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Objective: Brain electromagnetic activity in patients with epilepsy is characterized by abnormal high-amplitude transient events (spikes) and abnormal patterns of synchronization of brain rhythms that accompany epileptic seizures. With the aim of improving methods for identifying epileptogenic sources in magnetoencephalographic (MEG) recordings of brain data, we applied methods previously used in the study of oceanic 'rogue waves' and other freak events in complex systems., Approach: For data from three patients who were awaiting surgical treatment for epilepsy, we used a beamformer source model to produce volumetric maps showing areas with a high proportion of spikes that could be classified as 'rogue waves', and areas with high Hurst exponent (HE). The HE describes the extent to which a system is exhibiting persistent behavior, may predict the likelihood of freak events. These measures were compared with the more standard measure of kurtosis, which has been shown to be a reliable method for localizing interictal spikes., Main Results: There was partial concordance between the three different volumetric maps indicating that each measure provides different information about the underlying brain data. The HE, when combined with a simple connectivity analysis based on phase slope index (PSI), was able to identify the probable epileptogenic zone in all three patients, despite very different patterns of abnormal activity. The differences between distributions of high HE and high kurtosis values indicates that while spikes are propagated through cortex from the epileptogenic zone, the persistent dynamical conditions under which the spikes are generated may not be propagated in a similar way. Finally, the patterns of persistent activity, indicating a departure from 'healthy criticality' in brain networks may explain the wide range of social and cognitive impairments that are seen in epilepsy patients., Significance: The HE is a potentially useful addition to the clinician's battery of measures which may be used convergently to guide surgical intervention.

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.

Objective: Kurtosis beamforming is a useful technique for analysing magnetoencephalograpy (MEG) data containing epileptic spikes. However, the implementation varies and few studies measure concordance with subsequently resected areas. We evaluated kurtosis beamforming as a means of localizing spikes in drug-resistant epilepsy patients., Methods: We retrospectively applied kurtosis beamforming to MEG recordings of 22 epilepsy patients that had previously been analysed using equivalent current dipole (ECD) fitting. Virtual electrodes were placed in the kurtosis volumetric peaks and visually inspected to select a candidate source. The candidate sources were compared to the ECD localizations and resection areas., Results: The kurtosis beamformer produced interpretable localizations in 18/22 patients, of which the candidate source coincided with the resection lobe in 9/13 seizure-free patients and in 3/5 patients with persistent seizures. The sublobar accuracy of the kurtosis beamformer with respect to the resection zone was higher than ECD (56% and 50%, respectively), however, ECD resulted in a higher lobar accuracy (75%, 67%)., Conclusions: Kurtosis beamforming may provide additional value when spikes are not clearly discernible on the sensors and support ECD localizations when dipoles are scattered., Significance: Kurtosis beamforming should be integrated with existing clinical protocols to assist in localizing the epileptogenic zone., (Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

The auditory evoked N1m-P2m response complex presents a challenging case for MEG source-modelling, because symmetrical, phase-locked activity occurs in the hemispheres both contralateral and ipsilateral to stimulation. Beamformer methods, in particular, can be susceptible to localisation bias and spurious sources under these conditions. This study explored the accuracy and efficiency of event-related beamformer source models for auditory MEG data under typical experimental conditions: monaural and diotic stimulation; and whole-head beamformer analysis compared to a half-head analysis using only sensors from the hemisphere contralateral to stimulation. Event-related beamformer localisations were also compared with more traditional single-dipole models. At the group level, the event-related beamformer performed equally well as the single-dipole models in terms of accuracy for both the N1m and the P2m, and in terms of efficiency (number of successful source models) for the N1m. The results yielded by the half-head analysis did not differ significantly from those produced by the traditional whole-head analysis. Any localisation bias caused by the presence of correlated sources is minimal in the context of the inter-individual variability in source localisations. In conclusion, event-related beamformers provide a useful alternative to equivalent-current dipole models in localisation of auditory evoked responses.

The neural bases of altered consciousness in patients with epilepsy during seizures and at rest have raised significant interest in the last decade. This exponential growth has been supported by the parallel development of techniques and methods to investigate brain function noninvasively with unprecedented spatial and temporal resolution. In this article, we review the contribution of magnetoencephalography to deconvolve the bioelectrical changes associated with impaired consciousness during seizures. We use data collected from a patient with refractory absence seizures to discuss how spike-wave discharges are associated with perturbations in optimal connectivity within and between brain regions and discuss indirect evidence to suggest that this phenomenon might explain the cognitive deficits experienced during prolonged 3/s spike-wave discharges., (© 2013.)

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.

Magnetoencephalography (MEG) is a non-invasive brain imaging technique with the potential for very high temporal and spatial resolution of neuronal activity. The main stumbling block for the technique has been that the estimation of a neuronal current distribution, based on sensor data outside the head, is an inverse problem with an infinity of possible solutions. Many inversion techniques exist, all using different a-priori assumptions in order to reduce the number of possible solutions. Although all techniques can be thoroughly tested in simulation, implicit in the simulations are the experimenter's own assumptions about realistic brain function. To date, the only way to test the validity of inversions based on real MEG data has been through direct surgical validation, or through comparison with invasive primate data. In this work, we constructed a null hypothesis that the reconstruction of neuronal activity contains no information on the distribution of the cortical grey matter. To test this, we repeatedly compared rotated sections of grey matter with a beamformer estimate of neuronal activity to generate a distribution of mutual information values. The significance of the comparison between the un-rotated anatomical information and the electrical estimate was subsequently assessed against this distribution. We found that there was significant (P < 0.05) anatomical information contained in the beamformer images across a number of frequency bands. Based on the limited data presented here, we can say that the assumptions behind the beamformer algorithm are not unreasonable for the visual-motor task investigated.