A growing number of studies using fast sampling have demonstrated the persistence of functional connectivity (FC) in resting state (RS) networks beyond the conventional 0.1 Hz^[1-3]. However, some RS studies have reported frequencies (e.g., up to 5 Hz^[4]) not easily supported by canonical models of brain hemodynamic responses (the upper limit of which is about 0.3 Hz^[5]). It is thus questionable whether the observed high-frequency (HF) RSFC originates from BOLD mechanisms identical to the usual low-frequency (LF) components^[3], or could be caused by another mechanism such as flow^[6] or proton density changes^[7], or is artificially introduced by certain preprocessing steps. Here, we investigated the influence of a common preprocessing step – whole-band (the entire frequency band resolved by a short TR) linear nuisance regression (LNR) – on RSFC. We demonstrated via both simulation and real data that LNR can introduce network structures in HF bands, which may largely account for the observations of HF-RSFC.RS studies (< 0.1 Hz) commonly assume that various nuisance fluctuations (e.g., motion & physiological artifacts) are linearly superimposed on the spontaneous neural-related activities, and remove them from the raw dataset via linear regression (Fig. 1(a)). However, if both the observations and regressors are heavily dominated by LF fluctuations, fitting parameters in the regression model will be driven by the slow fluctuations in the observations/regressors. Therefore, any HF components present in the regressors will be introduced to observations as additional variance (Fig. 1(b)). Furthermore, it has recently been demonstrated that the regressed noise (fluctuations projected out of the original data) in the conventional LF band contains prominent network structures^[8]. The fitting parameters, which carry the network structure information in the LF band, may thus introduce spurious variance with network structure into the HF band of the fMRI time series (Fig. 1(b), red).Ten-minute RS scans from 10 healthy subjects aged 36 ± 12 yrs (4 females) were collected at 3T (GE Signa 750, 32 channel coil, simultaneous multi-slice EPI with blipped CAIPI sequence^[9], TR/TE = 350/30 ms, multiband acceleration factor of 6, CAIPI FOV shift factor of 3, flip angle = 40^o, 30 slices, voxel size 3.14 × 3.14 × 4 mm³). After correcting for susceptibility-induced distortions with FSL TOPUP toolbox (using an additional 7s scan with reversed phase encoding directions), further basic preprocessing steps included slice timing correction, removal of scanner drifts and physiological fluctuations synchronized with cardiac/respiratory cycles using RETROICOR^[10]. The subjects’ data were normalized to MNI template for the ensuing analysis.The simulation goal was to examine whether LNR could yield significant HF-RSFC in a ‘dummy’ dataset, which contained no network structures in the HF bands. The ‘dummy’ dataset was created by eliminating any possible HF correlations in the real dataset (post basic preprocessing). Specifically, we took the Fourier transform of each voxel’s time series for each subject, scrambled the phases of components above 0.2 Hz, and then inversely Fourier transformed back to the temporal domain. RSFC of the created ‘dummy’ dataset was identical with the real dataset below 0.2 Hz, but contained no structured patterns above 0.2 Hz. Then, LNR was performed on the ‘dummy’ dataset using regressors estimated from the real dataset. RSFC results with respect to a seed in the posterior cingulate cortex (PCC) and visual cortex of the ‘cleaned’ dataset (post LNR) are shown in Fig. 2. Contrasting the network patterns of the ‘cleaned’ dataset and that of the ‘dummy’ dataset, we observed that prominent network structures were artificially introduced by LNR, even if only a small fraction of regressors were included (e.g., ‘white matter+CSF’). The influence of LNR on real data was also evaluated. Instead of the desired de-noising (reducing noisy fluctuations) of fMRI time series in the HF bands, LNR added additional variance from the HF components of the nuisance regressors to the real dataset (Fig. 3, > 0.4/0.8 Hz). The introduced fluctuations, although small, contained network structures closely resembling the LF-RSFC (Fig. 4(a-c)). The similarity between the network patterns of the ‘cleaned dataset’ (Fig. 4(d-e)) and those introduced by nuisance regression (Fig. 4(c)) implies that the observed HF-RSFC is more likely to be artificially introduced by LNR.We have demonstrated that whole-band LNR can introduce artificial RSFC in the HF bands of fMRI signals. As LNR has been widely employed in studies of HF-RSFC, one should be cautious in drawing conclusions regarding neural bases of HF-RSFC in reported results.