J. Ashburner, A fast diffeomorphic image registration algorithm, NeuroImage, vol.38, pp.95-113, 2007.

G. Chételat, B. Desgranges, B. Landeau, F. Mézenge, J. B. Poline et al., Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease, Brain, vol.131, pp.60-71, 2008.

A. M. Dale, B. Fischl, and M. I. Sereno, Cortical surface-based analysis: I. segmentation and surface reconstruction, NeuroImage, vol.9, pp.179-194, 1999.

K. Erlandsson, I. Buvat, P. H. Pretorius, B. A. Thomas, and B. F. Hutton, A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology, Phys. Med. Biol, vol.57, pp.119-159, 2012.

M. Ewers, M. Brendel, A. Rizk-jackson, A. Rominger, P. Bartenstein et al., Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects, Neuroimage Clin, vol.4, pp.45-52, 2014.

B. Fischl, , 2012.

. Freesurfer, Neuroimage, vol.62, pp.774-781

B. Fischl and A. M. Dale, Measuring the thickness of the human cerebral cortex from magnetic resonance images, Proc. Natl. Acad. Sci. U.S.A, vol.97, pp.11050-11055, 2000.

B. Fischl, M. I. Sereno, and A. M. Dale, Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system, Neuroimage, vol.9, pp.195-207, 1999.

B. Fischl, M. I. Sereno, R. B. Tootell, and A. M. Dale, High-resolution intersubject averaging and a coordinate system for the cortical surface, Hum. Brain Mapp, vol.8, pp.272-284, 1999.

G. B. Frisoni, M. Boccardi, F. Barkhof, K. Blennow, S. Cappa et al., Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers, Lancet Neurol, vol.16, pp.674-687, 2014.

V. Garibotto, K. Herholz, M. Boccardi, A. Picco, A. Varrone et al., Clinical validity of brain fluorodeoxyglucose positron emission tomography as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework, Neurobiol. Aging, vol.52, pp.183-195, 2017.

K. Gorgolewski, C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko et al., Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python, Front. Neuroinformatics, vol.5, p.13, 2011.

K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci. Data, vol.3, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01345616

M. L. Gorno-tempini, A. E. Hillis, S. Weintraub, A. Kertesz, M. Mendez et al., Classification of primary progressive aphasia and its variants, Neurology, vol.76, pp.1006-1014, 2011.

K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers, R. et al., Random forest-based similarity measures for multimodal classification of Alzheimer's disease, Neuroimage, vol.65, pp.167-175, 2013.

M. Habert, S. Marie, H. Bertin, M. Reynal, J. Martini et al., Optimization of brain PET imaging for a multicentre trial: the French CATI experience, EJNMMI Phys, vol.3, p.6, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01300073

X. Han, J. Jovicich, D. Salat, A. Van-der-kouwe, B. Quinn et al., Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer, Neuroimage, vol.32, pp.180-194, 2006.

M. L. Henry and M. L. Gorno-tempini, The logopenic variant of primary progressive aphasia, Curr. Opin. Neurol, vol.23, pp.633-637, 2010.

K. Herholz, E. Salmon, D. Perani, J. Baron, V. Holthoff et al., Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET, Neuroimage, vol.17, pp.302-316, 2002.

S. Hess, B. A. Blomberg, H. J. Zhu, P. F. Høilund-carlsen, A. et al., The pivotal role of FDG-PET/CT in modern medicine, Acad. Radiol, vol.21, pp.232-249, 2014.

B. F. Hutton, B. A. Thomas, K. Erlandsson, A. Bousse, A. Reilhac-laborde et al., What approach to brain partial volume correction is best for PET/MRI?, Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spectrometers Detect. Assoc. Equip, vol.702, pp.29-33, 2013.

K. Ishii, F. Willoch, S. Minoshima, A. Drzezga, E. P. Ficaro et al., Statistical brain mapping of 18F-FDG PET in Alzheimer's Disease: validation of anatomic standardization for atrophied brains, J. Nucl. Med, vol.42, pp.548-557, 2001.

C. R. Jack, M. A. Bernstein, B. J. Borowski, J. L. Gunter, N. C. Fox et al., Update on the magnetic resonance imaging core of the Alzheimer's disease neuroimaging initiative, Alzheimers Dement, vol.6, pp.212-220, 2010.

W. Jagust, Positron emission tomography and magnetic resonance imaging in the diagnosis and prediction of dementia, Alzheimers Dement, vol.2, pp.36-42, 2006.

W. J. Jagust, S. M. Landau, R. A. Koeppe, E. M. Reiman, K. Chen et al., The Alzheimer's disease neuroimaging initiative 2 PET core, Alzheimers Dement, vol.11, pp.757-771, 2015.

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

Y. Jung, J. R. Duffy, J. , and K. A. , Primary progressive aphasia and apraxia of speech, Semin. Neurol, vol.33, pp.342-347, 2013.

G. Kalpouzos, G. Chételat, J. Baron, B. Landeau, K. Mevel et al., Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging, Neurobiol. Aging, vol.30, pp.112-124, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00277881

J. L. Lancaster, J. L. Summerlin, L. Rainey, C. S. Freitas, and P. T. Fox, The Talairach daemon a database server for talairach atlas labels, NeuroImage, vol.5, pp.238-242, 1997.

J. L. Lancaster, M. G. Woldorff, L. M. Parsons, M. Liotti, C. S. Freitas et al., Automated talairach atlas labels for functional brain mapping, Hum. Brain Mapp, vol.10, pp.120-131, 2000.

S. M. Landau, C. Breault, A. D. Joshi, M. Pontecorvo, C. A. Mathis et al., Amyloid-imaging with pittsburgh compound, B., and florbetapir: comparing radiotracers and quantification methods, J. Nucl. Med, vol.54, pp.70-77, 2013.

V. J. Lowe, G. Curran, P. Fang, A. M. Liesinger, K. A. Josephs et al., An autoradiographic evaluation of AV-1451 Tau PET in dementia, Acta Neuropathol. Commun, vol.4, p.58, 2016.

D. A. Macdonald, D. Avis, and A. C. Evans, Multiple surface identification and matching in magnetic resonance images, Visualization in Biomedical Computing, vol.2359, 1994.

A. Madhavan, J. L. Whitwell, S. D. Weigand, J. R. Duffy, E. A. Strand et al., FDG PET and MRI in logopenic primary progressive aphasia versus dementia of the Alzheimer's type, PLoS ONE, vol.8, p.62471, 2013.

J. A. Maldjian, P. J. Laurienti, R. A. Kraft, and J. H. Burdette, An automated method for neuroanatomic and cytoarchitectonic atlasbased interrogation of fMRI data sets, Neuroimage, vol.19, pp.1233-1239, 2003.

G. J. Matheson, P. Stenkrona, Z. Cselényi, P. Plavén-sigray, C. Halldin et al., Reliability of volumetric and surface-based normalisation and smoothing techniques for PET analysis of the cortex: a test-retest analysis using, Neuroimage, vol.155, pp.344-353, 2017.

J. M. Mazaux and J. M. Orgogozo, Echelle D'évaluation de L'aphasie Adaptée du Boston Diagnostic Aphasia Examination, 1982.

S. Minoshima, K. A. Frey, N. L. Foster, and D. E. Kuhl, Preserved pontine glucose metabolism in alzheimer disease: a reference region for functional brain image (PET) analysis, J. Comput. Assist. Tomogr, vol.19, pp.541-547, 1995.

S. Minoshima, K. A. Frey, R. A. Koeppe, N. L. Foster, and D. E. Kuhl, A diagnostic approach in Alzheimer's disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET, J. Nucl. Med, vol.36, pp.1238-1248, 1995.

F. Nobili, J. Arbizu, F. Bouwman, A. Drzezga, F. Agosta et al., European association of nuclear medicine and european academy of neurology recommendations for the use of brain 18 Ffluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: delphi consensus, Eur. J. Neurol, vol.25, pp.1201-1217, 2018.

G. Operto, M. Chupin, B. Batrancourt, M. Habert, O. Colliot et al., CATI: a large distributed infrastructure for the neuroimaging of cohorts, Neuroinformatics, vol.14, pp.253-264, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01303094

H. Park, J. D. Lee, J. W. Chun, J. H. Seok, M. Yun et al., Cortical surface-based analysis of 18F-FDG PET: measured metabolic abnormalities in schizophrenia are affected by cortical structural abnormalities, Neuroimage, vol.31, pp.1434-1444, 2006.

R. C. Petersen, P. S. Aisen, L. A. Beckett, M. C. Donohue, A. C. Gamst et al., Alzheimer's Disease neuroimaging initiative (ADNI) clinical characterization, Neurology, vol.74, pp.201-209, 2010.
DOI : 10.1212/wnl.0b013e3181cb3e25
URL : http://europepmc.org/articles/pmc2809036?pdf=render

G. D. Rabinovici, W. J. Jagust, A. J. Furst, J. M. Ogar, C. A. Racine et al., A? amyloid and glucose metabolism in three variants of primary progressive aphasia, Ann. Neurol, vol.64, pp.388-401, 2008.

A. Routier, J. Guillon, N. Burgos, J. Samper-gonzalez, J. Wen et al., Clinica: an open source software platform for reproducible clinical neuroscience studies, Annual Meeting of the Organization for Human Brain Mapping-OHBM 2018, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01760658

A. Routier, M. Habert, A. Bertrand, A. Kas, M. Sundqvist et al., Structural, microstructural, and metabolic alterations in primary progressive aphasia variants, Front. Neurol, vol.9, p.766, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01897015

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, 2018.

J. Samper-gonzález, N. Burgos, S. Fontanella, H. Bertin, M. Habert et al., Yet another ADNI machine learning paper? Paving the way towards fully-reproducible research on classification of Alzheimer's disease, Machine Learning in Medical Imaging Lecture Notes in Computer Science, pp.53-60, 2017.

D. H. Silverman, G. W. Small, C. Y. Chang, C. S. Lu, M. A. Aburto et al., Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome, JAMA, vol.286, pp.2120-2127, 2001.

Y. Tan, H. Kim, S. Lee, T. Tihan, L. Ver-hoef et al., Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias, Neuroimage, vol.166, pp.10-18, 2018.

B. A. Thomas, V. Cuplov, A. Bousse, A. Mendes, K. Thielemans et al., PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography, Phys. Med. Biol, vol.61, pp.7975-7993, 2016.

N. J. Tustison, P. A. Cook, A. Klein, G. Song, S. R. Das et al., , 2014.

, Large-scale evaluation of ANTs and freesurfer cortical thickness measurements, Neuroimage, vol.99, pp.166-179

M. Vanhoutte, F. Semah, A. Rollin-sillaire, A. Jaillard, G. Petyt et al., 18 F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer's disease, Neurobiol. Aging, vol.59, pp.184-196, 2017.

G. Varoquaux, P. R. Raamana, D. A. Engemann, A. Hoyos-idrobo, Y. Schwartz et al., Assessing and tuning brain decoders: cross-validation, caveats, and guidelines, Neuroimage, vol.145, pp.166-179, 2017.
DOI : 10.1016/j.neuroimage.2016.10.038
URL : https://hal.archives-ouvertes.fr/hal-01332785

D. F. Wong, P. B. Rosenberg, Y. Zhou, A. Kumar, V. Raymont et al., In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (Flobetapir F 18), J. Nucl. Med, vol.51, pp.913-920, 2010.

K. J. Worsley, M. Andermann, T. Koulis, D. Macdonald, and A. C. Evans, Detecting changes in nonisotropic images, Hum. Brain Mapp, vol.8, pp.98-101, 1999.
DOI : 10.1002/(sici)1097-0193(1999)8:2/3<98::aid-hbm5>3.3.co;2-6