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September 2014 Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data
Kuo-Jung Lee, Galin L. Jones, Brian S. Caffo, Susan S. Bassett
Bayesian Anal. 9(3): 699-732 (September 2014). DOI: 10.1214/14-BA873

Abstract

A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial and temporal dependence and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects in addition to other mechanisms of temporal drift in task-related signals. We study the properties of the model through its performance on simulated and real data sets.

Citation

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Kuo-Jung Lee. Galin L. Jones. Brian S. Caffo. Susan S. Bassett. "Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data." Bayesian Anal. 9 (3) 699 - 732, September 2014. https://doi.org/10.1214/14-BA873

Information

Published: September 2014
First available in Project Euclid: 5 September 2014

zbMATH: 1327.62507
MathSciNet: MR3256061
Digital Object Identifier: 10.1214/14-BA873

Rights: Copyright © 2014 International Society for Bayesian Analysis

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Vol.9 • No. 3 • September 2014
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