Implementation and evaluation of simultaneous video-electroencephalography and functional magnetic resonance imaging

The objective of this study was to demonstrate that the addition of simultaneous and synchronised video to electroencephalography (EEG)-correlated functional magnetic resonance imaging (fMRI) could increase recorded information without data quality reduction. We investigated the effect of placing EEG, video equipment and their required power supplies inside the scanner room, on EEG, video and MRI data quality, and evaluated video-EEG-fMRI by modelling a hand motor task. Gradient-echo, echo-planner images (EPI) were acquired on a 3-T MRI scanner at variable camera positions in a test object [with and without radiofrequency (RF) excitation], and human subjects. EEG was recorded using a commercial MR-compatible 64-channel cap and amplifiers. Video recording was performed using a two-camera custom-made system with EEG synchronization. An in-house script was used to calculate signal to fluctuation noise ratio (SFNR) from EPI in test object with variable camera positions and in human subjects with and without concurrent video recording. Five subjects were investigated with video-EEG-fMRI while performing hand motor task. The fMRI time series data was analysed using statistical parametric mapping, by building block design general linear models which were paradigm prescribed and video based. Introduction of the cameras did not alter the SFNR significantly, nor did it show any signs of spike noise during RF off conditions. Video and EEG quality also did not show any significant artefact. The Statistical Parametric Mapping{T} maps from video based design revealed additional blood oxygen level-dependent responses in the expected locations for non-compliant subjects compared to the paradigm prescribed design. We conclude that video-EEG-fMRI set up can be implemented without affecting the data quality significantly and may provide valuable information on behaviour to enhance the analysis of fMRI data

BOLD mapping of human epileptic spikes recorded during simultaneous intracranial EEG-fMRI: The impact of automated spike classification

Objectives: Simultaneous intracranial EEG and functional MRI (icEEG-fMRI) can be used to map the haemodynamic (BOLD) changes associated with the generation of IEDs. Unlike scalp EEG-fMRI, in most patients who undergo icEEG-fMRI, IEDs recorded intracranially are numerous and show variability in terms of field amplitude and morphology. Therefore, visual marking can be highly subjective and time consuming. In this study, we applied an automated spike classification algorithm, Wave_clus (WC), to IEDs marked visually on icEEG data acquired during simultaneous fMRI acquisition. The motivation of this work is to determine whether using a potentially more consistent and unbiased automated approach can produce more biologically meaningful BOLD patterns compared to the BOLD patterns obtained based on the conventional, visual classification. Methods: We analysed simultaneous icEEG-fMRI data from eight patients with severe drug resistant epilepsy, and who subsequently underwent resective surgery that resulted in a good outcome: confirmed epileptogenic zone (EZ). For each patient two fMRI analyses were performed: one based on the conventional visual IED classification and the other based on the automated classification. We used the concordance of the IED-related BOLD maps with the confirmed EZ as an indication of their biological meaning, which we compared for the automated and visual classifications for all IED originating in the EZ. Results: Across the group, the visual and automated classifications resulted in 32 and 24 EZ IED classes respectively, for which 75% vs 83% of the corresponding BOLD maps were concordant. At the single-subject level, the BOLD maps for the automated approach had greater concordance in four patients, and less concordance in one patient, compared to those obtained using the conventional visual classification, and equal concordance for three remaining patients. These differences did not reach statistical significance. Conclusion: We found automated IED classification on icEEG data recorded during fMRI to be feasible and to result in IED-related BOLD maps that may contain similar or greater biological meaning compared to the conventional approach in the majority of the cases studied. We anticipate that this approach will help to gain significant new insights into the brain networks associated with IEDs and in relation to postsurgical outcome