Nuisance Regression of High-frequency FMRI Data: De-noising Can Be Noisy.

Nuisance regression introduces spurious high-frequency (> 0.2 Hz) functional connectivity, ‘cleaned dummy’ post WB-LNR vs. ‘dummy'

Recently, emerging studies have demonstrated the existence of brain resting state (RS) spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood-oxygen-level dependent contrasts (the upper limit of the canonical model is ~ 0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity (FC) originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue), or rather is artificially introduced by improper preprocessing operations. Here, we examined the influence of a common preprocessing step - whole-band linear nuisance regression (WB-LNR) - on resting state functional connectivity (RSFC), and demonstrated via both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of fMRI signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.