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

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.

A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t‐tests

“…The task fMRI data used here comes from OpenfMRI (Poldrack et al, ), using a group of 16 subjects from ds000001 (release 2.0.4). It was processed using AFNI (Cox, ) following the “NIMH” set of steps described in Taylor et al (), with single‐subject processing including steps for motion correction, alignment to standard space, etc. (also note the comments about processing steps described therein; for the present purposes of showing statistical effects, the data are reasonable).…”

Processing of Real‐World, Dynamic Natural Stimuli in Autism is Linked to Corticobasal Function

“…MRI data were processed using Analysis of Functional NeuroImages (AFNI) software [Cox, ]. A nonlinear affine transformation of anatomical data to MNI space ( @SSwarper ) using the MNI152 template was first performed, the output of which was used for alignment of echo planar imaging (EPI) data using standard AFNI procedures ( afni_proc.py ) including accounting for steady‐state acquisition, despiking, temporal alignment, identification of motion outliers (>3 mm), 6 mm full width at half maximum blur, resampling of voxels to 3.5 mm 3 , and normalization of BOLD values to mean percent signal change (PSC) [Taylor et al, ]. In AFNI, the time series BOLD was scaled to the voxel‐wise mean using the BOLD signal across that run [Taylor et al, ].…”

“…A nonlinear affine transformation of anatomical data to MNI space ( @SSwarper ) using the MNI152 template was first performed, the output of which was used for alignment of echo planar imaging (EPI) data using standard AFNI procedures ( afni_proc.py ) including accounting for steady‐state acquisition, despiking, temporal alignment, identification of motion outliers (>3 mm), 6 mm full width at half maximum blur, resampling of voxels to 3.5 mm 3 , and normalization of BOLD values to mean percent signal change (PSC) [Taylor et al, ]. In AFNI, the time series BOLD was scaled to the voxel‐wise mean using the BOLD signal across that run [Taylor et al, ]. A per‐subject regression analysis used motion parameters per run as regressors of no interest, a second order polynomial modeled any linear drift in the signal, and general linear trends were based on timing files for the two conditions (AV vs. V).…”

Resting‐state functional connectivity of the human hippocampus in periadolescent children: Associations with age and memory performance

“…T2*-weighted MRI data were converted from DICOM to NIFTI, and then submitted to a processing pipeline using AFNI tools (Cox, 1996) generated with afni_proc.py (Taylor et al, 2018). The first two volumes of each run's EPI data were discarded, leaving 1,296 volumes (i.e., 9.967 min of rs-fMRI data).…”