title: Covariate-adjusted nonparametric analysis of magnetic resonance images using Markov chain Monte Carlo.
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.
authors.affiliation: Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics, 615 N. Wolfe Street, Baltimore, MD 21205-2179, USA,  hhedlin@jhsph.edu.
authors.forename: Haley
authors.initials: H
authors.lastname: Hedlin
authors.forename: Brian
authors.initials: B
authors.lastname: Caffo
authors.forename: Ziyad
authors.initials: Z
authors.lastname: Mahfoud
authors.forename: Susan Spear
authors.initials: SS
authors.lastname: Bassett