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In this paper, we propose an approach to modeling functional magnetic resonance imaging (fMRI) data that combines hierarchical polynomial models, Bayes estimation, and clustering. A cubic polynomial is used to fit the voxel time courses of event-related design experiments. The coefficients of the polynomials are estimated by Bayes estimation, in a two-level(More)
Modern methods for imaging the human brain, such as functional magnetic resonance imaging (fMRI) present a range of challenging statistical problems. In this paper, we first develop a large sample based test for between group comparisons and use it to determine the necessary sample size in order to obtain a target power via simulation under various(More)
Outline • What have we been doing • Motivation for a new Challenge: making things work (including endorsements) • What have we learned – Humans are perfect (they don't make mistakes during installation, wiring, upgrade, maintenance or repair) – Software will eventually be bug free (good programmers write bug-free code) – Hardware MTBF is already very large(More)
Acknowledgments The authors thank the many individuals who contributed directly and indirectly to this study. In particular we wish to recognize the following staff of many state and national organizations, state departments of education or state charter school offices who provided information, data, review or feedback at various stages of this study. The(More)
Since practically the first operational stored program computer, architects have dreamed of building bigger computers from many smaller ones. Unfortunately for architects, multiprocessor projects do not have a sterling track record. In our view the Achilles' heel of these projects has not been the design, construction, or reliability of the hardware; it has(More)
In this article, we model functional magnetic resonance imaging (fMRI) data for event-related experiment data using a fourth degree spline to fit voxel specific blood oxygenation level-dependent (BOLD) responses. The data are preprocessed for removing long term temporal components such as drifts using wavelet approximations. The spatial dependence is(More)
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