Statistics and Its Interface

Volume 3 (2010)

Number 1

Covariate-adjusted nonparametric analysis of magnetic resonance images using Markov chain Monte Carlo

Pages: 113 – 123

DOI: https://dx.doi.org/10.4310/SII.2010.v3.n1.a11

Authors

Susan Spear Bassett (Psychiatric Imaging, Johns Hopkins University School of Medicine, Baltimore, Maryland, U.S.A.)

Brian Caffo (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.)

Haley Hedlin (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.)

Ziyad Mahfoud (Department of Epidemiology and Population Health, American University of Beirut, Lebanon)

Abstract

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain under minimal assumptions. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our nonparametric method to remove potential bias due to the observed covariates is presented.

Keywords

covariate control, permutation testing, nonparametric inference, Markov chain Monte Carlo, imaging data

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Published 1 January 2010