. Introduction, N. Brain-cytoarchitecture, . Brain, and . Function,

F. A. Azevedo, L. R. Carvalho, L. T. Grinberg, J. M. Farfel, R. E. Ferretti et al., Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain, J. Comp. Neurol, vol.513, issue.5, pp.532-541, 2009.

J. M. Bekkers, Pyramidal neurons, Curr. Biol, vol.21, issue.24, p.975, 2011.

M. D. Waehnert, J. Dinse, M. Weiss, M. N. Streicher, P. Waehnert et al., Anatomically motivated modeling of cortical laminae, Neuroimage, vol.93, pp.210-220, 2014.

P. , R. S. Swenson, and M. D. , Review of Clinical and Functional Neuroscience, 2006.

S. T. Bok, Der Einfluß der in den Furchen und Windungen auftretenden Krümmungen der Großhirn-rinde auf die Rindenarchitektur, Z. Gesamte Neurol. Psychiatr, vol.121, issue.1, pp.682-750, 1929.

M. Mesulam and E. J. Mufson, Insula of the Old World Monkey . I : Architectonics in the Insuloorbito-temporal Component of the, J. Comp. Neurol, vol.22, pp.1-22, 1982.

K. Brodmann, Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt aufGrund des Zellaufbaues, 1909.

D. C. Van-essen, A tension-based theory of morphogenesis and compact wiring in the central nervous system, 1997.

W. Penfield and H. Jasper, Epilepsy and the Functional Anatomy of the Human Brain, 1954.

M. Sigman, The Secret Life of the Mind: How Our Brain Thinks, Feels, and Decides, 2017.

Z. M. Saygin, D. E. Osher, K. Koldewyn, G. Reynolds, J. D. Gabrieli et al., Anatomical connectivity patterns predict face selectivity in the fusiform gyrus, Nat. Neurosci, vol.15, pp.321-327, 2011.

S. L. Bressler and V. Menon, Large-scale brain networks in cognition: emerging methods and principles, Trends Cogn. Sci, vol.14, issue.6, pp.277-290, 2010.

J. M. Fuster, The Module: Crisis of a Paradigm, Neuron, vol.26, pp.51-53, 2000.

A. A. Ghazanfar and C. E. Schroeder, Is neocortex essentially multisensory?, Trends Cogn. Sci, vol.10, issue.6, pp.278-285, 2006.

T. E. Behrens, M. Woolrich, M. Jenkinson, H. Johansen-berg, R. Nunes et al., Characterization and propagation of uncertainty in diffusion-weighted MR imaging, Magn. Reson. Med, vol.50, pp.1077-1088, 2003.

K. H. Maier-hein, P. F. Neher, J. Houde, M. Côté, E. Garyfallidis et al., Nat. Commun, vol.8, p.1349, 2017.

M. Catani and M. Thiebaut-de-schotten, A diffusion tensor imaging tractography atlas for virtual in vivo dissections, Cortex, vol.44, issue.8, pp.1105-1132, 2008.
URL : https://hal.archives-ouvertes.fr/inserm-00322865

C. J. Donahue, S. N. Sotiropoulos, S. Jbabdi, M. Hernandez-fernandez, T. E. Behrens et al., Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey, J. Neurosci, vol.36, issue.25, pp.6758-6770, 2016.

A. D. Elster, Gradient-echo MR imaging: techniques and acronyms, Radiology, vol.186, issue.1, pp.1-8, 1993.

K. Brodmann, Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt aufGrund des Zellaufbaues, 1909.

D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sied, N. J. Kabani et al., Design and construction of a realistic digital brain phantom, IEEE Trans. Med. Imaging, vol.17, issue.3, pp.463-468, 1998.

B. T. Yeo, F. M. Krienen, J. Sepulcre, M. R. Sabuncu, D. Lashkari et al., The organization of the human cerebral cortex estimated by intrinsic functional connectivity, J. Neurophysiol, vol.106, pp.1125-1165, 2011.

G. Gong, Y. He, L. Concha, C. Lebel, D. W. Gross et al., Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography, Cereb. Cortex, vol.19, issue.3, pp.524-536, 2009.

T. Meynert, Der Bau der Gross-Hirnrinde und seine örtlichen Verschiedenheiten, nebst einem pathologischanatomischen Corollarium, Neuwied, JH Heuser'sche Ver-lagsbuchhandlung, pp.1-68, 1872.

C. Economo and G. N. Koskinas, Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Textband und Atlas, 1925.

W. Penfield and H. Jasper, Epilepsy and the Functional Anatomy of the Human Brain, 1954.

C. Der-malsburg, The Correlation Theory of Brain Function, Physics of Neural Networks, pp.95-119, 1994.

J. D. Schmahmann and D. N. Pandya, Fiber Pathways of the Brain, vol.1, 2006.

K. E. Stephan, The history of CoCoMac, Neuroimage, vol.80, pp.46-52, 2013.

N. Tzourio-mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard et al., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage, vol.15, issue.1, pp.273-289, 2002.

R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson et al., An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Neuroimage, vol.31, pp.968-980, 2006.

C. Destrieux, B. Fischl, A. Dale, and E. Halgren, Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature, Neuroimage, vol.53, issue.1, pp.1-15, 2010.

B. Thirion, G. Varoquaux, E. Dohmatob, and J. B. Poline, Which fMRI clustering gives good brain parcellations?, Front. Neurosci, vol.8, issue.8, pp.1-13, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01015172

S. Arslan, S. I. Ktena, A. Makropoulos, E. C. Robinson, D. Rueckert et al., Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex, 2018.

K. Amunts, A. Schleicher, and K. Zilles, Cytoarchitecture of the cerebral cortex -More than localization, Neuroimage, vol.37, issue.4, pp.1061-1065, 2007.

L. C. Triarhou, The Economo-Koskinas atlas revisited: Cytoarchitectonics and functional context, 2007.

S. Jbabdi and T. E. Behrens, Long-range connectomics, Ann. N. Y. Acad. Sci, vol.1305, pp.83-93, 2013.

M. A. De-reus, M. P. Van-den, and . Heuvel, The parcellation-based connectome: Limitations and extensions, Neuroimage, vol.80, pp.397-404, 2013.

S. B. Eickhoff, B. Thirion, G. Varoquaux, and D. Bzdok, Connectivity-based parcellation: Critique and implications, Hum. Brain Mapp, vol.36, issue.12, pp.4771-4792, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01184563

S. B. Eickhoff, B. T. Yeo, and S. Genon, Imaging-based parcellations of the human brain, Nat. Rev. Neurosci, 2018.

J. D. Power, A. L. Cohen, S. S. Nelson, G. S. Wig, K. A. Barnes et al., Functional network organization of the human brain, Neuron, vol.72, issue.4, pp.665-678, 2011.

A. G. Huth, W. A. De-heer, T. L. Griffiths, F. E. Theunissen, and J. L. Gallant, Natural speech reveals the semantic maps that tile human cerebral cortex, Nature, vol.532, issue.7600, pp.453-458, 2016.

C. A. Elsberg, The anatomy and surgery of the Edwin Smith surgical papyrus, J. Mt. Sinai Hoop, vol.12, pp.141-151, 1945.

M. Collice, R. Collice, and A. Riva, Who discovered the Sylvian fissure?, 2008.

J. Talairach and P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain, 1988.

A. C. Evans, A. L. Janke, D. L. Collins, and S. Baillet, NeuroImage Brain templates and atlases, Neuroimage, vol.62, issue.2, pp.911-922, 2012.

C. J. Holmes, R. Hoge, L. Collins, and A. C. Evans, Enhancement of T1 MR images using registration for signal averaging, NeuroImage, 2sd Int. Conf. Funct. Mapp. Hum. Brain, vol.3, p.28, 1996.

G. Auzias, O. Coulon, and A. Brovelli, MarsAtlas: A cortical parcellation atlas for functional mapping, Hum. Brain Mapp, vol.37, issue.4, pp.1573-1592, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01461819

A. W. Campbell, Histological Studies on the Localization of Cerebral Function, 1905.

E. G. Smith, A new topographical survey of the human cerebral cortex, being an account of the distribution of the anatomically distinct cortical areas and their relationship to the cerebral sulci, J Anat Physiol, vol.41, pp.237-254, 1907.

J. K. Peden and G. V. Bonin, The Neocortex of Hapale, J. Comp. Neurol, vol.86, pp.37-63, 1947.

A. Schleicher and K. Zilles, A quantitative approach to cytoarchitectonics: Analysis of structural inhomogeneities in nervous tissue using an image analyser, J. Microsc, vol.157, issue.3, pp.367-381, 1990.

S. B. Eickhoff, C. Rottschy, M. Kujovic, N. Palomero-gallagher, and K. Zilles, Organizational principles of human visual cortex revealed by receptor mapping, Cereb. Cortex, vol.18, issue.11, pp.2637-2645, 2008.

M. Rosenke, K. S. Weiner, M. A. Barnett, K. Zilles, K. Amunts et al., A cross-validated cytoarchitectonic atlas of the human ventral visual stream, Neuroimage, vol.170, pp.257-270, 2017.

H. Mohlberg, S. B. Eickhoff, A. Schleicher, K. Zilles, and A. K. , A new processing pipeline and release of cytoarchitectonic probabilistic maps -JuBrain, OHBM 2012, 2012.

S. L. Ding, J. J. Royall, S. M. Sunkin, L. Ng, B. A. Facer et al., Comprehensive cellularresolution atlas of the adult human brain, J. Comp. Neurol, vol.524, issue.16, pp.3127-3481, 2016.

J. M. Fuster, The Module: Crisis of a Paradigm, Neuron, vol.26, pp.51-53, 2000.

P. Johns, Clinical Neuroscience

D. Lashkari, E. Vul, N. Kanwisher, and P. Golland, Discovering structure in the space of fMRI selectivity profiles, Neuroimage, vol.50, issue.3, pp.1085-1098, 2010.

S. Ryali, T. Chen, K. Supekar, and V. Menon, A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI, Neuroimage, vol.65, pp.83-96, 2013.

T. Blumensath, S. Jbabdi, M. F. Glasser, D. C. Van-essen, K. Ugurbil et al., Spatially constrained hierarchical parcellation of the brain with resting-state fMRI, Neuroimage, vol.76, pp.313-324, 2013.

X. Shen, F. Tokoglu, X. Papademetris, and R. T. Constable, Groupwise whole-brain parcellation from resting-state fMRI data for network node identification, Neuroimage, vol.82, pp.403-415, 2013.

T. Kahnt, L. J. Chang, S. Q. Park, J. Heinzle, and J. Haynes, Connectivity-based parcellation of the human orbitofrontal cortex, J. Neurosci, issue.32, pp.6240-6250, 2012.

S. B. Eickhoff, D. Bzdok, A. R. Laird, C. Roski, S. Caspers et al., Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation, Neuroimage, vol.57, issue.938, 2011.

E. Eger, C. Keribin, V. Michel, A. Gramfort, and B. Thirion, A supervised clustering approach for fMRI -based inference of brain states, Pattern Recognit, vol.45, pp.2041-2049, 2012.
URL : https://hal.archives-ouvertes.fr/inria-00589201

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fmri datasets, Hum. Brain Mapp, vol.27, pp.678-693, 2006.

R. C. Craddock, G. A. James, P. E. Holtzheimer, X. P. Hu, and H. S. Mayberg, A whole brain fMRI atlas generated via spatially constrained spectral clustering, Hum. Brain Mapp, vol.33, issue.8, pp.1914-1928, 2012.

A. Schaefer, R. Kong, E. M. Gordon, T. O. Laumann, X. Zuo et al., Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI, Cereb. Cortex, pp.1-20, 2017.

E. M. Gordon, T. O. Laumann, B. Adeyemo, J. F. Huckins, W. M. Kelley et al., Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations, Cereb. Cortex, vol.26, issue.1, pp.288-303, 2016.

G. S. Wig, T. O. Laumann, A. L. Cohen, J. D. Power, S. M. Nelson et al., Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations, Cereb. Cortex, vol.24, issue.8, pp.2036-2054, 2014.

B. Deen, N. B. Pitskel, and K. A. Pelphrey, Three systems of insular functional connectivity identified with cluster analysis, Cereb. Cortex, vol.21, issue.7, pp.1498-1506, 2011.

L. Fan, H. Li, J. Zhuo, Y. Zhang, J. Wang et al., The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture, vol.26, pp.3508-3526, 2016.

M. Thiebaut-de-schotten, M. Urbanski, B. Batrancourt, R. Levy, B. Dubois et al., Rostro-caudal Architecture of the Frontal Lobes in Humans, Cereb. Cortex, pp.1-15, 2016.

G. Gallardo, W. Wells, R. Deriche, and D. Wassermann, Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach, Neuroimage, pp.1-14, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01426625

T. E. Behrens, H. Johansen-berg, M. W. Woolrich, S. M. Smith, C. A. Wheeler-kingshott et al., Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging, Nat. Neurosci, vol.6, pp.750-757, 2003.

S. Jbabdi, M. W. Woolrich, and T. E. Behrens, Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models, Neuroimage, vol.44, issue.2, pp.373-384, 2009.

M. J. Clarkson, I. B. Malone, M. Modat, K. K. Leung, N. Ryan et al., A Framework For Using Diffusion Weighted Imaging To Improve Cortical Parcellation, Lecture Notes in Computer Science, vol.6362, 2010.

S. Parisot, S. Arslan, J. Passerat-palmbach, W. M. Wells, and D. Rueckert, Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex, Inf. Process. Med. Imaging, vol.24, pp.600-612, 2015.

A. Anwander, M. Tittgemeyer, D. Von-cramon, A. Friederici, and T. Knosche, Connectivity-Based Parcellation of Broca's Area, Cereb. Cortex, vol.17, pp.816-825, 2006.

M. Thiebaut-de-schotten, M. Urbanski, R. Valabregue, D. J. Bayle, and E. Volle, Subdivision of the occipital lobes: An anatomical and functional MRI connectivity study, Cortex, vol.56, pp.121-137, 2014.
URL : https://hal.archives-ouvertes.fr/inserm-00790566

J. O'muircheartaigh and S. Jbabdi, Concurrent white matter bundles and grey matter networks using independent component analysis, 2018.

C. J. Bajada, R. L. Jackson, H. A. Haroon, H. Azadbakht, G. J. Parker et al., A graded tractographic parcellation of the temporal lobe, Neuroimage, vol.155, pp.503-512, 2017.

P. Roca, D. Rivière, P. Guevara, C. Poupon, and J. F. Mangin, Tractography-based parcellation of the cortex using a spatially-informed dimension reduction of the connectivity matrix, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), issue.1, pp.935-942, 2009.

S. Lefranc, P. Roca, M. Perrot, C. Poupon, D. L. Bihan et al., Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction, Med. Image Anal, vol.30, pp.11-29, 2016.

D. Moreno-dominguez, A. Anwander, and T. R. Knösche, A hierarchical method for wholebrain connectivity-based parcellation, Hum. Brain Mapp, vol.35, pp.5000-5025, 2014.

M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell et al., A multi-modal parcellation of human cerebral cortex, Nature, vol.536, issue.7615, pp.171-179, 2016.

I. Diez, P. Bonifazi, I. Escudero, B. Mateos, M. A. Muñoz et al., A novel brain partition highlights the modular skeleton shared by structure and function, Sci. Rep, vol.5, pp.1-13, 2015.

S. Parisot, B. Glocker, S. I. Ktena, S. Arslan, M. D. Schirmer et al., A flexible graphical model for multi-modal parcellation of the cortex, Neuroimage, vol.162, pp.226-248, 2017.

K. Zilles and K. Amunts, Individual variability is not noise, Trends Cogn. Sci, vol.17, pp.153-155, 2013.

S. L. Bressler and V. Menon, Large-scale brain networks in cognition: emerging methods and principles, Trends Cogn. Sci, vol.14, issue.6, pp.277-290, 2010.

R. E. Passingham, K. E. Stephan, and R. Kötter, The anatomical basis of functional localization in the cortex, Nat. Rev. Neurosci, vol.3, pp.606-616, 2002.

K. H. Maier-hein, P. Neher, J. Christophe, and M. Alexandre, Tractography -based connectomes are dominated by false -positive connections, pp.1-23, 2016.

G. Gallardo, R. Fick, W. Wells, R. Deriche, and D. Wassermann, Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models, MICCAI 2016 Work. Comput. Diffus. MRI, pp.99-112, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01358436

K. Brodmann, Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt aufGrund des Zellaufbaues, 1909.

M. Thiebaut-de-schotten, M. Urbanski, B. Batrancourt, R. Levy, B. Dubois et al., Rostro-caudal Architecture of the Frontal Lobes in Humans, Cereb. Cortex, pp.1-15, 2016.

J. D. Schmahmann and D. N. Pandya, Fiber Pathways of the Brain, vol.1, 2006.

R. E. Passingham, K. E. Stephan, and R. Kötter, The anatomical basis of functional localization in the cortex, Nat. Rev. Neurosci, vol.3, pp.606-616, 2002.

D. C. Van-essen, S. Jbabdi, S. N. Sotiropoulos, C. Chen, K. Dikranian et al., Mapping Connections in Humans and Non-Human Primates

N. Mri, , pp.337-358, 2014.

S. Jbabdi and T. E. Behrens, Long-range connectomics, Ann. N. Y. Acad. Sci, vol.1305, pp.83-93, 2013.

D. Moreno-dominguez, A. Anwander, and T. R. Knösche, A hierarchical method for wholebrain connectivity-based parcellation, Hum. Brain Mapp, vol.35, pp.5000-5025, 2014.

P. Roca, A. Tucholka, D. Rivière, P. Guevara, C. Poupon et al., Inter-subject connectivity-based parcellation of a patch of cerebral cortex, Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol.6362, issue.2, pp.347-354, 2010.

S. Parisot, S. Arslan, J. Passerat-palmbach, W. M. Wells, and D. Rueckert, Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex, Inf. Process. Med. Imaging, vol.24, pp.600-612, 2015.

J. F. Pendergast, S. J. Gange, M. A. Newton, M. J. Lindstrom, M. Palta et al., A Survey of Methods for Analyzing Clustered Binary Response Data, Int. Stat. Rev. / Rev. Int. Stat, vol.64, p.89, 1996.

T. Yarkoni, R. A. Poldrack, T. E. Nichols, D. C. Van-essen, and T. D. Wager, Large-scale automated synthesis of human functional neuroimaging data, Nat. Methods, vol.8, pp.665-670, 2011.

M. F. Glasser, S. N. Sotiropoulos, J. A. Wilson, T. S. Coalson, B. Fischl et al., The minimal preprocessing pipelines for the Human Connectome Project, Neuroimage, vol.80, pp.105-124, 2013.

M. F. Glasser, T. S. Coalson, E. C. Robinson, and C. D. ,

J. Hacker, E. Harwell, K. Yacoub, J. Ugurbil, C. F. Andersson et al., A multi-modal parcellation of human cerebral cortex, Nature, vol.536, issue.7615, pp.171-179, 2016.

J. A. Mccullagh and P. , Generalized Linear Models. London: Chapman and Hall/CRC, 1989.

K. M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. Mccarley et al., Using the logarithm of odds to define a vector space on probabilistic atlases, Med. Image Anal, vol.11, pp.465-477, 2007.

J. Ward, Hierarchical Grouping to Optimize an Objective Function, J. Am. Stat. Assoc, vol.58, issue.301, pp.236-244, 1963.

D. C. Van-essen, K. Ugurbil, E. Auerbach, D. Barch, T. E. Behrens et al., The Human Connectome Project: A data acquisition perspective, Neuroimage, vol.62, issue.4, pp.2222-2231, 2012.

D. M. Barch, G. C. Burgess, M. P. Harms, S. E. Petersen, B. L. Schlaggar et al., Function in the human connectome: Task-fMRI and individual differences in behavior, vol.80, pp.169-189, 2013.

J. Tournier, F. Calamante, D. G. Gadian, and A. Connelly, Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution, Neuroimage, vol.23, pp.1176-1185, 2004.

C. Reveley, A. K. Seth, C. Pierpaoli, A. C. Silva, D. Yu et al., Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography, Proc. Natl. Acad. Sci, vol.112, pp.2820-2828, 2015.

E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. Van-der-walt et al., Dipy, a library for the analysis of diffusion MRI data, Front. Neuroinform, vol.8, p.8, 2014.

F. Murtagh and P. Contreras, Methods of Hierarchical Clustering, Empir. Econ, vol.38, pp.23-45, 2011.

L. Hubert and P. Arabie, Comparing partitions, J. Classif, vol.2, pp.193-218, 1985.

K. J. Gorgolewski, G. Varoquaux, G. Rivera, Y. Schwartz, V. V. Sochat et al., NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain, Neuroimage, vol.124, pp.1242-1244, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01134575

F. Murtagh, Multidimensional Clustering Algorithms, 1985.

K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, When is "nearest neighbor" meaningful?, Database Theory-ICDT'99, pp.217-235, 1999.

B. G. Lance and W. T. Williams, A general theory of classificatory sorting strategies 1 . Hierarchical systems, Comput. J, vol.9, issue.4, pp.373-380, 1967.

J. Ghaziri, A. Tucholka, G. Girard, J. Houde, O. Boucher et al., The Corticocortical Structural Connectivity of the Human Insula, pp.1-13, 2015.

N. Kanwisher and G. Yovel, The fusiform face area: a cortical region specialized for the perception of faces, Philos. Trans. R. Soc. Lond. B. Biol. Sci, vol.361, issue.1476, pp.2109-2137, 2006.

J. D. Yeatman, K. S. Weiner, F. Pestilli, A. Rokem, A. Mezer et al., The vertical occipital fasciculus: A century of controversy resolved by in vivo measurements, Proc. Natl. Acad. Sci, vol.111, pp.5214-5223, 2014.

R. B. Mars, S. N. Sotiropoulos, and R. E. Passingham, Whole brain comparative anatomy using connectivity blueprints, p.245209, 2018.

D. Moreno-dominguez, A. Anwander, and T. R. Knösche, A hierarchical method for wholebrain connectivity-based parcellation, Hum. Brain Mapp, vol.35, pp.5000-5025, 2014.

S. Parisot, S. Arslan, J. Passerat-palmbach, W. M. Wells, and D. Rueckert, Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex, Inf. Process. Med. Imaging, vol.24, pp.600-612, 2015.

R. B. Mars, L. Verhagen, T. E. Gladwin, F. X. Neubert, J. Sallet et al., Comparing brains by matching connectivity profiles, Neurosci. Biobehav. Rev, vol.60, pp.90-97, 2016.

N. T. Gayraud, A. Rakotomamonjy, and M. Clerc, Optimal Transport Applied to Transfer Learning For P300 Detection, 7th Graz Brain-Computer Interface Conf, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01556603

D. Tuia, S. Member, and A. Rakotomamonjy, Optimal Transport for Domain Adaptation, IEEE Trans. Pattern Anal. Mach. Intel, vol.39, issue.9, pp.1853-1865, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02112785

M. A. De-reus, M. P. Van-den, and . Heuvel, The parcellation-based connectome: Limitations and extensions, Neuroimage, vol.80, pp.397-404, 2013.

D. M. Barch, G. C. Burgess, M. P. Harms, S. E. Petersen, B. L. Schlaggar et al., Function in the human connectome: Task-fMRI and individual differences in behavior, vol.80, pp.169-189, 2013.

D. E. Osher, R. R. Saxe, K. Koldewyn, J. D. Gabrieli, N. Kanwisher et al., Structural Connectivity Fingerprints Predict Cortical Selectivity for Multiple Visual Categories across Cortex, Cereb. Cortex, vol.26, issue.4, pp.1668-1683, 2016.

W. Penfield and H. Jasper, Epilepsy and the Functional Anatomy of the Human Brain, 1954.

M. Thiebaut-de-schotten, M. Urbanski, B. Batrancourt, R. Levy, B. Dubois et al., Rostro-caudal Architecture of the Frontal Lobes in Humans, Cereb. Cortex, pp.1-15, 2016.

M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Adv. Neural Inf. Process. Syst, pp.2292-2300, 2013.

M. F. Glasser, S. N. Sotiropoulos, J. A. Wilson, T. S. Coalson, B. Fischl et al., The minimal preprocessing pipelines for the Human Connectome Project, Neuroimage, vol.80, pp.105-124, 2013.

T. A. Huisman, Tumor-like lesions of the brain, Cancer Imaging, pp.10-13, 2009.

Y. I. Won, C. K. Chung, C. H. Kim, C. Park, B. Koo et al., White Matter Change Revealed by Diffusion Tensor Imaging in Gliomas, Brain Tumor Res Treat, vol.4, issue.2, pp.100-106, 2016.

M. J. Mcgirt, K. L. Chaichana, M. Gathinji, F. J. Attenello, K. Than et al., Independent association of extent of resection with survival in patients with malignant brain astrocytoma, J. Neurosurg, vol.110, pp.156-162, 2009.

B. J. Jellison, A. S. Field, J. Medow, M. Lazar, M. S. Salamat et al., Diffusion Tensor Imaging of Cerebral White Matter: A Pictorial Review of Physics, Fiber Tract Anatomy, and Tumor Imaging Patterns, 2004.

P. J. Basser and C. Pierpaoli, Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI, vol.111, pp.209-219, 1996.

A. J. Asman and B. A. Landman, Non-local statistical label fusion for multi-atlas segmentation, Med. Image Anal, vol.17, issue.2, pp.194-208, 2013.

L. Xu, A. Krzy?ak, and C. Y. Suen, Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition, IEEE Trans. Syst. Man Cybern, vol.22, issue.3, pp.418-435, 1992.

T. Rohlfing, D. B. Russakoff, and C. R. Maurer, Performance-based classifier combination in atlas-based image segmentation using expectationmaximization parameter estimation, IEEE Trans

, Med. Imaging, vol.23, issue.8, pp.983-994, 2004.

M. R. Sabuncu, B. T. Yeo, K. V. Leemput, B. Fischl, and P. Golland, A Generative Model for Image Segmentation Based on Label Fusion, IEEE Trans

, Med. Imaging, vol.29, issue.10, pp.1714-1729, 2010.

X. Artaechevarria, A. Muñoz-barrutia, and C. Ortiz-de-solórzano, Combination strategies in multi-atlas image segmentation: Application to brain MR data, IEEE Trans. Med. Imaging, vol.28, issue.8, pp.1266-1277, 2009.

G. Girard, A. Daducci, L. Petit, J. P. Thiran, K. Whittingstall et al., AxTract: Toward microstructure informed tractography, Hum. Brain Mapp, vol.38, issue.11, pp.5485-5500, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01564041

E. Caruyer, A. Daducci, M. Descoteaux, and J. ,

J. Houde, R. Thiran, and . Verma, Phantomas: a flexible software library to simulate diffusion MR phantoms, Ismrm, vol.17, p.20013, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00944644

D. S. Tuch, Q-ball imaging, Magn. Reson. Med, vol.52, pp.1358-1372, 2004.

K. V. Mardia and P. E. Jupp, Directional Statistics, 1999.

L. J. O'donnell, A. Daducci, D. Wassermann, and C. Lenglet, Advances in computational and statistical diffusion MRI, NMR Biomed, p.3805, 2017.

E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. Van-der-walt et al., Dipy, a library for the analysis of diffusion MRI data, Front. Neuroinform, vol.8, p.8, 2014.

D. Wassermann, N. Makris, Y. Rathi, M. Shenton, R. Kikinis et al., The white matter query language: a novel approach for describing human white matter anatomy, Brain Struct. Funct, vol.221, pp.4705-4721, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01247061

M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, Fsl, Neuroimage, vol.62, issue.2, pp.782-790, 2012.
URL : https://hal.archives-ouvertes.fr/inserm-01149484

M. Kuhn and K. Johnson, Applied Predictive Modeling, 2013.

G. Gallardo, N. Gayraud, R. Deriche, M. Clerc, S. Deslauriers-gauthier et al., Solving the Cross-Subject Parcel Matching Problem Using Optimal Transport, Med. Image Comput. Comput. Assist. Interv. -MICCAI, issue.11070, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01935684

G. Gallardo, B. Sylvain, and D. Wassermann, Diffusion Driven Label Fusion for White Matter MultiAtlas Segmentation, OHBM 2018 -Organ. Hum. Brain Mapp, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01737422

R. E. Passingham, K. E. Stephan, and R. Kötter, The anatomical basis of functional localization in the cortex, Nat. Rev. Neurosci, vol.3, pp.606-616, 2002.

H. Johansen-berg, T. E. Behrens, M. D. Robson, I. Drobnjak, M. F. Rushworth et al., Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex, Proc. Natl. Acad. Sci. U. S. A, vol.101, issue.36, pp.13335-13375, 2004.

C. J. Honey, O. Sporns, L. Cammoun, X. Gigandet, J. P. Thiran et al., Predicting human resting-state functional connectivity, Proc. Natl. Acad. Sci, vol.106, issue.6, pp.1-6, 2009.

S. B. Eickhoff, S. Jbabdi, S. Caspers, A. R. Laird, P. T. Fox et al., Anatomical and Functional Connectivity of Cytoarchitectonic Areas within the Human Parietal Operculum, J. Neurosci, vol.30, issue.18, pp.6409-6421, 2010.

D. Moreno-dominguez, A. Anwander, and T. R. Knösche, A hierarchical method for wholebrain connectivity-based parcellation, Hum. Brain Mapp, vol.35, pp.5000-5025, 2014.

F. Murtagh and P. Contreras, Methods of Hierarchical Clustering, Empir. Econ, vol.38, pp.23-45, 2011.

K. M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. Mccarley et al., Using the logarithm of odds to define a vector space on probabilistic atlases, Med. Image Anal, vol.11, pp.465-477, 2007.

K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, When is "nearest neighbor" meaningful?, Database Theory-ICDT'99, pp.217-235, 1999.

D. C. Van-essen, S. Jbabdi, S. N. Sotiropoulos, C. Chen, K. Dikranian et al., Mapping Connections in Humans and Non-Human Primates

N. Mri, , pp.337-358, 2014.

S. Jbabdi and T. E. Behrens, Long-range connectomics, Ann. N. Y. Acad. Sci, vol.1305, pp.83-93, 2013.

K. H. Maier-hein, P. Neher, J. Christophe, and M. Alexandre, Tractography -based connectomes are dominated by false -positive connections, pp.1-23, 2016.

I. Asimov, The Relativity of Wrong. Doubleday, 1988.

K. Zilles and K. Amunts, Individual variability is not noise, Trends Cogn. Sci, vol.17, pp.153-155, 2013.

R. B. Mars, L. Verhagen, T. E. Gladwin, F. X. Neubert, J. Sallet et al., Comparing brains by matching connectivity profiles, Neurosci. Biobehav. Rev, vol.60, pp.90-97, 2016.

R. B. Mars, S. N. Sotiropoulos, and R. E. Passingham, Whole brain comparative anatomy using connectivity blueprints, p.245209, 2018.

L. Xu, A. Krzy?ak, and C. Y. Suen, Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition, IEEE Trans. Syst. Man Cybern, vol.22, issue.3, pp.418-435, 1992.

L. J. O'donnell, A. Daducci, D. Wassermann, and C. Lenglet, Advances in computational and statistical diffusion MRI, NMR Biomed, p.3805, 2017.

E. S. Lutkenhoff, M. Rosenberg, J. Chiang, and K. Zhang, Optimized Brain Extraction for Pathological Brains ( optiBET ), PLoS One, pp.1-13, 2014.

G. Gallardo, D. Wassermann, R. Deriche, M. Descoteaux, and S. Deslauriers-gauthier, Information Flow in the White Matter During a Motor Task: A Structural Connectivity Driven Approach, Organ. Hum. Brain Mapp. Annu. Meet, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01534978

M. Frigo, G. Gallardo, I. Costantini, A. Daducci, D. Wassermann et al., Reducing false positive connection in tractograms using joint structure-function filtering, Organ. Hum. Brain Mapp, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01737434