Bayesian Analysis

Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data

Kuo-Jung Lee, Galin L. Jones, Brian S. Caffo, and Susan S. Bassett

Full-text: Open access

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.

Article information

Source
Bayesian Anal. Volume 9, Number 3 (2014), 699-732.

Dates
First available in Project Euclid: 5 September 2014

Permanent link to this document
http://projecteuclid.org/euclid.ba/1409921111

Digital Object Identifier
doi:10.1214/14-BA873

Mathematical Reviews number (MathSciNet)
MR3256061

Zentralblatt MATH identifier
1327.62507

Keywords
Bayesian variable selection fMRI Ising distribution Markov chain Monte Carlo

Citation

Lee, Kuo-Jung; Jones, Galin L.; Caffo, Brian S.; Bassett, Susan S. Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data. Bayesian Anal. 9 (2014), no. 3, 699--732. doi:10.1214/14-BA873. http://projecteuclid.org/euclid.ba/1409921111.


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