de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Book Chapter

Characterization of Multisensory Integration with fMRI: Experimental Design, Statistical Analysis, and Interpretation

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84112

Noppeney,  U
Research Group Cognitive Neuroimaging, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Noppeney, U. (2012). Characterization of Multisensory Integration with fMRI: Experimental Design, Statistical Analysis, and Interpretation. In The neural bases of multisensory processes (pp. 233-252). Boca Raton, FL, USA: CRC Press.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B88C-E
Abstract
This chapter reviews the potential and limitations of functional magnetic resonance imaging (fMRI) in characterizing the neural processes underlying multisensory integration. The neural basis of multisensory integration can be characterized from two distinct perspectives. From the perspective of functional specialization, we aim to identify regions where information from different senses converges and/or is integrated. From the perspective of functional integration, we investigate how information from multiple sensory regions is integrated via interactions among brain regions. Combining these two perspectives, this chapter discusses experimental design, analysis approaches, and interpretational limitations of fMRI results. The first section describes univariate statistical analyses of fMRI data and emphasizes the interpretational ambiguities of various statistical criteria that are commonly used for the identification of multisensory integration sites. The second section explores the potential and limitations of multivariate and pattern classifier approaches in multisensory integration. The third section introduces effective connectivity analyses that investigate how multisensory integration emerges from distinct interactions among brain regions. The complementary strengths of data-driven and hypothesis-driven effective connectivity analyses will be discussed. We conclude by emphasizing that the combined potentials of these various analysis approaches may help us to overcome or at least ameliorate the interpretational ambiguities associated with each analysis when applied in isolation.