Covariate adjustment in partial least squares (PLS) regression for the extraction of the spatial-temporal pattern from positron emission tomography data

Positron emission tomography (PET) imaging can be used to study the effects of pharmacologic intervention on brain function. Partial least squares (PLS) regression is a standard tool that can be applied to characterize such effects throughout the brain volume and across time. We have extended the PLS regression methodology to adjust for covariate effects that may influence spatial and temporal aspects of the functional image data over the brain volume. The extension involves multi-dimensional latent variables, experimental design variables based upon sequential PET scanning, and covariates. An illustration is provided using a sequential PET data set acquired to study the effect of d-amphetamine on cerebral blood flow in baboons. An iterative algorithm is developed and implemented and validation results are provided through computer simulation studies.