G. Auzias, M. Viellard, S. Takerkart, N. Villeneuve, F. Poinso et al., Atypical sulcal anatomy in young children with autism spectrum disorder, NeuroImage: Clinical, vol.4, pp.593-603, 2014.
DOI : 10.1016/j.nicl.2014.03.008

M. Boucher, S. Whitesides, and A. Evans, Depth potential function for folding pattern representation, registration and analysis. Medical image analysis 13, pp.203-217, 2009.

E. T. Bullmore, J. Suckling, S. Overmeyer, S. Rabe-hesketh, E. Taylor et al., Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain, IEEE Transactions on Medical Imaging, vol.18, issue.1, pp.32-42, 1999.
DOI : 10.1109/42.750253

A. Cachia, M. L. Pailì-ere-martinot, A. Galinowski, D. Januel, R. De-beaurepaire et al., Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations, NeuroImage, vol.39, issue.3, pp.927-935, 2008.
DOI : 10.1016/j.neuroimage.2007.08.049

O. Coulon, J. F. Mangin, J. B. Poline, M. Zilbovicius, D. Roumenov et al., Structural Group Analysis of Functional Activation Maps, NeuroImage, vol.11, issue.6, pp.767-782, 2000.
DOI : 10.1006/nimg.2000.0580

A. Dale, B. Fischl, and M. I. Sereno, Cortical Surface-Based Analysis, NeuroImage, vol.9, issue.2, pp.179-194, 1999.
DOI : 10.1006/nimg.1998.0395

E. Duchesnay, A. Cachia, A. Roche, D. Rivì-ere, Y. Cointepas et al., Classification Based on Cortical Folding Patterns, IEEE Transactions on Medical Imaging, vol.26, issue.4, pp.553-565, 2007.
DOI : 10.1109/TMI.2007.892501

J. A. Etzel, J. M. Zacks, and T. S. Braver, Searchlight analysis: Promise, pitfalls, and potential, NeuroImage, vol.78, pp.261-269, 2013.
DOI : 10.1016/j.neuroimage.2013.03.041
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3988828

B. Fischl, M. I. Sereno, R. B. Tootell, A. M. Dale, and . Others, High-resolution intersubject averaging and a coordinate system for the cortical surface, Human Brain Mapping, vol.179, issue.4, pp.272-284, 1999.
DOI : 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4

T. Gärtner, P. A. Flach, and S. Wrobel, On Graph Kernels: Hardness Results and Efficient Alternatives, Proc. of the 16th Conf. on Computational Learning Theory, 2003.
DOI : 10.1007/978-3-540-45167-9_11

C. D. Good, I. Johnsrude, J. Ashburner, R. N. Henson, K. J. Friston et al., Cerebral Asymmetry and the Effects of Sex and Handedness on Brain Structure: A Voxel-Based Morphometric Analysis of 465 Normal Adult Human Brains, NeuroImage, vol.14, issue.3, pp.685-700, 2001.
DOI : 10.1006/nimg.2001.0857

D. N. Greve, L. Van-der-haegen, Q. Cai, S. Stufflebeam, M. R. Sabuncu et al., A Surface-based Analysis of Language Lateralization and Cortical Asymmetry, Journal of Cognitive Neuroscience, vol.211, issue.9, pp.1477-1492, 2013.
DOI : 10.1016/j.neuroimage.2010.01.101

D. Haussler, Convolution kernels on discrete structures, 1999.

K. Im, H. J. Jo, J. F. Mangin, A. C. Evans, S. I. Kim et al., Spatial Distribution of Deep Sulcal Landmarks and Hemispherical Asymmetry on the Cortical Surface, Cerebral Cortex, vol.20, issue.3, pp.602-613, 2010.
DOI : 10.1093/cercor/bhp127

K. Im, J. M. Lee, J. Lee, Y. W. Shin, I. Y. Kim et al., Gender difference analysis of cortical thickness in healthy young adults with surface-based methods, NeuroImage, vol.31, issue.1, pp.31-38, 2006.
DOI : 10.1016/j.neuroimage.2005.11.042

K. Im, R. Pienaar, J. M. Lee, J. K. Seong, Y. Y. Choi et al., Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: A twin study, NeuroImage, vol.57, issue.3, pp.1077-86, 2011.
DOI : 10.1016/j.neuroimage.2011.04.062

K. Im, R. Pienaar, M. J. Paldino, N. Gaab, A. M. Galaburda et al., Quantification and Discrimination of Abnormal Sulcal Patterns in Polymicrogyria, Cerebral Cortex, vol.23, issue.12, pp.3007-3022, 2012.
DOI : 10.1093/cercor/bhs292

K. Im, N. M. Raschle, S. A. Smith, E. Grant, P. Gaab et al., Atypical Sulcal Pattern in Children with Developmental Dyslexia and At-Risk Kindergarteners, Cerebral Cortex, vol.26, issue.3, 2015.
DOI : 10.1093/cercor/bhu305

J. J. Koenderink, The structure of images, Biological Cybernetics, vol.27, issue.269, pp.363-370, 1984.
DOI : 10.1007/BF00336961

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, pp.1137-1143, 1995.

N. Kriegeskorte, R. Goebel, and P. Bandettini, Information-based functional brain mapping, Proceedings of the National Academy of Sciences, vol.103, issue.10, pp.3863-3868, 2006.
DOI : 10.1073/pnas.0600244103
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1383651

T. Lindeberg, Scale-Space Theory in Computer Vision, 1994.
DOI : 10.1007/978-1-4757-6465-9

G. Lohmann, D. Y. Von-cramon, and A. C. Colchester, Deep Sulcal Landmarks Provide an Organizing Framework for Human Cortical Folding, Cerebral Cortex, vol.18, issue.6, pp.1415-1420, 2008.
DOI : 10.1093/cercor/bhm174
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.499.4876

B. Lv, J. Li, H. He, M. Li, M. Zhao et al., Gender consistency and difference in healthy adults revealed by cortical thickness, NeuroImage, vol.53, issue.2, pp.373-382, 2010.
DOI : 10.1016/j.neuroimage.2010.05.020

J. F. Mangin, D. Rivì-ere, A. Cachia, E. Duchesnay, Y. Cointepas et al., Object-Based Morphometry of the Cerebral Cortex, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.968-982, 2004.
DOI : 10.1109/TMI.2004.831204

Y. Meng, G. Li, W. Lin, J. H. Gilmore, and D. Shen, Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants, NeuroImage, vol.100, pp.206-218, 2014.
DOI : 10.1016/j.neuroimage.2014.06.004

T. E. Nichols and A. P. Holmes, Nonparametric permutation tests for functional neuroimaging: A primer with examples, Human Brain Mapping, vol.4, issue.1, pp.1-25, 2002.
DOI : 10.1002/hbm.1058

H. Niederreiter and I. H. Sloan, Integration of nonperiodic functions of two variables by fibonacci lattice rules URL: http://www.sciencedirect.com/ science, Journal of Computational and Applied Mathematics, vol.5192, pp.57-700377, 1994.

G. Operto, D. Rivì-ere, B. Fertil, R. Bulot, J. F. Mangin et al., Structural analysis of fMRI data: A surface-based framework for multi-subject studies, Medical Image Analysis, vol.16, issue.5, pp.976-990, 2012.
DOI : 10.1016/j.media.2012.02.007

J. B. Poline and B. M. Mazoyer, Analysis of Individual Positron Emission Tomography Activation Maps by Detection of High Signal-to-Noise-Ratio Pixel Clusters, Journal of Cerebral Blood Flow & Metabolism, vol.114, issue.3, pp.425-437, 1993.
DOI : 10.1038/331585a0

J. Régis, J. F. Mangin, T. Ochiai, V. Frouin, D. Rivì-ere et al., ???Sulcal Root??? Generic Model: a Hypothesis to Overcome the Variability of the Human Cortex Folding Patterns, Neurologia medico-chirurgica, vol.45, issue.1, pp.1-17, 2005.
DOI : 10.2176/nmc.45.1

A. N. Ruigrok, G. Salimi-khorshidi, M. C. Lai, S. Baron-cohen, M. V. Lombardo et al., A meta-analysis of sex differences in human brain structure, Neuroscience & Biobehavioral Reviews, vol.39, pp.34-50, 2014.
DOI : 10.1016/j.neubiorev.2013.12.004

B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001.

J. Stelzer, Y. Chen, and R. Turner, Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control, NeuroImage, vol.65, pp.69-82, 2013.
DOI : 10.1016/j.neuroimage.2012.09.063

Z. Y. Sun, S. Klöppel, D. Rivì-ere, M. Perrot, R. Frackowiak et al., The effect of handedness on the shape of the central sulcus, NeuroImage, vol.60, issue.1, pp.332-339, 2012.
DOI : 10.1016/j.neuroimage.2011.12.050

Z. Y. Sun, S. Klöppel, D. Rivì-ere, M. Perrot, R. S. Frackowiak et al., The effect of handedness on the shape of the central sulcus, NeuroImage, vol.60, issue.1, pp.332-341, 2012.
DOI : 10.1016/j.neuroimage.2011.12.050

S. Takerkart, G. Auzias, L. Brun, and O. Coulon, Mapping cortical shape differences using a searchlight approach based on classification of sulcal pit graphs, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015.
DOI : 10.1109/ISBI.2015.7164165
URL : https://hal.archives-ouvertes.fr/hal-01112776

S. Takerkart, G. Auzias, B. Thirion, and L. Ralaivola, Graph-Based Inter-Subject Pattern Analysis of fMRI Data, PLoS ONE, vol.0, issue.8, 2014.
DOI : 10.1371/journal.pone.0104586.s001
URL : https://hal.archives-ouvertes.fr/hal-01027769

A. W. Toga and P. M. Thompson, Mapping brain asymmetry, Nature Reviews Neuroscience, vol.87, issue.1, pp.37-48, 2003.
DOI : 10.1038/nrn1009

D. C. Van-essen, H. A. Drury, J. Dickson, J. Harwell, D. Hanlon et al., An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex, Journal of the American Medical Informatics Association, vol.8, issue.5, pp.443-459, 2001.
DOI : 10.1136/jamia.2001.0080443

D. C. Van-essen, M. F. Glasser, D. L. Dierker, J. Harwell, and T. Coalson, Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases, Cerebral Cortex, vol.22, issue.10, pp.2241-2262, 2012.
DOI : 10.1093/cercor/bhr291

G. Wu, M. Kim, G. Sanroma, Q. Wang, B. C. Munsell et al., Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition, NeuroImage, vol.106, pp.34-46, 2015.
DOI : 10.1016/j.neuroimage.2014.11.025
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4285661

H. Zhang, T. Nichols, and T. Johnson, Cluster mass inference via random field theory, NeuroImage, vol.44, issue.1, pp.51-61, 2009.
DOI : 10.1016/j.neuroimage.2008.08.017