Electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to localize brain activity and their spatial resolutions have been compared in several publications. While most clinical studies demonstrated higher accuracy of MEG source localization, simulation studies suggested a more accurate EEG than MEG localization for the same number of channels. However, studies comparing real MEG and EEG data with equivalent number of channels are scarce. We investigated 14 right-handed healthy subjects performing a motor task in MEG, high-density-(hd-) EEG and fMRI as well as a somatosensory task in MEG and hd-EEG and compared source analysis results of the evoked brain activity between modalities with different head models. Using individual head models, hd-EEG localized significantly closer to the anatomical reference point obtained by fMRI than MEG. Source analysis results were least accurate for hd-EEG based on a standard head model. Further, hd-EEG and MEG localized more medially than fMRI. Localization accuracy of electric source imaging is dependent on the head model used with more accurate results obtained with individual head models. If this is taken into account, EEG localization can be more accurate than MEG localization for the same number of channels.

Colored noise in event-related fMRI obtained at short TRs originates mainly from neural sources and calls for more sophisticated correction of serial autocorrelations which cannot be achieved with standard methods relying on AR(1)+w models with globally fixed AR coefficients. Magn Reson Med 76:1805-1813, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Idiopathic/genetic generalized epilepsy (IGE/GGE) is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.