References

Software

dcm2niix

Li X, Morgan PS, Ashburner J, Smith J, Rorden C (2016) The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 264:47-56. doi: 10.1016/j.jneumeth.2016.03.001. PMID: 26945974

Freesurfer

Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781. https://doi.org/10.1016/j.neuroimage.2012.01.021

FSL

Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782-790. https://doi.org/10.1016/j.neuroimage.2011.09.015

AFNI

Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research, 29(3), 162-173. https://doi.org/10.1006/cbmr.1996.0014

MRtrix3

Tournier, J.-D.; Smith, R. E.; Raffelt, D.; Tabbara, R.; Dhollander, T., Pietsch, M.; Christiaens, D.; Jeurissen, B.; Yeh, C.-H. & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202, 116137. https://doi.org/10.1016/j.neuroimage.2019.116137

ANTs

Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 54(3), 2033-2044. https://doi.org/10.1016/j.neuroimage.2010.09.025

Workbench

Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, and Van Essen DC. (2011). Informatics and data mining: Tools and strategies for the Human Connectome Project. Frontiers in Neuroinformatics 5:4. https://doi.org/10.3389/fninf.2011.00004

FSL-FIX

Salimi-Khorshidi G, G. Douaud, C.F. Beckmann, M.F. Glasser, L. Griffanti S.M. Smith. (2014). Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90:449-68

Griffanti, L., G. Salimi-Khorshidi, C.F. Beckmann, E.J. Auerbach, G. Douaud, C.E. Sexton, E. Zsoldos, K. Ebmeier, N. Filippini, C.E. Mackay, S. Moeller, J.G. Xu, E. Yacoub, G. Baselli, K. Ugurbil, K.L. Miller, and S.M. Smith. (2014). ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage, 95:232-47

`R` Packages

R 3.6.3: Team, R. C. (2020). R: A language and environment for statistical computing [Internet]. (2018). R Foundation for Statistical Computing. Available from: _ http://www. R-project. org/_.
ggplot2: Villanueva, R. A. M., & Chen, Z. J. (2019). ggplot2: elegant graphics for data analysis. https://doi.org/10.1080/15366367.2019.1565254
tidyr: Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D. A., François, R., … & Yutani, H. (2019). Welcome to the Tidyverse. Journal of open source software, 4(43), 1686. https://doi.org/10.21105/joss.01686
viridis: Garnier, S. (2018). viridis: Default color maps from ’matplotlib’. Retrieved from https://CRAN.R-project.org/package=viridis
plotly: Sievert, C. (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC. https://plotly-r.com
networkD3: Allaire, J. J., Ellis, P., Gandrud, C., Kuo, K., Lewis, B. W., Owen, J., … & Gandrud, M. C. (2017). Package ‘networkD3’. D3 JavaScript Network Graphs from R. https://cran.irsn.fr/web/packages/networkD3/networkD3.pdf
htmlwidgets: Vaidyanathan, Ramnath, Yihui Xie, JJ Allaire, Joe Cheng, Carson Sievert, and Kenton Russell. (2020). Htmlwidgets: HTML Widgets for r. https://github.com/ramnathv/htmlwidgets.
freesurferformats: Tim Schäfer, T., (2020). freesurferformats: Read and Write ‘FreeSurfer’ Neuroimaging File Formats. R package version 0.1.9. CRAN, github
fsbrain: Schäfer, T., & Ecker, C. (2020). fsbrain: an R package for the visualization of structural neuroimaging data. bioRxiv. https://doi.org/10.1101/2020.09.18.302935

`python` packages

brainspace: Vos de Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, Xu T, Hong S, Langs G, Valk S, Misic B, Milham M, Margulies D, Smallwood J, Bernhardt B. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 3, 103.
numpy: Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in science & engineering, 13(2), 22-30. 10.1109/MCSE.2011.37
nibabel: Matthew Brett (MB), Chris Markiewicz (CM), Michael Hanke (MH), Marc-Alexandre Côté (MC), Ben Cipollini (BC), Paul McCarthy (PM), Chris Cheng (CC), Yaroslav Halchenko (YOH), Satra Ghosh (SG), Eric Larson (EL), Demian Wassermann, Stephan Gerhard and Ross Markello (RM). (2020). NiBabel 3.1.1. Jun 30. https://nipy.org/nibabel/
sklearn: Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
scipy: Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., … & Van Mulbregt, P. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2
argparse: Davis, M. T. L. (2015). Package ‘argparse’.
matplotlib: Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in science & engineering, 9(03), 90-95. https://doi.ieeecomputersociety.org/10.1109/MCSE.2007.55
nilearn: Nilearn: statistics for neuroimaging in Python. Available https://nilearn.github.io/
vtk: Schroeder, W. J., Avila, L. S., & Hoffman, W. (2000). Visualizing with VTK: a tutorial. IEEE Computer graphics and applications, 20(5), 20-27. https://doi.org/10.1109/38.865875

Datasets

MICs: Royer, J., Rodriguez-Cruces, R., Tavakol, S., Lariviere, S., Herholz, P., Li, Q, Vos de Wael, R., Paquola, C., Benkarim, O., Park, B., Lowe, A. J., Margulies, D., Smallwood, J., Bernasconi, A., Bernasconi, N., Frauscher, B., Bernhardt, B. C.. (2021). An open MRI dataset for multiscale neuroscience. bioRxiv. https://doi.org/10.1101/2021.08.04.454795
EpiC: Rodríguez-Cruces, R., Bernhardt, B. C., & Concha, L. (2020). Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. NeuroImage, Volume 213, June 2020, 116706.
Cam-CAN: Shafto, M.A., Tyler, L.K., Dixon, M., Taylor, J.R., Rowe, J.B., Cusack, R., Calder, A.J., Marslen-Wilson, W.D., Duncan, J., Dalgleish, T., Henson, R.N., Brayne, C., Cam-CAN, & Matthews, F.E. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology, 14(204). doi:10.1186/s12883-014-0204-1
Midnight Scan Club: Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., … & Dosenbach, N. U. (2017). Precision functional mapping of individual human brains. Neuron, 95(4), 791-807. https://doi.org/10.1016/j.neuron.2017.07.011
Audiopath: Sitek, K. R., Gulban, O. F., Calabrese, E., Johnson, G. A., Lage-Castellanos, A., Moerel, M., Satrajit S Ghosh & De Martino, F. (2019). Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T. Elife, 8, e48932. 10.7554/eLife.48932. https://doi.org/10.7554/eLife.48932
SUDMEX_CONN: Angeles-Valdez, D., Rasgado-Toledo, J., Issa-Garcia, V., Balducci, T., Villicana, V., Valencia, A., Gonzalez-Olvera, J. J., Reyes-Zamorano, E., Garza-Villarreal, E. A. (2021). SUDMEX CONN: The Mexican MRI dataset of patients with cocaine use disorder. medRxiv 2021.09.03.21263048

Parcellations

Desikan-Killiany (aparc): Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968-980.
Destrieux (aparc-a2009s): Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage, 53(1), 1-15.
Economo: Scholtens, L. H., de Reus, M. A., de Lange, S. C., Schmidt, R., & van den Heuvel, M. P. (2018). An mri von economo–koskinas atlas. NeuroImage, 170, 249-256.
Glasser: Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., … & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
Schaefer 100-1000: Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., … & Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.
vosdewael 100-400: Vos de Wael, R., Benkarim, O., Paquola, C., Lariviere, S., Royer, J., Tavakol, S., … & Bernhardt, B. C. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications biology, 3(1), 1-10.

Other sources

Microstructural profile covariance

Paquola C, Vos de Wael R, Wagstyl K, Bethlehem R, Seidlitz J, Hong S, Bullmore ET, Evans AC, Misic B, Margulies DS, Smallwood J, Bernhardt BC (2019) Dissociations between microstructural and functional hierarchies within regions of transmodal cortex. PLoS Biology, in press

Paquola C, Bethlehem RAI, Seidlitz J, Wagstyl K, Romero-Garcia, Whitaker KJ, Vos de Wael R, Williams GB, NSPN Consortium, Vertes PE, Bernhardt BC, Bullmore ET (2019). A moment of change: shifts in myeloarchitecture profiles characterize adolescent development of cortical gradients. Preprint: https://www.biorxiv.org/content/10.1101/706341v1

BIDS

Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., … & Poldrack, R. A. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS computational biology, 13(3), e1005209. https://doi.org/10.1371/journal.pcbi.1005209

Functions

ANTs - antsRegistrationSyN.sh

Tustison, N. J. (2013). Explicit B-spline regularization in diffeomorphic image registration. Frontiers in neuroinformatics, 7, 39. https://doi.org/10.3389/fninf.2013.00039

ANTs - N4BiasFieldCorrection

Tustison et al., N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010

FSL - bet

Smith, S. M. (2000). BET: Brain extraction tool. FMRIB TR00SMS2b, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, UK.

FSL - flirt

Jenkinson, M., Bannister, P., Brady, J. M. and Smith, S. M. (2002). Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841

Jenkinson, M. and Smith, S. M. A. (2001). Global Optimisation Method for Robust Affine Registration of Brain Images. Medical Image Analysis, 5(2), 143-156

Greve, D.N. and Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1):63-72.

FSL - fast

Zhang, Y. and Brady, M. and Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imag, 20(1):45-57.

FSL - first

Patenaude, B., Smith, S.M., Kennedy, D., and Jenkinson M. (2011). A Bayesian Model of Shape and Appearance for Subcortical Brain NeuroImage, 56(3):907-922.

FSL - eddy_quad

Bastiani, M., Cottaar, M., Fitzgibbon, S.P., Suri, S., Alfaro-Almagro, F., Sotiropoulos, S.N., Jbabdi, S., and Andersson, J.L.R. (2019). Automated quality control for within and between diffusion MRI studies using a non-parametric framework for movement and distortion correction. NeuroImage, 184:801-812.

FSL - eddy

Andersson, J.L.R. and Sotiropoulos, S.N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125:1063-1078.

FSL - eddy --repol

Andersson, J.L.R., Graham, M.S., Zsoldos, E., and Sotiropoulos, S.N. (2016). Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage, 141:556-572.

FSL - topup description

Andersson 2003] J.L.R. Andersson, S. Skare, J. Ashburner. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 20(2):870-888.

FSL - topup implementation

Smith S.M. , M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219.

MRtrix3 - tckgen -act

Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. (2012). Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 62, 1924-1938

MRtrix3 - dwidenoise

Veraart, J.; Novikov, D.S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. & Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. NeuroImage, 142, 394-406, doi: 10.1016/j.neuroimage.2016.08.016

Veraart, J.; Fieremans, E. & Novikov, D.S. (2016). Diffusion MRI noise mapping using random matrix theory. Magn. Res. Med., 76(5), 1582-1593, doi:10.1002/mrm.26059

Cordero-Grande, L.; Christiaens, D.; Hutter, J.; Price, A.N.; Hajnal, J.V. (2019). Complex diffusion-weighted image estimation via matrix recovery under general noise models. NeuroImage, 200, 391-404, doi:10.1016/j.neuroimage.2019.06.039

MRtrix3 - mrdegibbs

Kellner, E; Dhital, B; Kiselev, V.G & Reisert, M. (2016). Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 76, 1574–1581.

MRtrix3 - dwi2fod

Jeurissen, B; Tournier, J-D; Dhollander, T; Connelly, A & Sijbers, J. (2014). Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 103, 411-426

Tournier, J.-D.; Calamante, F., Gadian, D.G. & Connelly, A. (2004). Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 23, 1176-1185

MRtrix3 - mtnormalise

Raffelt, D.; Dhollander, T.; Tournier, J.-D.; Tabbara, R.; Smith, R. E.; Pierre, E. & Connelly, (2017). A. Bias Field Correction and Intensity Normalisation for Quantitative Analysis of Apparent Fibre Density. In Proc. ISMRM, 26, 3541

MRtrix3 - 5tt2gmwmi

Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. (2012). Anatomically-constrained tractography:Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 62, 1924-1938

MRtrix3 - tckmap -tdi

Calamante, F.; Tournier, J.-D.; Jackson, G. D. & Connelly, A. (2010). Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage, 53, 1233-1243

MRtrix3 - tckgen iFOD1 or SD_STREAM:

Tournier, J.-D.; Calamante, F. & Connelly, A. (2012). MRtrix: Diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol., 22, 53-66

MRtrix3 - tckgen iFOD2:

Tournier, J.-D.; Calamante, F. & Connelly, A. (2010). Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the International Society for Magnetic Resonance in Medicine, 1670

MRtrix3 - dwifslpreproc

Smith, S. M.; Jenkinson, M.; Woolrich, M. W.; Beckmann, C. F.; Behrens, T. E.; Johansen-Berg, H.; Bannister, P. R.; De Luca, M.; Drobnjak, I.; Flitney, D. E.; Niazy, R. K.; Saunders, J.; Vickers, J.; Zhang, Y.; De Stefano, N.; Brady, J. M. & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-S219

Skare, S. & Bammer, R. (2010). Jacobian weighting of distortion corrected EPI data. Proceedings of the International Society for Magnetic Resonance in Medicine, 5063

MRtrix3 - tck2sift2

Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 2015, 119, 338-351

MRtrix3 - tck2connectome

Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage, 2015, 104, 253-265