title: Detection and detrending in fMRI data analysis.
abstract: 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.
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. friman@bwh.harvard.edu
authors.forename: Ola
authors.initials: O
authors.lastname: Friman
authors.forename: Magnus
authors.initials: M
authors.lastname: Borga
authors.forename: Peter
authors.initials: P
authors.lastname: Lundberg
authors.forename: Hans
authors.initials: H
authors.lastname: Knutsson
mesh_terms: 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