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. Author manuscript; available in PMC: 2014 Nov 15.
Published in final edited form as: Neuroimage. 2013 Jun 6;0:208–225. doi: 10.1016/j.neuroimage.2013.05.116

Figure 1.

Figure 1

The effects of bandpass filtering and nuisance regression on the spectral composition of simulated time series. The left panel depicts the simulated time series, X and C, over time, as well as X following different approaches to bandpass regression and nuisance regression. The right panel depicts the spectral power of the series, and vertical gray rectangles denote the nuisance frequencies present in C that one wishes to suppress. Xlow contains the low frequency components of X after bandpass filtering, .009 Hz < f < .08 Hz. RegBp = nuisance regression, then bandpass filtering; Simult = simultaneous bandpass filtering and nuisance regression; BpReg = bandpass filtering, then nuisance regression.