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Activity Number:
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63
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307230 |
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Title:
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Spatio-Temporal Modeling of Functional Magnetic Resonance Imaging Data
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Author(s):
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Qihua Lin*+ and Patrick S. Carmack and Richard F. Gunst and William R. Schucany and Jeffrey S. Spence
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Companies:
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Southern Methodist University and The University of Texas Southwestern Medical Center at Dallas and Southern Methodist University and Southern Methodist University and The University of Texas Southwestern Medical Center at Dallas
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Address:
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Department of Statistical Science, Dallas, TX, 75275-0332,
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Keywords:
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spatiotemporal models ; fMRI
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Abstract:
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Functional magnetic resonance imaging (fMRI) is used in fields, such as neuroscience, to study the functioning of human brains. Data from fMRI experiments are very complex. A rich spatial and temporal correlation structure is inherent in such data and the signal-to-noise ratio is generally low. A class of spatiotemporal models is introduced to model such data. Spatially varying drift and hemodynamic response functions are modeled to explain some large-scale variation. Spatially and temporally correlated small-scale variations are modeled by autoregressive moving average processes. An algorithm for model identification and parameter estimation is outlined. Comparisons to SPM analysis are made using real fMRI data. These methods offer a strong alternative to SPM for analyzing fMRI data.
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