Justin K. Rajendra

The paper introduces FMRI quality control tools in AFNI, providing systematic, in-depth, and interactive QC features for preprocessing pipelines.

This paper introduces Bayesian multilevel modeling to address inefficiency and over-penalization in neuroimaging by improving error control and inference efficiency.

The paper shows that using two one-sided t-tests without correction doubles the false positive rate compared to a single two-sided test.

The paper comments on and corrects a previous study comparing FMRI analysis software, focusing on AFNI's processing pipeline and its implementation.