Abstracts

Rationale: The analysis of ictal events using electroencephalography (EEG) correlated functional magnetic resonance imaging (EEG-fMRI) to reveal ictal Blood Oxygen Level Dependent (BOLD) changes remains a challenge due to the potential neurophysiological complexity of the events and effects of head movement on the fMRI data. A variety of fMRI data modelling strategies mainly using the EEG to mark ictal activity have been applied (Ann.Neurol. 2003; 53(5): 663). Addition of simultaneous, synchronized video recording to EEG-fMRI can supply information on ictal semiology and may increase the technique’s utility. We implemented synchronised video-EEG-fMRI, evaluated its effect on data quality, and investigated the role of video in EEG-fMRI to study ictal epileptic activity. Methods: A custom made system consisting of two video cameras, video-multiplexer and quad-processors synchronised with the EEG was used to record video during EEG-fMRI acquisition (Figure1). Gradient echo BOLD-sensitive T2*-weighted single-shot echo-planar images (EPI) were acquired on a 3Tesla GE Signa® Excite HDX echo-speed MRI scanner (Milwaukee, USA) with Tx/Rx head coil. EEG was recorded using a commercial system with MR-compatible 64-channel cap and amplifiers and was transmitted to the control-room for display. An in-house script was used to calculate signal to fluctuation noise-ratio (SFNR) from the raw EPI time series to assess image data quality, and the results were compared, in data from subjects with (N=12) and without (N=12) concurrent video recording. Four subjects with frequent seizures were investigated with video-EEG-fMRI. Ictal events were identified on video and EEG after removing scanner and pulse artefact using software Brain Analyzer2 (Brain Products, Germany). fMRI time series data was pre-processed and analysed using Statistical Parametric Mapping (SPM5). SPM{F} maps were generated using event related video and EEG based general linear model. Events on EEG and video were modelled as separate blocks and convolved with canonical hemodynamic responses (HRF) with temporal and dispersion derivatives. Realignment parameters and cardiac regressors were included in the model as confounds. Results: Introduction of the cameras did not alter the SFNR significantly in EPI of the subjects scanned with video cameras in place. Video and EEG were free of major artefact. Two subjects had electroclinical seizures observed on video and EEG both, and two subjects had ictal activity observed either on video or EEG only. Global maximum BOLD signal changes on SPM{F} maps for events on EEG and video were concordant with available electroclinical information for localization of seizures. The events identified on video only also revealed additional BOLD signal changes. Conclusions: Video-EEG-fMRI can be implemented without affecting the data quality. Identification of ictal events on video along with EEG may provide valuable clinical information to be included in models of the fMRI data to reveal ictal related BOLD changes.