MEG and multimodal integration

Abstract

Functional brain imaging methods provide measures of various physiological processes with a range of spatial and temporal scales. Because the sensitivity properties of the imaging modalities differ, combining multimodal data is expected to provide more information about the brain activity than is available by a single method. In direct data fusion, multimodal data can be described as complementary or supportive. Complementary modalities have the same type of sources, such as electroencephalography (EEG) and magnetoencephalography (MEG), which are both generated by cortical primary currents, but with different sensitivity characteristics. Combination of EEG and MEG data can resolve ambiguities in data from only one of the modalities. In a supportive role data from one imaging modality guides the analysis and interpretation of another modality. Structural magnetic resonance imaging (MRI) provides supportive data for MEG source estimation, e.g., by indicating allowable locations and orientations of MEG source currents. Functional MRI (fMRI) can be used in a supportive role to suggest a likely source distribution for MEG among multiple alternatives. MEG and fMRI can also be considered complementary if the different source types, i.e., primary currents for MEG and blood oxygenation level dependent (BOLD) contrast for fMRI, are both derived from a common physiological model.