Multimodal Integration: fMRI, MRI, EEG, and MEG - Advance Image Processing in Magnetic Resonance Imaging - page 216

8.4.3.4 Linear Inverse Methods ...............................................248
8.4.3.5 Beamforming................................................................250
8.4.3.6 Bayesian Inference .......................................................250
8.5 Considerations and Future Directions.....................................................253
References .........................................................................................................254

OVERVIEW

This chapter provides a comprehensive survey of the motivations, assumptions and
pitfalls associated with combining signals such as functional magnetic resonance
imaging (fMRI) with electroencephalography (EEG) or magnetoencephalography
(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.
Specifically, we look at correlative analysis, decomposition techniques, equivalent
dipole fitting, distributed sources modeling, beamforming, and Bayesian methods.
Due to difficulties in assessing the ground truth of a combined signal in any realistic
experiment — a difficulty further confounded by lack of accurate biophysical mod-
els of BOLD signal — we are cautious about being optimistic in regard to multi-
modal integration. Nonetheless, as we highlight and explore the technical and
methodological difficulties 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, multimodal integration will
eventually become a useful tool in brain-imaging research.

8.1 INTRODUCTION

Noninvasive functional brain imaging has become an important tool used by neu-
rophysiologists, cognitive psychologists, cognitive scientists, and other researchers
interested in brain function. In the last five decades, the technology of noninvasive
functional imaging has flowered, and researchers today can choose from EEG,
MEG, positron emission tomography (PET), single-photon computed tomography
(SPECT), magnetic resonance imaging (MRI), and fMRI. Each method has its own
strengths and weaknesses, and no single method is best suited for all experimental
or clinical conditions. Because of the inadequacies of individual techniques, there
is increased interest in finding ways to combine existing techniques in order to
synthesize the strengths inherent in each. In this chapter, we will (a) examine
specific noninvasive imaging techniques (EEG, MEG, MRI, and fMRI), (b) com-
pare approaches used to analyze the data obtained from these techniques, and (c)
discuss the potential for successfully combining methodologies and analyses.
Localizing neuronal activity in the brain, both in time and in space, is a central
challenge to progress in understanding brain function. Localizing neural activity
from EEG or MEG data is called electromagnetic source imaging (EMSI). EEG and
MEG each provide data with high temporal resolution (measured in milliseconds)
but limited spatial resolution. In contrast, fMRI provides good spatial but relatively