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Neuroimage. 2014 Nov 15;102 Pt 1:3-10. doi: 10.1016/j.neuroimage.2014.05.018. Epub 2014 May 16.

General overview on the merits of multimodal neuroimaging data fusion.

Author information

1
Department of Cognitive Neuroscience, Maastricht Brain Imaging Centre (MBIC), Faculty of Psychology & Neuroscience, Maastricht University, PO Box 616, 6200MD, Maastricht, The Netherlands. Electronic address: kamil.uludag@maastrichtuniversity.nl.
2
Department of Cognitive Neuroscience, Maastricht Brain Imaging Centre (MBIC), Faculty of Psychology & Neuroscience, Maastricht University, PO Box 616, 6200MD, Maastricht, The Netherlands. Electronic address: a.roebroeck@maastrichtuniversity.nl.

Abstract

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.

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