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FMRI processing with AFNI: Some comments and corrections on “Exploring the Impact of Analysis Software on Task fMRI Results”

Paul A. Taylor, Gang Chen, Daniel R. Glen, Justin K. Rajendra, Richard C. Reynolds, Robert W. Cox
doi: https://doi.org/10.1101/308643
Paul A. Taylor
Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, MD, USA
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Gang Chen
Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, MD, USA
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Daniel R. Glen
Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, MD, USA
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Justin K. Rajendra
Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, MD, USA
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Richard C. Reynolds
Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, MD, USA
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Robert W. Cox
Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, MD, USA
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ABSTRACT

A recent study posted on bioRxiv by Bowring, Maumet and Nichols aimed to compare results of FMRI data that had been processed with three commonly used software packages (AFNI, FSL and SPM). Their stated purpose was to use “default” settings of each software’s pipeline for task-based FMRI, and then to quantify overlaps in final clustering results and to measure similarity/dissimilarity in the final outcomes of packages. While in theory the setup sounds simple (implement each package’s defaults and compare results), practical realities make this difficult. For example, different softwares would recommend different spatial resolutions of the final data, but for the sake of comparisons, the same value must be used across all. Moreover, we would say that AFNI does not have an explicit default pipeline available: a wide diversity of datasets and study designs are acquired across the neuroimaging community, often requiring bespoke tailoring of basic processing rather than a “one-size-fits-all” pipeline. However, we do have strong recommendations for certain steps, and we are also aware that the choice of a given step might place requirements on other processing steps. Given the very clear reporting of the AFNI pipeline used in Bowring et al. paper, we take this opportunity to comment on some of these aspects of processing with AFNI here, clarifying a few mistakes therein and also offering recommendations. We provide point-by-point considerations of using AFNI’s processing pipeline design tool at the individual level, afni_proc.py, along with supplementary programs; while specifically discussed in the context of the present usage, many of these choices may serve as useful starting points for broader processing. It is our intention/hope that the user should examine data quality at every step, and we demonstrate how this is facilitated in AFNI, as well.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 28, 2018.
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FMRI processing with AFNI: Some comments and corrections on “Exploring the Impact of Analysis Software on Task fMRI Results”
Paul A. Taylor, Gang Chen, Daniel R. Glen, Justin K. Rajendra, Richard C. Reynolds, Robert W. Cox
bioRxiv 308643; doi: https://doi.org/10.1101/308643
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FMRI processing with AFNI: Some comments and corrections on “Exploring the Impact of Analysis Software on Task fMRI Results”
Paul A. Taylor, Gang Chen, Daniel R. Glen, Justin K. Rajendra, Richard C. Reynolds, Robert W. Cox
bioRxiv 308643; doi: https://doi.org/10.1101/308643

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