JSM 2005 Online Program

Abstract #303089

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	521
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303089
Title:	A Hierarchical Bayesian Multivariate Stochastic Volatility Model on fMRI Motion Correction Data
Author(s):	Jun Ying*+ and Siu Hui and Tie-Qiang Li and Yang Wang
Companies:	Indiana University School of Medicine and Indiana University School of Medicine and National Institute of Neurological Disorders and Stroke and Indiana University School of Medicine
Address:	13883 Golden Saddle Ct, Carmel, IN, 46032, United States
Keywords:	fMRI ; Hierarchical Bayesian analysis ; Markov Chain Monte Carlo ; motion correction ; stochastic volatility
Abstract:	Six motion correction measurements (three axis measurements: "IS," "RL," and "AP" and three angle measurements: "roll," "pitch," and "yaw") were used to compare head movements between disruptive behavior disorder (DBD) children and normal children during fMRI scans in both resting and tasking states. A simple and conventional method was to compare sample standard deviations of each measurement. This method, however, ignored that the measurements were correlated and the variation of the observed measurements changed over time. We developed a multilevel hierarchical Bayesian model to incorporate the properties of motion correction data in the study. The estimation of unknown parameters was carried out by the Markov chain Monte Carlo (MCMC) method.

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