Semiparametric detection of significant activation for brain fMRI

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dc.creatorZhang, Chunming
dc.creatorYu, Tao
dc.date2008-08-07
dc.date.accessioned2026-07-07T09:55:23Z
dc.date.available2026-07-07T09:55:23Z
dc.descriptionFunctional 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 $χ^2$ 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.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AOS519 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0808.0989
dc.identifierhttp://arxiv.org/abs/0808.0989
dc.identifierAnnals of Statistics 2008, Vol. 36, No. 4, 1693-1725
dc.identifierdoi:10.1214/07-AOS519
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/166608
dc.subjectStatistics Theory
dc.subject62G08, 62G10 (Primary) 62F30, 65F50 (Secondary)
dc.titleSemiparametric detection of significant activation for brain fMRI
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