Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal

Functional 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). We 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. SDR allowed detection of FC with small brain regions. For 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. The 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. •Seed-based dual regression (SDR) can replace seed-based connectivity analysis (SCA).•SDR provides more accurate functional connectivity (FC) than SCA in some cases.•FC maps from SDR and SCA become identical after global signal regression.•Dual regression can result in spurious negative correlations that bias FC maps.•Pronounced spurious negative FC bias can easily be identified and corrected.