Commentary and Opinion: I. Principal Component Analysis, Variance Partitioning, and “Functional Connectivity”

Abstract

We briefly review the need for careful study of “variance partitioning” and “optimal model selection” in functional positron emission tomography (PET) data analysis, emphasizing the use of principal component analysis (PCA) and the importance of data analytic techniques that allow for heterogeneous spatial covariance structures. Using an [15O]water dataset, we demonstrate that—even after data processing—the intrasubject signal component of primary interest in baseline activation studies constitutes a very small fraction of the intersubject variance. This small intrasubject variance component is subtly but significantly changed by using analysis of covariance instead of scaled subprofile model processing before applying PCA. Finally, we argue that the concept of “functional connectivity” should be interpreted very generally until the relative roles of inter- and intrasubject variability in both disease and normal PET datasets are better understood.