Voxel-wise information theoretic EEG-fMRI feature integration

We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.P., 2010. An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage. 49, 498-516). In our previous experimental evaluation of the information theoretic framework, we defined the data subsets from which to calculate the ITQs using a priori constraints. In the case of EEG, this meant that data were extracted from a single electrode, while for fMRI the analysed data came from voxels contained within a sphere surrounding the most responsive voxel of visual cortex. While this approach was a natural starting point for the evaluation of the framework in the application to combined EEG-fMRI data sets, a more principled approach to data selection is desirable. Here, we propose to combine standard fMRI data pre-processing and low-resolution electromagnetic tomography (LORETA) for the evaluation of ITQs across the entire three-dimensional brain space. We apply the proposed method to a simultaneous EEG-fMRI data set acquired during checkerboard stimulation and assess the topographical informativeness of EEG (time and frequency domain) and fMRI features with respect to the stimulus and each other. The resulting information theoretic effect size maps are supplemented with a statistical evaluation based on Gaussian null model simulations using a false-discovery rate procedure. Given the contamination of EEG recordings by artefacts induced by the MR scanning environment we further assessed the influence of different advanced EEG pre-processing methods (independent component analysis and functional source separation) on the information topography. The results of this analysis provide evidence for the topographically focussed informativeness of both EEG and fMRI features with respect to the stimulus, but for the current feature selection do not detect EEG-fMRI activity dependence. More advanced EEG data pre-processing rendered the feature distributions more stimulus-informative, but did not alter the EEG-fMRI activity and conditional dependencies.