavioral and mental activity are originated in the periods of short-term metastable 2 states of the whole brain or its individual subsystems see reviews [ 9 , 22 , 43 , 48 , 49 , 59 , 60 ]. The results of these studies suggest that the quasi-stationary segments reflect the operational acts of nervous activity which continue to occur even without external stimulation.

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  5. From this viewpoint, it is justified to use the calculation of individual spectral estimations of the elementary EEG segments. The work of Bodenstein and Praetorious [ 58 ], Bodunov [ 2 ], Jansen et al.

    Considering that a single EEG spectrum illustrates the particular integral dynamics of tens and hundreds of thousands of neurons in a given cortical area at a particular point in time [ 50 ], it can be suggested that the SPs within each class are generated by the same or similar dynamics with the same or similar driving force [ 61 ]. SPs from different classes, however, have had in effect different driving forces and therefore have been generated by different dynamics.