Sample Size Estimation for Comparing Parameters Using Dynamic Causal Modeling

Abstract

Functional magnetic resonance imaging (fMRI) has proved to be useful for analyzing the effects of illness and pharmacological agents on brain activation. Many fMRI studies now incorporate effective connectivity analyses on data to assess the networks recruited during task performance. The assessment of the sample size that is necessary for carrying out such calculations would be useful if these techniques are to be confidently applied. Here, we present a method of estimating the sample size that is required for a study to have sufficient power. Our approach uses Bayesian Model Selection to find a best fitting model and then uses a bootstrapping technique to provide an estimate of the parameter variance. As illustrative examples, we apply this technique to two different tasks and show that for our data, ~20 volunteers per group is sufficient. Due to variability between task, volunteers, scanner, and acquisition parameters, this would need to be evaluated on individual datasets. This approach will be a useful guide for Dynamic Causal Modeling studies.