Abstract Details
Activity Number:
|
637
|
Type:
|
Topic Contributed
|
Date/Time:
|
Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
International Indian Statistical Association
|
Abstract - #308472 |
Title:
|
Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI Data
|
Author(s):
|
Marco Ferreira*+ and Nilotpal Sanyal
|
Companies:
|
University of Missouri and University of Missouri
|
Keywords:
|
Bayesian inference ;
Image smoothing ;
Mixture prior ;
Multiple subjects ;
Spatiotemporal analysis ;
Wavelet modeling
|
Abstract:
|
We develop methodology for Bayesian hierarchical multi-subject multiscale analysis of functional Magnetic Resonance Imaging (fMRI) data. Specifically, we model the brain images temporally with a standard general linear model and transform the resulting estimated standardized regression coefficient maps through a discrete wavelet transformation to obtain a sparse representation in the wavelet space. Subsequently, we assign to the wavelet coefficients a prior that is a mixture of a point mass at zero and a Gaussian distribution, and assume that the mixture probabilities for wavelet coefficients at same location and level are common across subjects. Further, we assign for the mixture probabilities a prior that depends on few hyperparameters. We develop empirical Bayes methodology that leads to fast computations. An application to computer simulated synthetic data has shown that, when compared to single-subject analysis, our multi-subject methodology performs better in terms of mean squared error. Finally, we illustrate the utility and flexibility of our multi-subject methodology with an application to an event-related fMRI dataset generated by Postle (2005).
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.