Semiparametric detection of significant activation for brain fMRI
Chunming Zhang and Tao Yu
Source: Ann. Statist. Volume 36, Number 4 (2008), 1693-1725.
Abstract
Functional magnetic resonance imaging (fMRI) aims to locate activated regions in human brains when specific tasks are performed. The conventional tool for analyzing fMRI data applies some variant of the linear model, which is restrictive in modeling assumptions. To yield more accurate prediction of the time-course behavior of neuronal responses, the semiparametric inference for the underlying hemodynamic response function is developed to identify significantly activated voxels. Under mild regularity conditions, we demonstrate that a class of the proposed semiparametric test statistics, based on the local linear estimation technique, follow χ2 distributions under null hypotheses for a number of useful hypotheses. Furthermore, the asymptotic power functions of the constructed tests are derived under the fixed and contiguous alternatives. Simulation evaluations and real fMRI data application suggest that the semiparametric inference procedure provides more efficient detection of activated brain areas than the popular imaging analysis tools AFNI and FSL.
Primary Subjects: 62G08, 62G10
Secondary Subjects: 62F30, 65F50
Keywords: deconvolution; local polynomial regression; nonparametric test; spatio-temporal data; stimuli; time resolution
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.aos/1216237297
Digital Object Identifier: doi:10.1214/07-AOS519
Mathematical Reviews number (MathSciNet):
MR2435453
Zentralblatt MATH identifier:
1142.62026
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