To date, source imaging (SI) has been performed using epileptiform discharges (ED) detected by magnetoencephalography (MEG) or electroencephalography (EEG). A few case studies has combined MEG and EEG recordings and performed electromagnetic source imaging (cEMSI). This study tries to elucidate the role of cEMSI in presurgical evaluation. A MEG whole-head 306-channel Elekta Neuromag® system, and simultaneous high density EEG (70 electrodes, range 58–80) using a non-magnetic cap (EASYCAP) were recorded in 141 consecutive patients. Fifty patients were operated and had one year follow up. MEG-EEG was inspected for EDs. Signals were analyzed using CURRY 7 Neuroimaging Suite. For each cluster, using the visually detected EDs as templates, automated algorithms scanned the recordings, and detected EDs were visually checked. To improve the signal-to-noise ratio, EDs with similar topography were averaged. Two different inverse solutions were applied: equivalent current dipole (ECD) and a distributed source model (DSM): sLORETA. We performed electric SI, magnetic SI, and cEMSI. All analyses were performed using individual head models from the patients’ MRIs. We calculated the odds ratio (OR) of becoming seizure-free when operation was concordant vs discordant with the localization of the SI. Combining both EEG and MEG signals (cEMSI) gave an OR of 5.8 for ECD and 19.2 for DSM. OR of cEMSI using DSM was significantly higher than both ESI (p = 0.02) and MSI (p = 0.03). Combined EMSI achieved significantly higher odds ratio for becoming seizure-free compared to electric SI and magnetic SI.

Objective To determine the diagnostic accuracy and clinical utility of electromagnetic source imaging (EMSI) in presurgical evaluation of patients with epilepsy. Methods We prospectively recorded magnetoencephalography (MEG) simultaneously with EEG and performed EMSI, comprising electric source imaging, magnetic source imaging, and analysis of combined MEG-EEG datasets, using 2 different software packages. As reference standard for irritative zone (IZ) and seizure onset zone (SOZ), we used intracranial recordings and for localization accuracy, outcome 1 year after operation. Results We included 141 consecutive patients. EMSI showed localized epileptiform discharges in 94 patients (67%). Most of the epileptiform discharge clusters (72%) were identified by both modalities, 15% only by EEG, and 14% only by MEG. Agreement was substantial between inverse solutions and moderate between software packages. EMSI provided new information that changed the management plan in 34% of the patients, and these changes were useful in 80%. Depending on the method, EMSI had a concordance of 53% to 89% with IZ and 35% to 73% with SOZ. Localization accuracy of EMSI was between 44% and 57%, which was not significantly different from MRI (49%-76%) and PET (54%-85%). Combined EMSI achieved significantly higher odds ratio compared to electric source imaging and magnetic source imaging. Conclusion EMSI has accuracy similar to established imaging methods and provides clinically useful, new information in 34% of the patients. Classification of evidence This study provides Class IV evidence that EMSI had a concordance of 53%-89% and 35%-73% (depending on analysis) for the localization of epileptic focus as compared with intracranial recordings-IZ and SOZ, respectively.

The greatest challenge in epilepsy surgery lies in the presurgical evaluation. On their own, none of the diagnostic methods can identify the epileptogenic zone. Therefore, a multimodal approach is used. Over the last decades, advances in recording techniques and in signal analysis made it possible to estimate the source of the epileptiform discharges recorded by electroencephalography (EEG source imaging: ESI) and magnetoencephalography (MEG source imaging: MSI). This prospective study investigates the role of electromagnetic source imaging (EMSI) as a non-invasive tool to guide the multidisciplinary epilepsy surgery team. MEG (306 channels) and simultaneous high-density EEG was recorded in 85 consecutive patients with refractory focal epilepsy, referred for conventional non-invasive presurgical evaluation. EMSI, comprising of electric, magnetic source imaging and analysis of combined MEG-EEG datasets, using commercially available software (BESA and CURRY) was used. The Danish epilepsy surgery team evaluated the patients first blinded to EMSI and then including the data from EMSI. At both sessions the multidisciplinary team (MDT) determined the presumed localisation of the epileptogenic zone and decided on location of surgery, intracranial registration (ICR) placement, or not offering epilepsy surgery. The clinical utility of EMSI was defined as the proportion of patients in whom EMSI changed the decision of the MDT. A change was defined useful as follows: (a) change from stop to ICR: the ICR localized the source; (b) change in implantation strategy: the electrode(s) implanted based on the EMSI identified the source; and (c) change from implantation to operation: the patient became seizure-free. The impact of EMSI on patient management-plan was assessed in 85 patients (50 men) in whom EMSI was part of the decision-making process. The age of these patients was between 10 and 70 years (median: 32 years). Thirty-eight patients were MRI negative; in the remaining patients, there were discordance between MRI and data from long-term video-EEG monitoring (semiology and EEG). EMSI changed the management plan in 28/85 patients (33%). For 16.5% (14/85) of the patients the ICR plan changed (additional structures were implanted). For 7% (6/85) of the patients, in whom neither operation nor ICR was suggested, after discussing the EMSI results, ICR was offered. For 9.4% (8/85) of the patients ICR was suggested prior to EMSI; after EMSI these patients skipped ICR and went directly to operation. Finally, for one patient it was decided not to offer operation, after discussing the EMSI. At one-year follow-up 80% (16/20) of these changes proved to be useful, meaning that the EMSI changes located the seizure onset zone, irritative zone or the patient became seizure free. Electromagnetic source imagening provides clinically relevant information that supplements the decision-making process in presurgery evaluation for epilepsy surgery.

Objective: To elucidate the possible additional diagnostic yield of MEG in the workup of patients with suspected epilepsy, where repeated EEGs, including sleep-recordings failed to identify abnormalities.Methods: Fifty-two consecutive patients with clinical suspicion of epilepsy and at least three normal EEGs, including sleep-EEG, were prospectively analyzed. The reference standard was inferred from the diagnosis obtained from the medical charts, after at least one-year follow-up. MEG (306-channel, whole-head) and simultaneous EEG (MEG-EEG) was recorded for one hour. The added sensitivity of MEG was calculated from the cases where abnormalities were seen in MEG but not EEG.Results: Twenty-two patients had the diagnosis epilepsy according to the reference standard. MEG-EEG detected abnormalities, and supported the diagnosis in nine of the 22 patients with the diagnosis epilepsy at one-year follow-up. Sensitivity of MEG-EEG was 41%. The added sensitivity of MEG was 18%. MEG-EEG was normal in 28 of the 30 patients categorized as 'not epilepsy' at one year follow-up, yielding a specificity of 93%.Conclusions: MEG provides additional diagnostic information in patients suspected for epilepsy, where repeated EEG recordings fail to demonstrate abnormality.Significance: MEG should be included in the diagnostic workup of patients where the conventional, widely available methods are unrevealing. (C) 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

OBJECTIVE:Reviewing magnetoencephalography (MEG) recordings is time-consuming: signals from the 306 MEG-sensors are typically reviewed divided into six arrays of 51 sensors each, thus browsing each recording six times in order to evaluate all signals. A novel method of reconstructing the MEG signals in source-space was developed using a source-montage of 29 brain-regions and two spatial components to remove magnetocardiographic (MKG) artefacts. Our objective was to evaluate the accuracy of reviewing MEG in source-space. METHODS:In 60 consecutive patients with epilepsy, we prospectively evaluated the accuracy of reviewing the MEG signals in source-space as compared to the classical method of reviewing them in sensor-space. RESULTS:All 46 spike-clusters identified in sensor-space were also identified in source-space. Two additional spike-clusters were identified in source-space. As 29 source-channels can be easily displayed simultaneously, MEG recordings had to be browsed only once. Yet, this yielded a global coverage of the recorded signals and enhanced detectability of epileptiform discharges because MKG-artefacts were suppressed and did not impede evaluation in source-space. CONCLUSIONS:Our results show that reviewing MEG recordings in source-space is accurate and much more rapid than the classical method of reviewing in sensor-space. SIGNIFICANCE:This novel method facilitates the clinical use of MEG.

Electromyography (EMG) is the recording of the electrical activity of the muscle. The electrical activity is muscle action potentials from depolarised muscle fibres. Muscle fibres that belong to one motor unit are scattered throughout the muscle in such a way that fibres belonging to the same motor unit are rarely next to each other. When the motor unit is activated, all muscle fibres belonging to it are depolarised nearly synchronously and these muscle fibre action potentials summate to a motor unit action potential (MUAP).

Background and objective Tissue injury is accompanied by pain and results in increased energy expenditure, which may promote catabolism. The extent to which pain contributes to this sequence of events is not known. Methods In a cross-over design, 10 healthy volunteers were examined on three occasions; first, during self-controlled nontraumatic electrical painful stimulus to the abdominal skin, maintaining an intensity of 8 on the visual analogue scale (0–10). Next, the electrical stimulus was reproduced during local analgesia and, finally, there was a control session without stimulus. Indirect calorimetry and blood and urine sampling was done in order to calculate energy expenditure and substrate utilization. Results During pain stimulus, energy expenditure increased acutely and reversibly by 62% (95% confidence interval, 43–83), which was abolished by local analgesia. Energy expenditure paralleled both heart rate and blood catecholamine levels. The energy expenditure increase was fuelled by all energy sources, with the largest increase in glucose utilization. Conclusion The pain-related increase in energy expenditure was possibly mediated by adrenergic activity and was probably to a large extent due to increased muscle tone. These effects may be enhanced by cortical events related to the pain. The increase in glucose consumption favours catabolism. Our findings emphasize the clinical importance of pain management.