G. K. Aguirre, J. Detre, E. Zarahn, and D. C. Alsop, Experimental Design and the Relative Sensitivity of BOLD and Perfusion fMRI, NeuroImage, vol.15, issue.3, pp.488-500, 2002.
DOI : 10.1006/nimg.2001.0990

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

J. Ashburner and K. J. Friston, Unified segmentation, NeuroImage, vol.26, issue.3, pp.839-851, 2005.
DOI : 10.1016/j.neuroimage.2005.02.018

S. Aslan and H. Lu, On the sensitivity of ASL MRI in detecting regional differences in cerebral blood flow, Magnetic Resonance Imaging, vol.28, issue.7, pp.928-935, 2010.
DOI : 10.1016/j.mri.2010.03.037

C. F. Beckmann, M. Jenkinson, and S. M. Smith, General multilevel linear modeling for group analysis in FMRI, NeuroImage, vol.20, issue.2, pp.1052-1063, 2003.
DOI : 10.1016/S1053-8119(03)00435-X

Y. Benjamini and Y. Hochberg, Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society. Series B (Methodological), vol.57, pp.289-300, 1995.

R. B. Buxton, L. R. Frank, E. C. Wong, B. Siewert, S. Warach et al., A general kinetic model for quantitative perfusion imaging with arterial spin labeling, Magnetic Resonance in Medicine, vol.37, issue.3, pp.383-396, 1998.
DOI : 10.1002/mrm.1910400308

G. Chen, Z. S. Saad, A. R. Nath, M. S. Beauchamp, and R. W. Cox, FMRI group analysis combining effect estimates and their variances, NeuroImage, vol.60, issue.1, pp.747-765, 2012.
DOI : 10.1016/j.neuroimage.2011.12.060

R. W. Cox, AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and biomedical research, an international journal 29, pp.162-73, 1996.

J. C. Ferré, J. Petr, E. Bannier, C. Barillot, and J. Y. Gauvrit, Improving quality of arterial spin labeling MR imaging at 3 tesla with a 32-channel coil and parallel imaging, Journal of Magnetic Resonance Imaging, vol.28, issue.5, pp.1233-1239, 2012.
DOI : 10.1002/jmri.23586

K. J. Friston, K. E. Stephan, T. E. Lund, A. Morcom, and S. Kiebel, Mixed-effects and fMRI studies, NeuroImage, vol.24, issue.1, pp.244-52, 2005.
DOI : 10.1016/j.neuroimage.2004.08.055

A. Holmes and K. Friston, Generalisability, Random Effects & Population Inference, Proceedings of Fourth International Conference on Functional Mapping of the Human Brain, 1998.

M. A. Lindquist, J. Spicer, I. Asllani, and T. D. Wager, Estimating and testing variance components in a multi-level GLM, NeuroImage, vol.59, issue.1, pp.490-501, 2012.
DOI : 10.1016/j.neuroimage.2011.07.077

B. J. Macintosh, N. Filippini, M. A. Chappell, M. W. Woolrich, C. E. Mackay et al., Assessment of arterial arrival times derived from multiple inversion time pulsed arterial spin labeling MRI, Magnetic Resonance in Medicine, vol.26, issue.3, pp.641-647, 2010.
DOI : 10.1002/mrm.22256

S. Mériaux, A. Roche, G. Dehaene-lambertz, B. Thirion, and J. B. Poline, Combined permutation test and mixed-effect model for group average analysis in fMRI, Human Brain Mapping, vol.15, issue.5, pp.402-412, 2006.
DOI : 10.1002/hbm.20251

J. Mumford, L. Hernandez-garcia, G. R. Lee, and T. E. Nichols, Estimation efficiency and statistical power in arterial spin labeling fMRI, NeuroImage, vol.33, issue.1, pp.103-117, 2006.
DOI : 10.1016/j.neuroimage.2006.05.040

J. A. Mumford and T. Nichols, Simple group fMRI modeling and inference, NeuroImage, vol.47, issue.4, pp.1469-1475, 2009.
DOI : 10.1016/j.neuroimage.2009.05.034

T. Noguchi, T. Yoshiura, A. Hiwatashi, O. Togao, K. Yamashita et al., Perfusion Imaging of Brain Tumors Using Arterial Spin-Labeling: Correlation with Histopathologic Vascular Density, American Journal of Neuroradiology, vol.29, issue.4, pp.688-93, 2008.
DOI : 10.3174/ajnr.A0903

L. Ostergaard, R. M. Weisskoff, D. A. Chesler, C. Gyldensted, and B. R. Rosen, High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis, Magnetic Resonance in Medicine, vol.20, issue.5, pp.715-725, 1996.
DOI : 10.1002/mrm.1910360510

W. D. Penny and A. J. Holmes, Random-Effects Analysis Human Brain Function, pp.843-850, 2004.

J. Petr, J. C. Ferré, H. Raoult, E. Bannier, J. Y. Gauvrit et al., Template-based approach for detecting motor task activation-related hyperperfusion in pulsed ASL data, Human Brain Mapping, vol.120, issue.4
DOI : 10.1002/hbm.22243
URL : https://hal.archives-ouvertes.fr/inserm-00800899

R. A. Poldrack, J. Mumford, and T. Nichols, Handbook of functional MRI data analysis, pp.Cam- bridge, 2011.
DOI : 10.1017/CBO9780511895029

B. Rosner, Percentage Points for a Generalized ESD Many-Outlier Procedure, Technometrics, vol.6, issue.2, pp.165-172, 1983.
DOI : 10.1080/00401706.1983.10487848

P. Skudlarski, R. T. Constable, and J. C. Gore, ROC Analysis of Statistical Methods Used in Functional MRI: Individual Subjects, NeuroImage, vol.9, issue.3, pp.311-329, 1999.
DOI : 10.1006/nimg.1999.0402

B. Thirion, P. Pinel, S. Mériaux, A. Roche, S. Dehaene et al., Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses, NeuroImage, vol.35, issue.1, pp.105-125, 2007.
DOI : 10.1016/j.neuroimage.2006.11.054
URL : https://hal.archives-ouvertes.fr/cea-00371089

R. Viviani, E. J. Sim, H. Lo, S. Richter, S. Haffer et al., Components of variance in brain perfusion and the design of studies of individual differences: The baseline study, NeuroImage, vol.46, issue.1, pp.12-22, 2009.
DOI : 10.1016/j.neuroimage.2009.01.041

Y. Wang, A. J. Saykin, J. Pfeuffer, C. Lin, K. M. Mosier et al., Regional reproducibility of pulsed arterial spin labeling perfusion imaging at 3T, NeuroImage, vol.54, issue.2, pp.1188-1195, 2011.
DOI : 10.1016/j.neuroimage.2010.08.043

Z. Wang, Improving cerebral blood flow quantification for arterial spin labeled perfusion MRI by removing residual motion artifacts and global signal fluctuations. Magnetic resonance imag- ing, 2012.

M. Wintermark, M. Sesay, E. Barbier, K. Borbély, W. P. Dillon et al., Comparative overview of brain perfusion imaging techniques, Journal of Neuroradiology, vol.32, issue.5, pp.83-99, 2005.
DOI : 10.1016/S0150-9861(05)83159-1
URL : https://hal.archives-ouvertes.fr/inserm-00410400

E. C. Wong, R. B. Buxton, and L. R. Frank, Quantitative imaging of perfusion using a single subtraction (QUIPSS and QUIPSS II), Magnetic Resonance in Medicine, vol.6, issue.5, pp.702-708, 1998.
DOI : 10.1002/mrm.1910390506

M. W. Woolrich, T. E. Behrens, C. F. Beckmann, M. Jenkinson, and S. M. Smith, Multilevel linear modelling for FMRI group analysis using Bayesian inference, NeuroImage, vol.21, issue.4, pp.1732-1779, 2004.
DOI : 10.1016/j.neuroimage.2003.12.023

K. J. Worsley, C. H. Liao, J. Aston, V. Petre, G. H. Duncan et al., A General Statistical Analysis for fMRI Data, NeuroImage, vol.15, issue.1, pp.1-15, 2002.
DOI : 10.1006/nimg.2001.0933

G. Zaharchuk, R. Bammer, M. Straka, A. Shankaranarayan, D. C. Alsop et al., Arterial Spin-Label Imaging in Patients with Normal Bolus Perfusion-weighted MR Imaging Findings: Pilot Identification of the Borderzone Sign, Radiology, vol.252, issue.3, pp.797-807, 2009.
DOI : 10.1148/radiol.2523082018