#default 
Functional and anatomical data were preprocessed using a flexible preprocessing pipeline [CITATION2][VALUE 1 liststeps].

#functional_realignandunwarpandfieldmap
Functional data were realigned using SPM realign & unwarp procedure [CITATION3] integrating fieldmaps for susceptibility distortion correction, where all scans were coregistered to a reference image [IF rtm 0 (first scan of the first session)][IF rtm 1 (mean functional volume)] using a least squares approach and a 6 parameter (rigid body) transformation, and resampled using b-spline interpolation [CITATION9] to simultaneously correct for motion, magnetic susceptibility geometric distortions, and their interaction.

#functional_realignandunwarp
Functional data were realigned using SPM realign & unwarp procedure [CITATION3], where all scans were coregistered to a reference image [IF rtm 0 (first scan of the first session)][IF rtm 1 (mean functional volume)] using a least squares approach and a 6 parameter (rigid body) transformation [CITATION9], and resampled using b-spline interpolation to correct for motion and magnetic susceptibility interactions. 

#functional_realign
Functional data were coregistered to a reference image [IF rtm 0 (first scan of the first session)][IF rtm 1 (mean functional volume)] using a least squares approach and a 6 parameter (rigid body) transformation [CITATION9], and resampled using b-spline interpolation. 

#functional_realign_noreslice
Functional data were coregistered to a reference image [IF rtm 0 (first scan of the first session)][IF rtm 1 (mean functional volume)] using a least squares approach and a 6 parameter (rigid body) transformation without resampling [CITATION9]. 

#functional_slicetime
Temporal misalignment between different slices of the functional data [IF sliceorder_select 1 (acquired in ascending order) ][IF sliceorder_select 2 (acquired in descending order) ][IF sliceorder_select 3 (acquired in interleaved middle-top order) ][IF sliceorder_select 4 (acquired in interleaved bottom-up order) ][IF sliceorder_select 5 (acquired in interleaved top-down order) ][IF sliceorder_select 6 (acquired in interleaved Siemens order) ][IF sliceorder_select 7 (acquired in interleaved Philips order) ]was corrected following SPM slice-timing correction (STC) procedure [CITATION4][CITATION10], using sinc temporal interpolation to resample each slice BOLD timeseries to a common mid-acquisition time. 

#functional_art
Potential outlier scans were identified using ART [CITATION5] as acquisitions with framewise displacement above [VALUE 2 art_thresholds] mm or global BOLD signal changes above [VALUE 1 art_thresholds] standard deviations [CITATION6][CITATION7][IF coregtomean 1 , and a reference BOLD image was computed for each subject by averaging all scans excluding outliers].

#functional_segmentandnormalize_direct
Functional [IF steps structural_segment&normalize and anatomical ]data were normalized into standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to [VALUE 1 voxelsize_func] mm isotropic voxels following a direct normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_segmentandnormalize_indirect
Functional and anatomical data were coregistered and normalized into standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to [VALUE 1 voxelsize_func] mm isotropic voxels following an indirect normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_smooth
Functional data were smoothed using spatial convolution with a Gaussian kernel of [VALUE 1 fwhm] mm full width half maximum (FWHM). 


#structural_segmentandnormalize
[IFNOT steps functional_segment&normalize_direct Anatomical data were normalized into standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to [VALUE 1 voxelsize_anat] mm isotropic voxels using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].]

#structural_normalize
Anatomical data were normalized into standard MNI space and resampled to [VALUE 1 voxelsize_anat] mm isotropic voxels using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#structural_segment
Anatomical data were segmented into grey matter, white matter, and CSF tissue classes using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#structural_segmentandnormalize_withlesion
Anatomical data were normalized into standard MNI space, segmented into grey matter, white matter, CSF, and lesion tissue classes, and resampled to [VALUE 1 voxelsize_anat] mm isotropic voxels using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template] extended to include custom lesion masks.

#functional_segmentandnormalize_indirect_withlesion
Functional and anatomical data were coregistered and normalized into standard MNI space, segmented into grey matter, white matter, CSF, and lesion tissue classes, and resampled to [VALUE 1 voxelsize_func] mm isotropic voxels following an indirect normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template] extended to include custom lesion masks.

#structural_normalize_preservemasks
Anatomical data and gray matter, white matter, and CSF masks were normalized into standard MNI space and resampled to [VALUE 1 voxelsize_anat] mm isotropic voxels using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#structural_mask
Anatomical images were masked setting all voxels [IF mask_inclusive_anat 1 outside][IF mask_inclusive_anat 0 within] a custom mask area to zero.

#functional_removescans
The [IF removescans_first 1 first][IF removescans_first 0 last] [IF removescans_value 1 scan in each functional run was removed][IFNOT removescans_value 1 [VALUE 1 removescans_value] scans in each functional run were removed]. 

#functional_bandpass
BOLD signal timeseries were bandpass filtered between [VALUE 1 bp_filter] Hz and [VALUE 2 bp_filter] Hz.

#functional_regression
[IF reg_skip 0 Components from [VALUE all reg_names_list] were removed from the BOLD signal timeseries using a separate linear regression model at each individual voxel.][IF reg_skip 1 Potential confounding effects on the BOLD signal were identified from [VALUE all reg_names_list].] 

#functional_roiextract
The average BOLD signal as well as the largest principal components within [VALUE 1 roi_names_list] were extracted[IF roi_scale 1  in percent signal change (PSC) units][VALUE 1 roi_covs_list].

#functional_mask
Functional images were masked setting all voxels [IF mask_inclusive_func 1 outside][IF mask_inclusive_func 0 within] a custom mask area to zero.

#functional_segment
Functional data were segmented into grey matter, white matter, and CSF tissue classes using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_motionmask
Six motion masks were created separately for each subject identifying the spatial gradient of each subject's reference BOLD image with respect to six motion parameters (three translation and three rotation parameters).

#functional_normalize_indirect
Functional and anatomical data were coregistered and normalized into standard MNI space, and resampled to [VALUE 1 voxelsize_func] mm isotropic voxels following an indirect normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_normalize_indirect_preservemasks
Functional and anatomical data were coregistered and, together with gray matter, white matter, and CSF masks, they were normalized into standard MNI space and resampled to [VALUE 1 voxelsize_func] mm isotropic voxels following an indirect normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_normalize_direct
Functional data were normalized into standard MNI space and resampled to [VALUE 1 voxelsize_func] mm isotropic voxels following a direct normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_coregister_affine_noreslice
Functional and anatomical data were coregistered using SPM intermodality coregistration procedure [CITATION13] with a normalised mutual information objective function [CITATION14]. 

#functional_coregister_affine_reslice
Functional and anatomical data were coregistered using SPM intermodality coregistration procedure [CITATION13] with a normalised mutual information objective function [CITATION14], and resampled using b-spline interpolation. 

#functional_coregister_nonlinear
Functional and anatomical data were coregistered following a direct normalization procedure [CITATION7][CITATION11] using SPM unified segmentation and normalization algorithm [CITATION8][CITATION12] [IFEMPTY tpm_template 1 with the default IXI-549 tissue probability map template][IFEMPTY tpm_template 0 with an alternative tissue probability map (TPM) template].

#functional_smooth_masked
Functional data were smoothed using spatial convolution with a Gaussian kernel of [VALUE 1 fwhm] mm full width half maximum (FWHM) while disregarding all voxels [IF mask_inclusive_anat 1 outside][IF mask_inclusive_anat 0 within] a custom mask area. 

#functional_vdm_apply
Magnetic susceptibility distortions in the functional data were corrected using b-spline interpolation to spatially resample all functional images in each run based on the estimated voxel-displacement maps. 

#functional_surface_coregandresample
Functional and anatomical data were coregistered using SPM intermodality coregistration procedure [CITATION13] with a normalised mutual information objective function [CITATION14]. Functional images were then resampled at the cortical surface by averaging the functional data from ten locations between the pial and white matter cortical surfaces of each subject at each individual vertex in FreeSurfer fsaverage level-8 tessellation, with 163,842 vertices per hemisphere [CITATION16]. 

#functional_surface_resample
Functional images were resampled at the cortical surface by averaging the functional data from ten locations between the pial and white matter cortical surfaces of each subject at each individual vertex in FreeSurfer fsaverage level-8 tessellation, with 163,842 vertices per hemisphere [CITATION16].

#functional_surface_smooth
Surface-level functional data were smoothed using [VALUE 1 diffusionsteps] iterative diffusion steps, approximately a [VALUE 1 diffusionstepsfwhm] mm FWHM smoothing kernel within the cortical surface [CITATION15]. 

#functional_vdm_create
#structural_manualorient
#structural_center
#structural_manualspatialdef
#functional_manualorient
#functional_center
#functional_centertostruct
#functional_manualspatialdef


#CITATION2 Nieto-Castanon, A. (2020). FMRI minimal preprocessing pipeline. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (pp. 3–16). Hilbert Press.
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#CITATION4 Henson, R. N. A., Buechel, C., Josephs, O., & Friston, K. J. (1999). The slice-timing problem in event-related fMRI. NeuroImage, 9, 125.
#CITATION5 Whitfield-Gabrieli, S., Nieto-Castanon, A., & Ghosh, S. (2011). Artifact detection tools (ART). Cambridge, MA. Release Version, 7(19), 11.
#CITATION6 Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84, 320-341.
#CITATION7 Nieto-Castanon, A. (submitted). Preparing fMRI Data for Statistical Analysis. In M. Filippi (Ed.). fMRI techniques and protocols. Springer. doi:10.48550/arXiv.2210.13564
#CITATION8 Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26(3), 839-851.
#CITATION9 Friston, K. J., Ashburner, J., Frith, C. D., Poline, J. B., Heather, J. D., & Frackowiak, R. S. (1995). Spatial registration and normalization of images. Human brain mapping, 3(3), 165-189.
#CITATION10 Sladky, R., Friston, K. J., Tröstl, J., Cunnington, R., Moser, E., & Windischberger, C. (2011). Slice-timing effects and their correction in functional MRI. Neuroimage, 58(2), 588-594.
#CITATION11 Calhoun, V.D., Wager, T.D., Krishnan, A., Rosch, K.S., Seymour, K.E., Nebel, M.B., Mostofsky, S.H., Nyalakanai, P. and Kiehl, K. (2017). The impact of T1 versus EPI spatial normalization templates for fMRI data analyses (Vol. 38, No. 11, pp. 5331-5342).
#CITATION12 Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95-113.
#CITATION13 Ashburner, J., & Friston, K. (1997). Multimodal image coregistration and partitioning—a unified framework. Neuroimage, 6(3), 209-217.
#CITATION14 Studholme, C., Hawkes, D. J., & Hill, D. L. (1998, June). Normalized entropy measure for multimodality image alignment. In Medical imaging 1998: image processing (Vol. 3338, pp. 132-143). SPIE.
#CITATION15 Hagler Jr, D. J., Saygin, A. P., & Sereno, M. I. (2006). Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. Neuroimage, 33(4), 1093-1103.
#CITATION16 Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.

