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Reference (APA Format)

Strother S.C., Kanno I. & Rottenberg D.A. 1995. Commentary and opinion .1. Principal component analysis, variance partitioning, and functional connectivity. Journal of Cerebral Blood Flow & Metabolism, 15 (3), 353-360.

Title

Commentary and opinion .1. Principal component analysis, variance partitioning, and functional connectivity

Journal Name

Journal of Cerebral Blood Flow & Metabolism

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 [O-15]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.

Volume

15 (3)

Year

1995

Pages

353-360

Authors

Strother S.C., Kanno I. & Rottenberg D.A.

More Information

Editorial Material Keywords: BRAIN ACTIVATION; FUNCTIONAL CONNECTIVITY; POSITRON EMISSION TOMOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODELS Publisher: LIPPINCOTT-RAVEN PUBL, 227 EAST WASHINGTON SQ, PHILADELPHIA, PA 19106 ISSN: 0271-678X