To tackle these technical and fundamental issues, we have proposed a new framework for the fMRI-EEG/MEG integrated neuroimaging. As illustrated in Fig. 5, this approach rests on a unified system underlying the signal generation and analysis for both modalities. The system assumes a common neuronal source (i.e. synaptic activity), from which fMRI and EEG/MEG signals are generated via a temporal low-pass filter and a spatial low-pass filter, respectively. The EEG/MEG inverse problems essentially deal with the spatial deconvolution – the process of reversing the head volume conduction. The inverse solutions retain the temporal source evolution even though it may fail to reconstruct the spatial source distribution. In other words, at every source location, the souce waveform estimated from EEG/MEG is much less distorted (in terms of its normalized “shape”) than its absolute magnitude, since the filtering applies to the spatial domain instead of the time domain. This feature is as opposed to the temporal regression of fMRI data (e.g. the GLM analysis), which theoretically ends up with high-resolution spatial maps of brain activations but with little or no temporal information.
