J. Dubois, J. Lefèvre, H. Angleys, F. Leroy, C. Fischer et al., The dynamics of cortical folding waves and prematurity-related deviations revealed by spatial and spectral analysis of gyrification. Neu-roImage, vol.185, pp.934-946, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01781242

A. Giorgio, D. Stefano, and N. , Clinical use of brain volumetry, Journal of Magnetic Resonance Imaging, vol.37, issue.1, pp.1-14, 2013.

T. C. Durazzo, D. Tosun, S. Buckley, S. Gazdzinski, A. Mon et al., Cortical thickness, surface area, and volume of the brain reward system in alcohol dependence: Relationships to relapse and extended abstinence, Alcoholism: Clinical and Experimental Research, vol.35, issue.6, pp.1187-1200, 2011.

G. B. Frisoni, N. C. Fox, C. R. Jack, P. Scheltens, and P. M. Thompson, The clinical use of structural MRI in Alzheimer disease, 2010.

K. Novak, T. Czech, D. Prayer, W. Dietrich, W. Serles et al., Individual variations in the sulcal anatomy of the basal temporal lobe and its relevance for epilepsy surgery: an anatomical study performed using magnetic resonance imaging, Journal of Neurosurgery, vol.96, issue.3, pp.464-473, 2009.

D. Tosun, P. Siddarth, J. Levitt, and R. Caplan, Cortical thickness and sulcal depth: insights on development and psychopathology in paediatric epilepsy, BJPsych Open, 2015.

E. H. Aylward, J. Schwartz, S. Machlin, and G. Pearlson, Bicaudate ratio as a measure of caudate volume on MR images, American Journal of Neuroradiology, vol.12, issue.6, pp.1217-1222, 1991.

R. A. Bermel, R. Bakshi, C. Tjoa, S. R. Puli, and L. Jacobs, Bicaudate ratio as a magnetic resonance imaging marker of brain atrophy in multiple sclerosis, Archives of Neurology, vol.59, issue.2, pp.275-280, 2002.

S. Tich, . Nguyen, P. J. Anderson, R. W. Hunt, K. J. Lee et al., Neurodevelopmental and perinatal correlates of simple brain metrics in very preterm infants, Archives of Pediatrics and Adolescent Medicine, vol.165, issue.3, pp.216-222, 2011.

A. M. Winkler, M. R. Sabuncu, B. Yeo, B. Fischl, D. N. Greve et al., Measuring and comparing brain cortical surface area and other areal quantities, NeuroImage, vol.61, issue.4, pp.1428-1443, 2012.

J. N. Giedd and J. L. Rapoport, Structural MRI of Pediatric Brain Development: What Have We Learned and Where Are We Going?, Neuron, vol.67, issue.5, pp.728-734, 2010.

J. Lefèvre, D. Germanaud, J. Dubois, F. Rousseau, D. Santos et al., Are developmental trajectories of cortical folding comparable between cross-sectional datasets of fetuses and preterm newborns?, Cerebral Cortex, vol.26, issue.7, pp.3023-3035, 2016.

K. Hamano, N. Iwasaki, K. Kawashima, and H. Takita, Volumetric quantification of brain volume in children using sequential CT scans, Neuroradiology, vol.32, issue.4, pp.300-303, 1990.

A. Pfefferbaum, D. H. Mathalon, E. V. Sullivan, J. M. Rawles, R. B. Zipursky et al., A Quantitative Magnetic Resonance Imaging Study of Changes in Brain Morphology From Infancy to Late Adulthood, 1994.

A. L. Reiss, M. T. Abrams, H. S. Singer, J. L. Ross, and M. B. Denckla, Brain development, gender and IQ in children. A volumetric imaging study, Brain: a journal of neurology, vol.119, pp.1763-74, 1996.

M. Matsumae, R. Kikinis, I. A. Mórocz, A. V. Lorenzo, T. Sá-ndor et al., Age-related changes in intracranial compartment volumes in normal adults assessed by magnetic resonance imaging, Journal of Neurosurgery, vol.84, issue.6, pp.982-991, 1996.

D. G. Murphy, C. Decarli, A. R. Mcintosh, E. Daly, M. J. Mentis et al., Sex differences in human brain morphometry and metabolism: an in vivo quantitative magnetic resonance imaging and positron emission tomography study on the effect of aging. Archives of general psychiatry, vol.53, pp.585-94, 1996.

N. Iwasaki, K. Hamano, Y. Okada, Y. Horigome, J. Nakayama et al., Volumetric quantification of brain development using MRI, Neuroradiology, vol.39, issue.12, pp.841-846, 1997.

P. S. Hüppi, S. Warfield, R. Kikinis, P. D. Barnes, G. P. Zientara et al., Quantitative magnetic resonance imaging of brain development in premature and mature newborns, Annals of neurology, vol.43, issue.2, pp.224-235, 1998.

C. E. Coffey, Sex Differences in Brain Aging, Arch Neurol, 1998.

J. N. Giedd, J. Blumenthal, N. O. Jeffries, F. X. Castellanos, H. Liu et al., Brain development during childhood and adolescence: a longitudinal MRI study, Nature Neuroscience, vol.2, issue.10, pp.861-863, 1999.

H. Utsunomiya, K. Takano, M. Okazaki, and A. Mitsudome, Development of the temporal lobe in infants and children: Analysis by MR-based volumetry, American Journal of Neuroradiology, vol.20, issue.4, pp.717-723, 1999.

E. Courchesne, H. J. Chisum, J. Townsend, A. Cowles, J. Covington et al., Normal Brain Development and Aging: Quantitative Analysis at in Vivo MR Imaging in Healthy Volunteers, Radiology, vol.216, issue.3, pp.672-682, 2000.

I. S. Gousias, D. Rueckert, R. A. Heckemann, L. E. Dyet, J. P. Boardman et al., Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest, NeuroImage, vol.40, issue.2, pp.672-684, 2008.

R. C. Knickmeyer, S. Gouttard, C. Kang, D. Evans, J. K. Smith et al., A Structural MRI Study of Human Brain Development from Birth to 2 Years, Journal of Neuroscience, vol.28, issue.47, pp.12176-12182, 2010.

M. Kuklisova-murgasova, P. Aljabar, L. Srinivasan, S. J. Counsell, V. Doria et al., A dynamic 4D probabilistic atlas of the developing brain, NeuroImage, 2011.

N. Lange, Total and regional brain volumes in a population-based normative sample from 4 to 18 years: The NIH MRI study of normal brain development, Cerebral Cortex, vol.22, issue.1, pp.1-12, 2012.

C. Ms, S. Ortiz-mantilla, N. Makris, M. Gregas, J. Bacic et al., Regional Infant Brain Development: An MRI-Based Morphometric Analysis in 3 to 13 Month Olds, Cerebral Cortex, vol.23, issue.9, pp.2100-2117, 2013.

A. Makropoulos, P. Aljabar, R. Wright, B. Hüning, N. Merchant et al., Regional growth and atlasing of the developing human brain, NeuroImage, vol.125, pp.456-478, 2016.

M. Peterson, B. C. Warf, and S. J. Schiff, Normative human brain volume growth, Journal of Neurosurgery: Pediatrics, vol.21, pp.1-8, 2018.

W. M. Wells, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, Multi-Modal Volume Registration by Maximisation of Mutual Information, Medical Image Analysis, 1996.

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, Multimodality image registration by maximization of mutual information, IEEE Transactions on Medical Imaging, vol.16, issue.2, pp.187-198, 1997.

S. Ourselin, A. Roche, S. Prima, and N. Ayache, Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images, pp.557-566, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00615860

O. Commowick, N. Wiest-daessle, and S. Prima, Block-matching strategies for rigid registration of multimodal medical images, Proceedings-International Symposium on Biomedical Imaging, pp.700-703, 2012.
URL : https://hal.archives-ouvertes.fr/inserm-00681610

X. Pennec, L'incertitude dans les problèmes de reconnaissance et de recalage-Applications en imagerie mé dicale et biologie molé culaire, 1996.

B. Horn, Closed-form solution of absolute orientation using unit quaternions, Journal of the Optical Society of America A, vol.4, issue.4, p.629, 1987.

A. Guimond, J. Meunier, and J. P. Thirion, Average brain models: A convergence study, vol.77, pp.192-210, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00072934

A. Legouhy, O. Commowick, F. Rousseau, and C. Barillot, Unbiased longitudinal brain atlas creation using robust linear registration and log-Euclidean framework for diffeomorphisms, Proceedings-International Symposium on Biomedical Imaging. IEEE, pp.1038-1041, 2019.
URL : https://hal.archives-ouvertes.fr/inserm-02099958

V. Arsigny, O. Commowick, X. Pennec, and N. Ayache, A Log-Euclidean Framework for Statistics on Diffeomorphisms, pp.924-931, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00615594

T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach, Medical Image Computing and Computer Assisted Intervention
URL : https://hal.archives-ouvertes.fr/inria-00280602

, , pp.754-761, 2008.

M. Bossa, M. Hernandez, and S. Olmos, Contributions to 3D diffeomorphic atlas estimation: Application to brain images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, issue.1, pp.667-674, 2007.

I. S. Gousias, A. D. Edwards, M. A. Rutherford, S. J. Counsell, J. V. Hajnal et al., Magnetic resonance imaging of the newborn brain: Manual segmentation of labelled atlases in term-born and preterm infants, NeuroImage, vol.62, issue.3, pp.1499-1509, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00797903

I. S. Gousias, A. Hammers, S. J. Counsell, L. Srinivasan, M. A. Rutherford et al., Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions, PLoS ONE, vol.8, issue.4, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00969111

R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers, Automatic anatomical brain MRI segmentation combining label propagation and decision fusion, NeuroImage, vol.33, issue.1, pp.115-126, 2006.

S. Prima, S. Ourselin, and N. Ayache, Computation of the mid-sagittal plane in 3-D brain images, IEEE Transactions on Medical Imaging, vol.21, issue.2, pp.122-138, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615857

G. Seber and C. J. Wild, Nonlinear Regression, 1989.

A. N. Spiess and N. Neumeyer, An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: A Monte Carlo approach, BMC Pharmacology, vol.10, pp.1-11, 2010.

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.

K. P. Burnham and D. R. Anderson, Model Selection and Multimodel Inference, 2002.

K. P. Burnham and D. R. Anderson, Multimodel inference: Understanding AIC and BIC in model selection, Sociological Methods and Research, vol.33, issue.2, pp.261-304, 2004.

E. J. Wagenmakers and S. Ferrel, AIC model selection using Akaike weights, Psychonomic Bulletin & Review, vol.11, issue.1, pp.192-196, 2004.

T. P. Lane and W. H. Dumouchel, Simultaneous Confidence Intervals in Multiple Regression, The American Statistician, vol.48, issue.4, pp.315-321, 1994.

Y. Benjamini and Y. Hochberg, Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society, vol.57, issue.1, pp.289-300, 1995.

G. Auzias, D. Guio, F. Pepe, A. Rousseau, F. Mangin et al., Model-driven parameterization of fetal cortical surfaces, Proceedings-International Symposium on Biomedical Imaging, 1260.
URL : https://hal.archives-ouvertes.fr/hal-01114993

S. H. Kim, I. Lyu, V. Fonov, C. Vachet, H. C. Hazlett et al., Development of cortical shape in the human brain from 6 to 24months of age via a novel measure of shape complexity, NeuroImage, vol.135, pp.163-176, 2016.

I. S. Gousias, A. Hammers, R. A. Heckemann, S. J. Counsell, L. E. Dyet et al., Atlas selection strategy for automatic segmentation of pediatric brain MRIs into 83 ROIs, IEEE International Conference on Imaging Systems and Techniques, IST 2010-Proceedings, pp.290-293, 2010.

P. Aljabar, R. A. Heckemann, A. Hammers, J. V. Hajnal, and D. Rueckert, Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy, NeuroImage, vol.46, issue.3, pp.726-738, 2009.

B. R. Collett, E. H. Aylward, J. Berg, C. Davidoff, J. Norden et al., Brain volume and shape in infants with deformational plagiocephaly. Child's Nervous System, vol.28, p.22447491, 2012.