H. Abdi, L. J. Williams, and D. Valentin, Multiple factor analysis : principal component analysis for multitable and multiblock data sets, Wiley Interdisciplinary Reviews : Computational Statistics, vol.5, issue.2, pp.149-179, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01259094

J. Acosta-cabronero, S. Alley, G. B. Williams, G. Pengas, and P. J. Nestor, Diffusion tensor metrics as biomarkers in Alzheimer's disease, PloS One, vol.7, issue.11, p.49072, 2012.

S. Adaszewski, J. Dukart, F. Kherif, R. Frackowiak, and B. Draganski, How early can we predict Alzheimer's disease using computational anatomy ?, Neurobiology of Aging, vol.34, issue.12, pp.2815-2826, 2013.

F. Agosta, M. Rovaris, E. Pagani, M. P. Sormani, G. Comi et al., Magnetization transfer MRI metrics predict the accumulation of disability 8 years later in patients with multiple sclerosis, Brain : A Journal of Neurology, vol.129, pp.2620-2627, 2006.

J. Almhana, Z. Liu, V. Choulakian, and R. Mcgorman, A recursive algorithm for gamma mixture models, 2006 IEEE International Conference on Communications, vol.1, pp.197-202, 2006.

J. Almhana, Z. Liu, V. Choulakian, and R. Mcgorman, A Recursive Algorithm for Gamma Mixture Models, 2006 IEEE International Conference on Communications, vol.1, pp.197-202, 2006.

A. Alzheimer, Über einen eigenartigen schweren Erkrankungsprozeß derHirnrinde, Neurol Central, p.25, 1134.

A. Al-radaideh, O. E. Mougin, S. Lim, I. Chou, C. S. Constantinescu et al., Histogram analysis of quantitative T1 and MT maps from ultrahigh field MRI in clinically isolated syndrome and relapsing-remitting multiple sclerosis, NMR in Biomedicine, vol.28, issue.11, pp.1374-1382, 2015.

S. Amari and H. Nagaoka, Methods of Information Geometry, Translations of Mathematical Monographs, vol.191, 2000.

I. K. Amlien and A. M. Fjell, Diffusion tensor imaging of white matter degeneration in Alzheimer's disease and mild cognitive impairment, Neuroscience, vol.276, pp.206-215, 2014.

L. Amoroso, Ricerche intorno alla curva dei redditi, vol.2, pp.123-159, 1925.

T. W. Anderson, On the Distribution of the Two-Sample Cramer-von Mises Criterion, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1148-1159, 1962.

K. Arwini, C. Dodson, A. Doig, W. Sampson, J. Scharcanski et al., Information Geometry : Near Randomness and Near Independence. Information Geometry : Near Randomness and Near Independence, 2008.

K. Arwini and C. T. Dodson, Information Geometry : Near Randomness and Near Independence, Lecture Notes in Mathematics, 2008.

J. Ashburner, Computational Neuroanatomy, Chapter5 : Image Segmentation, 2000.

J. Ashburner and K. J. Friston, Voxel-based morphometry-the methods, NeuroImage, vol.11, issue.6, pp.805-821, 2000.

L. G. Astrakas and M. I. Argyropoulou, Shifting from region of interest (ROI) to voxelbased analysis in human brain mapping, Pediatric Radiology, vol.40, issue.12, pp.1857-1867, 2010.

C. J. Azevedo and D. Pelletier, Whole-brain atrophy : ready for implementation into clinical decision-making in multiple sclerosis ?, Current Opinion in Neurology, vol.29, issue.3, pp.237-242, 2016.

A. Bakkour, J. C. Morris, and B. C. Dickerson, The cortical signature of prodromal AD : regional thinning predicts mild AD dementia, Neurology, vol.72, issue.12, pp.1048-1055, 2009.

A. Bakkour, J. C. Morris, D. A. Wolk, and B. C. Dickerson, The effects of aging and Alzheimer's disease on cerebral cortical anatomy : specificity and differential relationships with cognition, NeuroImage, vol.76, pp.332-344, 2013.

C. Ballard, S. Gauthier, A. Corbett, C. Brayne, D. Aarsland et al., Alzheimer's disease, Lancet, vol.377, issue.9770, pp.1019-1031, 2011.

R. Bammer, M. Augustin, S. Strasser-fuchs, T. Seifert, P. Kapeller et al., Magnetic resonance diffusion tensor imaging for characterizing diffuse and focal white matter abnormalities in multiple sclerosis, Magnetic Resonance in Medicine, vol.44, issue.4, pp.583-591, 2000.

A. Barachant and M. Congedo, A Plug&Play P300 BCI Using Information Geometry, 2014.

F. Barbaresco, Innovative tools for radar signal processing Based on Cartan's geometry of SPD matrices amp ; Information Geometry, IEEE Radar Conference, pp.1-6, 2008.

F. Barkhof, The clinico-radiological paradox in multiple sclerosis revisited, Current Opinion in Neurology, vol.15, issue.3, pp.239-245, 2002.

R. E. Bellman, Dynamic Programming, 1957.

M. Bester, J. H. Jensen, J. S. Babb, A. Tabesh, L. Miles et al., Non-Gaussian diffusion MRI of gray matter is associated with cognitive impairment in multiple sclerosis, Multiple Sclerosis (Houndmills, vol.21, pp.935-944, 2015.

A. Bhattacharyya, On a Measure of Divergence between Two Multinomial Populations, The Indian Journal of Statistics, vol.7, pp.401-406, 1933.

B. Bigi, Using kullback-leibler distance for text categorization, Proceedings of the 25th European Conference on IR Research, ECIR'03, pp.305-319, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01392500

J. Bigot, R. Gouet, T. Klein, and A. López, Geodesic PCA in the Wasserstein space, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01978864

M. Bobinski, M. J. De-leon, J. Wegiel, S. Desanti, A. Convit et al., The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer's disease, Neuroscience, vol.95, issue.3, pp.721-725, 2000.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT '92, pp.144-152, 1992.

M. Bozzali, M. Cercignani, M. P. Sormani, G. Comi, and M. Filippi, Quantification of Brain Gray Matter Damage in Different MS Phenotypes by Use of Diffusion Tensor MR Imaging, American Journal of Neuroradiology, vol.23, issue.6, pp.985-988, 2002.

M. Bozzali, M. Franceschi, A. Falini, S. Pontesilli, M. Cercignani et al., Quantification of tissue damage in AD using diffusion tensor and magnetization transfer MRI, Neurology, vol.57, issue.6, pp.1135-1137, 2001.

H. Braak and E. Braak, Neuropathological stageing of Alzheimer-related changes, Acta Neuropathologica, vol.82, issue.4, pp.239-259, 1991.

C. Brayne and B. Miller, Dementia and aging populations-A global priority for contextualized research and health policy, PLoS Medicine, issue.3, p.14, 2017.

A. L. Brigant and S. Puechmorel, Quantization and clustering on riemannian manifolds with an application to air traffic analysis, Journal of Multivariate Analysis, vol.173, pp.685-703, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01816865

K. Brodmann, Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues, 1909.

M. A. Buchem, J. C. Mcgowan, D. L. Kolson, M. Polansky, and R. I. Grossman, Quantitative volumetric magnetization transfer analysis in multiple sclerosis : Estimation of macroscopic and microscopic disease burden, Magnetic Resonance in Medicine, vol.36, issue.4, pp.632-636, 1996.

E. Busovaca, M. E. Zimmerman, I. B. Meier, E. Y. Griffith, S. M. Grieve et al., Is the Alzheimer's disease cortical thickness signature a biological marker for memory ?, Brain imaging and behavior, vol.10, issue.2, pp.517-523, 2016.

K. Cai, H. Xu, H. Guan, W. Zhu, J. Jiang et al., Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices, PLoS ONE, issue.1, p.12, 2017.

R. Casanova, F. Hsu, K. M. Sink, S. R. Rapp, J. D. Williamson et al., Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods, PLOS ONE, vol.8, issue.11, p.77949, 2013.

E. Cassol, J. Ranjeva, D. Ibarrola, C. Mékies, C. Manelfe et al., Diffusion tensor imaging in multiple sclerosis : a tool for monitoring changes in normalappearing white matter, Multiple Sclerosis (Houndmills, vol.10, pp.188-196, 2004.

A. Castriota-scanderbeg, U. Sabatini, F. Fasano, R. Floris, L. Fraracci et al., Diffusion of water in large demyelinating lesions : a follow-up study, Neuroradiology, vol.44, issue.9, pp.764-767, 2002.

A. Ceccarelli, M. A. Rocca, A. Falini, P. Tortorella, E. Pagani et al., Normal-appearing white and grey matter damage in MS. A volumetric and diffusion tensor MRI study at 3.0 Tesla, Journal of Neurology, vol.254, issue.4, pp.513-518, 2007.

M. Cercignani, M. Bozzali, G. Iannucci, G. Comi, and M. Filippi, Magnetisation transfer ratio and mean diffusivity of normal appearing white and grey matter from patients with multiple sclerosis, Neurosurgery, and Psychiatry, vol.70, issue.3, pp.311-317, 2001.

M. Cercignani, N. G. Dowell, and P. S. Tofts, Quantitative MRI of the Brain : Principles of Physical Measurement, Series in medical physics and biomedical engineering, 2018.

M. Cercignani, G. Iannucci, and M. Filippi, Diffusion-weighted imaging in multiple sclerosis, Italian Journal of Neurological Sciences, vol.20, issue.5, pp.246-249, 1999.

M. Cercignani, G. Iannucci, M. A. Rocca, G. Comi, M. A. Horsfield et al., Pathologic damage in MS assessed by diffusion-weighted and magnetization transfer MRI, Neurology, vol.54, issue.5, pp.1139-1144, 2000.

M. Bibliographie-cercignani, M. Inglese, E. Pagani, G. Comi, and M. Filippi, Mean Diffusivity and Fractional Anisotropy Histograms of Patients with Multiple Sclerosis, American Journal of Neuroradiology, vol.22, issue.5, pp.952-958, 2001.

S. Cha, Comprehensive Survey on Distance/Similarity Measures Between Probability Density Functions, International Journal Of Mathematical Models And Methods In Applied Sciences, vol.1, issue.4, pp.300-307, 2007.

L. L. Chao, N. Schuff, J. H. Kramer, A. T. Du, A. A. Capizzano et al., Reduced medial temporal lobe N-acetylaspartate in cognitively impaired but nondemented patients, Neurology, vol.64, issue.2, pp.282-289, 2005.

D. T. Chard, C. M. Griffin, M. A. Mclean, P. Kapeller, R. Kapoor et al., Brain metabolite changes in cortical grey and normal-appearing white matter in clinically early relapsing-remitting multiple sclerosis, Brain : A Journal of Neurology, vol.125, pp.2342-2352, 2002.

D. T. Chard, C. M. Griffin, W. Rashid, G. R. Davies, D. R. Altmann et al., Progressive grey matter atrophy in clinically early relapsing-remitting multiple sclerosis, Multiple Sclerosis (Houndmills, vol.10, pp.387-391, 2004.

J. T. Chen, D. L. Collins, H. L. Atkins, M. S. Freedman, D. L. Arnold et al., Magnetization transfer ratio evolution with demyelination and remyelination in multiple sclerosis lesions, Annals of Neurology, vol.63, issue.2, pp.254-262, 2008.

Y. Chen, T. T. Georgiou, and A. Tannenbaum, Optimal transport for Gaussian mixture models, 2017.

N. N. Chentsov, Statistical Decision Rules and Optimal Inference, 1982.

O. Ciccarelli, D. J. Werring, C. A. Wheeler-kingshott, G. J. Barker, G. J. Parker et al., Investigation of MS normal-appearing brain using diffusion tensor MRI with clinical correlations, Neurology, vol.56, issue.7, pp.926-933, 2001.

A. Clim, R. D. Zota, and G. Tinic?, The Kullback-Leibler Divergence Used in Machine Learning Algorithms for Health Care Applications and Hypertension Prediction : A Literature Review, Procedia Computer Science, vol.141, pp.448-453, 2018.

C. Coclitu, C. S. Constantinescu, and R. Tanasescu, The future of multiple sclerosis treatments, Expert Review of Neurotherapeutics, vol.16, issue.12, pp.1341-1356, 2016.

J. Correale, M. I. Gaitán, M. C. Ysrraelit, and M. P. Fiol, Progressive multiple sclerosis : from pathogenic mechanisms to treatment, Brain, vol.140, issue.3, pp.527-546, 2017.

C. Cortes and V. Vapnik, Support-vector networks, Machine learning, vol.20, pp.273-297, 1995.

S. I. Costa, S. A. Santos, and J. E. Strapasson, Fisher information distance : a geometrical reading, 2014.

G. E. Crooks, The amoroso distribution, 2010.

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy et al., Automatic classification of patients with Alzheimer's disease from structural MRI : a comparison of ten methods using the ADNI database, NeuroImage, vol.56, issue.2, pp.766-781, 2011.

F. De-angelis, D. Plantone, and J. Chataway, Pharmacotherapy in Secondary Progressive Multiple Sclerosis : An Overview, CNS drugs, vol.32, issue.6, pp.499-526, 2018.

. Bibliographie,

N. De-stefano, G. Iannucci, M. P. Sormani, L. Guidi, M. L. Bartolozzi et al., MR correlates of cerebral atrophy in patients with multiple sclerosis, Journal of Neurology, vol.249, issue.8, pp.1072-1077, 2002.

N. De-stefano, P. M. Matthews, L. Fu, S. Narayanan, J. Stanley et al., Axonal damage correlates with disability in patients with relapsingremitting multiple sclerosis. Results of a longitudinal magnetic resonance spectroscopy study, Brain : A Journal of Neurology, vol.121, pp.1469-1477, 1998.

N. De-stefano, M. L. Stromillo, A. Giorgio, M. L. Bartolozzi, M. Battaglini et al., Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis, Neurosurgery, and Psychiatry, vol.87, issue.1, pp.93-99, 2016.

J. Dehmeshki, A. C. Ruto, S. Arridge, N. C. Silver, D. H. Miller et al., Analysis of MTR histograms in multiple sclerosis using principal components and multiple discriminant analysis, Magnetic Resonance in Medicine, vol.46, issue.3, pp.600-609, 2001.

J. Dehmeshki, N. C. Silver, S. M. Leary, P. S. Tofts, A. J. Thompson et al., Magnetisation transfer ratio histogram analysis of primary progressive and other multiple sclerosis subgroups, Journal of the Neurological Sciences, vol.185, issue.1, pp.11-17, 2001.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society. Series B (Methodological), vol.39, issue.1, pp.1-38, 1977.

M. M. Deza and E. Deza, Encyclopedia of Distances, 2014.

L. R. Dice, Measures of the Amount of Ecologic Association Between Species, Ecology, vol.26, issue.3, pp.297-302, 1945.

B. C. Dickerson, A. Bakkour, D. H. Salat, E. Feczko, J. Pacheco et al., The cortical signature of Alzheimer's disease : regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals, Cerebral Cortex, vol.19, issue.3, pp.497-510, 1991.

B. C. Dickerson, E. Feczko, J. C. Augustinack, J. Pacheco, J. C. Morris et al., Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area, Neurobiology of Aging, vol.30, issue.3, pp.432-440, 2009.

B. C. Dickerson, T. R. Stoub, R. C. Shah, R. A. Sperling, R. J. Killiany et al., Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults, Neurology, vol.76, issue.16, pp.1395-1402, 2011.

B. C. Dickerson and D. Wolk, Biomarker-based prediction of progression in MCI : Comparison of AD-signature and hippocampal volume with spinal fluid amyloid-and tau, Frontiers in Aging Neuroscience, p.5, 2013.

V. Dousset, R. I. Grossman, K. N. Ramer, M. D. Schnall, L. H. Young et al., Experimental allergic encephalomyelitis and multiple sclerosis : lesion characterization with magnetization transfer imaging, Radiology, vol.182, issue.2, pp.483-491, 1992.

A. G. Droogan, C. A. Clark, D. J. Werring, G. J. Barker, W. I. Mcdonald et al., Comparison of multiple sclerosis clinical subgroups using navigated spin echo diffusionweighted imaging, Magnetic Resonance Imaging, vol.17, issue.5, pp.653-661, 1999.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2000.

J. Duistermaat, On hessian riemannian structures, Asian journal of mathematics, vol.5, pp.79-91, 2001.

C. Dutilleul, Evaluation de traitement de la sclérose en plaques par analyses morphométriques de données d'imagerie par résonance magnétique, 2015.

A. C. Es, J. V. Grond, V. H. Dam, A. J. Craen, G. J. Blauw et al., Associations between Total Cerebral Blood Flow and Age Related Changes of the Brain, vol.5, p.9825, 2010.

B. Escofier and J. Pagès, Analyses factorielles simples et multiples : objectifs, méthodes et interprétation. Dunod, 1990.

A. Eshaghi, R. V. Marinescu, A. L. Young, N. C. Firth, F. Prados et al., , p.190116, 2017.

N. Evangelou, D. Konz, M. M. Esiri, S. Smith, J. Palace et al., Regional axonal loss in the corpus callosum correlates with cerebral white matter lesion volume and distribution in multiple sclerosis, Brain : A Journal of Neurology, vol.123, pp.1845-1849, 2000.

L. Ferini-strambi, M. Bozzali, M. Cercignani, A. Oldani, M. Zucconi et al., Magnetization transfer and diffusion-weighted imaging in nocturnal frontal lobe epilepsy, Neurology, vol.54, issue.12, pp.2331-2333, 2000.

K. T. Fernando, D. J. Tozer, K. A. Miszkiel, R. M. Gordon, J. K. Swanton et al., Magnetization transfer histograms in clinically isolated syndromes suggestive of multiple sclerosis, Brain, vol.128, issue.12, pp.2911-2925, 2005.

M. Filippi, Linking structural, metabolic and functional changes in multiple sclerosis, European Journal of Neurology, vol.8, issue.4, pp.291-297, 2001.

M. Filippi, Oxford Textbooks in Clinical Neurology, 2015.

M. Filippi, M. Cercignani, M. Inglese, M. A. Horsfield, and G. Comi, Diffusion tensor magnetic resonance imaging in multiple sclerosis, Neurology, vol.56, issue.3, pp.304-311, 2001.

M. Filippi, N. De-stefano, V. Dousset, and J. Mcgowan, MR Imaging in White Matter Diseases of the Brain and Spinal Cord, medical radiology -diagnostic imaging and radiation, 2005.

M. Filippi, V. Dousset, H. F. Mcfarland, D. H. Miller, and R. I. Grossman, Role of magnetic resonance imaging in the diagnosis and monitoring of multiple sclerosis : consensus report of the White Matter Study Group, Journal of magnetic resonance imaging, vol.15, issue.5, pp.499-504, 2002.

M. Filippi, G. Iannucci, M. Cercignani, M. Assunta-rocca, A. Pratesi et al., A quantitative study of water diffusion in multiple sclerosis lesions and normal-appearing white matter using echo-planar imaging, Archives of Neurology, vol.57, issue.7, pp.1017-1021, 2000.

M. Filippi, G. Iannucci, C. Tortorella, L. Minicucci, M. A. Horsfield et al., Comparison of MS clinical phenotypes using conventional and magnetization transfer MRI, Neurology, vol.52, issue.3, pp.588-594, 1999.

M. Filippi, P. Preziosa, and M. A. Rocca, Magnetic resonance outcome measures in multiple sclerosis trials : time to rethink ?, Current Opinion in Neurology, vol.27, issue.3, pp.290-299, 2014.

M. Filippi, P. Preziosa, and M. A. Rocca, Microstructural MR Imaging Techniques in Multiple Sclerosis, Neuroimaging Clinics of North America, vol.27, issue.2, pp.313-333, 2017.

. Bibliographie,

M. Filippi, C. Tortorella, M. Rovaris, M. Bozzali, F. Possa et al., Changes in the normal appearing brain tissue and cognitive impairment in multiple sclerosis, Neurosurgery, and Psychiatry, vol.68, issue.2, pp.157-161, 2000.

P. Fillard, Riemannian processing of tensors for diffusion MRI and computational anatomy of the brain, 2008.
URL : https://hal.archives-ouvertes.fr/tel-00265129

R. A. Fisher, The Use of Multiple Measurements in Taxonomic Problems, Annals of Eugenics, vol.7, issue.2, pp.179-188, 1936.

A. M. Fjell, K. B. Walhovd, C. Fennema-notestine, L. K. Mcevoy, D. J. Hagler et al., One year brain atrophy evident in healthy aging, The Journal of neuroscience : the official journal of the Society for Neuroscience, vol.29, issue.48, pp.15223-15231, 2009.

P. T. Fletcher and S. Joshi, Principal Geodesic Analysis on Symmetric Spaces : Statistics of Diffusion Tensors, Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, pp.87-98, 2004.

M. Garey, D. Johnson, and H. Witsenhausen, The complexity of the generalized lloydmax problem (corresp.), IEEE Transactions on Information Theory, vol.28, issue.2, pp.255-256, 1982.

Y. Ge, M. Law, G. Johnson, J. Herbert, J. S. Babb et al., Preferential occult injury of corpus callosum in multiple sclerosis measured by diffusion tensor imaging, Journal of Magnetic Resonance Imaging, vol.20, issue.1, pp.1-7, 2004.

G. Giulietti, M. Torso, L. Serra, B. Spanò, C. Marra et al., Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer's disease patients, Disease Neuroimaging Initiative (ADNI), 2018.

B. G. Goodyear, N. M. Zayed, F. Cortese, J. Trufyn, and F. Costello, Skewness of Fractional Anisotropy Detects Decreased White Matter Integrity Resulting From Acute Optic Neuritis, Investigative Ophthalmology & Visual Science, vol.56, issue.12, pp.7597-7603, 2015.

J. Graff-radford and K. Kantarci, Magnetic resonance spectroscopy in Alzheimer's disease, Neuropsychiatric Disease and Treatment, vol.9, pp.687-696, 2013.

J. Grande-barreto and M. Del-pilar-gomez-gil, Partial Volume Segmentation inMagnetic Resonance Imaging (MRI), 2017.

Y. Grignon, C. Duyckaerts, M. Bennecib, and J. J. Hauw, Cytoarchitectonic alterations in the supramarginal gyrus of late onset Alzheimer's disease, Acta Neuropathologica, vol.95, issue.4, pp.395-406, 1998.

D. Gross, J. Shortle, J. Thompson, and C. Harris, Fundamentals of Queueing Theory. Wiley Series in Probability and Statistics, 2011.

Y. Han and H. W. Park, Automatic brain MR image registration based on Talairach reference system, Proceedings 2003 International Conference on Image Processing, 2003.

D. J. Hand and C. C. Taylor, Multivariate analysis of variance and repeated measures : A practical approach for behavioural scientists. Multivariate analysis of variance and repeated measures : A practical approach for behavioural scientists, 1987.

H. Hanyu, T. Asano, T. Iwamoto, M. Takasaki, H. Shindo et al., Magnetization transfer measurements of the hippocampus in patients with Alzheimer's disease, vascular dementia, and other types of dementia, AJNR. American journal of neuroradiology, vol.21, issue.7, pp.1235-1242, 2000.

J. F. Hiehle, R. E. Lenkinski, R. I. Grossman, V. Dousset, K. N. Ramer et al., Correlation of spectroscopy and magnetization transfer imaging in the evaluation of demyelinating lesions and normal appearing white matter in multiple sclerosis, Magnetic Resonance in Medicine, vol.32, issue.3, pp.285-293, 1994.

D. Hoa, L'Irm Pas Pas, 2007.

J. R. Hodges, Alzheimer's centennial legacy : origins, landmarks and the current status of knowledge concerning cognitive aspects, Brain : A Journal of Neurology, vol.129, pp.2811-2822, 2006.

Y. Huang and Y. Fong, Identifying optimal biomarker combinations for treatment selection via a robust kernel method, Biometrics, vol.70, issue.4, pp.891-901, 2014.

G. Iannucci, L. Minicucci, M. Rodegher, M. P. Sormani, G. Comi et al., Correlations between clinical and MRI involvement in multiple sclerosis : assessment using T(1), T(2) and MT histograms, Journal of the Neurological Sciences, vol.171, issue.2, pp.121-129, 1999.

G. Iannucci, C. Tortorella, M. Rovaris, M. P. Sormani, G. Comi et al., , 2000.

, Prognostic Value of MR and Magnetization Transfer Imaging Findings in Patients withClinically Isolated Syndromes Suggestive ofMultiple Sclerosis at Presentation, American Journal of Neuroradiology, vol.21, issue.6, pp.1034-1038

M. Inglese, M. Rovaris, S. Bianchi, L. La-mantia, G. L. Mancardi et al., Magnetic resonance imaging, magnetisation transfer imaging, and diffusion weighted imaging correlates of optic nerve, brain, and cervical cord damage in Leber's hereditary optic neuropathy, Neurosurgery, and Psychiatry, vol.70, issue.4, pp.444-449, 2001.

P. Jaccard, Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines, Bulletin de la Société Vaudoise des Sciences Naturelles, vol.37, pp.241-272, 1901.

J. Harold, An invariant form for the prior probability in estimation problems, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, vol.186, pp.453-461, 1007.

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, ICML, 1994.

I. T. Jolliffe, Principal Component Analysis. Springer Series in Statistics, 1986.

S. E. Jones, B. R. Buchbinder, A. , and I. , Three-dimensional mapping of cortical thickness using Laplace's equation, Human Brain Mapping, vol.11, issue.1, pp.12-32, 2000.

J. Josse, Principal component methods -hierarchical clustering -partitional clustering : why would we need to choose for visualizing data ?, 2010.

K. Juottonen, M. P. Laakso, K. Partanen, and H. Soininen, Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease, AJNR. American journal of neuroradiology, vol.20, issue.1, pp.139-144, 1999.

L. P. Kaelbling, M. L. Littman, and A. W. Moore, Reinforcement Learning : A Survey, Journal of Artificial Intelligence Research, vol.4, pp.237-285, 1996.

T. Kailath, The Divergence and Bhattacharyya Distance Measures in Signal Selection, IEEE Transactions on Communication Technology, vol.15, issue.1, pp.52-60, 1967.

. Bibliographie,

H. F. Kaiser, The Application of Electronic Computers to Factor Analysis, Educational and Psychological Measurement, vol.20, issue.1, pp.141-151, 1960.

N. F. Kalkers, R. Q. Hintzen, J. H. Waesberghe, R. H. Lazeron, R. A. Schijndel et al., Magnetization transfer histogram parameters reflect all dimensions of MS pathology, including atrophy, Journal of the Neurological Sciences, vol.184, issue.2, pp.155-162, 2001.

L. Kang, A. Liu, and L. Tian, Linear combination methods to improve diagnostic/prognostic accuracy on future observations, Statistical methods in medical research, vol.25, issue.4, pp.1359-1380, 2016.

Y. Kang, S. H. Choi, Y. Kim, K. G. Kim, C. Sohn et al., Gliomas : Histogram analysis of apparent diffusion coefficient maps with standardor high-b-value diffusion-weighted MR imaging-correlation with tumor grade, Radiology, vol.261, issue.3, pp.882-890, 2011.

K. Kantarci, S. D. Weigand, R. C. Petersen, B. F. Boeve, D. S. Knopman et al., Longitudinal 1h MRS changes in mild cognitive impairment and Alzheimer's disease, Neurobiology of aging, vol.28, issue.9, pp.1330-1339, 2007.

K. Kantarci, Y. Xu, M. M. Shiung, P. C. O'brien, R. H. Cha et al., COMPARATIVE DIAGNOSTIC UTILITY OF DIFFERENT MR MODALITIES IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER'S DISEASE, vol.14, pp.198-207, 2002.

Y. Karaca, Y. Zhang, C. Cattani, A. , and U. , The Differential Diagnosis of Multiple Sclerosis Using Convex Combination of Infinite Kernels, CNS & neurological disorders drug targets, vol.16, issue.1, pp.36-43, 2017.

L. Kaufman and P. Rousseeuw, Clustering by means of Medoids. Statistical Data Analysis Based on the L1-Norm and Related Methods, pp.405-416, 1987.

U. W. Kaunzner and S. A. Gauthier, MRI in the assessment and monitoring of multiple sclerosis : an update on best practice, Therapeutic Advances in Neurological Disorders, vol.10, issue.6, pp.247-261, 2017.

Z. Khaleeli, D. R. Altmann, M. Cercignani, O. Ciccarelli, D. H. Miller et al., Magnetization transfer ratio in gray matter : a potential surrogate marker for progression in early primary progressive multiple sclerosis, Archives of Neurology, vol.65, issue.11, pp.1454-1459, 2008.

R. J. Killiany, B. T. Hyman, T. Gomez-isla, M. B. Moss, R. Kikinis et al., MRI measures of entorhinal cortex vs hippocampus in preclinical AD, Neurology, vol.58, issue.8, pp.1188-1196, 2002.

W. E. Klunk, K. Panchalingam, J. Moossy, R. J. Mcclure, and J. W. Pettegrew, N-acetyl-Laspartate and other amino acid metabolites in Alzheimer's disease brain : a preliminary proton nuclear magnetic resonance study, Neurology, vol.42, issue.8, pp.1578-1585, 1992.

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artif. Intell, 1997.

S. Kolouri, S. R. Park, M. Thorpe, D. Slepcev, and G. K. Rohde, Optimal Mass Transport : Signal processing and machine-learning applications, IEEE Signal Processing Magazine, vol.34, issue.4, pp.43-59, 2017.

I. Kouskoumvekaki, Z. Yang, S. O. Jónsdóttir, L. Olsson, and G. Panagiotou, Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification, BMC bioinformatics, vol.9, p.59, 2008.

S. Kullback and R. A. Leibler, On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.

J. F. Kurtzke, Rating neurologic impairment in multiple sclerosis : an expanded disability status scale (EDSS), Neurology, vol.33, issue.11, pp.1444-1452, 1983.

R. K. Lama, J. Gwak, J. Park, and S. Lee, Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features, Journal of Healthcare Engineering, 2017.

S. Lan, J. Clarke, and C. Barnhart, Planning for Robust Airline Operations : Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions, Transportation Science, vol.40, issue.1, pp.15-28, 2006.

M. Law, R. Young, J. Babb, E. Pollack, J. et al., Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas, AJNR. American journal of neuroradiology, vol.28, issue.4, pp.761-766, 2007.

L. Bihan, D. Breton, and E. , Imagerie de diffusion in-vivo par résonance magnétique nucléaire. Comptes-Rendus de l'Académie des, Sciences, vol.93, issue.5, pp.27-34, 1985.

J. P. Lerch and A. C. Evans, Cortical thickness analysis examined through power analysis and a population simulation, NeuroImage, vol.24, issue.1, pp.163-173, 2005.

C. Liu, A. Liu, and S. Halabi, A min-max combination of biomarkers to improve diagnostic accuracy, Statistics in Medicine, vol.30, issue.16, pp.2005-2014, 2011.

M. Liu, D. Zhang, and D. Shen, View-centralized multi-atlas classification for Alzheimer's disease diagnosis, Human Brain Mapping, vol.36, issue.5, pp.1847-1865, 2015.

S. Lloyd, Least squares quantization in pcm, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.

L. Magy, La sclérose en plaques, Actualités Pharmaceutiques Hospitalières, vol.5, issue.19, pp.14-19, 2009.

C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, 2008.

M. C. Martínez-bisbal, E. Arana, L. Martí-bonmatí, E. Mollá, and B. Celda, Cognitive impairment : classification by 1h magnetic resonance spectroscopy, European Journal of Neurology, vol.11, issue.3, pp.187-193, 2004.

M. Mascalchi, A. Ginestroni, V. Bessi, N. Toschi, S. Padiglioni et al., Regional analysis of the magnetization transfer ratio of the brain in mild Alzheimer disease and amnestic mild cognitive impairment, AJNR. American journal of neuroradiology, vol.34, issue.11, pp.2098-2104, 2013.

J. M. Mateos-pérez, M. Dadar, M. Lacalle-aurioles, Y. Iturria-medina, Y. Zeighami et al., Structural neuroimaging as clinical predictor : A review of machine learning applications, NeuroImage : Clinical, vol.20, pp.506-522, 2018.

K. Matusita, On the theory of statistical decision functions, Annals of the Institute of Statistical Mathematics, vol.3, issue.1, pp.17-35, 1951.

W. I. Mcdonald, A. Compston, G. Edan, D. Goodkin, H. P. Hartung et al., Recommended diagnostic criteria for multiple sclerosis : guidelines from the International Panel on the diagnosis of multiple sclerosis, Annals of Neurology, vol.50, issue.1, pp.121-127, 2001.

. Bibliographie,

H. F. Mcfarland, F. Barkhof, J. Antel, and D. H. Miller, The role of MRI as a surrogate outcome measure in multiple sclerosis, Multiple Sclerosis (Houndmills, vol.8, pp.40-51, 2002.

G. Mckhann, D. Drachman, M. Folstein, R. Katzman, D. Price et al., Clinical diagnosis of Alzheimer's disease : report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, Neurology, vol.34, issue.7, pp.939-944, 1984.

G. M. Mckhann, D. S. Knopman, H. Chertkow, B. T. Hyman, C. R. Jack et al., The diagnosis of dementia due to Alzheimer's disease : recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease, Alzheimer's & Dementia : The Journal of the Alzheimer's Association, vol.7, issue.3, pp.263-269, 2011.

S. Messick and R. Abelson, The additive constant problem in multidimensional scaling, Psychometrika, vol.21, issue.1, pp.1-15, 1956.

A. Metastasio, P. Rinaldi, R. Tarducci, E. Mariani, F. T. Feliziani et al., Conversion of MCI to dementia : Role of proton magnetic resonance spectroscopy, Neurobiology of Aging, vol.27, issue.7, pp.926-932, 2006.

A. E. Miller and R. W. Rhoades, Treatment of relapsing-remitting multiple sclerosis : current approaches and unmet needs, Current Opinion in Neurology, vol.25, pp.4-10, 2012.

R. Min, G. Wu, J. Cheng, Q. Wang, and D. Shen, Multi-atlas based representations for Alzheimer's disease diagnosis, Human Brain Mapping, vol.35, issue.10, pp.5052-5070, 2014.

T. P. Minka, Estimating a Gamma distribution, 2002.

P. D. Molyneux, G. J. Barker, F. Barkhof, K. Beckmann, F. Dahlke et al., Clinical-MRI correlations in a European trial of interferon beta-1b in secondary progressive MS, and European Study Group on Interferon Beta-1b in Secondary Progressive MS, vol.57, pp.2191-2197, 2001.

O. Monga and S. Benayoun, Using partial derivatives of 3d images to extract typical surface features, Computer Vision and Image Understanding, vol.61, issue.2, pp.171-189, 1995.
URL : https://hal.archives-ouvertes.fr/inria-00615546

M. C. Morris, The role of nutrition in Alzheimer's disease : epidemiological evidence, European Journal of Neurology, vol.16, issue.1, pp.1-7, 2009.

E. Mueller and G. Chatterji, Analysis of Aircraft Arrival and Departure Delay Characteristics, AIAA's Aircraft Technology, Integration, and Operations (ATIO) 2002 Technical Forum, 2002.

S. G. Mueller, N. Schuff, and M. W. Weiner, Evaluation of treatment effects in Alzheimer's and other neurodegenerative diseases by MRI and MRS, NMR in biomedicine, vol.19, issue.6, pp.655-668, 2006.

S. G. Mueller, M. W. Weiner, L. J. Thal, R. C. Petersen, C. Jack et al., The Alzheimer's Disease Neuroimaging Initiative, Neuroimaging clinics of North America, vol.15, issue.4, p.869, 2005.

M. Muñoz-ruiz, P. Hartikainen, J. Koikkalainen, R. Wolz, V. Julkunen et al., , 2012.

, Tensor-Based Morphometry and Voxel-Based Morphometry, Structural MRI in Frontotemporal Dementia : Comparisons between Hippocampal Volumetry, vol.7, p.52531

S. Niarchakou and M. Cech, ATFCM OPERATIONS MANUAL, Network Manager -EUROCONTROL, vol.22, 2018.

T. M. Nir, N. Jahanshad, J. E. Villalon-reina, A. W. Toga, C. R. Jack et al., Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging, NeuroImage : Clinical, vol.3, pp.180-195, 2013.

K. Novianingsih and R. Hadianti, Modeling flight departure delay distributions, 2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), pp.30-34, 2014.

A. O. Nusbaum, C. Y. Tang, T. Wei, M. S. Buchsbaum, and S. W. Atlas, Whole-brain diffusion MR histograms differ between MS subtypes, Neurology, vol.54, issue.7, pp.1421-1427, 2000.

G. Orrù, W. Pettersson-yeo, A. F. Marquand, G. Sartori, and A. Mechelli, Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease : a critical review, Neuroscience and Biobehavioral Reviews, vol.36, issue.4, pp.1140-1152, 2012.

H. Park, J. Yang, J. Seo, and J. Lee, Dimensionality reduced cortical features and their use in the classification of Alzheimer's disease and mild cognitive impairment, Neuroscience Letters, vol.529, issue.2, pp.123-127, 2012.

K. Pearson, Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material, Philosophical Transactions of the Royal Society of London Series A, vol.186, pp.343-414, 1895.

K. Pearson, Das Fehlergesetz und Seine Verallgemeinerungen Durch Fechner und Pearson, A Rejoinder. Biometrika, vol.4, issue.1, pp.169-212, 1905.

E. Pellegrini, L. Ballerini, M. D. Hernandez, F. M. Chappell, V. González-castro et al., Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia : A systematic review, Assessment & Disease Monitoring, vol.10, pp.519-535, 2018.

M. S. Pepe and M. L. Thompson, Combining diagnostic test results to increase accuracy, Biostatistics, vol.1, issue.2, pp.123-140, 2000.

R. C. Petersen, G. E. Smith, S. C. Waring, R. J. Ivnik, E. G. Tangalos et al., Mild cognitive impairment : clinical characterization and outcome, Archives of Neurology, vol.56, issue.3, pp.303-308, 1999.

P. Paul, L. Debruyne, D. Bernard, D. Mock, D. M. Defer et al., Pharmacokinetics and pharmacodynamics of MD1003 (high-dose biotin) in the treatment of progressive multiple sclerosis, Expert Opinion on Drug Metabolism & Toxicology, vol.12, issue.3, pp.327-344, 2016.

G. B. Pike, N. D. Stefano, S. Narayanan, G. S. Francis, J. P. Antel et al., Combined Magnetization Transfer and Proton Spectroscopic Imaging in the Assessment of Pathologic Brain Lesions in Multiple Sclerosis, American Journal of Neuroradiology, vol.20, issue.5, pp.829-837, 1999.

L. Pini, M. Pievani, M. Bocchetta, D. Altomare, P. Bosco et al., Brain atrophy in Alzheimer's Disease and aging, Ageing Research Reviews, vol.30, pp.25-48, 2016.

C. Pinson, Intérêt des séquences de transfert de magnétisation pour l'évaluation de l'activité des fistules périnéales dans la maladie de Crohn, p.103, 2015.

C. H. Polman, S. C. Reingold, B. Banwell, M. Clanet, J. A. Cohen et al., Diagnostic criteria for multiple sclerosis : 2010 Revisions to the McDonald criteria, Annals of Neurology, vol.69, issue.2, pp.292-302, 2011.

. Bibliographie,

C. H. Polman, S. C. Reingold, G. Edan, M. Filippi, H. Hartung et al., Diagnostic criteria for multiple sclerosis : 2005 revisions to the "McDonald Criteria, Annals of Neurology, vol.58, issue.6, pp.840-846, 2005.

C. Poupon, Detection des faisceaux de fibres de la substance blanche pour l'etude de la connectivite anatomique cerebrale. thesis, 1999.

D. C. Preston, MRI Basics" by, 2011.

D. S. Priyadharshini, A. , and A. , Kullback-Leibler Divergence Measurement for Clustering Based On Probability Distribution Similarity, International Journal of Innovative Research in Science, Engineering and Technology, vol.3, issue.3, 2014.

C. Promteangtrong, M. Kolber, P. Ramchandra, M. Moghbel, S. Houshmand et al., Multimodality Imaging Approach in Alzheimer disease. Part I : Structural MRI, Functional MRI, Diffusion Tensor Imaging and Magnetization Transfer Imaging, Dementia & Neuropsychologia, vol.9, issue.4, pp.318-329, 2015.

O. Querbes, Mesure de l'épaisseur corticale en IRM : application au diagnostic précoce individuel de la maladie d'Alzheimer età la notion de réserve cognitive, 2009.

O. Querbes, F. Aubry, J. Pariente, J. Lotterie, J. Démonet et al., Early diagnosis of Alzheimer's disease using cortical thickness : impact of cognitive reserve, Brain : A Journal of Neurology, vol.132, pp.2036-2047, 2009.

A. M. Racine, M. Brickhouse, D. A. Wolk, and B. C. Dickerson, The personalized Alzheimer's disease cortical thickness index predicts likely pathology and clinical progression in mild cognitive impairment, Assessment & Disease Monitoring, vol.10, pp.301-310, 2018.

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.66, issue.336, pp.846-850, 1971.

C. R. Rao, Information and accuracy attainable in the estimation of statistical parameters, Bulletin of the Calcutta Mathematical Society, vol.37, pp.81-91, 1945.

S. Rebbah, D. Delahaye, S. Puechmorel, P. Maréchal, F. Nicol et al., Classification of Multiple Sclerosis patients using a histogram-based K-Nearest Neighbors algorithm, Organization for Human Brain Mapping (OHBM2019), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02156448

S. Rebbah, D. Delahaye, S. Puechmorel, F. Nicol, P. Marechal et al., A Combined MRI Biomarker Approach Using a Non-Standard Multiple Factor Analysis, 11th International Congress on Image and Signal Processing, pp.1-6, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01998086

S. Rebbah, D. Delahaye, S. Puechmorel, F. Nicol, P. Maréchal et al., Support Vector Machine-based classification of Alzheimer's Disease population using a combination of structural MRI biomarkers, 2018.

S. Rebbah, F. Nicol, and S. Puechmorel, The Geometry of the Generalized Gamma Manifold and an Application to, Medical Imaging. Mathematics, vol.7, issue.8, p.674, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02159244

N. D. Richert, J. L. Ostuni, C. N. Bash, J. H. Duyn, H. F. Mcfarland et al., Serial whole-brain magnetization transfer imaging in patients with relapsing-remitting multiple sclerosis at baseline and during treatment with interferon beta-1b, AJNR. American journal of neuroradiology, vol.19, issue.9, pp.1705-1713, 1998.

N. D. Richert, J. L. Ostuni, C. N. Bash, T. P. Leist, H. F. Mcfarland et al., Interferon beta-1b and intravenous methylprednisolone promote lesion recovery in multiple sclerosis, Multiple Sclerosis, vol.7, issue.1, pp.49-58, 2001.

J. Richiardi, M. Gschwind, S. Simioni, J. Annoni, B. Greco et al., Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity, NeuroImage, vol.62, issue.3, pp.2021-2033, 2012.

B. H. Ridha, M. R. Symms, D. J. Tozer, K. C. Stockton, C. Frost et al., Magnetization transfer ratio in Alzheimer disease : comparison with volumetric measurements, AJNR. American journal of neuroradiology, vol.28, issue.5, pp.965-970, 2007.

P. Robert and Y. Escoufier, A Unifying Tool for Linear Multivariate Statistical Methods : The RV-Coefficient, Journal of the Royal Statistical Society Series C, vol.25, issue.3, pp.257-265, 1976.

M. A. Rocca, G. Iannucci, M. Rovaris, G. Comi, and M. Filippi, Occult tissue damage in patients with primary progressive multiple sclerosis is independent of T2-visible lesions-a diffusion tensor MR study, Journal of Neurology, vol.250, issue.4, pp.456-460, 2003.

S. Ropele, R. Schmidt, C. Enzinger, M. Windisch, N. P. Martinez et al., Longitudinal magnetization transfer imaging in mild to severe Alzheimer disease, AJNR. American journal of neuroradiology, vol.33, issue.3, pp.570-575, 2012.

S. Ross, Introduction to Probability and Statistics for Engineers and Scientists, 2009.

M. Rovaris, F. Agosta, M. P. Sormani, M. Inglese, V. Martinelli et al., Conventional and magnetization transfer MRI predictors of clinical multiple sclerosis evolution : a medium-term follow-up study, Brain, vol.126, issue.10, pp.2323-2332, 2003.

M. Rovaris, M. Bozzali, G. Iannucci, A. Ghezzi, D. Caputo et al., Assessment of normal-appearing white and gray matter in patients with primary progressive multiple sclerosis : a diffusion-tensor magnetic resonance imaging study, Archives of Neurology, vol.59, issue.9, pp.1406-1412, 2002.

M. Rovaris, M. Filippi, M. Falautano, L. Minicucci, M. A. Rocca et al., Relation between MR abnormalities and patterns of cognitive impairment in multiple sclerosis, Neurology, vol.50, issue.6, pp.1601-1608, 1998.

M. Rovaris, M. Filippi, L. Minicucci, G. Iannucci, G. Santuccio et al., , 2000.

, Cortical/subcortical disease burden and cognitive impairment in patients with multiple sclerosis, AJNR. American journal of neuroradiology, vol.21, issue.2, pp.402-408

M. Rovaris, A. Gass, R. Bammer, S. J. Hickman, O. Ciccarelli et al., Diffusion MRI in multiple sclerosis, Neurology, vol.65, issue.10, pp.1526-1532, 2005.

M. Rovaris, G. Iannucci, M. Falautano, F. Possa, V. Martinelli et al., Cognitive dysfunction in patients with mildly disabling relapsing-remitting multiple sclerosis : an exploratory study with diffusion tensor MR imaging, Journal of the Neurological Sciences, vol.195, issue.2, pp.103-109, 2002.

E. Ruiz, J. Ramírez, J. M. Górriz, and J. Casillas, Alzheimer's Disease Computer-Aided Diagnosis : Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification, Journal of Alzheimer's disease : JAD, vol.65, issue.3, pp.819-842, 2018.

J. Río, C. Auger, and R. , MR Imaging in Monitoring and Predicting Treatment Response in Multiple Sclerosis, Neuroimaging Clinics of North America, vol.27, issue.2, pp.277-287, 2017.

J. Samper-gonzález, N. Burgos, S. Bottani, S. Fontanella, P. Lu et al., Reproducible evaluation of classification methods in Alzheimer's disease : Framework and application to MRI and PET data, NeuroImage, vol.183, pp.504-521, 2018.

A. C. Santos, S. Narayanan, N. De-stefano, M. C. Tartaglia, S. J. Francis et al., Magnetization transfer can predict clinical evolution in patients with multiple sclerosis, Journal of Neurology, vol.249, issue.6, pp.662-668, 2002.

A. J. Schaefer, E. L. Johnson, A. J. Kleywegt, and G. L. Nemhauser, Airline Crew Scheduling Under Uncertainty, Transportation Science, vol.39, issue.3, pp.340-348, 2005.

K. Schmierer, F. Scaravilli, D. R. Altmann, G. J. Barker, and D. H. Miller, Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain, Annals of Neurology, vol.56, issue.3, pp.407-415, 2004.

B. Schmitzer and C. Schnörr, Object Segmentation by Shape Matching with Wasserstein Modes, Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.123-136, 2013.

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

N. Schuff, A. A. Capizzano, A. T. Du, D. L. Amend, J. O'neill et al., Selective reduction of N-acetylaspartate in medial temporal and parietal lobes in AD, Neurology, vol.58, issue.6, pp.928-935, 2002.

F. Sedel, C. Papeix, A. Bellanger, V. Touitou, C. Lebrun-frenay et al., High doses of biotin in chronic progressive multiple sclerosis : a pilot study, Multiple Sclerosis and Related Disorders, vol.4, issue.2, pp.159-169, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01251510

H. Shima, Geometry of hessian structures, Geometric Science of Information, pp.37-55, 2013.

T. K. Shonk, R. A. Moats, P. Gifford, T. Michaelis, J. C. Mandigo et al., Probable Alzheimer disease : diagnosis with proton MR spectroscopy, Radiology, vol.195, issue.1, pp.65-72, 1995.

A. D. Smith, Imaging the progression of Alzheimer pathology through the brain, Proceedings of the National Academy of Sciences, vol.99, issue.7, pp.4135-4137, 2002.

S. Song, J. H. Kim, S. Lin, R. P. Brendza, and D. M. Holtzman, Diffusion tensor imaging detects age-dependent white matter changes in a transgenic mouse model with amyloid deposition, Neurobiology of Disease, vol.15, issue.3, pp.640-647, 2004.

S. Song, S. Sun, M. J. Ramsbottom, C. Chang, J. Russell et al., Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water, NeuroImage, vol.17, issue.3, pp.1429-1436, 2002.

E. W. Stacy, A generalization of the gamma distribution, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1187-1192, 1962.

E. W. Stacy and G. A. Mihram, Parameter estimation for a generalized gamma distribution, Technometrics, vol.7, issue.3, pp.349-358, 1965.

M. D. Steenwijk, J. J. Geurts, M. Daams, B. M. Tijms, A. M. Wink et al., Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant, Brain : A Journal of Neurology, vol.139, pp.115-126, 2016.

. Bibliographie,

E. A. Steffen-smith, J. E. Sarlls, C. Pierpaoli, J. H. Shih, R. S. Bent et al., Diffusion Tensor Histogram Analysis of Pediatric Diffuse Intrinsic Pontine Glioma, 2014.

E. O. Stejskal and J. E. Tanner, Spin Diffusion Measurements : Spin Echoes in the Presence of a Time-Dependent Field Gradient, The Journal of Chemical Physics, vol.42, issue.1, pp.288-292, 1965.

Y. Stern, What is cognitive reserve ? Theory and research application of the reserve concept, Journal of the International Neuropsychological Society, vol.8, issue.3, pp.448-460, 2002.

D. Strozyk, K. Blennow, L. R. White, and L. J. Launer, CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study, Neurology, vol.60, issue.4, pp.652-656, 2003.

J. Q. Su and J. S. Liu, Linear Combinations of Multiple Diagnostic Markers, Journal of the American Statistical Association, vol.88, issue.424, pp.1350-1355, 1993.

Z. Su, W. Zeng, Y. Wang, Z. Lu, and X. Gu, Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis. Information Processing in Medical Imaging : Proceedings of the ... Conference, vol.24, pp.411-423, 2015.

N. Tambasco, P. Nigro, M. Romoli, S. Simoni, L. Parnetti et al., Magnetization transfer MRI in dementia disorders, Huntington's disease and parkinsonism, Journal of the Neurological Sciences, vol.353, issue.1-2, pp.1-8, 2015.

M. Thambisetty, J. Wan, A. Carass, Y. An, J. L. Prince et al., Longitudinal changes in cortical thickness associated with normal aging, NeuroImage, vol.52, issue.4, pp.1215-1223, 2010.

A. J. Thompson, B. L. Banwell, F. Barkhof, W. M. Carroll, T. Coetzee et al., Diagnosis of multiple sclerosis : 2017 revisions of the McDonald criteria, The Lancet Neurology, vol.17, issue.2, pp.162-173, 2018.

A. L. Tievsky, T. Ptak, and J. Farkas, Investigation of apparent diffusion coefficient and diffusion tensor anisotrophy in acute and chronic multiple sclerosis lesions, AJNR. American journal of neuroradiology, vol.20, issue.8, pp.1491-1499, 1999.

P. S. Tofts, G. R. Davies, and J. Dehmeshki, Histograms : Measuring Subtle Diffuse Disease, Quantitative MRI of the Brain, pp.581-610, 2004.

P. S. Tofts, G. R. Davies, and J. Dehmeshki, Quantitative MRI of the Brain : Principles of Physical Measurement, 2004.

J. Tohka, Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images : A review, World Journal of Radiology, vol.6, issue.11, pp.855-864, 2014.

C. Tortorella, B. Viti, M. Bozzali, M. P. Sormani, G. Rizzo et al., A magnetization transfer histogram study of normal-appearing brain tissue in MS, Neurology, vol.54, issue.1, pp.186-193, 2000.

A. Tourbah and I. Berry, Contribution des techniques de résonance magnétiqueà la SEP, Pathol Biol, vol.48, pp.151-61, 2000.

A. Tourbah, C. Lebrun-frenay, G. Edan, M. Clanet, C. Papeix et al., MD1003 (high-dose biotin) for the treatment of progressive multiple sclerosis : A randomised, double-blind, placebo-controlled study, Multiple Sclerosis, vol.22, issue.13, pp.1719-1731, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01414958

A. Tourbah, C. Lebrun-frenay, G. Edan, M. Clanet, C. Papeix et al., 24-Month-Treatment with-MD1003 (High Doses of Biotin) in Progressive Multiple Sclerosis : Results of the MS-SPI Trial Extension Phase (S49.004), Neurology, p.86, 2016.

G. Toussaint, Bibliography on estimation of misclassification, IEEE Transactions on Information Theory, vol.20, issue.4, pp.472-479, 1974.

N. Toussaint, J. Souplet, and P. Fillard, MedINRIA : Medical Image Navigation and Research Tool by INRIA, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00616047

D. J. Tozer, G. R. Davies, D. R. Altmann, D. H. Miller, and P. S. Tofts, Principal component and linear discriminant analysis of T1 histograms of white and grey matter in multiple sclerosis, Magnetic Resonance Imaging, vol.24, issue.6, pp.793-800, 2006.

D. J. Tozer and P. S. Tofts, Removing spikes caused by quantization noise from highresolution histograms, Magnetic Resonance in Medicine, vol.50, issue.3, pp.649-653, 2003.

A. Traboulsee, J. Dehmeshki, K. R. Peters, C. M. Griffin, P. A. Brex et al., Disability in multiple sclerosis is related to normal appearing brain tissue MTR histogram abnormalities, Multiple Sclerosis (Houndmills, vol.9, pp.566-573, 2003.

Y. Tu, M. O. Ball, and W. S. Jank, Estimating Flight Departure Delay Distributions-A Statistical Approach With Long-Term Trend and Short-Term Pattern, Journal of the American Statistical Association, vol.103, issue.481, pp.112-125, 2008.

O. Tuzel, F. Porikli, and P. Meer, Pedestrian Detection via Classification on Riemannian Manifolds, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.10, pp.1713-1727, 2008.

M. A. Van-buchem, R. I. Grossman, C. Armstrong, M. Polansky, Y. Miki et al., Correlation of volumetric magnetization transfer imaging with clinical data in MS, Neurology, vol.50, issue.6, pp.1609-1617, 1998.

W. M. Van-der-flier, D. M. Van-den-heuvel, A. W. Weverling-rijnsburger, E. L. Bollen, R. G. Westendorp et al., Magnetization transfer imaging in normal aging, mild cognitive impairment, and Alzheimer's disease, Annals of Neurology, vol.52, issue.1, pp.62-67, 2002.

J. H. Van-waesberghe, W. Kamphorst, C. J. De-groot, M. A. Van-walderveen, J. A. Castelijns et al., Axonal loss in multiple sclerosis lesions : magnetic resonance imaging insights into substrates of disability, Annals of Neurology, vol.46, issue.5, pp.747-754, 1999.

M. A. Van-walderveen, F. Barkhof, O. R. Hommes, C. H. Polman, H. Tobi et al., Correlating MRI and clinical disease activity in multiple sclerosis : relevance of hypointense lesions on short-TR/short-TE (T1-weighted) spin-echo images, Neurology, vol.45, issue.9, pp.1684-1690, 1995.

L. Vanquin, Biomarqueurs de la morphologie du cortex cérébral par imagerie par résonance magnétique (IRM) anatomique : applicationà la maladie d'Alzheimer. thesis, 2015.

P. Vemuri and C. R. Jack, Role of structural MRI in Alzheimer's disease, Research & Therapy, vol.2, issue.4, p.23, 2010.

M. Vågberg, T. Lindqvist, K. Ambarki, J. B. Warntjes, P. Sundström et al., Automated determination of brain parenchymal fraction in multiple sclerosis, AJNR. American journal of neuroradiology, vol.34, issue.3, pp.498-504, 2013.

M. W. Wagner, A. K. Narayan, T. Bosemani, T. A. Huisman, and A. Poretti, , 2016.

, Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors, Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging, vol.26, issue.3, pp.360-365

L. Wasserman, Optimal Transport and Wasserstein Distance, 2018.

L. N. Wasserstein, Markov processes over denumerable products of spaces describing large systems of automata, Prob. Inf. Transmission, p.5, 1969.

A. R. Webb and K. D. Copsey, Introduction to Statistical Pattern Recognition, Statistical Pattern Recognition, pp.1-32, 2011.

B. Weill and F. Batteux, Immunopathologie et réactions inflammatoires, De Boeck Supérieur. Google-Books, 2003.

D. J. Werring, D. Brassat, A. G. Droogan, C. A. Clark, M. R. Symms et al., The pathogenesis of lesions and normalappearing white matter changes in multiple sclerosis : a serial diffusion MRI study, Brain : A Journal of Neurology, vol.123, pp.1667-1676, 2000.

D. J. Werring, C. A. Clark, G. J. Barker, A. J. Thompson, and D. H. Miller, Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis, Neurology, vol.52, issue.8, pp.1626-1632, 1999.

P. S. Weston, I. J. Simpson, N. S. Ryan, S. Ourselin, and N. C. Fox, Diffusion imaging changes in grey matter in Alzheimer's disease : a potential marker of early neurodegeneration, Alzheimer's Research & Therapy, vol.7, issue.1, 2015.

M. Wilson, P. Morgan, X. Lin, B. Turner, and L. Blumhardt, Quantitative diffusion weighted magnetic resonance imaging, cerebral atrophy, and disability in multiple sclerosis, Neurosurgery, and Psychiatry, vol.70, issue.3, pp.318-322, 2001.

S. D. Wolff and R. S. Balaban, Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo, Magnetic Resonance in Medicine, vol.10, issue.1, pp.135-144, 1989.

R. Wolz, V. Julkunen, J. Koikkalainen, E. Niskanen, D. P. Zhang et al., Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease, PLOS ONE, vol.6, issue.10, p.25446, 2011.

V. Wottschel, Supervised machine learning in multiple sclerosis : applications to clinically isolated syndromes, Doctoral, 2017.

N. Xu, L. Sherry, and K. B. Laskey, Multifactor Model for Predicting Delays at, U.S. Airports. Transportation Research Record, vol.2052, issue.1, pp.62-71, 2008.

T. Xu, Y. Fang, A. Rong, W. , and J. , Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy, BMC Medical Research Methodology, p.15, 2015.

D. H. Ye, K. M. Pohl, and C. Davatzikos, Semi-supervised Pattern Classification : Application to Structural MRI of Alzheimer's Disease, 2011 International Workshop on Pattern Recognition in NeuroImaging, pp.1-4, 2011.

J. Yin and L. Tian, Optimal linear combinations of multiple diagnostic biomarkers based on Youden index, Statistics in Medicine, vol.33, issue.8, pp.1426-1440, 2014.

R. Young, J. Babb, M. Law, E. Pollack, J. et al., Comparison of regionof-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas, Journal of magnetic resonance imaging : JMRI, vol.26, issue.4, pp.1053-1063, 2007.

. Bibliographie,

C. S. Yu, F. C. Lin, Y. Liu, Y. Duan, H. Lei et al., Histogram analysis of diffusion measures in clinically isolated syndromes and relapsing-remitting multiple sclerosis, European Journal of Radiology, vol.68, issue.2, pp.328-334, 2008.

X. Zhu, Semi-Supervised Learning Literature Survey, 2006.

X. Zhu, N. Schuff, J. Kornak, B. Soher, K. Yaffe et al., Effects of Alzheimer disease on fronto-parietal brain N-acetyl aspartate and myo-inositol using magnetic resonance spectroscopic imaging, Alzheimer Disease and Associated Disorders, vol.20, issue.2, pp.77-85, 2006.

S. Zieger, N. Weik, and N. Nießen, The influence of buffer time distributions in delay propagation modelling of railway networks, Journal of Rail Transport Planning & Management, vol.8, issue.3, pp.220-232, 2018.

R. Zivadinov, T. Uher, J. Hagemeier, M. Vaneckova, D. P. Ramasamy et al., A serial 10-year follow-up study of brain atrophy and disability progression in RRMS patients, Multiple Sclerosis (Houndmills, vol.22, p.674, 2016.

C. Internationales,

S. ?-rebbah, D. Delahaye, S. Puechmorel, P. Maréchal, F. Nicol et al., Classification of Multiple Sclerosis patients using a histogram-based K-Nearest Neighbors algorithm. Presented at the Organization for Human Brain Mapping, 2019.

S. ?-rebbah, D. Delahaye, S. Puechmorel, F. Nicol, P. Maréchal et al., Support Vector Machine-based classification of Alzheimer's Disease population using a combination of structural MRI biomarkers, Presented at the ISMRM Workshop on Machine Learning Part II, 2018.

S. ?-rebbah, D. Delahaye, S. Puechmorel, F. Nicol, P. Marechal et al., A Combined MRI Biomarker Approach Using a Non-Standard Multiple Factor Analysis, 11th International Congress on Image and Signal Processing, pp.1-6, 2018.

,

C. Nationales,

S. ?-rebbah and S. Puechmorel, The geometry of the generalized gamma manifold with an application in medical imaging, Presented at the Société de Mathématiques Appliquées et Industrielles (SMAI 2019), 2019.

S. ?-rebbah, D. Delahaye, S. Puechmorel, F. Nicol, P. Marechal et al., Aggregation of MRI biomarkers in multiple sclerosis clinical trials using geometric PCA. Presented at ARSEP -Spain FRENCH MS MEETING, 2018.

S. ?-rebbah, N. Chauveau, I. Berry, and F. Aubry, Vector generalized linear model applied to cortical thickness in neurodegenerative disease follow-up. Presented at ARSEP -12th annual MRI workshop, 2017.