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Activity Number:
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322
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #308458 |
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Title:
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Semiparametric Detection of Significant Activation for Brain fMRI
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Author(s):
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Chunming Zhang*+ and Tao Yu
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Companies:
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University of Wisconsin-Madison and University of Wisconsin-Madison
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Address:
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Department of Statistics, Madison, WI, 53706,
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Keywords:
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Deconvolution ; Local polynomial regression ; Nonparametric test ; Spatial temporal data ; Stimuli ; Time resolution
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Abstract:
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Functional MRI (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 identification of significantly activated voxels. 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. Simulation evaluations and real fMRI data application endorse that the semiparametric inference procedure delivers more efficient detection of activated brain areas than popular imaging analysis tools.
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