Abstract

PURPOSE: To determine a simple yet robust method to generate parsimonious design matrices that accurately estimate the "pharmacological MRI" (phMRI) response amplitude in the presence of both confounding signals and variability in temporal profile. Variability in the temporal response profile of phMRI time series data is often observed. If not properly accounted for, this variation can result in inaccurate and unevenly biased signal amplitude estimates when modeled within a general linear model (GLM) framework.
MATERIALS AND METHODS: The approach uses a low-rank singular value decomposition (SVD) approximation to a set of vectors capturing anticipated variations of no interest around the signal model to generate additional regressors for the design matrix. The method is demonstrated for both plateau and bolus type phMRI response profiles in the presence of variation in signal onset and/or shape, and applied to an in vivo blood oxygenation level-dependent (BOLD) phMRI study of buprenorphine in healthy human subjects.
RESULTS: In general, 2-3 additional regressors, capturing >75% of the anticipated variance, resulted in robust and unbiased signal amplitude estimates in the presence of substantial variability.
CONCLUSION: This method provides a simple and flexible means to provide robust phMRI amplitude estimates within a GLM framework.