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A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-site Experimental fMRI Study

Published online by Cambridge University Press:  01 January 2025

Bo Zhou,
Anna Konstorum,
Thao Duong,
Kinh H. Tieu,
William M. Wells,
Gregory G. Brown,
Hal S. Stern and
Babak Shahbaba
Bo Zhou
Affiliation:
University of California, Irvine
Anna Konstorum
Affiliation:
University of California, Irvine
Thao Duong
Affiliation:
University of California, Irvine
Kinh H. Tieu
Affiliation:
Harvard Medical School
William M. Wells
Affiliation:
Harvard Medical School
Gregory G. Brown
Affiliation:
University of California, San Diego
Hal S. Stern
Affiliation:
University of California, Irvine
Babak Shahbaba*
Affiliation:
University of California, Irvine
*
Requests for reprints should be sent to Babak Shahbaba, University of California, Irvine, Irvine, USA. E-mail: babaks@uci.edu
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Abstract

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size.

Keywords

multi-center studyfunctional magnetic resonance imagingBayesian modelmultilevel analysis

Information

Type
Original Paper
Information
Psychometrika , Volume 78 , Issue 2 , April 2013, pp. 260 - 278
Copyright
Copyright © 2012 The Psychometric Society

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Footnotes

Bo Zhou and Kinh H. Tieu are co-first authors.

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