Search

Functional connectivity in electroencephalography (EEG) and magnetoencephalography (MEG) data is commonly assessed by using measures that are insensitive to instantaneously interacting sources and as such would not give rise to false positive interactions caused by instantaneous mixing of true source signals (first-order mixing). Recent studies, however, have drawn attention to the fact that such measures are still susceptible to instantaneous mixing from lagged sources (i.e. second-order mixing) and that this can lead to a large number of false positive interactions. In this study we relate first- and second-order mixing effects on the cross-spectra of reconstructed source activity to the properties of the resolution operators that are used for the reconstruction. We derive two identities that relate first- and second-order mixing effects to the transformation properties of measurement and source configurations and exploit them to establish several basic properties of signal mixing. First, we provide a characterization of the configurations that are maximally and minimally sensitive to second-order mixing. It turns out that second-order mixing effects are maximal when the measurement locations are far apart and the sources coincide with the measurement locations. Second, we provide a description of second-order mixing effects in the vicinity of the measurement locations in terms of the local geometry of the point-spread functions of the resolution operator. Third, we derive a version of Lagrange's identity for cross-talk functions that establishes the existence of a trade-off between the magnitude of first- and second-order mixing effects. It also shows that, whereas the magnitude of first-order mixing is determined by the inner product of cross-talk functions, the magnitude of second-order mixing is determined by a generalized cross-product of cross-talk functions (the wedge product) which leads to an intuitive geometric understanding of the trade-off. All results are derived within the general framework of random neural fields on cortical manifolds. [ABSTRACT FROM AUTHOR]

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1–2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5–6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1–2 time coverage (τbs < 0.20, ps >.002), while the lateralization of deviance detection in MMN was associated with mMS 5–6 time coverage (τbs < 0.16, ps >.012). No temporal correlation was found between EEG and MEG microstates (ps >.05), despite some overlap in brain sources and global explained variance between mMS 2–3 and EEG microstates B-C (rs > 0.60, ps <.002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity. [ABSTRACT FROM AUTHOR]

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity. Key points: • Non-Linear brain activity trajectories: The study challenges linear brain activity changes with age, revealing distinct trajectories in oscillatory patterns based on frequency and brain region. • Contradicting age-related slowing: slow wave activity decreases and fast wave activity increases during healthy aging, contrasting with pathological conditions. • Cognitive implications and structural integrity: Oscillatory power changes correlate with cognitive decline and reduced structural integrity, challenging the brain scaffolding theory of aging and offering insights into neurodevelopment and aging. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is clinically used to localize interictal spikes in discrete brain areas of epilepsy patients through the equivalent current dipole (ECD) method, but does not account for the temporal dynamics of spike activity. Recent studies found that interictal spike propagation beyond the temporal lobe may be associated with worse postsurgical outcomes, but studies using whole-brain data such as in MEG remain limited. In this pilot study, we developed a tool that visualizes the spatiotemporal dynamics of interictal MEG spikes normalized to spike-free sleep activity to assess their onset and propagation patterns in patients with temporal lobe epilepsy (TLE). We extracted interictal source data containing focal epileptiform activity in awake and asleep states from seven patients whose MEG ECD clusters localized to the temporal lobe and normalized the data against spike-free sleep recordings. We calculated the normalized activity over time per cortical label, confirmed maximal activity at onset, and mapped the activity over a 10 ms interval onto each patient's brain using a custom-built Multi-Modal Visualization Tool. The onset of activity in all patients appeared near the clinically determined epileptogenic zone. By 10 ms, four of the patients had propagated source activity restricted to within the temporal lobe, and three had propagated source activity spread to extratemporal regions. Using this tool, we show that noninvasively identifying the onset and propagation of interictal spike activity in MEG can be achieved, which may help provide further insight into epileptic networks and guide surgical planning and interventions in patients with TLE. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

In this study of early functional changes in Parkinson's disease (PD), we aimed to provide a comprehensive assessment of the development of changes in both cortical and subcortical neurophysiological brain activity, including their association with clinical measures of disease severity. Repeated resting-state MEG recordings and clinical assessments were obtained in the context of a unique longitudinal cohort study over a seven-year period using a multiple longitudinal design. We used linear mixed-models to analyze the relationship between neurophysiological (spectral power and functional connectivity) and clinical data. At baseline, early-stage (drug-naïve) PD patients demonstrated spectral slowing compared to healthy controls in both subcortical and cortical brain regions, most outspoken in the latter. Over time, spectral slowing progressed in strong association with clinical measures of disease progression (cognitive and motor). Global functional connectivity was not different between groups at baseline and hardly changed over time. Therefore, investigation of associations with clinical measures of disease progression were not deemed useful. An analysis of individual connections demonstrated differences between groups at baseline (higher frontal theta, lower parieto-occipital alpha2 band functional connectivity) and over time in PD patients (increase in frontal delta and theta band functional connectivity). Our results suggest that spectral measures are promising candidates in the search for non-invasive markers of both early-stage PD and of the ongoing disease process. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising. [ABSTRACT FROM AUTHOR]

Background Magnetoencephalography (MEG) and electroencephalography (EEG) record two main types of data: continuous measurements at rest or during sleep, and event-related potentials/evoked magnetic fields (ERPs/EMFs) that involve specific and repetitive tasks. In this systematic review, we summarized longitudinal studies on recovery from post-stroke aphasia that used continuous or event-related temporal imaging (EEG or MEG). Methods We searched PubMed and Scopus for English articles published from 1950 to May 31, 2022. Results 34 studies were included in this review: 11 were non-interventional studies and 23 were clinical trials that used specific rehabilitation methods, neuromodulation, or drugs. The results of the non-interventional studies suggested that poor language recovery was associated with slow-wave activity persisting over time. The results of some clinical trials indicated that behavioral improvements were correlated with significant modulation of the N400 component. Discussion Compared with continuous EEG, ERP/EMF may more reliably identify biomarkers of therapy-induced effects. Electrophysiology should be used more often to explore language processes that are impaired after a stroke, as it may highlight treatment challenges for patients with post-stroke aphasia. [ABSTRACT FROM AUTHOR]

To evaluate the relationship between the network metrics of 68 brain regions and duration of temporal lobe epilepsy (TLE). Magnetoencephalography (MEG) data from 53 patients with TLE (28 left TLE, 25 right TLE) were recorded between seizures at resting state and analyzed in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), lower alpha (8–10 Hz), upper alpha (10–13 Hz), beta (13–30 Hz), and lower gamma (30–48 Hz). Three local network metrics, betweenness centrality, nodal degree, and nodal efficiency, were chosen to analyze the functional brain network. In Left, Right, and All (Left + Right) TLE groups, different metrics provide significant positive or negative correlations with the duration of TLE, in different frequency bands, and in different brain regions. In the Left TLE group, significant correlation between TLE duration and metric exists in the delta, beta, or lower gamma band, with network betweenness centrality, nodal degree, or nodal efficiency, in left caudal middle frontal, left middle temporal, or left supramarginal. In the Right TLE group, significant correlation exists in lower gamma or delta band, with nodal degree, or nodal efficiency, in left precuneus or right temporal pole. In the All TLE group, the significant correlation exists in delta, theta, beta, or lower gamma band, with nodal degree, or betweenness centrality, in either left or right hemisphere. Network metrics for some specific brain regions changed in patients with TLE as the duration of their TLE increased. Further researching these changes may be important for studying the pathogenesis, presurgical evaluation, and clinical treatment of long-term TLE. [ABSTRACT FROM AUTHOR]

Abnormal functional brain networks of temporal lobe epilepsy (TLE) patients with structural abnormalities may partially reflect structural lesions rather than either TLE per se or functional compensatory processes. In this study, we sought to investigate the brain-network properties of intractable TLE patients apart from the effects of structural abnormalities. The brain network properties of 20 left and 23 right MRI-negative TLE patients and 22 healthy controls were evaluated using magnetoencephalographic recordings in six main frequency bands. A slowing of oscillatory brain activity was observed for the left or right TLE group vs. healthy controls. The TLE groups presented significantly increased functional connectivity in the delta, theta, lower alpha and beta bands, and significantly greater values in the normalized clustering coefficient and path length, and significantly smaller values in the weighted small-world measure in the theta band when compared to healthy controls. Alterations in global and regional band powers can be attributed to spectral slowing in TLE patients. The brain networks of TLE patients displayed abnormally high synchronization in multi-frequency bands and shifted toward a more regular architecture with worse network efficiency in the theta band. Without the contamination of structural lesions, these significant findings can be helpful for better understanding of the pathophysiological mechanism of TLE. The theta band can be considered as a preferred frequency band for investigating the brain-network dysfunction of MRI-negative intractable TLE patients. [ABSTRACT FROM AUTHOR]

The present work aims at validating a Bayesian multi-dipole modeling algorithm (SESAME) in the clinical scenario consisting of localizing the generators of single interictal epileptiform discharges from resting state magnetoencephalographic recordings. We use the results of Equivalent Current Dipole fitting, performed by an expert user, as a benchmark, and compare the results of SESAME with those of two widely used source localization methods, RAP-MUSIC and wMNE. In addition, we investigate the relation between post-surgical outcome and concordance of the surgical plan with the cerebral lobes singled out by the methods. Unlike dipole fitting, the tested algorithms do not rely on any subjective channel selection and thus contribute towards making source localization more unbiased and automatic. We show that the two dipolar methods, SESAME and RAP-MUSIC, generally agree with dipole fitting in terms of identified cerebral lobes and that the results of the former are closer to the fitted equivalent current dipoles than those of the latter. In addition, for all the tested methods and particularly for SESAME, concordance with surgical plan is a good predictor of seizure freedom while discordance is not a good predictor of poor post-surgical outcome. The results suggest that the dipolar methods, especially SESAME, represent a reliable and more objective alternative to manual dipole fitting for clinical applications in the field of epilepsy surgery. [ABSTRACT FROM AUTHOR]

Auditory attention allows us to focus on relevant target sounds in the acoustic environment while maintaining the capability to orient to unpredictable (novel) sound changes. An open question is whether orienting to expected vs. unexpected auditory events are governed by anatomically distinct attention pathways, respectively, or by differing communication patterns within a common system. To address this question, we applied a recently developed PeSCAR analysis method to evaluate spectrotemporal functional connectivity patterns across subregions of broader cortical regions of interest (ROIs) to analyze magnetoencephalography data obtained during a cued auditory attention task. Subjects were instructed to detect a predictable harmonic target sound embedded among standard tones in one ear and to ignore the standard tones and occasional unpredictable novel sounds presented in the opposite ear. Phase coherence of estimated source activity was calculated between subregions of superior temporal, frontal, inferior parietal, and superior parietal cortex ROIs. Functional connectivity was stronger in response to target than novel stimuli between left superior temporal and left parietal ROIs and between left frontal and right parietal ROIs, with the largest effects observed in the beta band (15–35 Hz). In contrast, functional connectivity was stronger in response to novel than target stimuli in inter-hemispheric connections between left and right frontal ROIs, observed in early time windows in the alpha band (8–12 Hz). Our findings suggest that auditory processing of expected target vs. unexpected novel sounds involves different spatially, temporally, and spectrally distributed oscillatory connectivity patterns across temporal, parietal, and frontal areas. [ABSTRACT FROM AUTHOR]

K-Means is one of the most popular clustering algorithms that partitions observations into nonoverlapping subgroups based on a predefined similarity metric. Its drawbacks include a sensitivity to noisy features and a dependency of its resulting clusters upon the initial selection of cluster centroids resulting in the algorithm converging to local optima. Minkowski weighted K-Means (MWK-Means) addresses the issue of sensitivity to noisy features, but is sensitive to the initialization of clusters, and so the algorithm may similarly converge to local optima. Particle Swarm Optimization (PSO) uses a globalized search method to solve this issue. We present a hybrid Particle Swarm Optimization (PSO) + MWK-Means clustering algorithm to address all the above problems in a single framework, while maintaining benefits of PSO and MWK Means methods. This study investigated the utility of this approach in lateralizing the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Using MEG-CSI, we analyzed preoperative resting state MEG data from 17 adults TLE patients with Engel class I outcomes to determine coherence at 54 anatomical sites and compared the results with 17 age- and gender-matched controls. Fiber-tracking was performed through the same anatomical sites using DTI data. Indices of both MEG coherence and DTI nodal degree were calculated. A PSO + MWK-Means clustering algorithm was applied to identify the side of temporal lobe epileptogenicity and distinguish between normal and TLE cases. The PSO module was aimed at identifying initial cluster centroids and assigning initial feature weights to cluster centroids and, hence, transferring to the MWK-Means module for the final optimal clustering solution. We demonstrated improvements with the use of the PSO + MWK-Means clustering algorithm compared to that of K-Means and MWK-Means independently. PSO + MWK-Means was able to successfully distinguish between normal and TLE in 97.2% and 82.3% of cases for DTI and MEG data, respectively. It also lateralized left and right TLE in 82.3% and 93.6% of cases for DTI and MEG data, respectively. The proposed optimization and clustering methodology for MEG and DTI features, as they relate to focal epileptogenicity, would enhance the identification of the TLE laterality in cases of unilateral epileptogenicity. [ABSTRACT FROM AUTHOR]

A number of beamformers have been introduced to localize neuronal activity using magnetoencephalography (MEG) and electroencephalography (EEG). However, currently available information about the major aspects of existing beamformers is incomplete. In the present study, detailed analyses are performed to study the commonalities and differences among vectorized versions of existing beamformers in both theory and practice. In addition, a novel beamformer based on higher-order covariance analysis is introduced. Theoretical formulas are provided on all major aspects of each beamformer; to examine their performance, computer simulations with different levels of correlation and signal-to-noise ratio are studied. Then, an empirical data set of human MEG median-nerve responses with a large number of neuronal generators is analyzed using the different beamformers. The results show substantial differences among existing MEG/EEG beamformers in their ways of describing the spatial map of neuronal activity. Differences in performance are observed among existing beamformers in terms of their spatial resolution, false-positive background activity, and robustness to highly correlated signals. Superior performance is obtained using our novel beamformer with higher-order covariance analysis in simulated data. Excellent agreement is also found between the results of our beamformer and the known neurophysiology of the median-nerve MEG response.

The mapping of the sensorimotor cortex gives information about the cortical motor and sensory functions. Typical mapping methods are navigated transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG). The differences between these mapping methods are, however, not fully known. TMS center of gravities (CoGs), MEG somatosensory evoked fields (SEFs), corticomuscular coherence (CMC), and corticokinematic coherence (CKC) were mapped in ten healthy adults. TMS mapping was performed for first dorsal interosseous (FDI) and extensor carpi radialis (ECR) muscles. SEFs were induced by tactile stimulation of the index finger. CMC and CKC were determined as the coherence between MEG signals and the electromyography or accelerometer signals, respectively, during voluntary muscle activity. CMC was mapped during the activation of FDI and ECR muscles separately, whereas CKC was measured during the waving of the index finger at a rate of 3–4 Hz. The maximum CMC was found at beta frequency range, whereas maximum CKC was found at the movement frequency. The mean Euclidean distances between different localizations were within 20 mm. The smallest distance was found between TMS FDI and TMS ECR CoGs and longest between CMC FDI and CMC ECR sites. TMS-inferred localizations (CoGs) were less variable across participants than MEG-inferred localizations (CMC, CKC). On average, SEF locations were 8 mm lateral to the TMS CoGs (p < 0.01). No differences between hemispheres were found. Based on the results, TMS appears to be more viable than MEG in locating motor cortical areas. [ABSTRACT FROM AUTHOR]

Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR. [ABSTRACT FROM AUTHOR]

Establishing language dominance is an important step in the presurgical evaluation of patients with refractory epilepsy. In the absence of a universally accepted gold-standard non-invasive method to determine language dominance in the preoperative assessment, a range of tools and methodologies have recently received attention. When applied to pediatric age, many of the proposed methods, such as functional magnetic resonance imaging (fMRI), may present some challenges due to the time-varying effects of epileptogenic lesions and of on-going seizures on maturational phenomena. Magnetoencephalography (MEG) has the advantage of being insensitive to the distortive effects of anatomical lesions on brain microvasculature and to differences in the metabolism or vascularization of the developing brain and also provides a less intimidating recording environment for younger children. In this study we investigated the reliability of lateralized synchronous cortical activation during a verb generation task in a group of 28 children (10 males and 18 females, mean age 12 years) with refractory epilepsy who were evaluated for epilepsy surgery. The verb generation task was associated with significant decreases in beta oscillatory power (13–30 Hz) in frontal and temporal lobes. The MEG data were compared with other available presurgical non-invasive data including cortical stimulation, neuropsychological and fMRI data on language lateralization where available. We found that the lateralization of MEG beta power reduction was concordant with language dominance determined by one or more different assessment methods (i.e. cortical stimulation mapping, neuropsychological, fMRI or post-operative data) in 89% of patients. Our data suggest that qualitative hemispheric differences in task-related changes of spectral power could offer a promising insight into the contribution of dominant and non-dominant hemispheres in language processing and may help to characterize the specialization and lateralization of language processes in children. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer's disease and Parkinson's disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms. [ABSTRACT FROM AUTHOR]

Childhood absence epilepsy (CAE), the most common pediatric epilepsy syndrome, is usually treated with valproic acid (VPA) and lamotrigine (LTG) in China. This study aimed to investigate the ictal source locations and functional connectivity (FC) networks between the cortices and thalamus that are related to treatment response. Magnetoencephalography (MEG) data from 25 patients with CAE were recorded at 300 Hz and analyzed in 1-30 Hz frequency bands. Neuromagnetic sources were volumetrically scanned with accumulated source imaging. The FC networks between the cortices and thalamus were evaluated at the source level through a connectivity analysis. Treatment outcome was assessed after 36-66 months following MEG recording. The children with CAE were divided into LTG responder, LTG non-responder, VPA responder and VPA non-responder groups. The ictal source locations and cortico-thalamic FC networks were compared to the treatment response. The ictal source locations in the post-dorsal medial frontal cortex (post-DMFC, including the medial primary motor cortex and the supplementary sensorimotor area) were observed in all LTG non-responders but in all LTG responders. At 1-7 Hz, patients with fronto-thalamo-parietal/occipital (F-T-P/O) networks were older than those with fronto-thalamic (F-T) networks or other cortico-thalamic networks (p = 0.000). The duration of seizures in patients with F-T-P/O networks at 1-7 Hz was longer than that in patients with F-T networks or other cortico-thalamic networks (p = 0.001). The ictal post-DMFC source localizations suggest that children with CAE might experience initial LTG monotherapy failure. Moreover, the cortico-thalamo-cortical network is associated with age. Finally, the cortico-thalamo-cortical network consists of anterior and posterior cortices and might contribute to the maintenance of discharges. [ABSTRACT FROM AUTHOR]

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual’s head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be <4mm and <10∘, respectively. [ABSTRACT FROM AUTHOR]

Paired-pulse depression (PPD) has been widely used to investigate the functional profiles of somatosensory cortical inhibition. However, PPD induced by somatosensory stimulation is variable, and the reasons for between- and within-subject PPD variability remains unclear. Therefore, the purpose of this study was to clarify the factors influencing PPD variability induced by somatosensory stimulation. The study participants were 19 healthy volunteers. First, we investigated the relationship between the PPD ratio of each component (N20m, P35m, and P60m) of the somatosensory magnetic field, and the alpha, beta, and gamma band changes in power [event-related desynchronization (ERD) and event-related synchronization (ERS)] induced by median nerve stimulation. Second, because brain-derived neurotrophic factor (BDNF) gene polymorphisms reportedly influence the PPD ratio, we assessed whether BDNF genotype influences PPD ratio variability. Finally, we evaluated the test-retest reliability of PPD and the alpha, beta, and gamma ERD/ERS induced by somatosensory stimulation. Significant positive correlations were observed between the P60m_PPD ratio and beta power change, and the P60m_PPD ratio was significantly smaller for the beta ERD group than for the beta ERS group. P35m_PPD was found to be robust and highly reproducible; however, P60m_PPD reproducibility was poor. In addition, the ICC values for alpha, beta, and gamma ERD/ERS were 0.680, 0.760, and 0.552 respectively. These results suggest that the variability of PPD for the P60m deflection may be influenced by the ERD/ERS magnitude, which is induced by median nerve stimulation. [ABSTRACT FROM AUTHOR]

This study investigates whether movement kinematics modulates similarly the rolandic α and β rhythm amplitude during executed and observed goal-directed hand movements. It also assesses if this modulation relates to the corticokinematic coherence (CKC), which is the coupling observed between cortical activity and movement kinematics during such motor actions. Magnetoencephalography (MEG) signals were recorded from 11 right-handed healthy subjects while they performed or observed an actor performing the same repetitive hand pinching action. Subjects’ and actor’s forefinger movements were monitored with an accelerometer. Coherence was computed between acceleration signals and the amplitude of α (8-12 Hz) or β (15-25 Hz) oscillations. The coherence was also evaluated between source-projected MEG signals and their β amplitude. Coherence was mainly observed between acceleration and the amplitude of β oscillations at movement frequency within bilateral primary sensorimotor (SM1) cortex with no difference between executed and observed movements. Cross-correlation between the amplitude of β oscillations at the SM1 cortex and movement acceleration was maximal when acceleration was delayed by ~ 100 ms, both during movement execution and observation. Coherence between source-projected MEG signals and their β amplitude during movement observation and execution was not significantly different from that during rest. This study shows that observing others’ actions engages in the viewer’s brain similar dynamic modulations of SM1 cortex β rhythm as during action execution. Results support the view that different neural mechanisms might account for this modulation and CKC. These two kinematic-related phenomena might help humans to understand how observed motor actions are actually performed. [ABSTRACT FROM AUTHOR]

One common assumption has been that prefrontal executive control is mostly required for target detection (Posner and Petersen in Ann Rev Neurosci 13:25-42, 1990). Alternatively, cognitive control has also been related to anticipatory updating of task-set (contextual) information, a view that highlights proactive control processes. Frontoparietal cortical networks contribute to both proactive control and reactive target detection, although their fast dynamics are still largely unexplored. To examine this, we analyzed rapid magnetoencephalographic (MEG) source activations elicited by task cues and target cards in a task-cueing analogue of the Wisconsin Card Sorting Test. A single-task (color sorting) condition with equivalent perceptual and motor demands was used as a control. Our results revealed fast, transient and largely switch-specific MEG activations across frontoparietal and cingulo-opercular regions in anticipation of target cards, including (1) early (100-200 ms) cue-locked MEG signals at visual, temporo-parietal and prefrontal cortices of the right hemisphere (i.e., calcarine sulcus, precuneus, inferior frontal gyrus, anterior insula and supramarginal gyrus); and (2) later cue-locked MEG signals at the right anterior and posterior insula (200-300 ms) and the left temporo-parietal junction (300-500 ms). In all cases larger MEG signal intensity was observed in switch relative to repeat cueing conditions. Finally, behavioral restart costs and test scores of working memory capacity (forward digit span) correlated with cue-locked MEG activations at key nodes of the frontoparietal network. Together, our findings suggest that proactive cognitive control of task rule updating can be fast and transiently implemented within less than a second and in anticipation of target detection. [ABSTRACT FROM AUTHOR]

The mismatch negativity (MMN) reflects the early detection of changes in sensory stimuli at the cortical level. The mechanisms underlying its genesis remain debated. This magnetoencephalography study investigates the spatio-temporal dynamics and the neural mechanisms of the magnetic somatosensory MMN. Somatosensory evoked magnetic fields elicited by tactile stimulation of the right fingertip (Single), tactile stimulation of the right middle phalanx and fingertip (Double) or omissions (Omitted) of tactile stimuli were studied in different paradigms: in oddballs where Double/Omitted followed a sequence of four Single, in sequences of two stimuli where Double occurred after one Single, and in random presentation of Double only. The predictability of Double occurrence in oddballs was also manipulated. Cortical sources of evoked responses were identified using equivalent current dipole modeling. Evoked responses elicited by Double were significantly different from those elicited by Single at the contralateral secondary somatosensory (cSII) cortex. Double elicited higher cSII cortex responses than Single when preceded by a sequence of four Single, compared to when they were preceded by one Single. Double elicited higher cSII cortex response when presented alone compared to when Double were preceded by one or a sequence of Single. Omitted elicited similar cSII cortex response than Single. Double in oddballs led to higher cSII cortex responses when less predictable. These data suggest that early tactile change detection involves mainly cSII cortex. The predictive coding framework probably accounts for the SII cortex response features observed in the different tactile paradigms. [ABSTRACT FROM AUTHOR]

Using multi-frequency magnetoencephalography (MEG) data, we investigated whether the effective connectivity (EC) network of patients with childhood absence epilepsy (CAE) is altered during the inter-ictal period in comparison with healthy controls. MEG data from 13 untreated CAE patients and 10 healthy controls were recorded. Correlation analysis and Granger causality analysis were used to construct an EC network at the source level in eight frequency bands. Alterations in the spatial pattern and topology of the network in CAE were investigated by comparing the patients with the controls. The network pattern was altered mainly in 1-4 Hz, showing strong connections within the frontal cortex and weak connections in the anterior-posterior pathways. The EC involving the precuneus/posterior cingulate cortex (PC/PCC) significantly decreased in low-frequency bands. In addition, the parameters of graph theory were significantly altered in several low- and high-frequency bands. CAE patients display frequency-specific abnormalities in the network pattern even during the inter-ictal period, and the frontal cortex and PC/PCC might play crucial roles in the pathophysiology of CAE. The EC network of CAE patients was over-connective and random during the inter-ictal period. This study is the first to reveal the frequency-specific alteration in the EC network during the inter-ictal period in CAE patients. Multiple-frequency MEG data are useful in investigating the pathophysiology of CAE, which can serve as new biomarkers of this disorder. [ABSTRACT FROM AUTHOR]

Unlike EEG sensors, which are attached to the head, MEG sensors are located outside the head surface on a fixed external device. Subject head movements during acquisition thus distort the magnetic field distributions measured by the sensors. Previous studies have looked at the effect of head movements, but no study has comprehensively looked at the effect of head movements across age groups, particularly in infants. Using MEG recordings from subjects ranging in age from 3 months through adults, here we first quantify the variability in head position as a function of age group. We then combine these measured head movements with brain activity simulations to determine how head movements bias source localization from sensor magnetic fields measured during movement. We find that large amounts of head movement, especially common in infant age groups, can result in large localization errors. We then show that proper application of head movement compensation techniques can restore localization accuracy to pre-movement levels. We also find that proper noise covariance estimation (e.g., during the baseline period) is important to minimize localization bias following head movement compensation. Our findings suggest that head position measurement during acquisition and compensation during analysis is recommended for researchers working with subject populations or age groups that could have substantial head movements. This is especially important in infant MEG studies. [ABSTRACT FROM AUTHOR]

The aim of this study was to define the pattern of reduction in absolute power spectral density (PSD) of magnetoencephalography (MEG) signals throughout development. Specifically, we wanted to explore whether the human skull's high permeability for electromagnetic fields would allow us to question whether the pattern of absolute PSD reduction observed in the human electroencephalogram is due to an increase in the skull's resistive properties with age. Furthermore, the topography of the MEG signals during maturation was explored, providing additional insights about the areas and brain rhythms related to late maturation in the human brain. To attain these goals, spontaneous MEG activity was recorded from 148 sensors in a sample of 59 subjects divided into three age groups: children/adolescents (7-14 years), young adults (17-20 years) and adults (21-26 years). Statistical testing was carried out by means of an analysis of variance (ANOVA), with 'age group' as between-subject factor and 'sensor group' as within-subject factor. Additionally, correlations of absolute PSD with age were computed to assess the influence of age on the spectral content of MEG signals. Results showed a broadband PSD decrease in frontal areas, which suggests the late maturation of this region, but also a mild increase in high frequency PSD with age in posterior areas. These findings suggest that the intensity of the neural sources during spontaneous brain activity decreases with age, which may be related to synaptic pruning. [ABSTRACT FROM AUTHOR]

There is currently a void in the scientific literature on the cortical beta oscillatory activity that is associated with the production of leg motor actions. In addition, we have limited data on how these cortical oscillations may progressively change as a function of development. This study began to fill this vast knowledge gap by using high-density magnetoencephalography to quantify the beta cortical oscillatory activity over a cross-section of typically developing children as they performed an isometric knee target matching task. Advanced beamforming methods were used to identify the spatiotemporal changes in beta oscillatory activity during the motor planning and motor action time frames. Our results showed that a widespread beta event-related desynchronization (ERD) was present across the pre/postcentral gyri, supplementary motor area, and the parietal cortices during the motor planning stage. The strength of this beta ERD sharply diminished across this fronto-parietal network as the children initiated the isometric force needed to match the target. Rank order correlations indicated that the older children were more likely to initiate their force production sooner, took less time to match the targets, and tended to have a weaker beta ERD during the motor planning stage. Lastly, we determined that there was a relationship between the child's age and the strength of the beta ERD within the parietal cortices during isometric force production. Altogether our results suggest that there are notable maturational changes during childhood and adolescence in beta cortical oscillatory activity that are associated with the planning and execution of leg motor actions. [ABSTRACT FROM AUTHOR]

We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data., (© 2022. The Author(s).)

To clarify characteristics of each human somatosensory evoked field (SEF) component following passive movement (PM), PM1, PM2, and PM3, using high spatiotemporal resolution 306-channel magnetoencephalography and varying PM range and angular velocity. We recorded SEFs following PM under three conditions [normal range-normal velocity (NN), small range-normal velocity (SN), and small range-slow velocity (SS)] with changing movement range and angular velocity in 12 participants and calculated the amplitude, equivalent current dipole (ECD) location, and the ECD strength for each component. All components were observed in six participants, whereas only PM1 and PM3 in the other six. Clear response deflections at the ipsilateral hemisphere to PM side were observed in seven participants. PM1 amplitude was larger under NN and SN conditions, and mean ECD location for PM1 was at primary motor area. PM3 amplitude was larger under SN condition and mean ECD location for PM3 under SS condition was at primary somatosensory area. PM1 amplitude was dependent on the angular velocity of PM, suggesting that PM1 reflects afferent input from muscle spindle, whereas PM3 amplitude was dependent on the duration. The ECD for PM3 was located in the primary somatosensory cortex, suggesting that PM3 reflects cutaneous input. We confirmed the hypothesis for locally distinct generators and characteristics of each SEF component. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a noninvasive imaging method for localization of focal epileptiform activity in patients with epilepsy. Diffusion tensor imaging (DTI) is a noninvasive imaging method for measuring the diffusion properties of the underlying white matter tracts through which epileptiform activity is propagated. This study investigates the relationship between the cerebral functional abnormalities quantified by MEG coherence and structural abnormalities quantified by DTI in mesial temporal lobe epilepsy (mTLE). Resting state MEG data was analyzed using MEG coherence source imaging (MEG-CSI) method to determine the coherence in 54 anatomical sites in 17 adult mTLE patients with surgical resection and Engel class I outcome, and 17 age- and gender- matched controls. DTI tractography identified the fiber tracts passing through these same anatomical sites of the same subjects. Then, DTI nodal degree and laterality index were calculated and compared with the corresponding MEG coherence and laterality index. MEG coherence laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in insular cortex and both lateral orbitofrontal and superior temporal gyri (p < 0.017). Likewise, DTI nodal degree laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in gyrus rectus, insular cortex, precuneus and superior temporal gyrus (p < 0.017). In insular cortex, MEG coherence laterality correlated with DTI nodal degree laterality ( $$R^{2} = 0.46; p = 0.003)$$ in the cases of mTLE. None of these anatomical sites showed statistically significant differences in coherence laterality between right and left sides of the controls. Coherence laterality was in agreement with the declared side of epileptogenicity in insular cortex (in 82 % of patients) and both lateral orbitofrontal (88 %) and superior temporal gyri (88 %). Nodal degree laterality was also in agreement with the declared side of epileptogenicity in gyrus rectus (in 88 % of patients), insular cortex (71 %), precuneus (82 %) and superior temporal gyrus (94 %). Combining all significant laterality indices improved the lateralization accuracy to 94 % and 100 % for the coherence and nodal degree laterality indices, respectively. The associated variations in diffusion properties of fiber tracts quantified by DTI and coherence measures quantified by MEG with respect to epileptogenicity possibly reflect the chronic microstructural cerebral changes associated with functional interictal activity. The proposed methodology for using MEG and DTI to investigate diffusion abnormalities related to focal epileptogenicity and propagation may provide a further means of noninvasive lateralization. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are neuroimaging techniques that measure inherently different physiological processes, resulting in complementary estimates of brain activity in different regions. Combining the maps generated by each technique could thus provide a richer understanding of brain activation. However, present approaches to integration rely on a priori assumptions, such as expected patterns of brain activation in a task, or use fMRI to bias localization of MEG sources, diminishing fMRI-invisible sources. We aimed to optimize sensitivity to neural activity by developing a novel method of integrating data from the two imaging techniques. We present a data-driven method of integration that weights fMRI and MEG imaging data by estimates of data quality for each technique and region. This method was applied to a verbal object recognition task. As predicted, the two imaging techniques demonstrated sensitivity to activation in different regions. Activity was seen using fMRI, but not MEG, throughout the medial temporal lobes. Conversely, activation was seen using MEG, but not fMRI, in more lateral and anterior temporal lobe regions. Both imaging techniques were sensitive to activation in the inferior frontal gyrus. Importantly, integration maps retained activation from individual activation maps, and showed an increase in the extent of activation, owing to greater sensitivity of the integration map than either fMRI or MEG alone. [ABSTRACT FROM AUTHOR]

Aberrant brain activity in childhood absence epilepsy (CAE) during seizures has been well recognized as synchronous 3 Hz spike-and-wave discharges on electroencephalography. However, brain activity from low- to very high-frequency ranges in subjects with CAE between seizures (interictal) has rarely been studied. Using a high-sampling rate magnetoencephalography (MEG) system, we studied ten subjects with clinically diagnosed but untreated CAE in comparison with age- and gender-matched controls. MEG data were recorded from all subjects during the resting state. MEG sources were assessed with accumulated source imaging, a new method optimized for localizing and quantifying spontaneous brain activity. MEG data were analyzed in nine frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-55 Hz), high-gamma (65-90 Hz), ripple (90-200 Hz), high-frequency oscillation (HFO, 200-1,000 Hz), and very high-frequency oscillation (VHFO, 1,000-2,000 Hz). MEG source imaging revealed that subjects with CAE had higher odds of interictal brain activity in 200-1,000 and 1,000-2,000 Hz in the parieto-occipito-temporal junction and the medial frontal cortices as compared with controls. The strength of the interictal brain activity in these regions was significantly elevated in the frequency bands of 90-200, 200-1,000 and 1,000-2,000 Hz for subjects with CAE as compared with controls. The results indicate that CAE has significantly aberrant brain activity between seizures that can be noninvasively detected. The measurements of high-frequency neuromagnetic oscillations may open a new window for investigating the cerebral mechanisms of interictal abnormalities in CAE. [ABSTRACT FROM AUTHOR]

With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject's reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements. [ABSTRACT FROM AUTHOR]

Structural brain connections develop atypically in very preterm children, and altered functional connectivity is also evident in fMRI studies. Such alterations in brain network connectivity are associated with cognitive difficulties in this population. Little is known, however, about electrophysiological interactions among specific brain networks in children born very preterm. In the present study, we recorded magnetoencephalography while very preterm children and full-term controls performed a visual short-term memory task. Regions expressing task-dependent activity changes were identified using beamformer analysis, and inter-regional phase synchrony was calculated. Very preterm children expressed altered regional recruitment in distributed networks of brain areas, across standard physiological frequency ranges including the theta, alpha, beta and gamma bands. Reduced oscillatory synchrony was observed among task-activated brain regions in very preterm children, particularly for connections involving areas critical for executive abilities, including middle frontal gyrus. These findings suggest that inability to recruit neurophysiological activity and interactions in distributed networks including frontal regions may contribute to difficulties in cognitive development in children born very preterm. [ABSTRACT FROM AUTHOR]

Recent studies have shown the importance of graph theory in analyzing characteristic features of functional networks of the human brain. However, many of these explorations have focused on static patterns of a representative graph that describe the relatively long-term brain activity. Therefore, this study established and characterized functional networks based on the synchronization likelihood and graph theory. Quasidynamic graphs were constructed simply by dividing a long-term static graph into a sequence of subgraphs that each had a timescale of 1 s. Irregular changes were then used to investigate differences in human brain networks between resting and math-operation states using magnetoencephalography, which may provide insights into the functional substrates underlying logical reasoning. We found that graph properties could differ from brain frequency rhythms, with a higher frequency indicating a lower small-worldness, while changes in human brain state altered the functional networks into more-centralized and segregated distributions according to the task requirements. Time-varying connectivity maps could provide detailed information about the structure distribution. The frontal theta activity represents the essential foundation and may subsequently interact with high-frequency activity in cognitive processing. [ABSTRACT FROM AUTHOR]

Reporting the ink color of a written word when it is itself a color name incongruent with the ink color (e.g. 'red' printed in blue) induces a robust interference known as the Stroop effect. Although this effect has been the subject of numerous functional neuroimaging studies, its neuronal substrate is still a matter of debate. Here, we investigated the spatiotemporal dynamics of interference-related neural events using magnetoencephalography (MEG) and voxel-based analyses (SPM8). Evoked magnetic fields (EMFs) were acquired in 12 right-handed healthy subjects performing a color-word Stroop task. Behavioral results disclosed a classic interference effect with longer mean reaction times for incongruent than congruent stimuli. At the group level, EMFs' differences between incongruent and congruent trials spanned from 380 to 700 ms post-stimulus onset. Underlying neural sources were identified in the left pre-supplementary motor area (pre-SMA) and in the left posterior parietal cortex (PPC) confirming the role of these regions in conflict processing. [ABSTRACT FROM AUTHOR]

The conventional analysis estimates both the locations and strengths of neural source activations from event-related magnetoencephalography data that are averaged across about a hundred trials. In the present report, we propose a new method based on a minimum modified- l-norm to obtain the dependence of strengths on the presented stimuli from a limited number of trial data. It estimates the strengths from 10-trial average data and the locations from 100-trial average data. The method can be applied to neural activations whose strengths, but not locations, depend on the presented stimuli. For instance, it can be used in experiments in which the activation in the anterior temporal area (aT) is measured by varying semantic relatedness between stimuli in linguistic experiments. We conducted a realistic simulation, following previous experiments on lexico-semantic processing, in which five neural sources were simultaneously activated. The results showed that when the signal-to-noise ratio was one for non-averaged data, the proposed method had standard deviations of 13 % for the normalized strengths in the aT. It is inferred on the basis of the general linear model in which the strength has a linear dependence on the stimulus parameters that the proposed method can detect the dependence at a significance level of 1 % if the peak-to-peak change in normalized strength is more than 49 %. It is smaller than 66 % for the conventional method, which estimated locations and strengths from 10-trial data for each point. Thus, the proposed method can plot an activation-strength versus stimulus-parameter curve with better sensitivity. [ABSTRACT FROM AUTHOR]

To investigate if Magnetoencephalography (MEG) can add non-redundant information to guide implantation sites for intracranial recordings (IR). The contribution of MEG to intracranial recording planning was evaluated in 12 consecutive patients assessed pre-surgically with MEG followed by IR. Primary outcome measures were the identification of focal seizure onset in IR and favorable surgical outcome. Outcome measures were compared to those of 12 patients matched for implantation type in whom non-invasive pre-surgical assessment suggested clear hypotheses for implantation (non-MEG group). In the MEG group, non-invasive assessment without MEG was inconclusive, and MEG was then used to further help identify implantation sites. In all MEG patients, at least one virtual MEG electrode generated suitable hypotheses for the location of implantations. No differences in outcome measures were found between non-MEG and MEG groups. Although the MEG group included more complex patients, it showed similar percentage of successful implantations as the non-MEG group. This suggests that MEG can contribute to identify implantation sites where standard methods failed. [ABSTRACT FROM AUTHOR]