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Localizing neuronal activity in the brain, both in time and in space, is acentral challenge to advance the understanding of brain function. Because ofthe inability of any single neuroimaging techniques to cover all aspects atonce, there is a growing interest to combine signals from multiple modalitiesin order to benefit from the advantages of each acquisition method. Due to thecomplexity and unknown parameterization of any suggested complete model of BOLDresponse in functional magnetic resonance imaging (fMRI), the development of areliable ultimate fusion approach remains difficult. But besides the primarygoal of superior temporal and spatial resolution, conjoint analysis of datafrom multiple imaging modalities can alternatively be used to segregate neuralinformation from physiological and acquisition noise. In this paper we suggesta novel methodology which relies on constructing a quantifiable mapping of datafrom one modality (electroencephalography; EEG) into another (fMRI), calledtransmodal analysis of neural signals (TRANSfusion). TRANSfusion attempts tomap neural data embedded within the EEG signal into its reflection in fMRIdata. Assessing the mapping performance on unseen data allows to localize brainareas where a significant portion of the signal could be reliablyreconstructed, hence the areas neural activity of which is reflected in bothEEG and fMRI data. Consecutive analysis of the learnt model allows to localizeareas associated with specific frequency bands of EEG, or areas functionallyrelated (connected or coherent) to any given EEG sensor. We demonstrate theperformance of TRANSfusion on artificial and real data from an auditoryexperiment. We further speculate on possible alternative uses: cross-modal datafiltering and EEG-driven interpolation of fMRI signals to obtain arbitrarilyhigh temporal sampling of BOLD.

Transmodal Analysis of Neural Signals