Data-driven analysis of simultaneous EEG-fMRI using a time-frequency approach

Background and Aim: For some patients suffering from abnormalities in their nervous system, brain surgery is the only chance of survival. In that case, the surgeon needs a high-resolution (temporally and spatially) picture of the brain, showing the location(s) of brain abnormalities. EEG and blood oxygenation level dependent changes encoded in fMRI are the two most commonly used noninvasive functional neuroimaging techniques that exhibit highly complementary characteristics; i.e. EEG has a good time resolution (but a poor spatial resolution), while fMRI has a good spatial resolution (but a poor time resolution). Therefore, their multimodal integration has been actively sought as a powerful non-invasive imaging tool. Methods: The proposed methodology consists of ۳ main steps as follows: (S۱) On the EEG side, the multichannel signals are pre-processed, i.e. down-sampled and band-pass filtered, and based on the region of interest(s) (ROIs), interested channels are selected. Then, the signals are segmented based on the repetition time (TR) of the fMRI data. Finally, from the resulted multichannel EEGs, the phase synchrony (PS) is calculated using a multivariate PS measure. At the output of this stage, a time-series with sampling period of TR is obtained. (S۲) On the fMRI side, ۴D fMRI data are converted to ۳D, preprocessed, i.e. realigned, normalized, and smoothed, and the time-series in the ROI(s) are extracted. Then, the local connectivity in the resulted multivariate time-series is calculated using a dynamic regional phase synchrony measure. (S۳) Time-frequency coherence analysis is applied to the resulted time-series in (S۱) and (S۲) and after voxel-wise thresholding and spatial thresholding, maps are obtained which shows dynamic coupling between fMRI and EEG data. Results: To (۱) introduce a perceptual decision making EEG/fMRI data set, (۲) present a time-frequency approach for quantitatively investigating the relationship between simultaneous acquired EEG and fMRI data, and (۳) apply the proposed methodology to the data set introduced in (۱). Conclusion: The methodology was applied to a data set composed of behavioral, EEG, and fMRI data acquired from human subjects performing a perceptual decision making task. The data set is publicly available at https://osf.io under a Data Use Agreement. The proposed methodology can be used as a neuroimaging tool for studying epilepsy as well as neuro-vascular coupling and cognitive studies. It can also be deployed to get better constrain solutions of the inverse problem of source localization of EEG activity.

کلیدواژه ها:

EEG ، fMRI ، multivariate phase synchrony ، dynamic regional phase synchrony