ArticleJournal of neuroscience methods2022
Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal.
Article in Journal of neuroscience methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Functional and structural connectivity of the subregions of the amygdala in ADHD children with or without ODD.BMC psychiatry · 2025Article
- Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration.Nature communications · 2023Article
- Functional connectivity between tumor region and resting-state networks as imaging biomarker for overall survival in recurrent gliomas diagnosed byNeuro-oncology advancesArticle
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Abstract
backgroundFunctional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala). NEW
methodWe explored seed-based dual regression (SDR) from theoretical and practical points of view. SDR is a modified implementation of dual regression where the set of spatial regressors is replaced by a single binary spatial map of the seed region.
resultsSDR allowed detection of FC with small brain regions. COMPARISON WITH EXISTING
methodFor both synthetic and natural fMRI data, detection of FC with SDR was identical to that obtained with SCA after removal of global signal from fMRI data with global signal regression (GSR). In the absence of GSR, detection of FC was significantly improved when using SDR compared with SCA.
conclusionThe improved FC detection achieved with SDR was related to a partial filtering of the global signal that occurred during spatial regression, an integral part of dual regression. This filtering can sometimes lead to spurious negative correlations that result in a widespread negative bias in FC derived with any application of dual regression. We provide guidelines for how to identify and correct this potential problem.
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