Fusion of simultaneous fMRI/EEG data based on the electro-metabolic coupling

This paper presents an algorithm of data fusion for simultaneous EEG and fMRI recordings, which provides an estimation of ´neural´ signals on the cortical surface. The technique described here is applied on simulation data, and can be generically applied to any event-related paradigm. The main originality of this method relative to EEG source reconstruction techniques is to take into account the observed relationship between sources energy and the size of corresponding BOLD responses, for each source and across event occurrences, resulting in a better estimation of the sources´ energy. In presence of an EEC/BOLD link, simulation studies show that the method proposed here 1) has better spatial discrimination 2) better estimates the temporal course of sources 3) better estimates the coherence of sources, relative to a similar method using EEG information only. The method is also robust to noise on fMRI and EEG. To our knowledge, this is the first attempt to take advantage of the temporal variability across events.