Accurate autocorrelation modeling substantially improves fMRI reliability.
Nature Publishing Group
MetadataShow full item record
Olszowy, W., Aston, J., Rua, C., & Williams, G. (2019). Accurate autocorrelation modeling substantially improves fMRI reliability.. Nature communications, 10 (1), 1220. https://doi.org/10.1038/s41467-019-09230-w
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 autocorre- lation 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.
Brain, Humans, Magnetic Resonance Imaging, Artifacts, Linear Models, Reproducibility of Results, Algorithms, Computer Simulation, Image Processing, Computer-Assisted, Functional Neuroimaging, Datasets as Topic
MEDICAL RESEARCH COUNCIL (MR/M009041/1)
MEDICAL RESEARCH COUNCIL (MR/M024873/1)
Embargo Lift Date
External DOI: https://doi.org/10.1038/s41467-019-09230-w
This record's URL: https://www.repository.cam.ac.uk/handle/1810/290313