I. S. Abramson, On bandwidth variation in kernel estimates-A square root law, Ann. Stat, vol.10, pp.1217-1223, 1982.
DOI : 10.1214/aos/1176345986
URL : https://doi.org/10.1214/aos/1176345986

A. Baddeley, I. Bárány, R. Schneider, and W. Weil, Lecture Notes in Mathematics Stochastic Geometry, 2004.

C. D. Barr and F. P. Schoenberg, On the Voronoi estimator for the intensity of an inhomogeneous planar Poisson process, Biometrika, vol.97, pp.977-984, 2010.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

E. T. Bullmore, J. Suckling, S. Overmeyer, S. Rabe-hesketh, E. Taylor et al., Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain, IEEE Trans. Med. Imaging, vol.18, pp.32-42, 1999.

K. Chung, J. Wallace, S. Y. Kim, S. Kalyanasundaram, A. S. Andalman et al., Structural and molecular interrogation of intact biological systems, Nature, vol.497, pp.332-337, 2013.
DOI : 10.1038/nature12107
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092167

J. Dauguet, T. Delzescaux, F. Condé, J. F. Mangin, N. Ayache et al., Three-dimensional reconstruction of stained histological slices and 3D non-linear registration with in-vivo MRI for whole baboon brain, J. Neurosci. Methods, vol.164, pp.191-204, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00616027

H. U. Dodt, U. Leischner, A. Schierloh, N. Jährling, C. P. Mauch et al., Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain, Nat. Methods, vol.4, pp.331-336, 2007.

A. Dorr, J. Lerch, S. Spring, N. Kabani, and R. Henkelman, High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice, NeuroImage, vol.42, pp.60-69, 2008.
DOI : 10.1016/j.neuroimage.2008.03.037

A. Dubois, J. Dauguet, A. Herard, L. Besret, E. Duchesnay et al., Automated three-dimensional analysis of histological and autoradiographic rat brain sections: application to an activation study, J. Cereb. Blood Flow Metab, vol.27, pp.1742-1755, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02155676

A. Dubois, A. S. Hérard, B. Delatour, P. Hantraye, G. Bonvento et al., Detection by voxel-wise statistical analysis of significant changes in regional cerebral glucose uptake in an APP/PS1 transgenic mouse model of Alzheimer's disease, NeuroImage, vol.51, pp.586-598, 2010.

C. Duyckaerts, G. Godefroy, and J. J. Hauw, Evaluation of neuronal numerical density by Dirichlet tessellation, J. Neurosci. Methods, vol.51, pp.90025-90031, 1994.
DOI : 10.1016/0165-0270(94)90025-6

A. Ertürk, K. Becker, N. Jährling, C. P. Mauch, C. D. Hojer et al., Three-dimensional imaging of solvent-cleared organs using 3DISCO, Nat. Protoc, vol.7, 1983.

K. J. Friston, C. D. Frith, P. F. Liddle, R. J. Dolan, A. A. Lammertsma et al., The relationship between global and local changes in PET scans, J. Cereb. Blood Flow Metab, vol.10, pp.458-466, 1990.
DOI : 10.1038/jcbfm.1990.88
URL : http://www.fil.ion.ucl.ac.uk/~karl/The%20Relationship%20Between%20Global%20and%20Local%20Changes%20in%20PET%20Scans.pdf

C. R. Genovese, N. A. Lazar, N. , and T. , Thresholding of statistical maps in functional neuroimaging using the false discovery rate, NeuroImage, vol.15, pp.870-878, 2002.

M. Grand'maison, S. P. Zehntner, M. K. Ho, F. Hébert, A. Wood et al., Early cortical thickness changes predict ?-amyloid deposition in a mouse model of Alzheimer's disease, Neurobiol. Dis, vol.54, pp.59-67, 2013.

H. Hama, H. Hioki, K. Namiki, T. Hoshida, H. Kurokawa et al., ScaleS: an optical clearing palette for biological imaging, Nat. Neurosci, vol.18, pp.1518-1529, 2015.
DOI : 10.1038/nn.4107

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Springer Series in Statistics, 2009.

M. T. Ke, S. Fujimoto, and T. Imai, SeeDB: a simple and morphologypreserving optical clearing agent for neuronal circuit reconstruction, Nat. Neurosci, vol.16, pp.1154-1161, 2013.
DOI : 10.1038/nn.3447

P. Kochunov, J. Lancaster, P. Thompson, A. W. Toga, P. Brewer et al., An optimized individual target brain in the Talairach coordinate system, NeuroImage, vol.17, pp.922-927, 2002.
DOI : 10.1016/s1053-8119(02)91084-0

J. Lebenberg, A. S. Hérard, A. Dubois, J. Dauguet, V. Frouin et al., Validation of MRI-based 3D digital atlas registration with histological and autoradiographic volumes: an anatomofunctional transgenic mouse brain imaging study, NeuroImage, vol.51, pp.1037-1046, 2010.

E. S. Lein, M. J. Hawrylycz, N. Ao, M. Ayres, A. Bensinger et al., Genome-wide atlas of gene expression in the adult mouse brain, Nature, vol.445, pp.168-176, 2007.

A. Lenartowicz and A. R. Mcintosh, The role of anterior cingulate cortex in working memory is shaped by functional connectivity, J. Cogn. Neurosci, vol.17, pp.1026-1042, 2005.

N. Lepore, C. Brun, Y. Y. Chou, A. D. Lee, M. Barysheva et al., Best individual template selection from deformation tensor minimization, 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.460-463, 2008.
DOI : 10.1109/isbi.2008.4541032
URL : https://hal.archives-ouvertes.fr/inria-00616172

F. Letronne, G. Laumet, A. M. Ayral, J. Chapuis, F. Demiautte et al., ADAM30 downregulates APP-linked defects through cathepsin D activation in Alzheimer's disease, vol.9, pp.278-292, 2016.
DOI : 10.1016/j.ebiom.2016.06.002
URL : https://doi.org/10.1016/j.ebiom.2016.06.002

D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, PET-CT image registration in the chest using free-form deformations, IEEE Trans. Med. Imaging, vol.22, pp.120-128, 2003.
DOI : 10.1109/tmi.2003.809072

L. Mucke, E. Masliah, G. Q. Yu, M. Mallory, E. M. Rockenstein et al., High-level neuronal expression of abeta 1-42 in wild-type human amyloid protein precursor transgenic mice: synaptotoxicity without plaque formation, J. Neurosci, vol.20, pp.4050-4058, 2000.

T. E. Nichols, Multiple testing corrections, nonparametric methods, and random field theory, NeuroImage, vol.62, pp.811-815, 2012.
DOI : 10.1016/j.neuroimage.2012.04.014
URL : http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/publications/Nichols_20yrsfMRI_MTP.pdf

H. Oh, C. Habeck, C. Madison, J. , and W. , Covarying alterations in A? deposition, glucose metabolism, and gray matter volume in cognitively normal elderly, Hum. Brain Mapp, vol.35, pp.297-308, 2014.

S. Ourselin, A. Roche, G. Subsol, X. Pennec, A. et al., Reconstructing a 3D structure from serial histological sections, Image Vis. Comput, vol.19, pp.25-31, 2001.
DOI : 10.1016/s0262-8856(00)00052-4
URL : https://hal.archives-ouvertes.fr/cea-00333669

G. Paxinos, F. , and K. , The Mouse Brain in Stereotaxic Coordinates, 2001.

G. Pengas, J. R. Hodges, P. Watson, and P. J. Nestor, Focal posterior cingulate atrophy in incipient Alzheimer's disease, Neurobiol. Aging, vol.31, pp.25-33, 2010.
DOI : 10.1016/j.neurobiolaging.2008.03.014

T. Ragan, L. R. Kadiri, K. U. Venkataraju, K. Bahlmann, J. Sutin et al., Serial two-photon tomography for automated ex vivo mouse brain imaging, Nat. Methods, vol.9, pp.255-258, 2012.
DOI : 10.1038/nmeth.1854
URL : http://europepmc.org/articles/pmc3297424?pdf=render

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo et al., Mapping of brain activity by automated volume analysis of immediate early genes, Cell, vol.165, pp.1789-802, 2016.

G. Salimi-khorshidi, S. M. Smith, and T. E. Nichols, Adjusting the effect of nonstationarity in cluster-based and TFCE inference, NeuroImage, vol.54, 2006.

L. Silvestri, M. Paciscopi, P. Soda, F. Biamonte, G. Iannello et al., Quantitative neuroanatomy of all Purkinje cells with light sheet microscopy and high-throughput image analysis, Front. Neuroanatomy, vol.9, p.68, 2015.

R. N. Spreng, R. A. Mar, and A. S. Kim, The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis, J. Cogn. Neurosci, vol.21, pp.489-510, 2009.

E. A. Susaki, K. Tainaka, D. Perrin, F. Kishino, T. Tawara et al., Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis, Cell, vol.157, pp.726-739, 2014.
DOI : 10.1016/j.cell.2014.03.042
URL : https://doi.org/10.1016/j.cell.2014.03.042

M. Vandenberghe, 3D Whole-Brain Quantitative Histopathology: Methodology and Applications in Mouse Models of Alzheimer's Disease. Theses, 2015.

M. E. Vandenberghe, Y. Balbastre, N. Souedet, A. Herard, M. Dhenain et al., Robust supervised segmentation of neuropathology whole-slide microscopy images, IEEE Eng. Med. Biol. Soc, pp.3851-3854, 2015.

M. E. Vandenberghe, A. S. Hérard, N. Souedet, E. Sadouni, M. D. Santin et al., High-throughput 3D whole-brain quantitative histopathology in rodents, Sci. Rep, vol.6, p.20958, 2016.
DOI : 10.1038/srep20958
URL : https://hal.archives-ouvertes.fr/hal-02155737

S. D. Vann, J. P. Aggleton, and E. A. Maguire, What does the retrosplenial cortex do?, Nat. Rev. Neurosci, vol.10, pp.792-802, 2009.
DOI : 10.1038/nrn2733

P. Viola and W. Wells, Alignment by maximization of mutual information, Proceedings of IEEE International Conference on Computer Vision, pp.16-23, 1995.

D. A. Vousden, J. Epp, H. Okuno, B. J. Nieman, M. Van-eede et al., Whole-brain mapping of behaviourally induced neural activation in mice, Brain Struct. Funct, vol.220, pp.2043-2057, 2015.

H. Wang, J. Zhu, A. , and T. , Serial optical coherence scanner for largescale brain imaging at microscopic resolution, NeuroImage, vol.84, pp.1007-1017, 2014.
DOI : 10.1016/j.neuroimage.2013.09.063
URL : http://europepmc.org/articles/pmc3928112?pdf=render

A. M. Winkler, G. R. Ridgway, G. Douaud, T. E. Nichols, and S. M. Smith, Faster permutation inference in brain imaging, NeuroImage, vol.141, pp.502-516, 2016.
DOI : 10.1016/j.neuroimage.2016.05.068
URL : https://doi.org/10.1016/j.neuroimage.2016.05.068

J. Wu, Y. He, Z. Yang, C. Guo, Q. Luo et al., 3D BrainCV: simultaneous visualization and analysis of cells and capillaries in a whole mouse brain with one-micron voxel resolution, NeuroImage, vol.87, pp.199-208, 2014.

B. Zingg, H. Hintiryan, L. Gou, M. Y. Song, M. Bay et al., Neural networks of the mouse neocortex, Cell, vol.156, pp.1096-1111, 2014.