Dynamic Imaging Of Coherent Sources Research Articles

Age-related increases in right hemisphere support for prosodic processing in children

Language comprehension is a complex process involving an extensive brain network. Brain regions responsible for prosodic processing have been studied in adults; however, much less is known about the neural bases of prosodic processing in children. Using magnetoencephalography (MEG), we mapped regions supporting speech envelope tracking (a marker of prosodic processing) in 80 typically developing children, ages 4–18 years, completing a stories listening paradigm. Neuromagnetic signals coherent with the speech envelope were localized using dynamic imaging of coherent sources (DICS). Across the group, we observed coherence in bilateral perisylvian cortex. We observed age-related increases in coherence to the speech envelope in the right superior temporal gyrus (r = 0.31, df = 78, p = 0.0047) and primary auditory cortex (r = 0.27, df = 78, p = 0.016); age-related decreases in coherence to the speech envelope were observed in the left superior temporal gyrus (r = − 0.25, df = 78, p = 0.026). This pattern may indicate a refinement of the networks responsible for prosodic processing during development, where language areas in the right hemisphere become increasingly specialized for prosodic processing. Altogether, these results reveal a distinct neurodevelopmental trajectory for the processing of prosodic cues, highlighting the presence of supportive language functions in the right hemisphere. Findings from this dataset of typically developing children may serve as a potential reference timeline for assessing children with neurodevelopmental hearing and speech disorders.

Dynamic networks differentiate the language ability of children with cochlear implants.

Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill. Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children. Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy. Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear.In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC.Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies.We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

MEG language mapping using a novel automatic ECD algorithm in comparison with MNE, dSPM, and DICS beamformer.

The single equivalent current dipole (sECD) is the standard clinical procedure for presurgical language mapping in epilepsy using magnetoencephalography (MEG). However, the sECD approach has not been widely used in clinical assessments, mainly because it requires subjective judgements in selecting several critical parameters. To address this limitation, we developed an automatic sECD algorithm (AsECDa) for language mapping. The localization accuracy of the AsECDa was evaluated using synthetic MEG data. Subsequently, the reliability and efficiency of AsECDa were compared to three other common source localization methods using MEG data recorded during two sessions of a receptive language task in 21 epilepsy patients. These methods include minimum norm estimation (MNE), dynamic statistical parametric mapping (dSPM), and dynamic imaging of coherent sources (DICS) beamformer. For the synthetic single dipole MEG data with a typical signal-to-noise ratio, the average localization error of AsECDa was less than 2 mm for simulated superficial and deep dipoles. For the patient data, AsECDa showed better test-retest reliability (TRR) of the language laterality index (LI) than MNE, dSPM, and DICS beamformer. Specifically, the LI calculated with AsECDa revealed excellent TRR between the two MEG sessions across all patients (Cor = 0.80), while the LI for MNE, dSPM, DICS-event-related desynchronization (ERD) in the alpha band, and DICS-ERD in the low beta band ranged lower (Cor = 0.71, 0.64, 0.54, and 0.48, respectively). Furthermore, AsECDa identified 38% of patients with atypical language lateralization (i.e., right lateralization or bilateral), compared to 73%, 68%, 55%, and 50% identified by DICS-ERD in the low beta band, DICS-ERD in the alpha band, MNE, and dSPM, respectively. Compared to other methods, AsECDa's results were more consistent with previous studies that reported atypical language lateralization in 20-30% of epilepsy patients. Our study suggests that AsECDa is a promising approach for presurgical language mapping, and its fully automated nature makes it easy to implement and reliable for clinical evaluations.

P 80 How to avoid measurement of spurious inter-regional functional connectivity from EEG – a simulation study

Introduction: Aggregating statistical dependencies between multivariate time series is important to characterise functional connectivity (FC) between brain regions. However, it is still unclear how to reliably detect true FC from source-reconstructed M/EEG data. This study generates ground truth data and compares the performances of various pipelines that estimate directed FC (Time-Reversed Granger Causality TRGC 1 ) and undirected linear FC (imaginary part of coherency, absolute part of coherency and multivariate interaction measure MIM 2 ) between regions. Material and Methods: To simulate EEG-like sensor signals, we proceeded as follows: 1. Ground truth source activity was generated as white noise time series filtered in the alpha band. Between one and five pairs of sources interacted with certain time delays. 2. After adding individual 1/f noise, the source signals were projected to sensor space. 3. White sensor noise was added. Afterwards, sensor data were projected to source level by applying one of four tested inverse solutions: Dynamic imaging of coherent sources (DICS), Linearly Constrained Minimum Variance source projection (LCMV), eLORETA, and Champagne. All tested connectivity analysis pipelines calculate one connectivity score for every region combination. The pipelines‘ ability to detect the true interactions was evaluated by percentile rank. Results: The best-performing FC pipeline consists of the following steps: 1. Source projection with a beamformer inverse solution. 2. Principal component analysis within every region. 3. Selection of a fixed number of strongest principal components as basis for further analysis. 4. Calculation of the MIM for every region pair in case of undirected FC and calculation of the TRGC in case of directed FC. Worst performance was obtained with pipelines estimating undirected FC with the absolute value of coherency. DICS source projection resulted in good detection of undirected FC, but failure to detect the direction of FC with TRGC. Discussion: In this study, we tested several connectivity analysis pipelines that are used in the literature. Our simulation clearly shows that many of them do not detect true interactions reliably. To use the winning pipeline of this study could greatly increase the validity of future experimental connectivity studies.

Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG

In performing a gaming task, mental rotation (MR) is one of the important aspects of visuospatial processing. MR involves dorsal-ventral pathways of the brain. Visual objects/models used in computer-games play a crucial role in gaming experience of the users. The visuospatial characteristics of the objects used in the computer-game influence the engagement of dorsal-ventral visual pathways. The current study investigates how the objects’ visuospatial characteristics (i.e., angular disparity and dimensionality) in an MR-based computer-game influence the cortical activities in dorsal-ventral visual pathways. Both the factors have two levels, angular disparity: convex angle (CA) vs. reflex angle (RA) and dimensionality: 2D vs. 3D. Sixty healthy adults, aged, 18–29 years (M = 21.6) were recruited for the study and randomly assigned to four gaming conditions i.e., 15 participants in each group. The multichannel electroencephalogram (EEG) data were recorded from 60 healthy adults while playing the game. The source reconstruction was done for ∼3000 sources inside the brain using the Dynamic Imaging of Coherent Sources (DICS) beamforming method for θ1(4–5.75), θ2(5.75–7.5), α1(7.5–9), α2(9–11), α3(11–13), β1(13–17.25), β2(17.25–21.5) Hz frequency sub-bands. The reconstructed neuronal sources were segmented into 68 functionally parcellated brain regions, and the percentage of active sources for each region was computed. Further, the differences across the 68 regions among the four gaming conditions were evaluated using the percentage of active sources. The differences in activation for the dorsal-ventral pathways and some additional brain regions were observed among the four groups. The game with 2D objects and CA showed higher activation than that with 3D objects and RA, respectively. The dorsal pathway was found to be more active in contrast to the ventral pathway. The findings suggest that angular disparity and dimensionality in MR influence the engagement of dorsal-ventral visual pathways in such a way that angular disparity has a greater impact on cortical activation across this region than dimensionality. Also, higher activation for CA as compared to RA irrespective of dimensionality reflects the complexity of spatial information processing under CA. Similarly, greater activation was seen for 2D objects than 3D, indicating difficulty in information processing due to deficient visual features.

Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice

ObjectiveAge-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources. MethodsHealthy younger (n = 24, 18–24 y) and older (n = 24, 65–87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention. ResultsThe results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta connectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively. ConclusionAge-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks. SignificanceThe present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries.

Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window.

AimsTo improve the Magnetoencephalography (MEG) spatial localization precision of focal epileptic.Methods306‐channel simulated or real clinical MEG is estimated as a lower‐dimensional tensor by Tucker decomposition based on Higher‐order orthogonal iteration (HOOI) before the inverse problem using linearly constraint minimum variance (LCMV). For simulated MEG data, the proposed method is compared with dynamic imaging of coherent sources (DICS), multiple signal classification (MUSIC), and LCMV. For clinical real MEG of 31 epileptic patients, the ripples (80–250 Hz) were detected to compare the source location precision with spikes using the proposed method or the dipole‐fitting method.ResultsThe experimental results showed that the positional accuracy of the proposed method was higher than that of LCMV, DICS, and MUSIC for simulation data. For clinical real MEG data, the positional accuracy of the proposed method was higher than that of dipole‐fitting regardless of whether the time window was ripple window or spike window. Also, the positional accuracy of the ripple window was higher than that of the spike window regardless of whether the source location method was the proposed method or the dipole‐fitting method. For both shallow and deep sources, the proposed method provided effective performance.ConclusionTucker estimation of MEG for source imaging by ripple window is a promising approach toward the presurgical evaluation of epileptics.

Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System.

This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.

Frequency-specific network activity predicts bradykinesia severity in Parkinson’s disease

ObjectiveBradykinesia has been associated with beta and gamma band interactions in the basal ganglia-thalamo-cortical circuit in Parkinson’s disease. In this present cross-sectional study, we aimed to search for neural networks with electroencephalography whose frequency-specific actions may predict bradykinesia. MethodsTwenty Parkinsonian patients treated with bilateral subthalamic stimulation were first prescreened while we selected four levels of contralateral stimulation (0: OFF, 1–3: decreasing symptoms to ON state) individually, based on kinematics. In the screening period, we performed 64-channel electroencephalography measurements simultaneously with electromyography and motion detection during a resting state, finger tapping, hand grasping tasks, and pronation-supination of the arm, with the four levels of contralateral stimulation. We analyzed spectral power at the low (13–20 Hz) and high (21–30 Hz) beta frequency bands and low (31–60 Hz) and high (61–100 Hz) gamma frequency bands using the dynamic imaging of coherent sources. Structural equation modelling estimated causal relationships between the slope of changes in network beta and gamma activities and the slope of changes in bradykinesia measures. ResultsActivity in different subnetworks, including predominantly the primary motor and premotor cortex, the subthalamic nucleus predicted the slopes in amplitude and speed while switching between stimulation levels. These subnetwork dynamics on their preferred frequencies predicted distinct types and parameters of the movement only on the contralateral side. DiscussionConcurrent subnetworks affected in bradykinesia and their activity changes in the different frequency bands are specific to the type and parameters of the movement; and the primary motor and premotor cortex are common nodes.

Quantitative Evaluation in Estimating Sources Underlying Brain Oscillations Using Current Source Density Methods and Beamformer Approaches.

Brain oscillations from EEG and MEG shed light on neurophysiological mechanisms of human behavior. However, to extract information on cortical processing, researchers have to rely on source localization methods that can be very broadly classified into current density estimates such as exact low-resolution brain electromagnetic tomography (eLORETA), minimum norm estimates (MNE), and beamformers such as dynamic imaging of coherent sources (DICS) and linearly constrained minimum variance (LCMV). These algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. On the other hand, there are very few comparative analyses that evaluates the “ground truth detection” capabilities of these methods. The current article evaluates the reliability in estimation of sources of spectral event generators in the cortex using a two-pronged approach. First, simulated EEG data with point dipoles and distributed dipoles are used to validate the accuracy and sensitivity of each one of these methods of source localization. The abilities of the techniques were tested by comparing the localization error, focal width, false positive (FP) ratios while detecting already known location of neural activity generators under varying signal-to-noise ratios (SNRs). Second, empirical EEG data during auditory steady state responses (ASSRs) in human participants were used to compare the distributed nature of source localization. All methods were successful in recovery of point sources in favorable signal to noise scenarios and could achieve high hit rates if FPs are ignored. Interestingly, focal activation map is generated by LCMV and DICS when compared to eLORETA while control of FPs is much superior in eLORETA. Subsequently drawbacks and strengths of each method are highlighted with a detailed discussion on how to choose a technique based on empirical requirements.

Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson's disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21-35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4-6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

Bayesian multi-dipole modelling in the frequency domain

BackgroundMagneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are therefore well suited for source localization in the time and in the frequency domain. In particular, localization of the generators of neural oscillations is very important in the study of cognitive processes in the healthy and in the pathological brain. New methodWe introduce the use of a Bayesian multi-dipole localization method in the frequency domain. Given the Fourier Transform of the data at one or multiple frequencies and/or trials, the algorithm approximates numerically the posterior distribution with Monte Carlo techniques. ResultsWe use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios and correlated sources. We use dipole clusters to mimic the effect of extended sources. In addition, we test the algorithm on real MEG data to confirm its feasibility. Comparison with existing method(s)Throughout the whole study, DICS (Dynamic Imaging of Coherent Sources) is used systematically as a benchmark. The two methods provide similar general pictures; the posterior distributions of the Bayesian approach contain much richer information at the price of a higher computational cost. ConclusionsThe Bayesian method described in this paper represents a reliable approach for localization of multiple dipoles in the frequency domain.

Analysis of Functional Connectivity and Oscillatory Power Using DICS: From Raw MEG Data to Group-Level Statistics in Python.

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex- and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

PB15. Neurophysiological biomarker for the clinical development of tuberous sclerosis

Aim To investigate the neuronal networks in children with tuberous sclerosis complex (TS) undergoing treatment with Everolimus. Methods Sleep and wake electroencephalography (EEG) before and one year after the start of the treatment with Everolimus were investigated in 13 patients with TS. To investigate functional and effective connectivity within the network generating the delta and theta activity in the background sleep and wake EEG, the methods of dynamic imaging of coherent sources (DICS) and renormalized partial directed coherence (RPDC) were applied. Results Sources before the treatment . Independent of location of the tubera and severity of epilepsy, delta activity in the background EEG pattern in patients with TS was associated with the sources in the medial prefrontal cortex, the supplementary motor area and the putamen during sleep. Theta waves during sleep were associated with sources in the prefrontal cortex, sensory cortex, hippocampus and the thalamus. The sources of delta frequency during wakefulness were identified at the posterior parietal cortex, the parahippocampal gyrus and the Broca area. Sources at theta frequency were found at the sensorymotor cortex, the prefrontal cortex, the primary visual cortex and the thalamus at awake state. Sources after the treatment. The sources one year after the start of the therapy, for both delta and delta frequencies were located in the same areas as before, however with a significantly weaker strength of coherence. The RPDC analysis at baseline showed strong bidirectional connections between described sources. The RPDC analyses after the one year of treatment showed significantly weaker unidirectional connections within the described network. At the follow up patients were grouped in two groups; group 1: five patients with >50% reduction of seizures and spike wave index, group 2: eight patients with Conclusion The current study described the neuronal network in children with severe epilepsies due to TS. Regardless of the locations of the tubera the DICS analyses showed a complex network of cortical and subcortical sources with strong bidirectional connections. The described network was significantly weaker after one year under the treatment with Everolimus and appears to be characteristic for the children with TS and severe epilepsy.

Neuromagnetic Cerebellar Activity Entrains to the Kinematics of Executed Finger Movements

This magnetoencephalography (MEG) study aims at characterizing the coupling between cerebellar activity and the kinematics of repetitive self-paced finger movements. Neuromagnetic signals were recorded in 11 right-handed healthy adults while they performed repetitive flexion-extensions of right-hand fingers at three different movement rates: slow (~ 1Hz), medium (~ 2Hz), and fast (~ 3Hz). Right index finger acceleration was monitored with an accelerometer. Coherence analysis was used to index the coupling between right index finger acceleration and neuromagnetic signals. Dynamic imaging of coherent sources was used to locate coherent sources. Coupling directionality between primary sensorimotor (SM1), cerebellar, and accelerometer signals was assessed with renormalized partial directed coherence. Permutation-based statistics coupled with maximum statistic over the entire brain volume or restricted to the cerebellum were used. At all movement rates, maximum coherence peaked at SM1 cortex contralateral to finger movements at movement frequency (F0) and its first harmonic (F1). Significant (statistics restricted to the cerebellum) coherence consistently peaked at the right posterior lobe of the cerebellum at F0 with no influence of movement rate. Coupling between Acc and cerebellar signals was significantly stronger in the afferent than in the efferent direction with no effective contribution of cortico-cerebellar or cerebello-cortical pathways. This study demonstrates the existence of significant coupling between finger movement kinematics and neuromagnetic activity at the posterior cerebellar lobe ipsilateral to finger movement at F0. This coupling is mainly driven by spinocerebellar, presumably proprioceptive, afferences.

Primary Sensorimotor Cortex Drives the Common Cortical Network for Gamma Synchronization in Voluntary Hand Movements.

Background: Gamma synchronization (GS) may promote the processing between functionally related cortico-subcortical neural populations. Our aim was to identify the sources of GS and to analyze the direction of information flow in cerebral networks at the beginning of phasic movements, and during medium-strength isometric contraction of the hand.Methods: We measured 64-channel electroencephalography in 11 healthy volunteers (age: 25 ± 8 years; four females); surface electromyography detected the movements of the dominant hand. In Task 1, subjects kept a constant medium-strength contraction of the first dorsal interosseus muscle, and performed a superimposed repetitive voluntary self-paced brisk squeeze of an object. In Task 2, brisk, and in Task 3, constant contractions were performed. Time-frequency analysis of the EEG signal was performed with the multitaper method. GS sources were identified in five frequency bands (30–49, 51–75, 76–99, 101–125, and 126–149 Hz) with beamformer inverse solution dynamic imaging of coherent sources. The direction of information flow was estimated by renormalized partial directed coherence for each frequency band. The data-driven surrogate test, and the time reversal technique were performed to identify significant connections.Results: In all tasks, we depicted the first three common sources for the studied frequency bands that were as follows: contralateral primary sensorimotor cortex (S1M1), dorsolateral prefrontal cortex (dPFC) and supplementary motor cortex (SMA). GS was detected in narrower low- (∼30–60 Hz) and high-frequency bands (>51–60 Hz) in the contralateral thalamus and ipsilateral cerebellum in all three tasks. The contralateral posterior parietal cortex was activated only in Task 1. In every task, S1M1 had efferent information flow to the SMA and the dPFC while dPFC had no detected afferent connections to the network in the gamma range. Cortical-subcortical information flow captured by the GS was dynamically variable in the narrower frequency bands for the studied movements.Conclusion: A distinct cortical network was identified for GS in voluntary hand movement tasks. Our study revealed that S1M1 modulated the activity of interconnected cortical areas through GS, while subcortical structures modulated the motor network dynamically, and specifically for the studied movement program.

The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.

Neuronal networks in epileptic encephalopathies with CSWS.

The aim of our study was to investigate the neuronal networks underlying background oscillations of epileptic encephalopathy with continuous spikes and waves during slow sleep (CSWS). Sleep electroencephalography (EEG) studies before and after the treatment were investigated in 15 patients with CSWS. To investigate functional and effective connectivity within the network generating the delta activity in the background sleep EEG, the methods of dynamic imaging of coherent sources (DICS) and renormalized partial directed coherence (RPDC) were applied. Independent of etiology and severity of epilepsy, background EEG pattern in patients with CSWS before treatment is associated with the complex network of coherent sources in medial prefrontal cortex, somatosensory association cortex/posterior cingulate cortex, medial prefrontal cortex, middle temporal gyrus/parahippocampal gyrus/insular cortex, thalamus, and cerebellum. The analysis of information flow within this network revealed that the medial parietal cortex, the precuneus, and the thalamus act as central hubs, driving the information flow to other areas, especially to the temporal and frontal cortex. The described CSWS-specific pattern was no longer observed in patients with normalized sleep EEG. In addition, frequency of spiking showed a strong linear correlations with absolute source power, source coherence strength, and source RPDC strength at both time points: (1) Spike and wave index (SWI) versus absolute source power at EEG1 (r = 0.56; p = 0.008) and at EEG2 (r = 0.45; p = 0.009); (2) SWI versus source coherence strength at EEG1 (r = 0.71; p = 0.005) and at EEG2 (r = 0.52; p = 0.006); and (3) SWI versus source RPDC strength at EEG1 (r = 0.65; p = 0.003) and at EEG2 (r = 0.47; p = 0.009). The leading role of the precuneus and thalamus in the hierarchical organization of the network underlying the background EEG points toward the significance of fluctuations of vigilance in the generation of CSWS. This hierarchical network organization appears to be specific for CSWS as it is resolved after successful treatment.

EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network.

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful.