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General Advice Since the physical layout of all scanner rooms is different we recognize that you may not be able to position your EEG amplifiers outside the bore of the magnet. In this case a good compromise is to place the amplifiers on a thick rubber pad so as to decouple them from the scanner vibrations as much possible. If you find that the gradient artifact correction is not working well, then check the times between volume or slice markers, as it is likely in this case that the TR that has been input to the MR console is not precisely the TR that is being generated. In this case you will need to contact the relevant MR scanner manufacturer for further assistance.
The most important steps in the process of EEG data acquisition during simultaneous fMRI are those taken to ensure that all external noise sources have been minimized (e.g. cyrocooler pumps and vibration of the EEG equipment). To allow optimal gradient artifact correction it is important to ensure that the EEG and MR scanner clocks are synchronized, the slice TR is a multiple of the scanner clock period and that the subject is optimally positioned. To ensure optimal pulse artifact correction many techniques require a clean cardiac trace from which R-peaks can be detected, we suggest that this can be best achieved using a VCG, although it is also possible with a well-positioned ECG lead. If using the ECG then it is recommended to place this at the base of the back to maximize the signal to noise ratio of the R-peak with the added benefit of this being an easier site to access than a position near the heart23. Positioning the ECG lead on the chest results in motion artifacts due to respiration being added to the trace from this lead as well as causing the gradient artifact to vary over time. This can result in the trace saturating and/or gradient artifact correction not working due to template variability and therefore is not recommended.
General Discussion EEG-fMRI is a powerful tool for studying brain function, as the high temporal resolution of EEG can be combined with the high spatial resolution of fMRI. To date, a number of studies have used this multi-modal approach to gain a better understanding of brain function. EEG-fMRI has been applied to healthy volunteers in order to investigate the correlation between oscillatory rhythms (measured with EEG) and blood oxygenation responses (using BOLD fMRI) e.g. 2,3. It has also been used to study whether characteristics of the evoked signal can explain the variance in the BOLD signal on a trial-by- trial basis4,5. In clinical studies the main use of the technique has been to investigate the foci of interictal epileptic discharges which are inherently difficult to localize non-invasively6,7. These examples show the power of this multi-modal imaging tool. However, to enable the study of such phenomena, it is important to have access to the best possible quality of EEG and MRI data. To achieve this inside the MR scanner it is important to have the best experimental set-up and also to choose the most appropriate analysis methods. The optimal analysis methods will to some extent depend upon the research question of interest, as will the correction methods used for removal of artifacts. For example the size and number of movements that have occurred during the recording will determine the most effective combination of algorithms for removing the gradient artifact. However, the optimal experimental set-up of the EEG and fMRI hardware is relatively independent of particular research questions. The guidelines outlined here are therefore of general value and can be followed in experiments using different EEG and MR scanner hardware than we used.
Here we have demonstrated the acquisition methods which should be followed to acquire high quality EEG and fMRI data. We used a visual stimulus based on a previously employed stimulus paradigm 24. However, the same techniques for data acquisition can be applied regardless of the paradigm used to stimulate the brain activity of interest. When choosing your paradigm it should be noted that the quality of the EEG data that can be achieved when recording inside the MR environment with the techniques currently available to users (and described here) still place some limitations on the brain activity which may be studied: there are particular difficulties in recording EEG activity in low (<5 Hz) and high frequency (>80 Hz) bands where residual pulse and gradient artifacts may reside. Additionally, care must be taken when choosing the paradigm so that the possibility of subject movement related to the task is minimized. This is a problem because motion artifacts in the EEG data are often difficult to correct and small artifacts can be difficult to identify clearly, although they still may dominate neuronal signals. These motion artifacts can cause spurious but plausible correlations with the fMRI data17.
Post-processing methods for simultaneous EEG-fMRI are numerous and as such their discussion is beyond the scope of this work. As previously mentioned the gradient and pulse artifact can be removed using a number of techniques which include average artifact subtraction18,19, independent component analysis20,21, optimal basis sets22 and beamformers25. Often a combination of these methods may be employed23 and the performance of the methods is dependent upon factors such as the magnetic field strength and the paradigm used. The optimal post-processing methods for a specific study will also depend on the signals to extract from the data, whether these are oscillatory rhythms or evoked potentials may have an influence on the post-processing methods employed.
Whilst there is considerable on-going research targeting improved data acquisition and analysis methods for simultaneous EEG-fMRI, it is already possible, using the techniques described here, to answer important neuroscience questions which require the combination of the high spatial resolution of fMRI and the excellent temporal resolution of EEG.