Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

8-2009

Journal or Book Title

NeuroImage

Volume

47

Issue

1

First Page

88

Last Page

97

DOI

10.1016/j.neuroimage.2009.03.073

Abstract

Functional Magnetic Resonance Imaging (fMRI) is widely used to study activation in the human brain. In most cases, data are commonly used to construct activation maps corresponding to a given paradigm. Results can be very variable, hence quantifying certainty of identified activation and inactivation over studies is important. This paper provides a model-based approach to certainty estimation from data acquired over several replicates of the same experimental paradigm. Specifically, the p-values derived from the statistical analysis of the data are explicitly modeled as a mixture of their underlying distributions; thus, unlike the methodology currently in use, there is no subjective thresholding required in the estimation process. The parameters governing the mixture model are easily obtained by the principle of maximum likelihood. Further, the estimates can also be used to optimally identify voxel-specific activation regions along with their corresponding certainty measures. The methodology is applied to a study involving a motor paradigm performed on a single subject several times over a period of two months. Simulation experiments used to calibrate performance of the method are promising. The methodology is also seen to be robust in determining areas of activation and their corresponding certainties.

Comments

NOTICE: this is the author's version of a work that was accepted for publication in NeuroImage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this documents. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NeuroImage, [v.47,iss.1,(2009)] doi: 10.1016/j.neuroimage.2009.03.073.

Copyright Owner

Elsevier Inc.

Language

en

File Format

application/pdf

Published Version

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