Within-subject transformations of PET regional cerebral blood flow data: ANCOVA, ratio, and Z-score adjustments on empirical data

Abstract

In the statistical analysis of PET rCBF data, it has become routine to use a mathematical transformation to reduce individual differences in global metabolic rate and increase the power of statistical tests. Two methods of adjustment have been proposed: the ratio adjustment, dividing rCBF for a brain region by the whole-brain average CBF, and the ANCOVA adjustment based on a regression model. We compared these two transformations on the empirically derived PET data sets to assess which of these transforms worked best. Within-subject Z-scores were also considered as an additional transformation technique. Comparisons between the three transform techniques, and the untransformed raw data, were made on the detected significant differences, the size of the statistical effects, and the relative reduction in error variance. Two data sets were considered: 1) a visual perception study comparing object and spatial vision; 2) a data set from a parametric visual working memory study. There was no striking difference in the detected significant differences between any of the three transformed data sets and all transformed data sets detected more differences than the raw data set. There was little difference between the transformed data sets in terms of effect sizes, with the Z-score data set showing slightly lower total error variance that either the ratio-adjusted or ANCOVA-adjusted data sets. It was concluded that PET data sets should be closely examined for task-related changes in global CBF and subject heterogeneities (outliers) since these factors will influence the outcome of image-based statistical comparison more than the particular data transformation.