ReviewGeneral overview on the merits of multimodal neuroimaging data fusion

- Multimodal neuroimaging describes combination of non-redundant brain imaging data.
- Spatio-temporal complementarity is the main motivation to combine multimodal data.
- Generative models describe the physical and physiological underpinnings of imaging.
- Discrepancies in multimodal data can provide novel insight into brain processes.

Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain.