J. Chalela, Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison, The Lancet, vol.369, issue.9558, pp.293-98, 2007.
DOI : 10.1016/S0140-6736(07)60151-2

K. Crafton, Improved understanding of cortical injury by incorporating measures of functional anatomy, Brain, vol.126, issue.7, pp.1650-59, 2003.
DOI : 10.1093/brain/awg159

T. Domi, Corticospinal Tract Pre-Wallerian Degeneration: A Novel Outcome Predictor for Pediatric Stroke on Acute MRI, Stroke, vol.40, issue.3, pp.780-87, 2009.
DOI : 10.1161/STROKEAHA.108.529958

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.528.1235

R. Lo, Identification of critical areas for motor function recovery in chronic stroke subjects using voxel-based lesion symptom mapping, NeuroImage, vol.49, issue.1, pp.9-18, 2010.
DOI : 10.1016/j.neuroimage.2009.08.044

C. Rosso, Prediction of Infarct Growth Based on Apparent Diffusion Coefficients: Penumbral Assessment without Intravenous Contrast Material, Radiology, vol.250, issue.1, pp.184-92, 2009.
DOI : 10.1148/radiol.2493080107

URL : https://hal.archives-ouvertes.fr/hal-00805406

J. Ashburner and K. J. Friston, Voxel-Based Morphometry???The Methods, NeuroImage, vol.11, issue.6, pp.805-826, 2000.
DOI : 10.1006/nimg.2000.0582

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.9512

C. Davatzikos, Why voxel-based morphometric analysis should be used with great caution when characterizing group differences, NeuroImage, vol.23, issue.1, pp.17-20, 2004.
DOI : 10.1016/j.neuroimage.2004.05.010

V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.

B. Schölkopf and A. J. Smola, Learning with Kernels, 2001.

Z. Lao, Morphological classification of brains via high-dimensional shape transformations and machine learning methods, NeuroImage, vol.21, issue.1, pp.46-57, 2004.
DOI : 10.1016/j.neuroimage.2003.09.027

Y. Fan, COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements, IEEE Transactions on Medical Imaging, vol.26, issue.1, pp.93-105, 2007.
DOI : 10.1109/TMI.2006.886812

S. Klöppel, Automatic classification of MR scans in Alzheimer's disease, Brain, vol.131, issue.3, pp.681-690, 2008.
DOI : 10.1093/brain/awm319

P. Vemuri, Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies, NeuroImage, vol.39, issue.3, pp.1186-97, 2008.
DOI : 10.1016/j.neuroimage.2007.09.073

J. Mourão-miranda, Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data, NeuroImage, vol.28, issue.4, pp.980-95, 2005.
DOI : 10.1016/j.neuroimage.2005.06.070

F. R. Chung, Spectral Graph Theory. Number 92, AMS, 1992.

A. J. Smola and B. Schölkopf, On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion, Algorithmica, vol.22, issue.1-2, pp.211-242, 1998.
DOI : 10.1007/PL00013831

R. I. Kondor and J. D. Lafferty, Diffusion kernels on graphs and other discrete input spaces, Proc. International Conference on Machine Learning, pp.315-337, 2002.

L. Gómez-chova, Semisupervised Image Classification With Laplacian Support Vector Machines, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.3, pp.336-376, 2008.
DOI : 10.1109/LGRS.2008.916070

A. Smola and R. Kondor, Kernels and Regularization on Graphs, Proc. COLT, p.144, 2003.
DOI : 10.1007/978-3-540-45167-9_12

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.7020

F. Rapaport, Classification of microarray data using gene networks, BMC Bioinformatics, vol.8, issue.1, p.35, 2007.
DOI : 10.1186/1471-2105-8-35

URL : https://hal.archives-ouvertes.fr/hal-00433577

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. Roy. Stat. Soc. B. Met, pp.289-300, 1995.