Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps.

Publication Type	Academic Article
Authors	Kelly R, Hoptman M, Alexopoulos G, Gunning F, McKeown M
Journal	Hum Brain Mapp
Volume	40
Issue	14
Pagination	4005-4025
Date Published	06/12/2019
ISSN	1097-0193
Abstract	Functional connectivity (FC) maps from brain fMRI data can be derived with dual regression, a proposed alternative to traditional seed-based FC (SFC) methods that detect temporal correlation between a predefined region (seed) and other regions in the brain. As with SFC, incorporating nuisance regressors (NR) into the dual regression must be done carefully, to prevent potential bias and insensitivity of FC estimates. Here, we explore the potentially untoward effects on dual regression that may occur when NR correlate highly with the signal of interest, using both synthetic and real fMRI data to elucidate mechanisms responsible for loss of accuracy in FC maps. Our tests suggest significantly improved accuracy in FC maps derived with dual regression when highly correlated temporal NR were omitted. Single-map dual regression, a simplified form of dual regression that uses neither spatial nor temporal NR, offers a viable alternative whose FC maps may be more easily interpreted, and in some cases be more accurate than those derived with standard dual regression.
DOI	10.1002/hbm.24692
PubMed ID	31187917
PubMed Central ID	PMC6865788