Integrating EEG–fMRI Through Brain Simulation

Abstract

EEG and fMRI are thought to measure partly distinct, partly overlapping, and certainly incomplete aspects of neuronal activity. Brain network models (BNMs) are used to simulate neuronal activity, like the dynamics of postsynaptic potentials, or spike-firing activity, and may conjointly predict both, EEG and fMRI, and therefore allow for the integration and the analysis of the two signals. The usual motivation for EEG–fMRI integration is to use both techniques in a complementary fashion by combining their strengths, while ameliorating their weaknesses. For instance, EEG measures electric activity on the scalp with a high temporal sampling rate, but a low spatial resolution (e.g., due to volume conduction effects). On the other hand, fMRI BOLD contrast is an indirect (proxy) measure of neural activity that is sensitive for the fluctuation of blood oxygenation at a relatively low temporal resolution. Some of the appeal of brain simulation-based integration of EEG–fMRI data is related to the idea that after fitting a neural model to reproduce observed activity, the internal activity of the model can tell us something about unobservable activity, like neural firing, which can only be measured invasively and in a spatially restricted manner. Brain simulation-based approaches have the potential to not only integrate EEG and fMRI, but basically data from every modality that can either directly (like multi-electrode recordings) or indirectly (like fMRI) be linked with the neural model.