S. B. Vos, D. K. Jones, B. Jeurissen, M. A. Viergever, and A. Leemans, The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain, NeuroImage, vol.59, issue.3, pp.2208-2216, 2012.
DOI : 10.1016/j.neuroimage.2011.09.086

B. Jeurissen, A. Leemans, J. Tournier, D. K. Jones, and J. Sijbers, Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging, Human Brain Mapping, vol.45, issue.11, 2012.
DOI : 10.1002/hbm.22099

B. Scherrer and S. K. Warfield, Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI, PLoS ONE, vol.49, issue.11, p.48232, 2012.
DOI : 10.1371/journal.pone.0048232.g014

D. Tuch, T. Reese, M. Wiegell, N. Makris, J. Belliveau et al., High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity, Magnetic Resonance in Medicine, vol.147, issue.4, pp.577-582, 2002.
DOI : 10.1002/mrm.10268

Y. Assaf and P. Basser, Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain, NeuroImage, vol.27, issue.1, pp.48-58, 2005.
DOI : 10.1016/j.neuroimage.2005.03.042

H. Zhang, T. Schneider, C. A. Wheeler-kingshott, and D. C. Alexander, NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain, NeuroImage, vol.61, issue.4, 2012.
DOI : 10.1016/j.neuroimage.2012.03.072

B. Scherrer, A. Schwartzman, M. Taquet, S. P. Prabhu, M. Sahin et al., Characterizing the DIstribution of Anisotropic MicrO-structural eNvironments with Diffusion-Weighted Imaging (DIAMOND), Medical Image Computing and Computer-Assisted Intervention?MICCAI 2013
DOI : 10.1007/978-3-642-40760-4_65

O. Pasternak, C. Westin, S. Bouix, L. J. Seidman, J. M. Goldstein et al., Excessive Extracellular Volume Reveals a Neurodegenerative Pattern in Schizophrenia Onset, Journal of Neuroscience, vol.32, issue.48, pp.17-365, 2012.
DOI : 10.1523/JNEUROSCI.2904-12.2012

A. H. Klein and . Cross, Quantification of increased cellularity during inflammatory demyelination, Brain, vol.134, issue.12, pp.3590-3601, 2011.

N. S. White, T. B. Leergaard, H. D. 'arceuil, J. G. Bjaalie, and A. M. Dale, Probing tissue microstructure with restriction spectrum imaging: Histological and theoretical validation, Human Brain Mapping, vol.376, issue.8, pp.327-346, 2013.
DOI : 10.1002/hbm.21454

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538903

M. Taquet, B. Scherrer, N. Boumal, B. Macq, and S. K. Warfield, Estimation of a multifascicle model from single b-value data with a population-informed prior, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2013, 2013.

Y. Rathi, M. Kubicki, S. Bouix, C. Westin, J. Goldstein et al., Statistical analysis of fiber bundles using multi-tensor tractography: application to first-episode schizophrenia, Magnetic Resonance Imaging, vol.29, issue.4, pp.507-515, 2011.
DOI : 10.1016/j.mri.2010.10.005

S. Smith, H. Jenkinson, D. Johansen-berg, T. Rueckert, C. Nichols et al., Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data, NeuroImage, vol.31, issue.4, pp.1487-1505, 2006.
DOI : 10.1016/j.neuroimage.2006.02.024

O. Bergmann, G. Kindlmann, S. Peled, and C. Westin, TWO-TENSOR FIBER TRACTOGRAPHY, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.796-799, 2007.
DOI : 10.1109/ISBI.2007.356972

S. Jbabdi, T. E. Behrens, and S. M. Smith, Crossing fibres in tract-based spatial statistics, NeuroImage, vol.49, issue.1, pp.249-256, 2010.
DOI : 10.1016/j.neuroimage.2009.08.039

M. Taquet, B. Scherrer, O. Commowick, J. M. Peters, M. Sahin et al., Registration and Analysis of White Matter Group Differences with a Multi-fiber Model, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2012, pp.313-320, 2012.
DOI : 10.1007/978-3-642-33454-2_39

URL : https://hal.archives-ouvertes.fr/inserm-00716110

A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, The Journal of Machine Learning Research, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

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

J. Dhillon, Differential entropic clustering of multivariate Gaussians, Advances in Neural Information Processing Systems, p.337, 2007.

M. Taquet, B. Scherrer, C. Benjamin, S. Prabhu, B. Macq et al., Interpolating multi-fiber models by Gaussian mixture simplification, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.928-931, 2012.
DOI : 10.1109/ISBI.2012.6235708

T. Behrens, M. Woolrich, M. Jenkinson, H. Johansen-berg, R. Nunes et al., Characterization and propagation of uncertainty in diffusion-weighted MR imaging, Magnetic Resonance in Medicine, vol.36, issue.5, pp.1077-1088, 2003.
DOI : 10.1002/mrm.10609

Y. Assaf, R. Freidlin, G. Rohde, and P. Basser, New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter, Magnetic Resonance in Medicine, vol.121, issue.204, pp.965-978, 2004.
DOI : 10.1002/mrm.20274

A. Stamm, P. Perez, and C. Barillot, Diffusion directions imaging (DDI), 2011.
URL : https://hal.archives-ouvertes.fr/inria-00608706

J. Shi and J. Malik, Normalized cuts and image segmentation Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.22, issue.8, pp.888-905, 2000.

V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, Log-Euclidean metrics for fast and simple calculus on diffusion tensors, Magnetic Resonance in Medicine, vol.52, issue.2, pp.411-421, 2006.
DOI : 10.1002/mrm.20965

URL : https://hal.archives-ouvertes.fr/inria-00502678

H. Zhang, B. Avants, P. Yushkevich, J. Woo, S. Wang et al., High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis, IEEE Transactions on Medical Imaging, vol.26, issue.11, pp.1585-1597, 2007.
DOI : 10.1109/TMI.2007.906784

J. Ruiz-alzola, C. Westin, S. K. Warfield, C. Alberola, S. Maier et al., Nonrigid registration of 3D tensor medical data, Medical Image Analysis, vol.6, issue.2, pp.143-161, 2002.
DOI : 10.1016/S1361-8415(02)00055-5

S. Ourselin, A. Roche, S. Prima, and N. Ayache, Block Matching: A??General??Framework??to??Improve Robustness of??Rigid??Registration of Medical Images, Medical Image Computing and Computer-Assisted Intervention?MICCAI, pp.557-566, 2000.
DOI : 10.1007/978-3-540-40899-4_57

URL : https://hal.archives-ouvertes.fr/inria-00615860

O. Commowick, V. Arsigny, A. Isambert, J. Costa, F. Dhermain et al., An efficient locally affine framework for the smooth registration of anatomical structures, Medical Image Analysis, vol.12, issue.4, pp.427-441, 2008.
DOI : 10.1016/j.media.2008.01.002

URL : https://hal.archives-ouvertes.fr/inria-00616074

V. Garcia, O. Commowick, and G. Malandain, A robust and efficient block-matching framework for non linear registration of thoracic ct images Proceedings of A Grand Challenge on Pulmonary Image Registration (EMPIRE'10), held in conjunction with MICCAI Spatial transformations of diffusion tensor magnetic resonance images, Medical Imaging IEEE Transactions on, vol.10, issue.20 11, pp.1131-1139, 2001.

A. Guimond, J. Meunier, and J. P. Thirion, Average Brain Models: A Convergence Study, Computer Vision and Image Understanding, vol.77, issue.2, pp.192-210, 2000.
DOI : 10.1006/cviu.1999.0815

URL : https://hal.archives-ouvertes.fr/inria-00615030

J. M. Peters, M. Sahin, V. Vogel-farley, S. Jeste, C. Nelson et al., Loss of White Matter Microstructural Integrity Is Associated with Adverse Neurological Outcome in Tuberous Sclerosis Complex, Academic Radiology, vol.19, issue.1, pp.17-25, 2012.
DOI : 10.1016/j.acra.2011.08.016

M. C. Jeste, S. P. Gregas, C. A. Prabhu, S. K. Nelson, and . Warfield, Impaired language pathways in tuberous sclerosis complex patients with autism spectrum disorders, Cerebral Cortex, 2012.

C. F. Benjamin, J. M. Singh, S. P. Prabhu, and S. K. Warfield, Optimization of tractography of the optic radiations, Human Brain Mapping, vol.26, issue.2, 2012.
DOI : 10.1002/hbm.22204

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, 2001.
DOI : 10.1002/hbm.1058

O. Pasternak, M. Shenton, and C. Westin, Estimation of Extracellular Volume from Regularized Multi-shell Diffusion MRI, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2012, pp.305-312, 2012.
DOI : 10.1007/978-3-642-33418-4_38

T. G. Reese, O. Heid, R. M. Weisskoff, and V. J. Wedeen, Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo, Magnetic Resonance in Medicine, vol.111, issue.1, pp.177-182, 2003.
DOI : 10.1002/mrm.10308

M. Taquet, B. Macq, and S. K. Warfield, Spatially Adaptive Log-Euclidean Polyaffine Registration Based on Sparse Matches, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2011, pp.590-597, 2011.
DOI : 10.1007/3-540-47979-1_28

K. Ridler, E. Bullmore, P. De-vries, J. Suckling, G. Barker et al., Widespread anatomical abnormalities of grey and white matter structure in tuberous sclerosis, Psychological medicine, vol.31, issue.08, pp.1437-1446, 2001.

E. Gibson, A. Fenster, and A. Ward, Registration Accuracy: How Good Is Good Enough? A Statistical Power Calculation Incorporating Image Registration Uncertainty, Medical Image Computing and Computer-Assisted Intervention? MICCAI 2012, pp.643-650, 2012.
DOI : 10.1007/978-3-642-33418-4_79

J. Ashburner, C. Hutton, R. Frackowiak, I. Johnsrude, C. Price et al., Identifying global anatomical differences: deformationbased morphometry, Human Brain Mapping, vol.6, pp.5-6, 1998.

R. O. Suarez, O. Commowick, S. P. Prabhu, and S. K. Warfield, Automated delineation of white matter fiber tracts with a multiple region-of-interest approach, NeuroImage, vol.59, issue.4, pp.3690-3700, 2012.
DOI : 10.1016/j.neuroimage.2011.11.043

URL : https://hal.archives-ouvertes.fr/inserm-00657707

J. M. Peters, M. Taquet, C. Vega, S. S. Jeste, I. Sanchez-fernandez et al., Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity, BMC Medicine, vol.52, issue.1, p.54, 2013.
DOI : 10.1111/j.1528-1167.2010.02785.x

J. Radua, M. L. Phillips, T. Russell, N. Lawrence, N. Marshall et al., Neural response to specific components of fearful faces in healthy and schizophrenic adults, NeuroImage, vol.49, issue.1, pp.939-946, 2004.
DOI : 10.1016/j.neuroimage.2009.08.030

D. L. Vargas, C. Nascimbene, C. Krishnan, A. W. Zimmerman, and C. A. Pardo, Neuroglial activation and neuroinflammation in the brain of patients with autism, Annals of Neurology, vol.115, issue.1, pp.67-81, 2005.
DOI : 10.1002/ana.20315

A. Klin, D. J. Lin, P. Gorrindo, G. Ramsay, and W. Jones, Two-year-olds with autism orient to non-social contingencies rather than biological motion, Nature, vol.38, issue.7244, pp.257-261, 2009.
DOI : 10.1038/nature07868