Magnetoencephalography in the study of epilepsy and consciousness.

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.

Interhemispheric differences of spectral power in expressive language: a MEG study with clinical applications.

In the last decade we have seen an exponential growth of functional imaging studies investigating multiple aspects of language processing. These studies have sparked an interest in applying some of the paradigms to various clinically relevant questions, such as the identification of the cortical regions mediating language function in surgical candidates for refractory epilepsy. Here we present data from a group of adult control participants in order to investigate the potential of using frequency specific spectral power changes in MEG activation patterns to establish lateralisation of language function using expressive language tasks. In addition, we report on a paediatric patient whose language function was assessed before and after a left hemisphere amygdalo-hippocampectomy. Our verb generation task produced left hemisphere decreases in beta-band power accompanied by right hemisphere increases in low beta-band power in the majority of the control group, a previously unreported phenomenon. This pattern of spectral power was also found in the patient's post-surgery data, though not her pre-surgery data. Comparison of pre and post-operative results also provided some evidence of reorganisation in language related cortex both inter- and intra-hemispherically following surgery. The differences were not limited to changes in localisation of language specific cortex but also changes in the spectral and temporal profile of frontal brain regions during verb generation. While further investigation is required to establish concordance with invasive measures, our data suggest that the methods described may serve as a reliable lateralisation marker for clinical assessment. Furthermore, our findings highlight the potential utility of MEG for the investigation of cortical language functioning in both healthy development and pathology.

Automatic detection and visualisation of MEG ripple oscillations in epilepsy.

High frequency oscillations (HFOs, 80-500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80-250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.