Partial Directed Coherence Analysis Research Articles

Electrospinography for non-invasively recording spinal sensorimotor networks in humans

Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity changes during single-joint movements at the spinal and cortical levels. Approach. We synchronously recorded electroencephalography (EEG), electromyography, and ESG in ten neurologically intact adults while performing one of three lower-limb tasks (no movement, plantar-flexion and knee flexion) in the prone position. A multi-pronged approach was applied for removing artifacts using filtering, artifact subspace reconstruction and independent component (IC) analysis. Next, data were segmented by task and ICs of EEG were clustered across participants. Within-participant analysis of ICs and ESG data was conducted, and ESG was characterized in the time and frequency domains. Generalized partial directed coherence analysis was performed within ICs and between ICs and ESG data by participant and task. Results. K-means clustering resulted in five clusters of ICs at Brodmann areas (BAs) 9, BA 8, BA 39, BA 4, and BA 22. Areas associated with motor planning, working memory, visual processing, movement, and attention, respectively. Time-frequency analysis of ESG data found localized changes during movement execution when compared to no movement. Lastly, we found bi-directional changes in functional connectivity (p < 0.05, adjusted for multiple comparisons) within IC’s and between IC’s and ESG sensors during movement when compared to the no movement condition. Significance. To our knowledge this is the first report using high density ESG for characterizing single joint lower limb movements. Our findings provide support that ESG contains information about efferent and afferent signaling in neurologically intact adults and suggests that we can utilize ESG to directly study the spinal cord.

Multisensory Conflict Impairs Cortico-Muscular Network Connectivity and Postural Stability: Insights from Partial Directed Coherence Analysis.

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.

Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram.

Although the remission of self-limited epilepsy with centrotemporal spikes (SeLECTS) usually occurs by adolescence, deficits in cognition and behavior are not uncommon. Several functional magnetic resonance imaging (fMRI) studies have revealed connectivity disturbances in patients with SeLECTS associated with cognitive impairment. However, the disadvantages of fMRI are expensive, time-consuming, and motion sensitive. In the current study, we used a partial directed coherence (PDC) method to analyze electroencephalogram (EEG) for exploring brain connectivity in patients with SeLECTS. This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in the controls than in the patients with SeLECTS, whereas the inflow connectivity in the MIF_L area 4 was significantly higher in the patients with SeLECTS than in the controls. Our proposed approach of combining EEG with PDC provides a convenient and useful tool for investigating functional connectivity in patients with SeLECTS. This approach is time-saving and inexpensive compared with fMRI, but it achieves similar results to fMRI.

Fatigue stress estimation of metro bogie frame through frequency response functions by using limited sensors

In this paper, the fatigue state estimation of the metro bogie frame is discussed based on limited sensors through frequency domain methods. The middle-frequency and high-frequency vibration fatigue damage cases of one metro bogie frame is discussed. The main excitations come from the wheel/rail interactions including wheel out-of-roundness and rail irregularities. The most common modal vibration of the bogie frame, where the transom bending deformation is dominant, is taken as the research object to study the possibilities of fatigue failure monitoring on concerned points of the bogie frame transom focusing on specific transfer paths. To eliminate unnecessary input signals which are not closely related to the occurrence of large stress amplitudes, the coherence analysis of different loads including the transmission load, carbody load and wheel-rail impact load is conducted through various methods including the short-time Fourier transform spectrum analysis, the magnitude-squared coherence analysis and the partial directed coherence analysis. Then the power spectral densities (PSDs) of the measured accelerations and cross power spectral densities (CPSDs) are calculated to obtain the transfer functions, whose output is the Y-axis stress at the concerned point. The stress PSDs are obtained using the instantly measured input accelerations and the prepared transfer function. Then the spectral moments and the stress amplitudes distribution functions are calculated through the frequency domain method to overcome the computational burden. The stress reconstructions based on inverse fast Fourier transform are analyzed and the fatigue damage results show good consistency with the experimental ones.

Epileptogenic zone localization based on partial directed coherence and graph analysis: a case study

Epileptogenic zone localization based on partial directed coherence and graph analysis: a case study

Weakened Effective Connectivity Related to Electroacupuncture in Stroke Patients with Prolonged Flaccid Paralysis: An EEG Pilot Study

Flaccid paralysis in the upper extremity is a severe motor impairment after stroke, which exists for weeks, months, or even years. Electroacupuncture treatment is one of the most widely used TCM therapeutic interventions for poststroke flaccid paralysis. However, the response to electroacupuncture in different durations of flaccid stage poststroke as well as in the topological configuration of the cortical network remains unclear. The objectives of this study are to explore the disruption of the cortical network in patients in different durations of flaccid stage and observe dynamic network reorganization during and after electroacupuncture. Resting-state networks were constructed from 18 subjects with flaccid upper extremity by partial directed coherence (PDC) analysis of multichannel EEG. They were allocated to three groups according to time after flaccid paralysis: the short-duration group (those with flaccidity for less than two months), the medium-duration group (those with flaccidity between two months and six months), and the long-duration group (those with flaccidity over six months). Compared with short-duration flaccid subjects, weakened effective connectivity was presented in medium-duration and long-duration groups before electroacupuncture. The long-duration group has no response in the cortical network during electroacupuncture. The global network measures of EEG data (sPDC, mPDC, and N) indicated that there was no significant difference among the three groups. These results suggested that the network connectivity reduced and weakly responded to electroacupuncture in patients with flaccid paralysis for over six months. These findings may help us to modulate the formulation of electroacupuncture treatment according to different durations of the flaccid upper extremity.

Hippocampus-nidopallium caudolaterale interactions exist in the goal-directed behavior of pigeon

Avian hippocampus (Hp) and nidopallium caudolaterale (NCL) are believed to play key roles in goal-directed behavior. However, it is still unclear whether there are interactions between the two brain regions in the goal-directed behavior of pigeons. To investigate the interactions between the Hp and the NCL in the goal-directed behavior, we recorded local field potential (LFP) signals from the two regions simultaneously when the pigeons performed a goal-directed decision-making task. Amplitude-amplitude coupling analysis revealed that the coupling value between the LFP recorded from the Hp and that from the NCL increased significantly (P < 0.05) in slow gamma-band (40–60 Hz) during the turning area. In addition, the LFP functional network analysis demonstrated the LFP functional connections between the Hp and the NCL increased significantly (P < 0.05) in the turning area. The result of partial directed coherence (PDC) analysis showed that the predominant direction of information flow is thought to be from the Hp to the NCL. These findings suggest that there are causal functional interactions between the Hp and the NCL by which information is transmitted between the two regions relevant to goal-directed behavior.

Effects of an Exoskeleton-Assisted Gait Motor Imagery Training in Functional Brain Connectivity.

Lower-limb exoskeletons have been used in gait rehabilitation to facilitate the restoration of motor skills. These robotics systems could be complemented by Brain-Computer Interfaces (BCIs) to assist or rehabilitate people with walking disabilities. In this preliminary study, electroencephalography-based brain functional connectivity is analyzed during exoskeleton-assisted gait motor imagery (MI) training. Partial Directed Coherence (PDC) analysis was employed to assess the exchange of information flow between EEG signals during gait MI in four healthy subjects, two using an exoskeleton and two without using it. Besides, in order to explore the functional connectivity, an outflow index based on the number of significant directed connectivities revealed by the PDC analysis is proposed. We found that the outflow index increases in the central zone (C2, C3, C4) while decreases in the central-parietal (CP1, CP2) and fronto-central (FC1) zones when the training was assisted by an exoskeleton. The results obtained can be useful to obtain informative features for BCI applications as well as in motor rehabilitation.

Comparison of statistical tests in effective connectivity analysis of ECoG data

BackgroundThe effects of statistical testing on the results of multivariate autoregressive (MVAR)-based effective connectivity analysis have not been adequately investigated, and it is still unclear which statistical test can provide the most accurate results. New methodsUsing simulated and real electrocorticographic (ECoG) data, we investigated the performance of three nonparametric statistical tests – Monte Carlo permutation, bootstrap resampling, and surrogate data method in MVAR-based effective connectivity analysis. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to assess the performance of each statistical test method. In addition, we found optimal p-values for each method based on ROC analysis. Finally, we investigated the application of statistical tests on partial directed coherence analysis of ECoG data collected in a patient with epilepsy. ResultsThe bootstrap statistical test performed more accurately than other methods. The surrogate method slightly outperformed the Monte Carlo permutation method. Optimal p-values of statistical tests depended on signal-to-noise ratio (SNR) of data, and its value increased by reducing SNR of data. By considering the typical SNR range of electrophysiological data, we recommended an optimal p-value range for the application of each statistical test method. Comparison with existing methodsLimited studies have investigated the performance of statistical tests for MVAR-based effective connectivity analysis. For the first time, we have investigated the effects of baseline connections on the various performances of statistical tests. ConclusionsWe recommend utilizing the bootstrap statistical test with p-value between 0.05 and 0.1 for effective connectivity analysis of ECoG data.

Anterior Cingulate Cortex Modulation of the Ventral Tegmental Area in an Effort Task

Information gained during goal pursuit motivates adaptive behavior. The anterior cingulate cortex (ACC) supports adaptive behavior, but how ACC signalsare translated into motivational signals remains unclear. Rats with implants in the ACC and ventral tegmental area (VTA), a dopaminergic brain area implicated in motivation, were trained to run laps around a rectangular track for a fixed reward, where each lap varied in physical effort (a 30-cm climbable barrier). Partial directed coherence analysis of local field potentials revealed that ACC theta (4-12Hz) activity increased as rats entered the barrier-containing region of the maze in trials when the barrier was absent and predicted similar changes in VTA theta activity. This did not occur in effortful, barrier-present trials. These data suggest that the ACC provides a top-down modulating signal to the VTA that can influence the motivation with which to pursue a reward.

Epileptic Seizure Detection Based on Partial Directed Coherence Analysis.

Long-term video EEG epilepsy monitoring can help doctors diagnose and cure epilepsy. The workload of doctors to read the EEG signals of epilepsy patients can be effectively reduced by automatic seizure detection. The application of partial directed coherence (PDC) analysis as mechanism for feature extraction in the scalp EEG recordings for seizure detection could reflect the physiological changes of brain activity before and after seizure onsets. In this study, a new approach on the basis of PDC was proposed to detect the seizure intervals of epilepsy patients. First of all, the multivariate autoregressive model was established for a moving window and the direction and intensity of information flow based on PDC analysis was calculated. Then, the outflow information related to certain EEG channel could be obtained by summing up the intensity of information flow propagated to other EEG channels in order to reduce the feature dimensionality. At last, according to the pathological features of epileptic seizures, the outflow information was regarded as the input vectors to a support vector machine classifier for discriminating interictal periods and ictal periods of EEG signals. The proposed method had achieved a good performance with the correct rate of 98.3%, the selectivity rate of 67.88%, the sensitivity rate of 91.44%, the specificity rate of 99.34%, and the average detection rate of 95.39%, which demonstrated that this method was suitable for detecting the seizure intervals of epilepsy patients. By comparing with other existing techniques, the proposed method based on PDC analysis achieved significant improvement in terms of seizure detection.

Enhanced effective connectivity in mild occipital stroke patients with hemianopia.

Plasticity-based spontaneous recovery and rehabilitation intervention of stroke-induced hemianopia have drawn great attention in recent years. However, the underlying neural mechanism remains unknown. This study aims to investigate brain network disruption and reorganization in hemianopia patients due to mild occipital stroke. Resting-state networks were constructed from 12 hemianopia patients with right occipital infarct by partial directed coherence analysis of multi-channel electroencephalograms. Compared with control subjects, the patients presented enhanced connectivity owing to newly formed connections. Compensational connections mostly originated from the peri-infarct area and targeted contralesional frontal, central, and parietal cortices. These new ipsilesional-to-contralesional inter-hemispheric connections coordinately presented significant correlation with the extent of vision loss. The enhancement of connectivity might be the neural substrate for brain plasticity in stroke-induced hemianopia and may shed light on plasticity-based recovery or rehabilitation.

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

Time-variant partial directed coherence for analysing connectivity: a methodological study

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.

Impact of acoustic coordinated reset neuromodulation on effective connectivity in a neural network of phantom sound

Chronic subjective tinnitus is an auditory phantom phenomenon characterized by abnormal neuronal synchrony in the central auditory system. As recently shown in a proof of concept clinical trial, acoustic coordinated reset (CR) neuromodulation causes a significant relief of tinnitus symptoms combined with a significant decrease of pathological oscillatory activity in a network comprising auditory and non-auditory brain areas. The objective of the present study was to analyze whether CR therapy caused an alteration of the effective connectivity in a tinnitus related network of localized EEG brain sources. To determine which connections matter, in a first step, we considered a larger network of brain sources previously associated with tinnitus. To that network we applied a data-driven approach, combining empirical mode decomposition and partial directed coherence analysis, in patients with bilateral tinnitus before and after 12 weeks of CR therapy as well as in healthy controls. To increase the signal-to-noise ratio, we focused on the good responders, classified by a reliable-change-index (RCI). Prior to CR therapy and compared to the healthy controls, the good responders showed a significantly increased connectivity between the left primary cortex auditory cortex and the posterior cingulate cortex in the gamma and delta bands together with a significantly decreased effective connectivity between the right primary auditory cortex and the dorsolateral prefrontal cortex in the alpha band. Intriguingly, after 12 weeks of CR therapy most of the pathological interactions were gone, so that the connectivity patterns of good responders and healthy controls became statistically indistinguishable. In addition, we used dynamic causal modeling (DCM) to examine the types of interactions which were altered by CR therapy. Our DCM results show that CR therapy specifically counteracted the imbalance of excitation and inhibition. CR significantly weakened the excitatory connection between posterior cingulate cortex and primary auditory cortex and significantly strengthened inhibitory connections between auditory cortices and the dorsolateral prefrontal cortex. The overall impact of CR therapy on the entire tinnitus-related network showed up as a qualitative transformation of its spectral response, in terms of a drastic change of the shape of its averaged transfer function. Based on our findings we hypothesize that CR therapy restores a silence based cognitive auditory comparator function of the posterior cingulate cortex.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

Resilience in migraine brains: decrease of coherence after photic stimulation

Background: During migraine attacks, patients generally have photophobia and phonophobia and seek for environments with less sensorial stimulation. Present work aimed to quantify cortical partial directed coherence (PDC) of electroencephalographic (EEG) recordings from migraine patients and controls in occipital, parietal, and frontal areas with or without photic stimulation. Our hypothesis is that migraine patients with visual aura might have neuronal networks with higher coherence than controls even in interictal periods due to a predisposition in sensory cortical processing. Methods: Eleven adult women with migraine with visual aura (at least 48 h without previous attacks) and seven healthy adult woman were submitted to EEG recording in basal state and during photic stimulation. Results: When compared to healthy volunteers, migraine patients show different coherence profiles. Migraine patients had greater coherence than controls during the basal period (without photic stimulation), showing predisposition for sensory processing in many frequency ranges. After photic stimulation, patients showed a decrease in cortical coherence while controls had an increase. Conclusions: When compared to healty subjects, migraineurs show increased cortical coherence before photic stimulation, but a decrease when stimulation starts. This may be the expression of a resilience mechanism that allows migraineurs the interictal period. The PDC analysis permits to address a patient coherence profile, or “coherence map,” that can be utilized for management of the headache disorder or following up treatments.

Enhanced frontocentral EEG connectivity in photosensitive generalized epilepsies: A partial directed coherence study

Photosensitive epilepsy (PSE) is the most common form of reflex epilepsy presenting with electroencephalography (EEG) paroxysms elicited by intermittent photic stimulation (IPS). To investigate whether the neuronal network undergoes dynamic changes before and during the transition to an EEG epileptic discharge, we estimated EEG connectivity patterns in photosensitive (PS) patients with idiopathic generalized epilepsy. EEG signals were evaluated under resting conditions and during 14 Hz IPS, a frequency that consistently induces photoparoxysmal responses (PPRs) in PS patients. Partial directed coherence (PDC), a linear measure of effective connectivity based on multivariate autoregressive models, was used in 10 PS patients and 10 controls. Anterior versus posterior (F3, F4, C3, C4, and P3, P4, O1, O2) and interhemispheric connectivity patterns (F4, C4, P4, O2, and F3, C3, P3, O1) were estimated with focus on beta and gamma band activity. PDC analysis revealed an enhanced connectivity pattern in terms of both the number and strength of outflow connections in the PS patient group. Under resting condition, the greater connectivity in the PS patients occurred in the beta band, whereas it mainly involved the gamma band during IPS (i.e., the frequencies ranging from 40-60 Hz that include the higher harmonics of the stimulus frequency). Both at rest and during IPS, the differences between the PS patients and controls were due primarily to clearly increased connectivity involving the anterior cortical regions. Our findings indicate that PS patients are characterised by abnormal EEG hyperconnectivity, primarily involving the anterior cortical regions under resting conditions and during IPS. This suggests that, even if the occipital cortical regions are the recipient zone of the stimulus and probably hyperexcitable, the anterior cortical areas are prominently involved in generating the hypersynchronization underlying the spike-and wave discharges elicited by IPS.

Directional connectivity of resting state human fMRI data using cascaded ICA-PDC analysis

Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.

Dynamics of directed interactions between brain regions during interburst–burst EEG patterns in quiet sleep of full-term neonates

The study investigates time-variant directed interactions between brain regions during the interburst–burst EEG pattern (tracé alternant) characteristic of quiet sleep in healthy neonates. The transition from interburst to burst is of particular interest as the generation of the EEG characteristics at burst onset reflects timing and time-variant interplay between the cortical and the thalamo-cortical brain structures. To study the dynamics of the interactions, time-variant partial directed coherence (PDC), a measure of effective connectivity, was used which allows analysis in the time–frequency range. The main results of the grand mean PDC analysis are: (1) PDC time–frequency patterns are frequently associated with phase-locked oscillations. (2) Interhemispheric interactions are dominant between frontal, central and occipital electrodes and intrahemispheric interactions are much less substantial. (3) An interaction breakdown for the frequency ranges 1–4Hz (Fp1⇒Fp2) and 0.5–3Hz (Fp2⇒Fp1) exists which lasts about 2.5s and which is located at about burst onset. (4) Strong interactions in the high-frequency range 3.5–4.5Hz between the frontal electrodes can be observed for both directions at the burst onset. It can be concluded that the evolution of strong interactions in the high-frequency range, which starts shortly before or at the burst onset from frontal regions to anteroposterior directions as well as the frontal interhemispheric interactions, are associated with the burst onset generation. Additionally, the collapsing of the interactions before burst onset and after the burst are indicative of neuronal reorganisation processes.