https://pubmed.ncbi.nlm.nih.gov/23747457/ (Hallquist et al., 2013)

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ROBEX
https://sites.google.com/site/jeiglesias/ROBEX
Iglesias JE, Liu CY, Thompson P, Tu Z: "Robust Brain Extraction Across Datasets and Comparison with Publicly Available Methods", IEEE Transactions on Medical Imaging, 30(9), 2011, 1617-1634.

ANTS warping
http://stnava.github.io/ANTs/
Diffeomorphisms: SyN, Independent Evaluation: Klein, Murphy, Template Construction (2004)(2010), Similarity Metrics, Multivariate registration, Multiple modality analysis and statistical bias

c3d_affine_tool (Convert3d, ITK snap)
Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage. 2006 Jul 1; 31(3):1116-28.
doi:10.1016/j.neuroimage.2006.01.015


FSL
https://fsl.fmrib.ox.ac.uk/fsl/fslwiki
M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012  

AFNI
https://afni.nimh.nih.gov/afni_papers
https://afni.nimh.nih.gov/afni/community/board/read.php?1,148824,148855#msg-148855
RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29: 162-173, 1996.

MNI Tissue Probability
http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009

BRAIN WAVELET TOOLBOX
Patel AX and Bullmore ET (2016) A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs. NeuroImage. 142: 14-26.   (http://dx.doi.org/10.1016/j.neuroimage.2015.04.052).


NIPY (4dslicewarp)
https://nipype.readthedocs.io/en/0.12.0/about.html
Gorgolewski, Krzysztof J. ; Esteban, Oscar ; Burns, Christopher ; Ziegler, Erik ; Pinsard, Basile ; Madison, Cindee ; Waskom, Michael ; Ellis, David Gage ; Clark, Dav ; Dayan, Michael ; Manhães-Savio, Alexandre ; Notter, Michael Philipp ; Johnson, Hans ; Dewey, Blake E ; Halchenko, Yaroslav O. ; Hamalainen, Carlo ; Keshavan, Anisha ; Clark, Daniel ; Huntenburg, Julia M. ; Hanke, Michael ; Nichols, B. Nolan ; Wassermann , Demian ; Eshaghi, Arman ; Markiewicz, Christopher ; Varoquaux, Gael ; Acland, Benjamin ; Forbes, Jessica ; Rokem, Ariel ; Kong, Xiang-Zhen ; Gramfort, Alexandre ; Kleesiek, Jens ; Schaefer, Alexander ; Sikka, Sharad ; Perez-Guevara, Martin Felipe ; Glatard, Tristan ; Iqbal, Shariq ; Liu, Siqi ; Welch, David ; Sharp, Paul ; Warner, Joshua ; Kastman, Erik ; Lampe, Leonie ; Perkins, L. Nathan ; Craddock, R. Cameron ; Küttner, René ; Bielievtsov, Dmytro ; Geisler, Daniel ; Gerhard, Stephan ; Liem, Franziskus ; Linkersdörfer, Janosch ; Margulies, Daniel S. ; Andberg, Sami Kristian ; Stadler, Jörg ; Steele, Christopher John ; Broderick, William ; Cooper, Gavin ; Floren, Andrew ; Huang, Lijie ; Gonzalez, Ivan ; McNamee, Daniel ; Papadopoulos Orfanos, Dimitri ; Pellman, John ; Triplett, William ; Ghosh, Satrajit (2016). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.12.0-rc1. Zenodo. 10.5281/zenodo.50186


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https://www.biorxiv.org/content/10.1101/2022.05.02.490303v2.full.pdf
2.6. MR data preprocessing
Structural MRI data were preprocessed to extract the brain from the skull and warped to
the MNI standard using both linear (FLIRT) and non-linear (FNIRT) transformations. rsfMRI data
were preprocessed using a pipeline that minimized the effects of head motion (Hallquist et al.,
2013) including 4D slice-timing and head motion correction, skull stripping, intensity
thresholding, wavelet despiking (Patel et al., 2014), coregistration to the structural image and
nonlinear warping to MNI space, local spatial smoothing with a 5mm Gaussian kernel based on
the SUSAN algorithm, intensity normalization, and nuisance regression based on head motion (6
of translation/rotation and their first derivative) and non-gray matter signal (white matter and CSF
and their first derivative). Bandpass filtering between .009 and .08 Hz was done simultaneously
with nuisance regression. Frame-wise motion estimates were computed for resting-state data.
Functional volumes containing frame-wise displacement (FD) > 0.3 mm were excluded from
analyses (Siegel et al., 2013). Participants with more than 40% of TRs censored were excluded
altogether from rsfMRI analyses, resulting in the exclusion of 64 participants. Neuroimaging
analyses were performed in AFNI (Cox, 1996).