Statistics and Data Science Seminar
Prof. Chunming Zhang
University of Wisconsin at Madison
Semiparametric detection of significant activation for brain fMRI
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 chi-squared distributions under null hypotheses for a number of useful hypotheses. The
asymptotic power functions of the constructed tests are derived under the fixed and contiguous alternatives. Furthermore,
a new false discovery rate approach which incorporates spatial information of voxel-wise p-values is devised for detecting the regions of activation.
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.
Wednesday March 19, 2008 at 3:30 PM in SEO 712