Related Experiment Video
Updated: May 26, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Covariate-adjusted nonparametric analysis of magnetic resonance images using Markov chain Monte Carlo
Haley Hedlin1, Brian Caffo, Ziyad Mahfoud
1Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics, 615 N. Wolfe Street, Baltimore, MD 21205-2179, USA, hhedlin@jhsph.edu.
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.
More Related Videos
Related Concept Videos
Magnetic Resonance Imaging
Imaging Studies IV: Magnetic Resonance Imaging
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Imaging Studies for Cardiovascular System IV: CMRI
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

