Halchenko, Yaroslav O. and Hanson, Stephen Jose and Pearlmutter, Barak A. (2005) Multimodal Integration: fMRI, MRI, EEG, MEG. In: Advanced Image Processing in Magnetic Resonance Imaging. Taylor & Francis, pp. 223-265.

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Abstract

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research.

Item Type:	Book Section
Keywords:	Magnetic Resonance Imaging
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	562
Depositing User:	Barak Pearlmutter
Date Deposited:	23 Sep 2008
Journal or Publication Title:	Advanced Image Processing in Magnetic Resonance Imaging
Publisher:	Taylor & Francis
Refereed:	Yes
URI:	http://search.tandf.co.uk/results.asp
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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