Software references

Tool	Link (pref. code)	Citation(s)
FreeSurfer	https://github.com/freesurfer/freesurfer
recon-all	https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all	https://doi.org/10.1006/nimg.1998.0395
bbregister & BBR		https://doi.org/10.1016/j.neuroimage.2009.06.060
mri_robust_template	https://surfer.nmr.mgh.harvard.edu/fswiki/mri_robust_template	https://doi.org/10.1016/j.neuroimage.2012.02.084
mri_robust_register	https://surfer.nmr.mgh.harvard.edu/fswiki/mri_robust_register	http://dx.doi.org/10.1016/j.neuroimage.2010.07.020
ANTs	https://github.com/ANTsX/ANTs	https://doi.org/10.3389/fninf.2015.00005
antsRegistration		http://dx.doi.org/10.1016/j.media.2007.06.004 https://doi.org/10.1016/j.neuroimage.2010.09.025
N4BiasFieldCorrection		https://doi.org/10.1109/TMI.2010.2046908
antsBrainExtraction.sh	https://github.com/ANTsX/ANTs/blob/v2.2.0/Scripts/antsBrainExtraction.sh	https://doi.org/10.1038/sdata.2015.3
FSL		https://doi.org/10.1016/j.neuroimage.2004.07.051 https://doi.org/10.1016/j.neuroimage.2008.10.055 https://doi.org/10.1016/j.neuroimage.2011.09.015
FAST	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST	https://doi.org/10.1109/42.906424
BET	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET	https://doi.org/10.1002/hbm.10062
FLIRT	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT_BBR	https://doi.org/10.1006/nimg.2002.1132; https://doi.org/10.1016/S1361-8415(01)00036-6
MCFLIRT	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MCFLIRT	https://doi.org/10.1006/nimg.2002.1132
SUSAN	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/SUSAN	https://doi.org/10.1023/A:1007963824710
MELODIC	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC
ICA-AROMA	https://github.com/rhr-pruim/ICA-AROMA/	http://www.sciencedirect.com/science/article/pii/S1053811915001822
PRELUDE & FUGUE	https://nipype.readthedocs.io/en/latest/interfaces/generated/workflows.dmri/fsl.utils.html#cleanup-edge-pipeline	https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE/Guide
AFNI		https://doi.org/10.1006/cbmr.1996.0014; https://doi.org/10.1016/j.neuroimage.2011.08.056
3dvolreg	https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dvolreg.html
3dTshift	https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTshift.html
3dUnifize	https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dUnifize.html
3dAutomask	https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dAutomask.html
Power, et al. (2012) measures		https://doi.org/10.1016/j.neuroimage.2011.10.018
DVARS	https://nipype.readthedocs.io/en/latest/interfaces/generated/nipype.algorithms.confounds.html#computedvars	https://arxiv.org/abs/1704.01469 https://doi.org/10.1101/125021
Framewise displacement	https://nipype.readthedocs.io/en/latest/interfaces/generated/nipype.algorithms.confounds.html#framewisedisplacement
Other
a/tCompCor	http://nipype.readthedocs.io/en/latest/interfaces/generated/nipype.algorithms.confounds.html#compcor	https://doi.org/10.1016/j.neuroimage.2007.04.042
phdiff2fmap	https://fmriprep.readthedocs.io/en/stable/sdc/estimation.html#fmriprep.workflows.fieldmap.phdiff.phdiff2fmap	https://doi.org/10.1006/nimg.2001.1054
nibabel	https://github.com/nipy/nibabel/	https://doi.org/10.5281/zenodo.60808
nilearn	https://github.com/nilearn/nilearn/	https://doi.org/10.3389/fninf.2014.00014
nipype	https://github.com/nipy/nipype/	https://doi.org/10.5281/zenodo.581704
convert3d	https://sourceforge.net/projects/c3d/
Graphics
seaborn	https://github.com/mwaskom/seaborn	https://doi.org/10.5281/zenodo.883859
matplotlib 2.0.0	https://github.com/matplotlib/matplotlib	https://doi.org/10.5281/zenodo.248351
cwebp	https://developers.google.com/speed/webp/	https://developers.google.com/speed/webp/docs/webp_study https://developers.google.com/speed/webp/docs/webp_lossless_alpha_study
SVGO	https://github.com/svg/svgo

Additional references worth considering:

• HCP pipeline (for GIFTI surface modifications)