]. G. Cython, R. W. Ewing, S. Bradshaw, D. S. Behnel, and . Seljebotn, The Cython compiler The Python programming language The Enthought Python Distribution http://www.enthought.com/ products/epd.php [Pythonxy] P. Raybault, http://www.pythonxy.com Jython: Python for the Java platform The Python language, Fast numerical computa References [Wilbers] I. M. Wilbers, H. P. Langtangen, Å. Ødegård, Using Cython to Speed up Numerical Python Programs, Proceedings of MekIT'09 Proceedings of the 8th Python in Science Conferenceorg. [Pyrex] G. Ewing, Pyrex: C-Extensions for Python, 2009.

T. Oliphantscipy, ]. E. Jones, T. Oliphant, P. Peterson-inlining, C. C++-in-pythonsage et al., f2py: Fortran to Python interface generator Instant: Inlining of C/C++ in Python http:// fenics.org/instant. [Psyco] A. Rigo, Psyco: Python Specializing Compiler. http://psyco.sourceforge.net An Updated Set of Basic Linear Algebra Subprograms (BLAS) Minimizing development and maintenance costs in supporting persistently optimized BLAS, Software: Practice and Experience mpi4py: MPI bindings for PythonOpenMPI] Open MPI, pp.28-30, 2002.

]. D. Referencesabr03 and . Abrahams, Building Hybrid Systems with Boost Python, Bea95] D. Beazley. Simplified Wrapper and Interface Generator, p.1995, 2003.

]. T. Dru03, E. Drummond, and . Rosten, TooN: Tom's Object-oriented Numerics, 2003.

]. E. Jon01, T. Jones, P. Oliphant, and . Peterson, SciPy: Open Source Scientific tools for Python, p.2001

]. G. Gvr92, . Van-rossum, E. Python, and . Rosten, Writing Python Extensions in C++ with PyCXX, p.2004, 1991.

]. R. Yak09 and . Yakovenko, Py++: Language Binding Project, 2009.

]. D. Referencesand04, . E. Andersonbal89-]-h, J. G. Bal, A. S. Steiner, and . Fultz, Programming languages for distributed computing systems ACM computing Surveys Cieslik, PaPy: Parallel and distributed dataprocessing pipelines in Python DANSE: Distributed data analysis for neutron scattering experiments, BOINC: A system for publicresource computing and storage Proceedings of the 8th Python in Science Conference MapReduce: Simplified Data Processing on Large Clusters, OSDI'04: Sixth Symposium on Operating System Design and Implementation) [Jon09] I. de Jong: Pyro -Python Remote Objects) [Per09] F. Perez and B. Granger: IPython: a system for interactive scientific computingVan09] V. Vanovschi: Parallel Python, pp.4-10261, 1989.

P. Kienzle, N. Patel, M. Mckerns-referencescdk01, ]. R. Chandra, L. Dagum et al., Data-parallel computing Parallel Programming in OpenMP: Parallel and distributed data-processing pipelines in Python, MPIF09] Message Passing Interface Forum, MPI: A Message-Passing Interface Standard, Version 2.2, High Performance Computing Center Proc. SciPy, pp.17-24, 2001.

L. Brian, . Harvard-smithsonian, . Center-for-astrophysics, M. D. Stephen, . Harvard-smithsonian et al., The Design and Implementation of FFTW3 Special Issue on Program Generation, Optimization, and Platform Adaptation, harvard.edu) ? Harvard-Smithsonian Center for Astrophysics, USA Antonella Fruscione (antonell@head.cfa.harvard.edu) ? Harvard-Smithsonian Center for Astrophysics, USA Elizabeth C. Galle (egalle@head.cfa.harvard.edu) ? Harvard-Smithsonian Center for Astrophysics Proceedings of the IEEE Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spacesmpl] J.D. Hunter, Matplotlib: A 2D graphics environment . Computing in Science and Engineering, pp.216-231, 1978.

J. A. Nelder, R. Jeffrey, C. Lagarias, J. A. Reeds, M. H. Wright et al., Wright Convergence Properties of the Nelder-Mead Simplex Algorithm in Low Dimensions Direct Search Methods: Once Scorned, Now Respectable in Numerical Analysis Matplotlib: A 2D graphics environment, Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis) sourceforge.net/. [mayavi] P. Ramachandran, G. Varoquaux, Mayavi: Making 3D Data Visualization Reusable Proceedings of the 7th Python in Science conference SciPy: Open source scientific tools for Python http, pp.308-313, 1965.

R. Lee, K. Irizarry, T. Genemine, H. H. Kron, . S. Bitl-]-d et al., Programming In the Large Versus Programming In the Small Evolving from Bioinformatics in the Small to Bioinformatics in the Large [Pygr] The Pygr Consortium, Pygr: the Python Graph Database Framework Global analysis of exon creation vs. loss, and the role of alternative splicing, in 17 vertebrate genomes The ASAP II database: analysis and comparative genomics of alternative splicing in 15 animal species Wagner and Dollo: a stochastic duet by composing two parsimonious solos, The Comprehensive R Archive Network, pp.592-603, 1976.

]. S. Cython, R. Behnel, D. S. Bradshaw, G. Seljebotn, F. L. Van-rossum et al., Cython: C Extensions for Python, www.cython. org. [Python], 2001.

G. Aguirre, E. Zarahn, D. Esposito, and M. , Empirical Analyses of BOLD fMRI Statistics, NeuroImage, vol.5, issue.3, pp.199-212, 1997.
DOI : 10.1006/nimg.1997.0264

G. Aguirre, E. Zarahn, D. 'esposito, and M. , The Variability of Human, BOLD Hemodynamic Responses, NeuroImage, vol.8, issue.4, pp.360-369, 1998.
DOI : 10.1006/nimg.1998.0369

R. Cox and J. Hyde, Software tools for analysis and visualization of FMRI data Optimal Experimental Design for Event-Related fMRI, NMR in Biomedicine Human Brain Mapping, vol.10, issue.8, pp.171-178, 1997.

L. Favre and A. Fouque, A Comprehensive fMRI Processing Toolbox for BrainVISA Functional and Effective Connectivity in Neuroimaging: A Synthesis, Human Brain Mapping Human Brain Mapping, vol.47, issue.2, pp.56-78, 1994.

K. Friston and J. Ashburner, Spatial registration and normalization of images, Human Brain Mapping, vol.16, issue.3, pp.165-189, 1995.
DOI : 10.1002/hbm.460030303

D. Esposito and M. , A comparison of Granger causality and coherency in fMRI-based analysis of the motor system Top-down Flow of Visual Spatial Attention Signals from Parietal to Occipital Cortex, press. [Makni08], 2009.

S. Makni and J. Idier, A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI, NeuroImage, vol.41, issue.3, pp.941-969, 2008.
DOI : 10.1016/j.neuroimage.2008.02.017

URL : https://hal.archives-ouvertes.fr/cea-00333624

. Networkx, A. Hagberg, D. Schult, and P. Swart, Exploring network structure, dynamics, and function using NetworkX, Proc. 7th SciPy Conf, pp.11-15, 2008.

. Nipy, K. Millman, and M. Brett, Analysis of functional Magnetic Resonance Imaging in Python Numerical Recipes: The Art of Scientific Computing, Comp. Sci. Eng, vol.9, pp.52-55, 2007.

D. Percival, . Walden, and . At, Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques Region of interest analysis for fMRI, Soc. Cog. Aff. Neurosci, vol.2, pp.67-70, 1993.
DOI : 10.1017/CBO9780511622762

. Silver05, M. Silver, D. Ress, and D. Heeger, Topographic Maps of Visual Spatial Attention in Human Parietal Cortex, Journal of Neurophysiology, vol.94, issue.2, pp.1358-71, 2005.
DOI : 10.1152/jn.01316.2004

. Smith04, S. Smith, and M. Jenkinson, Advances in functional and structural MR image analysis and implementation as FSL, NeuroImage, vol.23, pp.208-219, 2004.
DOI : 10.1016/j.neuroimage.2004.07.051

. Sun05, F. Sun, L. Miller, D. Esposito, and M. , Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data, NeuroImage, vol.21, issue.2, pp.647-658, 2005.
DOI : 10.1016/j.neuroimage.2003.09.056

. Timeseries, P. Gerard-marchant, and M. Knox, Scikits .TimeSeries: Python time series analysis Visual field maps in human cortex, Neuron, vol.56, pp.366-83, 2007.

. References-[-numpy-]-t and . Oliphant, SymPy: Python library for symbolic mathematics http:// code.google.com/p/sympy, Urso References [SQU] G.L. Squires, Introduction to the Theory of Thermal Neutron Scattering, pp.90-95, 1978.

R. Pynn, N. Scattering, ]. R. Primerrie, and . Riedel, [CNC] Cold Neutron Chopper Spectrometer http: //neutrons.ornl.gov/instrument_systems/ beamline_05_cncs Overview of Data Acquisition at the SNS, talk given at NOBUGS 2004 conference, numpy.scipy. org/ [MAT] J.D. Hunter, Matplotlib: A 2D graphics environment . Computing in Science and Engineering, pp.90-95, 2000.

B. [. Perez and . Granger, IPython: A System for Interactive Scientific Computing, www. codeplex.com/IronPython References [Bran] Georg Brandl, Sphinx: Python Documentation Generator, pp.21-29, 2007.
DOI : 10.1109/MCSE.2007.53

G. Genibel, C. B. , D. Goodger, and G. Van-rossum, Ian Lynagh (graphs), et al. Debian Quality Assurance: Popularity contest statistics for python- numpy. http://qa.debian.org/popcon.php? package=python-numpy, Python Enhancement Proposal, vol.257, pp.257-2001, 2009.

P. Gre, R. Greenfield, and . Jedrzejewski, Using Python for Interactive Data Analysis, 2007.

[. Harrington, The SciPy Documentation Project http://conference.scipy.org/proceedings [Oli] Travis Oliphant, Guide to NumPy [reST] reStructuredText: Markup Syntax and Parser Component of Docutils, Proceedings of the 7th Python in Science ConferenceVir] Pauli Virtanen, Emmanuelle Gouillart, Gael Varoquaux , and Stefan van der Walt, et al. pydocweb: A tool for collaboratively documenting Python modules via the web, pp.33-35, 2006.