Volume Conduction Effects Research Articles

Hypnotic induction is followed by state-like changes in the organization of EEG functional connectivity in the theta and beta frequency bands in high-hypnotically susceptible individuals.

Altered state theories of hypnosis posit that a qualitatively distinct state of mental processing, which emerges in those with high hypnotic susceptibility following a hypnotic induction, enables the generation of anomalous experiences in response to specific hypnotic suggestions. If so then such a state should be observable as a discrete pattern of changes to functional connectivity (shared information) between brain regions following a hypnotic induction in high but not low hypnotically susceptible participants. Twenty-eight channel EEG was recorded from 12 high susceptible (highs) and 11 low susceptible (lows) participants with their eyes closed prior to and following a standard hypnotic induction. The EEG was used to provide a measure of functional connectivity using both coherence (COH) and the imaginary component of coherence (iCOH), which is insensitive to the effects of volume conduction. COH and iCOH were calculated between all electrode pairs for the frequency bands: delta (0.1–3.9 Hz), theta (4–7.9 Hz) alpha (8–12.9 Hz), beta1 (13–19.9 Hz), beta2 (20–29.9 Hz) and gamma (30–45 Hz). The results showed that there was an increase in theta iCOH from the pre-hypnosis to hypnosis condition in highs but not lows with a large proportion of significant links being focused on a central-parietal hub. There was also a decrease in beta1 iCOH from the pre-hypnosis to hypnosis condition with a focus on a fronto-central and an occipital hub that was greater in high compared to low susceptibles. There were no significant differences for COH or for spectral band amplitude in any frequency band. The results are interpreted as indicating that the hypnotic induction elicited a qualitative change in the organization of specific control systems within the brain for high as compared to low susceptible participants. This change in the functional organization of neural networks is a plausible indicator of the much theorized “hypnotic-state.”

Directed Transfer Function is not influenced by volume conduction-inexpedient pre-processing should be avoided.

Directed Transfer Function is not influenced by volume conduction-inexpedient pre-processing should be avoided.

Visualizing simulated electrical fields from electroencephalography and transcranial electric brain stimulation: A comparative evaluation

Electrical activity of neuronal populations is a crucial aspect of brain activity. This activity is not measured directly but recorded as electrical potential changes using head surface electrodes (electroencephalogram - EEG). Head surface electrodes can also be deployed to inject electrical currents in order to modulate brain activity (transcranial electric stimulation techniques) for therapeutic and neuroscientific purposes. In electroencephalography and noninvasive electric brain stimulation, electrical fields mediate between electrical signal sources and regions of interest (ROI). These fields can be very complicated in structure, and are influenced in a complex way by the conductivity profile of the human head. Visualization techniques play a central role to grasp the nature of those fields because such techniques allow for an effective conveyance of complex data and enable quick qualitative and quantitative assessments. The examination of volume conduction effects of particular head model parameterizations (e.g., skull thickness and layering), of brain anomalies (e.g., holes in the skull, tumors), location and extent of active brain areas (e.g., high concentrations of current densities) and around current injecting electrodes can be investigated using visualization. Here, we evaluate a number of widely used visualization techniques, based on either the potential distribution or on the current-flow. In particular, we focus on the extractability of quantitative and qualitative information from the obtained images, their effective integration of anatomical context information, and their interaction. We present illustrative examples from clinically and neuroscientifically relevant cases and discuss the pros and cons of the various visualization techniques.

Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model

To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.

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.

Resting-State EEG Oscillatory Dynamics in Fragile X Syndrome: Abnormal Functional Connectivity and Brain Network Organization

Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS) and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI), a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior) and short-range (frontal-frontal and posterior-posterior) clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

Investigation of tDCS volume conduction effects in a highly realistic head model

Objective. We investigate volume conduction effects in transcranial direct current stimulation (tDCS) and present a guideline for efficient and yet accurate volume conductor modeling in tDCS using our newly-developed finite element (FE) approach. Approach. We developed a new, accurate and fast isoparametric FE approach for high-resolution geometry-adapted hexahedral meshes and tissue anisotropy. To attain a deeper insight into tDCS, we performed computer simulations, starting with a homogenized three-compartment head model and extending this step by step to a six-compartment anisotropic model. Main results. We are able to demonstrate important tDCS effects. First, we find channeling effects of the skin, the skull spongiosa and the cerebrospinal fluid compartments. Second, current vectors tend to be oriented towards the closest higher conducting region. Third, anisotropic WM conductivity causes current flow in directions more parallel to the WM fiber tracts. Fourth, the highest cortical current magnitudes are not only found close to the stimulation sites. Fifth, the median brain current density decreases with increasing distance from the electrodes. Significance. Our results allow us to formulate a guideline for volume conductor modeling in tDCS. We recommend to accurately model the major tissues between the stimulating electrodes and the target areas, while for efficient yet accurate modeling, an exact representation of other tissues is less important. Because for the low-frequency regime in electrophysiology the quasi-static approach is justified, our results should also be valid for at least low-frequency (e.g., below 100 Hz) transcranial alternating current stimulation.

Measuring Time-Varying Information Flow in Scalp EEG Signals: Orthogonalized Partial Directed Coherence

This study aimed to develop a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction and amplitude scaling. We modified the time-varying generalized partial directed coherence (tv-gPDC) method, by orthogonalization of the strictly causal multivariate autoregressive model coefficients, to minimize the effect of mutual sources. The novel measure, generalized orthogonalized PDC (gOPDC), was tested first using two simulated models with feature dimensions relevant to EEG activities. We then used the method for assessing event-related directional information flow from flash-evoked responses in neonatal EEG. For testing statistical significance of the findings, we followed a thresholding procedure driven by baseline periods in the same EEG activity. The results suggest that the gOPDC method 1) is able to remove common components akin to volume conduction effect in the scalp EEG, 2) handles the potential challenge with different amplitude scaling within multichannel signals, and 3) can detect directed information flow within a subsecond time scale in nonstationary multichannel EEG datasets. This method holds promise for estimating directed interactions between scalp EEG channels that are commonly affected by the confounding impact of mutual cortical sources.

Discriminative Learning of Propagation and Spatial Pattern for Motor Imagery EEG Analysis

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

Designing discriminative spatial filter vectors in motor imagery brain–computer interface

The problem of a volume conduction effect in electroencephalography is considered one of the challenging issues in brain–computer interface (BCI) community. In this article, we propose a novel method of designing a class‐discriminative spatial filter assuming that a combination of spatial pattern vectors, irrespective of the eigenvalues of the common spatial pattern (CSP), can produce better performance in terms of classification accuracy. We select discriminative spatial filter vectors that determine features in a pairwise manner, that is, eigenvectors of the K largest eigenvalue and the K smallest eigenvalue. Although the pair of the eigenvectors of the K largest and the K smallest eigenvalues helps extract discriminative features, we believe that a different set of eigenvector pairs is more appropriate to extract class‐discriminative features. In our experimental results using the publicly available dataset of BCI Competition IV, we show that the proposed method outperformed the conventional CSP methods and a filter‐bank CSP. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 147–151, 2013

Editorial: Connectivity via magnetoencephalography

The capacity engendered by the fusion of imaging, neurophysiological recording, and complex data analysis to provide new insights into disease processes and recovery of the nervous system continues to expand. In their article “Resting state magnetoencephalography functional connectivity in traumatic brain injury,” Tarapore and colleagues4 offer a preliminary look into what is now possible with this approach, and how it may change our understanding of recovery and, eventually, allow us to monitor the effects of specific interventions. The authors describe 21 patients with various types and clinical severities of traumatic brain injuries (TBIs) who underwent functional connectivity studies via magnetoencephalography (MEG) between 4 and 42 months after injury, along with structural imaging. Five patients also had a second MEG study at intervals ranging from 21 to 31 months after the initial MEG study. Using a beamformer source analysis in combination with the imaginary coherence (IC) from a resting-state MEG measurement, the authors found a reduction in the IC in the alpha band in specific areas of the cortex compared with age-matched, healthy controls. The authors concluded that specific aspects of functional connectivity were decreased in patients compared with controls and that those with serial measurements had improvement (fewer regions with decreased connectivity) on average over time. Although this is a pilot study, if the findings are valid, it is a fascinating result that has implications for the treatment of patients with TBI. The paper contains striking images. On the more technical imaging side, although the results are likely complimentary and may be due to differences in analytic technique, it would be important to know whether these changes in functional connectivity correspond to the findings in other studies. For example, Huang with various authors1,2 and Lewine et al.3 found focal slowing (low-frequency discharges) that was not found with simultaneous surface electroencephalography. These were found with simple bandpass filtering and sequential equivalent current dipole (ECD) analysis or by using the distributed source analysis method (for example, VESTAL). The frequency of the focal slowing found in these studies was much lower in spectral frequency than the alpha frequencies used for the IC reductions found in this study. These apparent differences are likely reflections of cortical disconnections from white matter. From a practical standpoint, it would be important to know whether the changes in IC correspond to areas of focal slowing, since the focal slowing can be identified with the widely implemented ECD analysis of filtered data, without the need for a comparison with an age-matched control or advanced analysis of IC. There are some limitations of this study, including the use of IC for the functional connectivity. As the authors point out, using only the imaginary projection of coherence reduces overestimation of crosstalk that may occur locally due to the source analysis. It is worth pointing out that in the case of using a source analysis method, this is not due to volume conduction effects; this would be true in electroencephalography electrodes, but that is not the case in this study. As has been pointed out in other studies, IC requires a higher signal-to-noise ratio and also will suppress actual brain activity that has no phase lag, even if it occurs over long distances. There is evidence that this does occur over long distances in the human brain but perhaps is less likely in the alpha frequency band. It seems that the authors used the alpha band with the highest spectral power to mitigate the issue of requiring high signal-to-noise ratio for robust IC measures. The authors also speculate that increased functional connectivity in some of the 5 patients who underwent sequential MEG examinations suggests plasticity in the brain. If true, this would be an important observation that would allow for a biomarker of neuroplasticity in patients with TBI that could supplement traditional measures of neurocognitive improvement and that might indicate the mechanisms of recovery or response to intervention in an individual patient. These techniques show promise, and the authors are to be congratulated on exploring these new methods to further understand the common but frustratingly treatment-elusive condition of TBI. (http://thejns.org/doi/abs/10.3171/2012.10.JNS121408)

A three dimensional anatomical view of oscillatory resting-state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas-based beamforming

Parkinson's disease (PD) related dementia (PDD) develops in up to 80% of PD patients. The present study was performed to further unravel the underlying pathophysiological mechanisms by applying a new analysis approach that uses an atlas-based MEG beamformer to provide a detailed anatomical mapping of cortical rhythms and functional interactions. Importantly, we used the phase lag index (PLI) as a measure of functional connectivity to avoid any biases due to effects of volume conduction.MEG recordings were obtained in 13 PDD and 13 non-demented PD patients. Beamforming was used to estimate spectral power and PLI in delta, theta, alpha, beta and gamma frequency bands. Compared to PD patients, PDD patients had more delta and theta power in parieto-occipital and fronto-parietal areas, respectively. The PDD patients had less alpha and beta power in parieto-temporo-occipital and frontal areas, respectively. Compared to PD patients, PDD patients had lower mean PLI values in the delta and alpha bands in fronto-temporal and parieto-temporo-occipital areas, respectively. In addition, in PDD patients connectivity between pairs of regions of interest (Brodmann areas) was stronger in the theta band and weaker in the delta, alpha and beta bands.The added value of the present results over previous studies analysing frequency-specific changes in neuronal activity in PD patients, is the anatomical framework in which we demonstrated a slowing in neuronal activity and a reduction in functional connectivity in PD related dementia. Moreover, this study shows a widespread reduction in functional connectivity between different regions in PDD.

Comparing parametric and nonparametric methods for detecting phase synchronization in EEG

Detecting significant periods of phase synchronization in EEG recordings is a non-trivial task that is made especially difficult when considering the effects of volume conduction and common sources. In addition, EEG signals are often confounded by non-neural signals, such as artifacts arising from muscle activity or external electrical devices. A variety of phase synchronization analysis methods have been developed with each offering a different approach for dealing with these confounds. We investigate the use of a parametric estimation of the time–frequency transform as a means of improving the detection capability for a range of phase analysis methods. We argue that such an approach offers numerous benefits over using standard nonparametric approaches. We then demonstrate the utility of our technique using both simulated and actual EEG data by showing that the derived phase synchronization estimates are more robust to noise and volume conduction effects.

Simulation of Cortico-Basal Ganglia Oscillations and Their Suppression by Closed Loop Deep Brain Stimulation

A new model of deep brain stimulation (DBS) is presented that integrates volume conduction effects with a neural model of pathological beta-band oscillations in the cortico-basal ganglia network. The model is used to test the clinical hypothesis that closed-loop control of the amplitude of DBS may be possible, based on the average rectified value of beta-band oscillations in the local field potential. Simulation of closed-loop high-frequency DBS was shown to yield energy savings, with the magnitude of the energy saved dependent on the strength of coupling between the subthalamic nucleus and the remainder of the cortico-basal ganglia network. When closed-loop DBS was applied to a strongly coupled cortico-basal ganglia network, the stimulation energy delivered over a 480 s period was reduced by up to 42%. Greater energy reductions were observed for weakly coupled networks, as the stimulation amplitude reduced to zero once the initial desynchronization had occurred. The results provide support for the application of closed-loop high-frequency DBS based on electrophysiological biomarkers.

Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index

In this paper, we test the performance of a synchronicity estimator widely applied in Neuroscience, phase lag index (PLI), for brain network inference in EEG. We implement the four sphere head model to simulate the volume conduction problem present in EEG recordings and measure the activity at the scalp of surrogate sources located at the brain level. Then, networks are estimated under the null hypothesis (independent sources) using PLI, coherence (R) and phase coherence (PC) for the volume conduction and no volume conduction (NVC) cases. It is known that R and PC are highly influenced by volume conduction, leading to the inference of clustered grid networks. PLI was designed to solve this problem. Our simulations show that PLI is partially invariant to volume conduction. The networks found by PLI show small-worldness, with a clustering coefficient higher than random networks. On the contrary, PLI-NVC obtains networks whose distribution is closer to random networks indicating that the high clustering shown by PLI networks are caused by volume conduction. The influence of volume conduction in PLI might lead to biased results in brain network inference from EEG if this behaviour is ignored.

Brain Oscillatory Activity during Spatial Navigation: Theta and Gamma Activity Link Medial Temporal and Parietal Regions

Brain oscillatory correlates of spatial navigation were investigated using blind source separation (BSS) and standardized low resolution electromagnetic tomography (sLORETA) analyses of 62-channel EEG recordings. Twenty-five participants were instructed to navigate to distinct landmark buildings in a previously learned virtual reality town environment. Data from periods of navigation between landmarks were subject to BSS analyses to obtain source components. Two of these cortical sources were found to exhibit significant spectral power differences during navigation with respect to a resting eyes open condition and were subject to source localization using sLORETA. These two sources were localized as a right parietal component with gamma activation and a right medial-temporal-parietal component with activation in theta and gamma bandwidths. The parietal gamma activity was thought to reflect visuospatial processing associated with the task. The medial-temporal-parietal activity was thought to be more specific to the navigational processing, representing the integration of ego- and allo-centric representations of space required for successful navigation, suggesting theta and gamma oscillations may have a role in integrating information from parietal and medial-temporal regions. Theta activity on this medial-temporal-parietal source was positively correlated with more efficient navigation performance. Results are discussed in light of the depth and proposed closed field structure of the hippocampus and potential implications for scalp EEG data. The findings of the present study suggest that appropriate BSS methods are ideally suited to minimizing the effects of volume conduction in noninvasive recordings, allowing more accurate exploration of deep brain processes.

Volume conduction effects in tDCS using a 1mm geometry-adapted hexahedral finite element head model

Article Volume conduction effects in tDCS using a 1mm geometry-adapted hexahedral finite element head model was published on September 6, 2012 in the journal Biomedical Engineering / Biomedizinische Technik (volume 57, issue SI-1-Track-O).

A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

Studying brain connectivity has provided new insights to the understanding of brain function. While connectivity measures are conventionally computed from electroencephalogram (EEG) signals directly, the presence of volume conduction represents a serious confound affecting interpretation of results. A common solution is to use a two-stage approach which involves estimating underlying brain sources from scalp EEG recordings and subsequently estimating the connectivity between the inferred sources. Recently, a state-space framework which jointly models the instantaneous mixing effects of volume conduction and the causal relationships between underlying brain sources is proposed. In this paper, we extend the state-space framework and model the source activity by a generalized multivariate autoregressive (mAR) process with possibly non-Gaussian noise. A maximum likelihood estimation approach is developed which allows simultaneous estimation of both the mixing matrix and AR model parameters directly from scalp EEG. The proposed technique was verified with simulated EEG data generated using the single-shell spherical head model and demonstrated improved estimation accuracies compared to conventional two-stage connectivity estimation approaches. Furthermore, the proposed technique was applied to EEG data collected from normal and Parkinson's subjects performing a right-handed force-tracking task with differing amounts of visual feedback. The partial directed coherence (PDC) between sources showed significant differences between groups and conditions. These results suggest that the proposed technique is a powerful method to extract connectivity information from EEG recordings.

Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution

The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach.Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI).Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected.Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.

Development of brain synchronisation within school-age – Individual analysis of resting (alpha) coherence in a longitudinal data set

Objective Given evidence that synchronisation of neuronal activity may be a correlate of cognition, we examined EEG coherence as function of age and inter-electrode distance in healthy children and adolescents in order to elucidate basic information for a better understanding of developmental disorders associated with deficits in cognitive functions. Methods Based on a 64-channel eyes closed resting EEG we combined local and global coherence measures in order to reduce volume conduction and reference effects. We used a two point longitudinal design in order to analyze intraindividual change during school-age ( n = 40; 6–18 years). Coherence was analyzed within individually adjusted frequency bands and around iPF (= individual alpha peak frequency). Results Both local and global resting coherence was largest in the alpha range and particularly around iPF. Local synchronisation was larger in the left compared with the right hemisphere. Controlling for increases in iPF, synchronisation increased with age, with global changes being most pronounced in the alpha range. Moreover age-related changes suggest an earlier development in girls. Conclusions Our data provides evidence that both local and global functional integration increases during normal development within school-age. Significance This general pattern – combined with more specific effects of sex and frequency – may help to specify deviations in developmental disorders.