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Statistical criteria in fMRI studies of multisensory integration

Statistical criteria in fMRI studies of multisensory integration Inferences drawn from functional magnetic resonance imaging (fMRI) studies are dependent on the statistical criteria used to define different brain regions as “active” or “inactive” under the experimental manipulation. In fMRI studies of multisensory integration, additional criteria are used to classify a subset of the active brain regions as “multisensory.” Because there is no general agreement in the literature on the optimal criteria for performing this classification, we investigated the effects of seven different multisensory stat-istical criteria on a single test dataset collected as human subjects performed auditory, visual, and auditory-visual object recognition. Activation maps created using the different criteria differed dramatically. The classification of the superior temporal sulcus (STS) was used as a performance measure, because a large body of converging evidence demonstrates that the STS is important for auditory-visual integration. A commonly proposed criterion, “supra-additivity” or “super-additivity”, which requires the multisensory response to be larger than the summed unisensory responses, did not classify STS as multisensory. Alternative criteria, such as requiring the multisensory response to be larger than the maximum or the mean of the unisensory responses, successfully classified STS as multisensory. This practical demonstration strengthens theoretical arguments that the super-additivity is not an appropriate criterion for all studies of multisensory integration. Moreover, the importance of examining evoked fMRI responses, whole brain activation maps, maps from multiple individual subjects, and mixed-effect group maps are discussed in the context of selecting statistical criteria. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Statistical criteria in fMRI studies of multisensory integration

Beauchamp, Michael
Neuroinformatics , Volume 3 (2) – Jun 6, 2005
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21 pages

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Publisher
Springer Journals
Copyright
Copyright © 2005 by Humana Press Inc
Subject
Biomedicine; Neurosciences; Computer Appl. in Life Sciences; Neurology; Biotechnology; Computational Biology/Bioinformatics
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1385/NI:3:2:093
pmid
15988040
Publisher site
See Article on Publisher Site

Abstract

Inferences drawn from functional magnetic resonance imaging (fMRI) studies are dependent on the statistical criteria used to define different brain regions as “active” or “inactive” under the experimental manipulation. In fMRI studies of multisensory integration, additional criteria are used to classify a subset of the active brain regions as “multisensory.” Because there is no general agreement in the literature on the optimal criteria for performing this classification, we investigated the effects of seven different multisensory stat-istical criteria on a single test dataset collected as human subjects performed auditory, visual, and auditory-visual object recognition. Activation maps created using the different criteria differed dramatically. The classification of the superior temporal sulcus (STS) was used as a performance measure, because a large body of converging evidence demonstrates that the STS is important for auditory-visual integration. A commonly proposed criterion, “supra-additivity” or “super-additivity”, which requires the multisensory response to be larger than the summed unisensory responses, did not classify STS as multisensory. Alternative criteria, such as requiring the multisensory response to be larger than the maximum or the mean of the unisensory responses, successfully classified STS as multisensory. This practical demonstration strengthens theoretical arguments that the super-additivity is not an appropriate criterion for all studies of multisensory integration. Moreover, the importance of examining evoked fMRI responses, whole brain activation maps, maps from multiple individual subjects, and mixed-effect group maps are discussed in the context of selecting statistical criteria.

Journal

Neuroinformatics – Springer Journals

Published: Jun 6, 2005

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