ArticleHuman brain mapping2019
Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal.Journal of neuroscience methods · 2022Article
- Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps.Human brain mapping · 2019Article
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Authors and funding
5 authors.
Funding
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.
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