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Activity Number:
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463
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #309730 |
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Title:
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Bayesian Hierarchical Spatiotemporal Models for fMRI Data
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Author(s):
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Qihua Lin*+ and Richard Gunst and Jeffrey Spence and Patrick Carmack and William R. Schucany
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Companies:
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The University of Texas Southwestern Medical Center at Dallas and Southern Methodist University and The University of Texas Southwestern Medical Center at Dallas and The University of Texas Southwestern Medical Center at Dallas and Southern Methodist University
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Address:
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5323 Harry Hines Blvd, Dallas, TX, 75390-8896,
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Keywords:
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fMRI ; Spatiotemporal ; Bayesian
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Abstract:
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A rich spatial and temporal correlation structure is well-known to be inherent in fMRI data and the signal-to-noise ratio is generally low. A class of spatiotemporal models is introduced to model such data. Both low-frequency drift and hemodynamic response functions with spatially varying parameters are modeled to explain large-scale variations. Spatially and temporally correlated small-scale variations are modeled by autoregressive moving average processes. Parameters in this class of models are estimated in a Bayesian hierarchical setting, which provides a convenient way to account for spatial correlations. This method offers a strong alternative to SPM for analyzing fMRI data.
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