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
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
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
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
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
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
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
The Nature of Statistical Learning Theory, 1995. ,
Learning with Kernels, 2001. ,
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
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
Automatic classification of MR scans in Alzheimer's disease, Brain, vol.131, issue.3, pp.681-690, 2008. ,
DOI : 10.1093/brain/awm319
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
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
Spectral Graph Theory. Number 92, AMS, 1992. ,
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
Diffusion kernels on graphs and other discrete input spaces, Proc. International Conference on Machine Learning, pp.315-337, 2002. ,
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
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
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
Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. Roy. Stat. Soc. B. Met, pp.289-300, 1995. ,