J. Carp, Optimizing the order of operations for movement scrubbing: Comment on Power et al, Neuroimage, vol.76, pp.436-438, 2013.

J. Carp, On the plurality of (methodological) worlds: estimating the analytic flexibility of FMRI experiments, Front. Neurosci, vol.6, p.149, 2012.

J. Carp, The secret lives of experiments: methods reporting in the fMRI literature, Neuroimage, vol.63, pp.289-300, 2012.

J. R. Chumbley and K. J. Friston, False discovery rate revisited: FDR and topological inference using Gaussian random fields, Neuroimage, vol.44, pp.62-70, 2009.

R. W. Cox, AFNI: software for analysis and visualization of functional magnetic resonance neuroimages, Comput. Biomed. Res, vol.29, pp.162-173, 1996.

A. M. Dale, B. Fischl, and M. I. Sereno, Cortical surface-based analysis. I. Segmentation and surface reconstruction, Neuroimage, vol.9, pp.179-194, 1999.

A. Eklund, T. E. Nichols, and H. Knutsson, Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates, Proc. Natl. Acad. Sci. U. S. A, vol.113, pp.7900-7905, 2016.

E. D. Foster and A. D. , Open Science Framework (OSF), J. Med. Libr. Assoc, vol.105, p.203, 2017.

T. Glatard, L. B. Lewis, R. F. Da-silva, R. Adalat, N. Beck et al., Reproducibility of neuroimaging analyses across operating systems. Front Neuroinform. Frontiers 9, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01207394

K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data, vol.3, p.160044, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01345616

K. J. Gorgolewski, G. Varoquaux, G. Rivera, Y. Schwarz, S. S. Ghosh et al., NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain, Front. Neuroinform, vol.9, 2015.
URL : https://hal.archives-ouvertes.fr/inserm-01134575

E. H. Gronenschild, P. Habets, H. I. Jacobs, R. Mengelers, N. Rozendaal et al., The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements, PLoS One, vol.7, p.38234, 2012.

J. D. Hunter, Matplotlib: A 2D Graphics Environment, Comput. Sci. Eng, vol.9, pp.90-95, 2007.

J. P. Ioannidis, Why most published research findings are false, PLoS Med, vol.2, p.124, 2005.

M. Jenkinson, P. Bannister, M. Brady, and S. Smith, Improved optimization for the robust and accurate linear registration and motion correction of brain images, Neuroimage, vol.17, pp.825-841, 2002.

M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, Neuroimage, vol.62, pp.782-790, 2012.

T. Kluyver, B. Ragan-kelley, F. Pérez, B. E. Granger, M. Bussonnier et al., Others, 2016. Jupyter Notebooks-a publishing format for reproducible computational workflows, pp.87-90

T. E. Lund, M. D. Nørgaard, E. Rostrup, J. B. Rowe, and O. B. Paulson, Motion or activity: their role in intra-and inter-subject variation in fMRI, Neuroimage, vol.26, pp.960-964, 2005.

C. Maumet, T. Auer, A. Bowring, G. Chen, S. Das et al., Sharing brain mapping statistical results with the neuroimaging data model, p.160102, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01411025

W. Mckinney and . Others, Data structures for statistical computing in python, Proceedings of the 9th Python in Science Conference, pp.51-56, 2010.

J. M. Moran, E. Jolly, and J. P. Mitchell, Social-cognitive deficits in normal aging, J. Neurosci, vol.32, pp.5553-5561, 2012.

K. Mueller, J. Lepsien, H. E. Möller, and G. Lohmann, Commentary: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates, Front. Hum. Neurosci, vol.11, p.345, 2017.

T. E. Nichols and A. P. Holmes, Nonparametric permutation tests for functional neuroimaging: a primer with examples, Hum. Brain Mapp, vol.15, pp.1-25, 2002.

L. H. Nielsen and T. Smith, , 2014.

A. Padmanabhan, C. F. Geier, S. J. Ordaz, T. Teslovich, and B. Luna, Developmental changes in brain function underlying the influence of reward processing on inhibitory control, Dev. Cogn. Neurosci, vol.1, pp.517-529, 2011.

R. Pauli, A. Bowring, R. Reynolds, G. Chen, T. E. Nichols et al., Exploring fMRI Results Space: 31 Variants of an fMRI Analysis in AFNI, FSL, and SPM. Front. Neuroinform, vol.10, p.24, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01341946

R. D. Peng, Reproducible research in computational science, Science, vol.334, pp.1226-1227, 2011.

W. D. Penny, K. J. Friston, J. T. Ashburner, S. J. Kiebel, and T. E. Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images, 2011.

R. A. Poldrack, C. I. Baker, J. Durnez, K. J. Gorgolewski, P. M. Matthews et al., Scanning the horizon: towards transparent and reproducible neuroimaging research, Nat. Rev. Neurosci, vol.18, pp.115-126, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01896468

R. A. Poldrack, D. M. Barch, J. P. Mitchell, T. D. Wager, A. D. Wagner et al., Toward open sharing of task-based fMRI data: the OpenfMRI project, Front. Neuroinform, vol.7, p.12, 2013.

T. Schonberg, C. R. Fox, J. A. Mumford, E. Congdon, C. Trepel et al., Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an FMRI investigation of the balloon analog risk task, Front. Neurosci, vol.6, p.80, 2012.

P. Skudlarski, R. T. Constable, and J. C. Gore, ROC analysis of statistical methods used in functional MRI: individual subjects, Neuroimage, vol.9, pp.311-329, 1999.

S. M. Smith, Fast robust automated brain extraction, Hum. Brain Mapp, vol.17, pp.143-155, 2002.

S. C. Strother, J. Anderson, L. K. Hansen, U. Kjems, R. Kustra et al., The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework, Neuroimage, vol.15, pp.747-771, 2002.

P. A. Taylor, G. Chen, D. R. Glen, J. K. Rajendra, R. C. Reynolds et al., Exploring the Impact of Analysis Software on Task fMRI Results, 2018.

T. D. Wager, M. A. Lindquist, T. E. Nichols, H. Kober, and J. X. Van-snellenberg, Evaluating the consistency and specificity of neuroimaging data using meta-analysis, Neuroimage, vol.45, pp.210-231, 2009.

S. Walt, C. Van-der, S. C. Varoquaux, and G. , The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng, vol.13, pp.22-30, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00564007

A. M. Winkler, G. R. Ridgway, M. A. Webster, S. M. Smith, and T. E. Nichols, Permutation inference for the general linear model, Neuroimage, vol.92, pp.381-397, 2014.

M. W. Woolrich, B. D. Ripley, M. Brady, and S. M. Smith, Temporal autocorrelation in univariate linear modeling of FMRI data, Neuroimage, vol.14, pp.1370-1386, 2001.

A. W. Yeung, An Updated Survey on Statistical Thresholding and Sample Size of fMRI Studies, Front. Hum. Neurosci, vol.12, p.16, 2018.