Comparison of detrending methods for optimal fMRI preprocessing

Because of the inherently low signal to noise ratio (SNR) of fMRI data, removal of low frequency signal intensity drift is an important preprocessing step, particularly in those brain regions that weakly activate. Two known sources of drift are noise from the MR scanner and aliasing of physiological pulsations. However, the amount and direction of drift is difficult to predict, even between neighboring voxels. Further, there is no concensus on an optimal baseline drift removal algorithm. In this paper, five voxel-based detrending techniques were compared to each other and an auto-detrending algorithm, which automatically selected the optimal method for a given voxel time-series. For a significance level of P < 10(-6), linear and quadratic detrending moderately increased the percentage of activated voxels. Cubic detrending decreased activation, while a wavelet approach increased or decreased activation, depending on the dataset. Spline detrending was the best single algorithm. However, auto-detrending (selecting the best algorithm or none, if detrending is not useful) appears to be the most judicious choice, particularly for analyzing fMRI data with weak activations in the presence of baseline drift.