COMMENTARY AND OPINION .1. PRINCIPAL COMPONENT ANALYSIS, VARIANCE PARTITIONING, AND FUNCTIONAL CONNECTIVITY

We briefly review the need for careful study of ''variance partitionin g'' and ''optimal model selection'' in functional positron emission to mography (PET) data analysis, emphasizing the use of principal compone nt analysis (PCA) and the importance of data analytic techniques that allow for heterogeneous spatial covariance structures. Using an [O-15] water dataset, we demonstrate that-even after data processing-the intr asubject signal component of primary interest in baseline activation s tudies constitutes a very small fraction of the intersubject variance. This small intrasubject variance component is subtly but significantl y changed by using analysis of covariance instead of scaled subprofile model processing before applying PCA. Finally, we argue that the conc ept of ''functional connectivity'' should be interpreted very generall y until the relative roles of inter- and intrasubject variability in b oth disease and normal PET datasets are better understood.