Accurate autocorrelation modeling substantially improves fMRI reliability.

Details

Serval ID
serval:BIB_E6BD0A9EEFD9
Type
Article: article from journal or magazin.
Collection
Publications
Title
Accurate autocorrelation modeling substantially improves fMRI reliability.
Journal
Nature communications
Author(s)
Olszowy W., Aston J., Rua C., Williams G.B.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
25/12/2019
Peer-reviewed
Oui
Volume
10
Number
1
Pages
1220
Language
english
Notes
Publication types: Comparative Study ; Evaluation Studies ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Abstract
Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorrelation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. SPM's alternative pre-whitening method, FAST, performed better than SPM's default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems.
Keywords
Algorithms, Artifacts, Brain/diagnostic imaging, Computer Simulation, Datasets as Topic, Functional Neuroimaging/methods, Humans, Image Processing, Computer-Assisted/methods, Linear Models, Magnetic Resonance Imaging/methods, Reproducibility of Results
Pubmed
Web of science
Open Access
Yes
Create date
22/04/2019 15:37
Last modification date
09/05/2019 2:43
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