Detection and detrending in fMRI data analysis.
This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.
Friman
Ola
O
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. friman@bwh.harvard.edu
Borga
Magnus
M
Lundberg
Peter
P
Knutsson
Hans
H
D001921:Brain / Q000033:anatomy & histology; D001931:Brain Mapping / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D011336:Probability; D015203:Reproducibility of Results