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IEEE Rev Biomed Eng. 2011;4:26-58. doi: 10.1109/RBME.2011.2170675.

Analysis of multimodal neuroimaging data.

Author information

1
Department of Machine Learning, Berlin Institute of Technology, Berlin 10587, Germany.

Abstract

Each method for imaging brain activity has technical or physiological limits. Thus, combinations of neuroimaging modalities that can alleviate these limitations such as simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular. Multimodal imaging setups can take advantage of complementary views on neural activity and enhance our understanding about how neural information processing is reflected in each modality. However, dedicated analysis methods are needed to exploit the potential of multimodal methods. Many solutions to this data integration problem have been proposed, which often renders both comparisons of results and the choice of the right method for the data at hand difficult. In this review we will discuss different multimodal neuroimaging setups, the advances achieved in basic research and clinical application and the methods used. We will provide a comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.

PMID:
22273790
DOI:
10.1109/RBME.2011.2170675
[Indexed for MEDLINE]

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