, DSM-5 -Manuel diagnostique et statistique des troubles mentaux, Association AP, 2015.

A. Serrano-pozo, M. P. Frosch, E. Masliah, and B. T. Hyman, Neuropathological alterations in Alzheimer disease, Cold Spring Harb Perspect Med, vol.1, p.6189, 2011.

A. Fornito, A. Zalesky, and M. Breakspear, The connectomics of brain disorders, Nat Rev Neurosci, vol.16, pp.159-72, 2015.

M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, Default-mode network activity distinguishes

, Alzheimer's disease from healthy aging: evidence from functional MRI, Proc Natl Acad Sci U S A, vol.101, pp.4637-4642, 2004.

Y. I. Sheline and M. E. Raichle, Resting State Functional Connectivity in Preclinical Alzheimer's Disease, Biol Psychiatry, vol.74, pp.340-347, 2013.

M. Sourty, L. Thoraval, D. Roquet, J. Armspach, J. Foucher et al., Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models, Front Comput Neurosci, vol.10, 2016.

D. L. Bihan, J. Mangin, C. Poupon, C. A. Clark, S. Pappata et al., Diffusion tensor imaging: Concepts and applications, J Magn Reson Imaging, vol.13, pp.534-580, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00349820

J. M. Ringman, J. O'neill, D. Geschwind, L. Medina, L. G. Apostolova et al.,

, Diffusion tensor imaging in preclinical and presymptomatic carriers of familial Alzheimer's disease mutations, Brain J Neurol, vol.130, pp.1767-76, 2007.

L. Harsan, D. Paul, S. Schnell, B. W. Kreher, J. Hennig et al., In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain, NMR Biomed, vol.23, pp.884-96, 2010.

A. Gozzi and A. J. Schwarz, Large-scale functional connectivity networks in the rodent brain, NeuroImage, 2015.

J. Grandjean, A. Schroeter, P. He, M. Tanadini, R. Keist et al., Early Alterations in Functional Connectivity and White Matter Structure in a Transgenic Mouse Model of Cerebral Amyloidosis, J Neurosci, vol.34, pp.13780-13789, 2014.

D. Shah, J. Praet, L. Hernandez, A. Höfling, C. Anckaerts et al., Early pathologic amyloid induces hypersynchrony of BOLD resting-state networks in transgenic mice and provides an early therapeutic window before amyloid plaque deposition, Alzheimers Dement, vol.12, pp.964-76, 2016.

D. Liu, H. Lu, E. Stein, Z. Zhou, Y. Yang et al., Brain regional synchronous activity predicts tauopathy in 3×TgAD mice, Neurobiol Aging, vol.70, pp.160-169, 2018.

K. Schindowski, A. Bretteville, K. Leroy, S. Bégard, J. Brion et al., Alzheimer's Disease-Like Tau Neuropathology Leads to Memory Deficits and Loss of Functional Synapses in a Novel Mutated Tau Transgenic Mouse without Any Motor Deficits, Am J Pathol, vol.169, pp.599-616, 2006.

A. Van-der-jeugd, B. Vermaercke, M. Derisbourg, A. C. Lo, M. Hamdane et al., Progressive age-related cognitive decline in tau mice, J Alzheimers JAD, vol.37, pp.777-788, 2013.

C. Laurent, G. Dorothée, S. Hunot, E. Martin, Y. Monnet et al., Hippocampal T cell infiltration promotes neuroinflammation and cognitive decline in a mouse model of tauopathy, Brain, vol.140, pp.184-200, 2017.
URL : https://hal.archives-ouvertes.fr/inserm-01833350

V. Hamm, C. Héraud, J. Bott, K. Herbeaux, C. Strittmatter et al., Differential contribution of APP metabolites to early cognitive deficits in a TgCRND8 mouse model of Alzheimer's disease, Sci Adv, vol.3, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02166071

S. Chatterjee, R. Cassel, A. Schneider-anthony, K. Merienne, B. Cosquer et al., Reinstating plasticity and memory in a tauopathy mouse model with an acetyltransferase activator, EMBO Molecular Medicine, p.8587, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01930486

N. S. Hübner, A. E. Mechling, H. Lee, M. Reisert, T. Bienert et al., The connectomics of brain demyelination: Functional and structural patterns in the cuprizone mouse model, NeuroImage, vol.146, pp.1-18, 2017.

S. M. Smith, K. L. Miller, G. Salimi-khorshidi, M. Webster, C. F. Beckmann et al., Network modelling methods for FMRI, NeuroImage, vol.54, pp.875-91, 2011.

J. Veraart, D. S. Novikov, D. Christiaens, B. Ades-aron, J. Sijbers et al., Denoising of diffusion MRI using random matrix theory, NeuroImage, vol.142, pp.394-406, 2016.

L. Harsan, C. David, M. Reisert, S. Schnell, J. Hennig et al., Mapping remodeling of thalamocortical projections in the living reeler mouse brain by diffusion tractography, Proc Natl Acad Sci, vol.110, pp.1797-806, 2013.

M. Reisert and V. G. Kiselev, Fiber Continuity: An Anisotropic Prior for ODF Estimation, IEEE Trans Med Imaging, vol.30, pp.1274-83, 2011.

H. Braak, I. Alafuzoff, T. Arzberger, H. Kretzschmar, D. Tredici et al., Staging of Alzheimer diseaseassociated neurofibrillary pathology using paraffin sections and immunocytochemistry, Acta Neuropathol (Berl), vol.112, pp.389-404, 2006.

J. R. Clarke, M. Cammarota, A. Gruart, I. Izquierdo, and J. M. Delgado-garcía, Plastic modifications induced by object recognition memory processing, Proc Natl Acad Sci U S A, vol.107, pp.2652-2659, 2010.

I. Lee and F. Solivan, The roles of the medial prefrontal cortex and hippocampus in a spatial paired association task, Learn Mem, vol.15, pp.357-67, 2008.

T. Van-cauter, J. Camon, A. Alvernhe, C. Elduayen, F. Sargolini et al., Distinct Roles of Medial and Lateral Entorhinal Cortex in Spatial Cognition, Cereb Cortex, vol.23, pp.451-460, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01384091

K. Belarbi, K. Schindowski, S. Burnouf, R. Caillierez, M. Grosjean et al., Early Tau pathology involving the septo-hippocampal pathway in a Tau transgenic model: relevance to Alzheimer's disease, Curr Alzheimer Res, vol.6, pp.152-159, 2009.

M. Burlot, J. Braudeau, K. Michaelsen-preusse, B. Potier, S. Ayciriex et al., Cholesterol 24-hydroxylase defect is implicated in memory impairments associated with Alzheimerlike Tau pathology, Hum Mol Genet, vol.24, pp.5965-76, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01779042

J. M. Stephen, R. Montaño, C. H. Donahue, J. C. Adair, J. Knoefel et al., Somatosensory responses in normal aging, mild cognitive impairment, and Alzheimer's disease, J Neural Transm Vienna Austria, vol.117, pp.217-242, 1996.

A. Bakker, G. L. Krauss, M. S. Albert, C. L. Speck, L. R. Jones et al., Reduction of Hippocampal Hyperactivity Improves Cognition in Amnestic Mild Cognitive Impairment, Neuron, vol.74, pp.467-74, 2012.

B. C. Dickerson, D. H. Salat, D. N. Greve, E. F. Chua, E. Rand-giovannetti et al., Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD, Neurology, vol.65, pp.404-415, 2005.

P. Coupé, J. V. Manjón, E. Lanuza, and G. Catheline, Lifespan Changes of the Human Brain In Alzheimer's Disease, Sci Rep, vol.9, 2019.

P. W. Frankland and B. Bontempi, The organization of recent and remote memories, Nature Reviews Neuroscience, vol.6, p.119, 2005.

A. E. Mechling, N. S. Hübner, H. Lee, J. Hennig, D. Von-elverfeldt et al., Fine-grained mapping of mouse brain functional connectivity with resting-state fMRI, NeuroImage, vol.96, pp.203-218, 2014.

M. Sourty, L. Thoraval, D. Roquet, J. Armspach, J. Foucher et al., Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models, Frontiers in Computational Neuroscience, vol.10, 2016.

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein et al., A reproducible evaluation of ANTs similarity metric performance in brain image registration, Neuroimage, vol.54, pp.2033-2077, 2011.

E. Mohandas, V. Rajmohan, and B. Raghunath, Neurobiology of Alzheimer's disease, Indian Journal of Psychiatry, vol.51, p.55, 2009.

A. Fornito, A. Zalesky, and M. Breakspear, The connectomics of brain disorders, Nature Reviews Neuroscience, vol.16, pp.159-72, 2015.

H. Park and K. Friston, Structural and Functional Brain Networks: From Connections to Cognition, Science, vol.342, pp.1238411-1238411, 2013.

O. Sporns, Graph theory methods: applications in brain networks, Dialogues Clin Neurosci, vol.20, pp.111-132, 2018.

T. Yan, W. Wang, L. Yang, K. Chen, R. Chen et al., Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease, Theranostics, vol.8, pp.3237-55, 2018.

M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, Default-mode network activity distinguishes

, Alzheimer's disease from healthy aging: evidence from functional MRI, Proceedings of the National Academy of Sciences of the United States of America, vol.101, pp.4637-4642, 2004.

A. Burggren and J. Brown, Imaging markers of structural and functional brain changes that precede cognitive symptoms in risk for Alzheimer's disease, Brain Imaging Behav, vol.8, pp.251-61, 2014.

H. Yao, Y. Liu, B. Zhou, Z. Zhang, N. An et al., Decreased functional connectivity of the amygdala in Alzheimer's disease revealed by resting-state fMRI, Eur J Radiol, vol.82, pp.1531-1539, 2013.

K. Kantarci, M. E. Murray, C. G. Schwarz, R. Reid, S. A. Przybelski et al., White Matter Integrity on DTI and the Pathologic Staging of Alzheimer's Disease, Neurobiol Aging, vol.56, pp.172-181, 2017.

V. Planche, P. Coupé, C. Helmer, L. Goff, M. Amieva et al., Evolution of brain atrophy subtypes during aging predicts long-term cognitive decline and future Alzheimer's clinical syndrome, Neurobiology of Aging, vol.79, pp.22-31, 2019.

R. Sivera, H. Delingette, M. Lorenzi, X. Pennec, and N. Ayache, A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments, NeuroImage, vol.198, pp.255-70, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01948174

M. Mitolo, M. Stanzani-maserati, S. Capellari, C. Testa, P. Rucci et al., Predicting conversion from mild cognitive impairment to Alzheimer's disease using brain 1H-MRS and volumetric changes: A two-year retrospective follow-up study, Neuroimage Clin, vol.23, 2019.

L. Su, A. M. Blamire, R. Watson, J. He, . O'brien et al.,

, Alzheimer's Disease: A Longitudinal and Quantitative MRI Study, Current Alzheimer Research, 2016.

R. Schliebs and T. Arendt, The cholinergic system in aging and neuronal degeneration

, Behavioural Brain Research, vol.221, pp.555-63, 2011.

A. Serrano-pozo, M. P. Frosch, E. Masliah, and B. T. Hyman, Neuropathological alterations in Alzheimer disease, Cold Spring Harb Perspect Med, vol.1, p.6189, 2011.

J. Dong, R. Revilla-sanchez, S. Moss, and P. G. Haydon, Multiphoton in vivo imaging of amyloid in animal models of Alzheimer's disease, Neuropharmacology, vol.59, pp.268-75, 2010.

S. Sun, H. Liang, K. Trinkaus, A. H. Cross, R. C. Armstrong et al., Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum, Magn Reson Med, vol.55, pp.302-310, 2006.

J. Grandjean, A. Schroeter, P. He, M. Tanadini, R. Keist et al., Early Alterations in Functional Connectivity and White Matter Structure in a Transgenic Mouse Model of Cerebral Amyloidosis, J Neurosci, vol.34, pp.13780-13789, 2014.

D. Shah, J. Praet, L. Hernandez, A. Höfling, C. Anckaerts et al., Early pathologic amyloid induces hypersynchrony of BOLD resting-state networks in transgenic mice and provides an early therapeutic window before amyloid plaque deposition, Alzheimer's & Dementia, vol.12, pp.964-76, 2016.

P. T. Nelson, I. Alafuzoff, E. H. Bigio, C. Bouras, H. Braak et al., Correlation of Alzheimer Disease Neuropathologic Changes With Cognitive Status: A Review of the Literature, Journal of Neuropathology & Experimental Neurology, vol.71, pp.362-81, 2012.

H. Braak, I. Alafuzoff, T. Arzberger, H. Kretzschmar, D. Tredici et al., Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry, Acta Neuropathol, vol.112, pp.389-404, 2006.

T. Arendt, M. Holzer, M. K. Brückner, C. Janke, and U. Gärtner, The use of okadaic acid in vivo and the induction of molecular changes typical for Alzheimer's disease, Neuroscience, vol.85, pp.1337-1377, 1998.

C. X. Gong and T. J. Singh, Grundke-Iqbal I, Iqbal K. Phosphoprotein phosphatase activities in Alzheimer disease brain, J Neurochem, vol.61, pp.921-928, 1993.

T. Guo, W. Noble, and D. P. Hanger, Roles of tau protein in health and disease, Acta Neuropathol, vol.133, pp.665-704, 2017.

L. Buée, T. Bussière, V. Buée-scherrer, A. Delacourte, and P. R. Hof, Tau protein isoforms, phosphorylation and role in neurodegenerative disorders, Brain Res Brain Res Rev, vol.33, pp.95-130, 2000.

W. M. Snow, R. Dale, Z. O'brien-moran, R. Buist, D. Peirson et al., In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer's Disease with Diffusion Tensor Imaging, Journal of Alzheimer's Disease, vol.58, pp.841-53, 2017.

X. Nie, M. F. Falangola, R. Ward, E. T. Mckinnon, J. A. Helpern et al., Diffusion MRI detects longitudinal white matter changes in the 3xTg-AD mouse model of Alzheimer's disease, Magnetic Resonance Imaging, vol.57, pp.235-277, 2019.

D. Liu, H. Lu, E. Stein, Z. Zhou, Y. Yang et al., Brain regional synchronous activity predicts tauopathy in 3×TgAD mice, Neurobiology of Aging, vol.70, pp.160-169, 2018.

N. Sahara, P. D. Perez, W. Lin, D. W. Dickson, Y. Ren et al., Age-related decline in white matter integrity in a mouse model of tauopathy: an in vivo diffusion tensor magnetic resonance imaging study, Neurobiology of Aging, vol.35, pp.1364-74, 2014.

V. Hamm, C. Héraud, J. Bott, K. Herbeaux, C. Strittmatter et al., Differential contribution of APP metabolites to early cognitive deficits in a TgCRND8 mouse model of Alzheimer's disease, Sci Adv, vol.3, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02166071

S. Chatterjee, R. Cassel, A. Schneider-anthony, K. Merienne, B. Cosquer et al., Reinstating plasticity and memory in a tauopathy mouse model with an acetyltransferase activator, EMBO Molecular Medicine, p.8587, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01930486

J. Veraart, D. S. Novikov, D. Christiaens, B. Ades-aron, J. Sijbers et al., Denoising of diffusion MRI using random matrix theory, NeuroImage, vol.142, pp.394-406, 2016.

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein et al., A reproducible evaluation of ANTs similarity metric performance in brain image registration, Neuroimage, vol.54, pp.2033-2077, 2011.

M. Reisert, I. Mader, C. Anastasopoulos, M. Weigel, S. Schnell et al., Global fiber reconstruction becomes practical, Neuroimage, vol.54, pp.955-62, 2011.

N. S. Hübner, A. E. Mechling, H. Lee, M. Reisert, T. Bienert et al., The connectomics of brain demyelination: Functional and structural patterns in the cuprizone mouse model, NeuroImage, vol.146, pp.1-18, 2017.

S. M. Smith, K. L. Miller, G. Salimi-khorshidi, M. Webster, C. F. Beckmann et al., Network modelling methods for FMRI, Neuroimage, vol.54, pp.875-91, 2011.

S. A. Stouffer, E. A. Suchman, L. C. Devinney, S. A. Star, R. M. Williams et al., The American Soldier: Adjustment During Army Life, vol.599

S. A. Janis, L. S. Star, and J. R. Cottrell, Vols. I and II together, The American Soldier: Combat and Its Aftermath, vol.II, p.675, 1949.

C. Green, A. Sydow, S. Vogel, M. Anglada-huguet, D. Wiedermann et al., Functional networks are impaired by elevated tau-protein but reversible in a regulatable Alzheimer's disease mouse model, Mol Neurodegener, vol.14, 2019.

P. Dutar, M. H. Bassant, M. C. Senut, and Y. Lamour, The septohippocampal pathway: structure and function of a central cholinergic system, Physiological Reviews, vol.75, pp.393-427, 1995.

K. Belarbi, K. Schindowski, S. Burnouf, R. Caillierez, M. Grosjean et al., Early Tau pathology involving the septo-hippocampal pathway in a Tau transgenic model: relevance to Alzheimer's disease, Curr Alzheimer Res, vol.6, pp.152-159, 2009.

A. Badea, L. Kane, R. J. Anderson, Y. Qi, M. Foster et al., The fornix provides multiple biomarkers to characterize circuit disruption in a mouse model of Alzheimer's disease, NeuroImage, vol.142, pp.498-511, 2016.

A. Badea, N. A. Delpratt, R. J. Anderson, R. Dibb, Y. Qi et al., Multivariate MR biomarkers better predict cognitive dysfunction in mouse models of Alzheimer's disease, Magn Reson Imaging, vol.60, pp.52-67, 2019.

Y. Hara, Y. Motoi, K. Hikishima, H. Mizuma, H. Onoe et al., Involvement of the Septo-Hippocampal Cholinergic Pathway in Association with Septal Acetylcholinesterase Upregulation in a Mouse Model of Tauopathy, Curr Alzheimer Res, vol.14, pp.94-103, 2017.

C. Watson, G. Paxinos, and L. Puelles, The Mouse Nervous System, 2012.

J. D. Schmahmann, D. L. Rosene, and D. N. Pandya, Motor projections to the basis pontis in rhesus monkey, J Comp Neurol, vol.478, pp.248-68, 2004.

E. V. Sullivan, N. M. Zahr, T. Rohlfing, and A. Pfefferbaum, Fiber tracking functionally distinct components of the internal capsule, Neuropsychologia, vol.48, pp.4155-63, 2010.

S. Thillainadesan, W. Wen, L. Zhuang, J. Crawford, N. Kochan et al., Changes in mild cognitive impairment and its subtypes as seen on diffusion tensor imaging, Int Psychogeriatr, vol.24, pp.1483-93, 2012.

H. Cho, D. W. Yang, Y. M. Shon, B. S. Kim, Y. I. Kim et al., Abnormal Integrity

, Corticocortical Tracts in Mild Cognitive Impairment: A Diffusion Tensor Imaging Study, J Korean Med Sci, vol.23, pp.477-83, 2008.

E. Canu, D. G. Mclaren, M. E. Fitzgerald, B. B. Bendlin, G. Zoccatelli et al., Mapping the structural brain changes in Alzheimer's disease: The independent contribution of two imaging modalities, J Alzheimers Dis, vol.26, pp.263-74, 2011.

S. E. Rose, A. L. Janke, and J. B. Chalk, Gray and white matter changes in Alzheimer's disease: a diffusion tensor imaging study, J Magn Reson Imaging, vol.27, pp.20-26, 2008.

R. Xie, M. Fang, L. Zhou, S. Fan, J. Liu et al., Diffusion tensor imaging detects Wallerian degeneration of the corticospinal tract early after cerebral infarction, Neural Regen Res, vol.7, pp.900-905, 2012.

Z. Safadi, G. Grisot, S. Jbabdi, T. E. Behrens, S. R. Heilbronner et al., Functional Segmentation of the Anterior Limb of the Internal Capsule: Linking White Matter Abnormalities to Specific Connections, J Neurosci, vol.38, pp.2106-2123, 2018.

M. P. Hyett, A. Perry, M. Breakspear, W. Wen, and G. B. Parker, White matter alterations in the internal capsule and psychomotor impairment in melancholic depression, PLOS ONE, vol.13, p.195672, 2018.

K. Schindowski, A. Bretteville, K. Leroy, S. Bégard, J. Brion et al., Alzheimer's Disease-Like Tau Neuropathology Leads to Memory Deficits and Loss of Functional Synapses in a Novel Mutated Tau Transgenic Mouse without Any Motor Deficits, The American Journal of Pathology, vol.169, pp.599-616, 2006.

J. Graff-radford, L. Williams, D. T. Jones, and E. E. Benarroch, Caudate nucleus as a component of networks controlling behavior, Neurology, vol.89, pp.2192-2199, 2017.

X. Li, H. Wang, Y. Tian, S. Zhou, X. Li et al., Impaired White Matter Connections of the Limbic System Networks Associated with Impaired Emotional Memory in Alzheimer's Disease, Front Aging Neurosci, vol.8, p.250, 2016.

F. Sforazzini, A. J. Schwarz, A. Galbusera, A. Bifone, and A. Gozzi, Distributed BOLD and CBVweighted resting-state networks in the mouse brain, NeuroImage, vol.87, pp.403-418, 2014.

V. Zerbi, J. Grandjean, M. Rudin, and N. Wenderoth, Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification, NeuroImage, vol.123, pp.11-21, 2015.

J. Winson, Loss of hippocampal theta rhythm results in spatial memory deficit in the rat, Science, vol.201, pp.160-163, 1978.

S. D. Oddie, W. Stefanek, I. J. Kirk, and B. H. Bland, Intraseptal procaine abolishes hypothalamic stimulation-induced wheel-running and hippocampal theta field activity in rats, J Neurosci, vol.16, pp.1948-56, 1996.

O. Mamad, H. M. Mcnamara, R. B. Reilly, and M. Tsanov, Medial septum regulates the hippocampal spatial representation, Front Behav Neurosci, vol.9, 2015.

F. Khakpai, M. Nasehi, A. Haeri-rohani, A. Eidi, and M. R. Zarrindast, Septo-Hippocampo-Septal Loop and Memory Formation, Basic Clin Neurosci, vol.4, pp.5-23, 2013.

T. H. Ferreira-vieira, I. M. Guimaraes, F. R. Silva, and F. M. Ribeiro, Alzheimer's Disease: Targeting the Cholinergic System, Curr Neuropharmacol, vol.14, pp.101-116, 2016.

E. D. Plowey and J. L. Ziskin, Hippocampal phospho-tau/MAPT neuropathology in the fornix in Alzheimer disease: an immunohistochemical autopsy study, Acta Neuropathol Commun, vol.4, p.114, 2016.

S. Mondragón-rodríguez, N. Gu, C. Fasano, F. Peña-ortega, and S. Williams, Functional Connectivity between Hippocampus and Lateral Septum is Affected in Very Young Alzheimer's Transgenic Mouse Model, Neuroscience, vol.401, pp.96-105, 2019.

Y. T. Quiroz, A. E. Budson, K. Celone, A. Ruiz, R. Newmark et al., Hippocampal Hyperactivation in Presymptomatic Familial Alzheimer's Disease, Ann Neurol, vol.68, pp.865-75, 2010.

B. C. Dickerson, D. H. Salat, D. N. Greve, E. F. Chua, E. Rand-giovannetti et al., Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD, Neurology, vol.65, pp.404-415, 2005.

H. Li, X. Jia, Z. Qi, X. Fan, T. Ma et al., Altered Functional Connectivity of the Basal Nucleus of Meynert in Mild Cognitive Impairment: A Resting-State fMRI Study, Front Aging Neurosci, vol.9, 2017.

P. A. Chiesa, E. Cavedo, S. J. Teipel, M. J. Grothe, M. Habert et al., A FUNCTIONAL RESTING STATE STUDY OF BASAL FOREBRAIN FUNCTIONAL CONNECTIVITY IN ASYMPTOMATIC AT-RISK INDIVIDUALS FOR AD: THE INSIGHT-PREAD STUDY

, Alzheimer's & Dementia: The Journal of the Alzheimer's, Association, vol.13, p.790, 2017.

T. W. Schmitz, N. Spreng, and R. , The Alzheimer's Disease Neuroimaging Initiative

A. P. Mw and R. Petersen, Basal forebrain degeneration precedes and predicts the cortical spread of Alzheimer's pathology, Nature Communications, vol.7, p.13249, 2016.

A. Kamali, D. M. Yousem, D. D. Lin, H. I. Sair, S. P. Jasti et al., Mapping the trajectory of the stria terminalis of the human limbic system using high spatial resolution diffusion tensor tractography, Neuroscience Letters, vol.608, pp.45-50, 2015.

R. P. Vertes, Differential projections of the infralimbic and prelimbic cortex in the rat, Synapse, vol.51, pp.32-58, 2004.

L. W. Swanson and W. M. Cowan, The connections of the septal region in the rat, J Comp Neurol, vol.186, pp.621-55, 1979.

C. L. Masters, R. Bateman, K. Blennow, C. C. Rowe, R. A. Sperling et al., Alzheimer's disease, Nature Reviews Disease Primers, vol.1, 2015.
URL : https://hal.archives-ouvertes.fr/inserm-01723790

M. S. Fanselow and H. Dong, Are the Dorsal and Ventral Hippocampus Functionally Distinct Structures?, Neuron, vol.65, pp.7-19, 2010.

A. Pereira, S. Ribeiro, M. Wiest, L. C. Moore, J. Pantoja et al., Processing of tactile information by the hippocampus, Proc Natl Acad Sci, vol.104, pp.18286-91, 2007.

E. Bellistri, J. Aguilar, J. R. Brotons-mas, G. Foffani, and L. M. De-la-prida, Basic properties of somatosensory-evoked responses in the dorsal hippocampus of the rat, J Physiol, vol.591, pp.2667-86, 2013.

N. Crouzin, K. Baranger, M. Cavalier, Y. Marchalant, C. Cohen-solal et al., Areaspecific alterations of synaptic plasticity in the 5XFAD mouse model of Alzheimer's disease: dissociation between somatosensory cortex and hippocampus, PLoS ONE, vol.8, p.74667, 2013.

M. A. Daulatzai, Dysfunctional Sensory Modalities, Locus Coeruleus, and Basal Forebrain: Early Determinants that Promote Neuropathogenesis of Cognitive and Memory Decline and Alzheimer's Disease, Neurotoxicity Research, vol.30, pp.295-337, 2016.

G. A. Gates, M. L. Anderson, M. P. Feeney, S. M. Mccurry, and E. B. Larson, Central Auditory Dysfunction in Older People with Memory Impairment or Alzheimer's Dementia, Arch Otolaryngol Head Neck Surg, vol.134, pp.771-778, 2008.

Y. Wang, S. L. Risacher, J. D. West, B. C. Mcdonald, T. R. Magee et al., Altered Default Mode Network Connectivity in Older Adults with Cognitive Complaints and Amnestic Mild Cognitive Impairment, J Alzheimers Dis, vol.35, pp.751-60, 2013.

C. Petersen, The Functional Organization of the Barrel Cortex, Neuron, vol.56, pp.339-55, 2007.

M. Laramée and D. Boire, Visual cortical areas of the mouse: comparison of parcellation and network structure with primates, Front Neural Circuits, vol.8, 2015.

F. Cauda, D. 'agata, F. Sacco, K. Duca, S. Geminiani et al., Functional connectivity of the insula in the resting brain, Neuroimage, vol.55, pp.8-23, 2011.

D. J. Bonthius, A. Solodkin, and G. W. Van-hoesen, Pathology of the insular cortex in Alzheimer disease depends on cortical architecture, J Neuropathol Exp Neurol, vol.64, pp.910-932, 2005.

X. Liu, X. Chen, W. Zheng, M. Xia, Y. Han et al., Altered Functional Connectivity of Insular Subregions in Alzheimer's Disease, Front Aging Neurosci, vol.10, 2018.

D. Roquet, V. Noblet, P. Anthony, N. Philippi, C. Demuynck et al., Insular atrophy at the prodromal stage of dementia with Lewy bodies: a VBM DARTEL study, Sci Rep, vol.7, p.9437, 2017.

A. Botzung, N. Philippi, M. Constans-erbs, J. Kemp, M. Hamdaoui et al., Alzheimer's & Dementia: The Journal of the Alzheimer's, COGNITIVE IMPAIRMENT IN EARLY DEMENTIA WITH LEWY BODIES, vol.13, p.1462, 2017.

G. Albouy, B. R. King, P. Maquet, and J. Doyon, Hippocampus and striatum: dynamics and interaction during acquisition and sleep-related motor sequence memory consolidation, Hippocampus, vol.23, pp.985-1004, 2013.

J. C. Pych, Q. Chang, C. Colon-rivera, R. Haag, and P. E. Gold, Acetylcholine release in the hippocampus and striatum during place and response training, Learn Mem, vol.12, pp.564-72, 2005.

S. Salgado and M. G. Kaplitt, The Nucleus Accumbens: A Comprehensive Review, SFN, vol.93, pp.75-93, 2015.

G. P. Mark, P. Rada, E. Pothos, and B. G. Hoebel, Effects of feeding and drinking on acetylcholine release in the nucleus accumbens, striatum, and hippocampus of freely behaving rats, J Neurochem, vol.58, pp.2269-74, 1992.

D. C. Perry and J. H. Kramer, Reward processing in neurodegenerative disease, Neurocase, vol.21, pp.120-153, 2015.

S. Ortiz, M. S. Latsko, J. L. Fouty, S. Dutta, J. M. Adkins et al., Anterior cingulate cortex and ventral hippocampal inputs to the basolateral amygdala selectively control generalized fear, J Neurosci, pp.810-819, 2019.

S. A. Allsop, C. M. Vander-weele, R. Wichmann, and K. M. Tye, Optogenetic insights on the relationship between anxiety-related behaviors and social deficits, Front Behav Neurosci, vol.8, 2014.

E. K. Hebda-bauer, T. A. Simmons, A. Sugg, E. Ural, J. A. Stewart et al., 3xTg-AD Mice Exhibit an Activated Central Stress Axis during Early-Stage Pathology, J Alzheimers Dis, vol.33, pp.407-429, 2013.

R. A. Stelzmann, H. N. Schnitzlein, and F. R. Murtagh, An english translation of alzheimer's 1907 paper, Clin Anat, vol.8, pp.429-460, 1995.

J. Lambert, I. -. Verbaas, C. A. Harold, D. Naj, A. C. Sims et al., Metaanalysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease, Nat Genet, vol.45, pp.1452-1460, 2013.

I. E. Jansen, J. E. Savage, K. Watanabe, J. Bryois, D. M. Williams et al., Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk, Nat Genet, vol.51, pp.404-417, 2019.

, The Global Impact of Dementia: An analysis of prevalence, incidence, World Alzheimer Report, p.87, 2015.

L. Galea, K. M. Frick, E. Hampson, F. Sohrabji, and E. Choleris, Why estrogens matter for behavior and brain health, Neurosci Biobehav Rev, vol.76, pp.363-79, 2017.

H. M. Snyder, S. Asthana, L. Bain, R. Brinton, S. Craft et al., Sex biology contributions to vulnerability to Alzheimer's disease: A think tank convened by the Women's Alzheimer's Research Initiative, Alzheimers Dement J Alzheimers Assoc, vol.12, pp.1186-96, 2016.

M. K. Andrew and M. C. Tierney, The puzzle of sex, gender and Alzheimer's disease: Why are women more often affected than men?, Womens Health, vol.14, p.1745506518817995, 2018.

, Inserm -Sci Pour Santé n, 2019.

F. J. Wolters, M. A. Ikram, . Epidemiology, and . Dementia, Alzheimer's Dis, The Burden on Society, the Challenges for Research, vol.1750, pp.3-14, 2018.

D. La-maladie, Alzheimer en chiffres. Fr Alzheimer n, 2019.

R. E. Tanzi and L. Bertram, Twenty Years of the Alzheimer's Disease Amyloid Hypothesis: A Genetic Perspective, Cell, vol.120, pp.545-55, 2005.

H. W. Querfurth and F. M. Laferla, Alzheimer's Disease, N Engl J Med, vol.362, pp.329-373, 2010.

R. Kayed, E. Head, J. L. Thompson, T. M. Mcintire, S. C. Milton et al., Common Structure of Soluble Amyloid Oligomers Implies Common Mechanism of Pathogenesis, Science, vol.300, pp.486-495, 2003.

D. M. Walsh and D. J. Selkoe, A? Oligomers -a decade of discovery, J Neurochem, vol.101, pp.1172-84, 2007.

L. Lue, Y. Kuo, A. E. Roher, L. Brachova, Y. Shen et al., Soluble Amyloid ? Peptide Concentration as a Predictor of Synaptic Change in Alzheimer's Disease, Am J Pathol, vol.155, pp.853-62, 1999.

M. A. Mena, J. A. Rodríguez-navarro, G. De-yébenes, and J. , The multiple mechanisms of amyloid deposition, Prion, vol.3, pp.5-11, 2009.

G. Thinakaran and E. H. Koo, Amyloid Precursor Protein Trafficking, Processing, and Function, J Biol Chem, vol.283, pp.29615-29624, 2008.

J. H. Tam, C. Seah, and S. H. Pasternak, The Amyloid Precursor Protein is rapidly transported from the Golgi apparatus to the lysosome and where it is processed into beta-amyloid, Mol Brain, vol.7, p.54, 2014.

W. H. Yu, A. Kumar, C. Peterhoff, L. Shapiro-kulnane, Y. Uchiyama et al.,

, Autophagic vacuoles are enriched in amyloid precursor protein-secretase activities: implications for ?amyloid peptide over-production and localization in Alzheimer's disease, Int J Biochem Cell Biol, vol.36, pp.2531-2571, 2004.

W. H. Yu, A. M. Cuervo, A. Kumar, C. M. Peterhoff, S. D. Schmidt et al.,

, Macroautophagy-a novel ?-amyloid peptide-generating pathway activated in Alzheimer's disease, J Cell Biol, vol.171, pp.87-98, 2005.

D. J. Selkoe, Alzheimer's Disease Is a Synaptic Failure, Science, vol.298, pp.789-91, 2002.

H. Du, L. Guo, S. Yan, A. A. Sosunov, G. M. Mckhann et al., Early deficits in synaptic mitochondria in an Alzheimer's disease mouse model, Proc Natl Acad Sci, vol.107, pp.18670-18675, 2010.

G. A. Krafft and W. L. Klein, ADDLs and the signaling web that leads to Alzheimer's disease, Neuropharmacology, vol.59, pp.230-272, 2010.

H. Braak, I. Alafuzoff, T. Arzberger, H. Kretzschmar, D. Tredici et al., Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry, Acta Neuropathol (Berl), vol.112, pp.389-404, 2006.

T. Arendt, M. Holzer, M. K. Brückner, C. Janke, and U. Gärtner, The use of okadaic acid in vivo and the induction of molecular changes typical for Alzheimer's disease, Neuroscience, vol.85, pp.1337-1377, 1998.

C. X. Gong and T. J. Singh, Grundke-Iqbal I, Iqbal K. Phosphoprotein phosphatase activities in Alzheimer disease brain, J Neurochem, vol.61, pp.921-928, 1993.

T. Guo, W. Noble, and D. P. Hanger, Roles of tau protein in health and disease, Acta Neuropathol (Berl), vol.133, pp.665-704, 2017.

M. D. Weingarten, A. H. Lockwood, S. Y. Hwo, and M. W. Kirschner, A protein factor essential for microtubule assembly, Proc Natl Acad Sci, vol.72, pp.1858-62, 1975.

K. R. Patterson, C. Remmers, Y. Fu, S. Brooker, N. M. Kanaan et al., Characterization of prefibrillar Tau oligomers in vitro and in Alzheimer disease, J Biol Chem, vol.286, pp.23063-76, 2011.

J. P. Brion, H. Passareiro, J. Nunez, F. Durand, and J. , Mise en évidence immunologique de la protéine tau au niveau des lésions de dégénérescence neurofibrillaire de la maladie d'Alzheimer, Arch Biol (Liege), vol.95, pp.229-264, 1985.

L. Buée, T. Bussière, V. Buée-scherrer, A. Delacourte, and P. R. Hof, Tau protein isoforms, phosphorylation and role in neurodegenerative disorders, Brain Res Brain Res Rev, vol.33, pp.95-130, 2000.

E. Mandelkow, M. Von-bergen, J. Biernat, and E. Mandelkow, Structural principles of tau and the paired helical filaments of Alzheimer's disease, Brain Pathol Zurich Switz, vol.17, pp.83-90, 2007.

H. Braak and E. Braak, Frequency of Stages of Alzheimer-Related Lesions in Different Age Categories n.d, p.7

C. L. Masters, R. Bateman, K. Blennow, C. C. Rowe, R. A. Sperling et al., Alzheimer's disease, Nat Rev Dis Primer, vol.1, 2015.
URL : https://hal.archives-ouvertes.fr/inserm-01723790

J. A. Hardy and G. A. Higgins, Alzheimer's disease: the amyloid cascade hypothesis, Science, vol.256, pp.184-189, 1992.

T. Bilousova, C. A. Miller, W. W. Poon, H. V. Vinters, M. Corrada et al., Synaptic Amyloid-? Oligomers Precede p-Tau and Differentiate High Pathology Control Cases, Am J Pathol, vol.186, pp.185-98, 2016.

C. Duyckaerts, H. Braak, J. Brion, L. Buée, D. Tredici et al., PART is part of Alzheimer disease, Acta Neuropathol (Berl), vol.129, pp.749-56, 2015.

E. Karran, M. Mercken, D. Strooper, and B. , The amyloid cascade hypothesis for Alzheimer's disease: an appraisal for the development of therapeutics, Nat Rev Drug Discov, vol.10, pp.698-712, 2011.

A. D. Korczyn, The amyloid cascade hypothesis, Alzheimers Dement J Alzheimers Assoc, vol.4, pp.176-184, 2008.

H. Lee, G. Casadesus, X. Zhu, J. A. Joseph, G. Perry et al., Perspectives on the amyloidbeta cascade hypothesis, J Alzheimers Dis JAD, vol.6, pp.137-182, 2004.

S. W. Pimplikar, Reassessing the amyloid cascade hypothesis of Alzheimer's disease, Int J Biochem Cell Biol, vol.41, pp.1261-1269, 2009.

P. T. Nelson, I. Alafuzoff, E. H. Bigio, C. Bouras, H. Braak et al., Correlation of Alzheimer Disease Neuropathologic Changes With Cognitive Status: A Review of the Literature, J Neuropathol Exp Neurol, vol.71, pp.362-81, 2012.

V. Galvan, O. F. Gorostiza, S. Banwait, M. Ataie, A. V. Logvinova et al., Reversal of Alzheimer's-like pathology and behavior in human APP transgenic mice by mutation of Asp664, Proc Natl Acad Sci, vol.103, pp.7130-7135, 2006.

E. Mohandas, V. Rajmohan, and B. Raghunath, Neurobiology of Alzheimer's disease, Indian J Psychiatry, vol.51, p.55, 2009.

M. T. Heneka, M. J. Carson, E. Khoury, J. Landreth, G. E. Brosseron et al., Neuroinflammation in Alzheimer's Disease, Lancet Neurol, vol.14, issue.15, pp.70016-70021, 2015.

J. W. Kinney, S. M. Bemiller, A. S. Murtishaw, A. M. Leisgang, A. M. Salazar et al., Inflammation as a central mechanism in Alzheimer's disease, Alzheimers Dement Transl Res Clin Interv, vol.4, pp.575-90, 2018.

S. Carmona, K. Zahs, E. Wu, K. Dakin, J. Bras et al., The role of TREM2 in Alzheimer's disease and other neurodegenerative disorders, Lancet Neurol, vol.17, issue.18, pp.30232-30233, 2018.

H. Chun and C. J. Lee, Reactive astrocytes in Alzheimer's disease: A double-edged sword, Neurosci Res, vol.126, pp.44-52, 2018.

N. , , 2019.

, DSM-5 -Manuel diagnostique et statistique des troubles mentaux, Association AP, 2015.

A. Serrano-pozo, M. P. Frosch, E. Masliah, and B. T. Hyman, Neuropathological alterations in Alzheimer disease, Cold Spring Harb Perspect Med, vol.1, p.6189, 2011.

M. A. Conway, Episodic memories, Neuropsychologia, vol.47, pp.2305-2318, 2009.

M. Nicholas, L. T. Connor, L. K. Obler, and M. L. Albert, 12 -Aging, Language, and Language Disorders, pp.413-462, 1998.

B. R. Matthews, Memory Dysfunction. Contin Lifelong Learn Neurol, vol.21, pp.613-639, 2015.

F. Bature, B. Guinn, D. Pang, and Y. Pappas, Signs and symptoms preceding the diagnosis of

, Alzheimer's disease: a systematic scoping review of literature from 1937 to 2016, BMJ Open, vol.7, p.15746, 2017.

J. Attems and K. A. Jellinger, The overlap between vascular disease and Alzheimer's diseaselessons from pathology, BMC Med, vol.12, 2014.

J. Park, Mortality from Alzheimer's disease in Canada: A multiple-cause-of-death analysis, Health Rep, vol.27, p.7, 2004.

H. J. Tschampa, K. Kallenberg, H. Urbach, B. Meissner, C. Nicolay et al., MRI in the diagnosis of sporadic Creutzfeldt-Jakob disease: a study on inter-observer agreement, Brain, vol.128, pp.2026-2059, 2005.

C. A. Raji, O. L. Lopez, L. H. Kuller, O. T. Carmichael, and J. T. Becker, Age, Alzheimer disease, and brain structure, Neurology, vol.73, pp.1899-905, 2009.

E. Niemantsverdriet, S. Valckx, M. Bjerke, and S. Engelborghs, Alzheimer's disease CSF biomarkers: clinical indications and rational use, Acta Neurol Belg, vol.117, pp.591-602, 2017.

C. R. Jack, D. S. Knopman, W. J. Jagust, R. C. Petersen, M. W. Weiner et al., Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers, Lancet Neurol, vol.12, pp.207-223, 2013.

A. Burns and S. Iliffe, Alzheimer's disease, BMJ, vol.338, pp.158-158, 2009.

I. O. Korolev, Alzheimer's Disease: A Clinical and Basic, Science Review, vol.04, p.10, 2014.

S. E. Counts and D. K. Lahiri, Overview of Immunotherapy in Alzheimer's Disease (AD) and Mechanisms of IVIG Neuroprotection in Preclinical Models of AD, Curr Alzheimer Res, vol.11, pp.623-628, 2014.

F. M. Laferla and K. N. Green, Animal Models of Alzheimer Disease, Cold Spring Harb Perspect Med, vol.2, 2012.

H. Sasaguri, P. Nilsson, S. Hashimoto, K. Nagata, T. Saito et al., APP mouse models for Alzheimer's disease preclinical studies, EMBO J, vol.36, pp.2473-87, 2017.

S. Oddo, A. Caccamo, J. D. Shepherd, M. P. Murphy, T. E. Golde et al., Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Abeta and synaptic dysfunction, Neuron, vol.39, pp.409-430, 2003.

E. Drummond and T. Wisniewski, Alzheimer's Disease: Experimental Models and Reality, Acta Neuropathol (Berl), vol.133, pp.155-75, 2017.

A. Lossos, A. Reches, A. Gal, J. P. Newman, D. Soffer et al., Frontotemporal dementia and parkinsonism with the P301S tau gene mutation in a Jewish family, J Neurol, vol.250, pp.733-773, 2003.

B. Ghetti, A. L. Oblak, B. F. Boeve, K. A. Johnson, B. C. Dickerson et al., Invited review: Frontotemporal dementia caused by microtubule-associated protein tau gene ( MAPT ) mutations: a chameleon for neuropathology and neuroimaging: MAPT mutations and FTD, Neuropathol Appl Neurobiol, vol.41, pp.24-46, 2015.

S. Borrego-Écija, J. Morgado, L. Palencia-madrid, O. Grau-rivera, R. Reñé et al.,

, Frontotemporal Dementia Caused by the P301L Mutation in the MAPT Gene: Clinicopathological Features of 13 Cases from the Same Geographical Origin in, Dement Geriatr Cogn Disord, vol.44, pp.213-234, 2017.

K. E. Ameen-ali, S. B. Wharton, J. E. Simpson, P. R. Heath, P. Sharp et al., Review: Neuropathology and behavioural features of transgenic murine models of Alzheimer's disease, Neuropathol Appl Neurobiol, vol.43, pp.553-70, 2017.

J. Götz, M. Tolnay, R. Barmettler, F. Chen, A. Probst et al., Oligodendroglial tau filament formation in transgenic mice expressing G272V tau, Eur J Neurosci, vol.13, pp.2131-2171, 2001.

K. Tanemura, M. Murayama, T. Akagi, T. Hashikawa, T. Tominaga et al., Neurodegeneration with tau accumulation in a transgenic mouse expressing V337M human tau, J Neurosci Off J Soc Neurosci, vol.22, pp.133-174, 2002.

Y. Tatebayashi, T. Miyasaka, D. Chui, T. Akagi, K. Mishima et al., Tau filament formation and associative memory deficit in aged mice expressing mutant (R406W) human tau, Proc Natl Acad Sci U S A, vol.99, pp.13896-901, 2002.

J. L. Jankowsky and H. Zheng, Practical considerations for choosing a mouse model of Alzheimer's disease, Mol Neurodegener, vol.12, p.89, 2017.

C. Andorfer, Y. Kress, M. Espinoza, R. De-silva, K. L. Tucker et al.,

, Hyperphosphorylation and aggregation of tau in mice expressing normal human tau isoforms, J Neurochem, vol.86, pp.582-90, 2003.

K. Schindowski, A. Bretteville, K. Leroy, S. Bégard, J. Brion et al., Alzheimer's Disease-Like Tau Neuropathology Leads to Memory Deficits and Loss of Functional Synapses in a Novel Mutated Tau Transgenic Mouse without Any Motor Deficits, Am J Pathol, vol.169, pp.599-616, 2006.

A. Van-der-jeugd, B. Vermaercke, M. Derisbourg, A. C. Lo, M. Hamdane et al., Progressive age-related cognitive decline in tau mice, J Alzheimers JAD, vol.37, pp.777-788, 2013.

K. Belarbi, K. Schindowski, S. Burnouf, R. Caillierez, M. Grosjean et al., Early Tau pathology involving the septo-hippocampal pathway in a Tau transgenic model: relevance to Alzheimer's disease, Curr Alzheimer Res, vol.6, pp.152-159, 2009.

L. Pauling and C. D. Coryell, The Magnetic Properties and Structure of Hemoglobin, Oxyhemoglobin and Carbonmonoxyhemoglobin, Proc Natl Acad Sci U S A, vol.22, pp.210-216, 1936.

P. K. Shetty, F. Galeffi, and D. A. Turner, Cellular Links between Neuronal Activity and Energy Homeostasis, Front Pharmacol, vol.3, 2012.

S. Cinciute, Translating the hemodynamic response: why focused interdisciplinary integration should matter for the future of functional neuroimaging, PeerJ, vol.7, p.6621, 2019.

D. Ratering, C. Baltes, J. Nordmeyer-massner, D. Marek, and M. Rudin, Performance of a 200-MHz cryogenic RF probe designed for MRI and MRS of the murine brain, Magn Reson Med, vol.59, pp.1440-1447, 2008.

M. A. Bernstein, J. Huston, and H. A. Ward, Imaging artifacts at 3.0T, J Magn Reson Imaging, vol.24, pp.735-781, 2006.

E. Jonckers, D. Shah, J. Hamaide, M. Verhoye, and A. Van-der-linden, The power of using functional fMRI on small rodents to study brain pharmacology and disease, Front Pharmacol, vol.6, 2015.

E. Jonckers, R. Delgado-y-palacios, D. Shah, C. Guglielmetti, M. Verhoye et al., Different anesthesia regimes modulate the functional connectivity outcome in mice: Anesthesia and Functional Connectivity Outcome in Mice, Magn Reson Med, vol.72, pp.1103-1115, 2014.

J. Grandjean, A. Schroeter, I. Batata, and M. Rudin, Optimization of anesthesia protocol for restingstate fMRI in mice based on differential effects of anesthetics on functional connectivity patterns, NeuroImage, vol.102, pp.838-885, 2014.

J. A. King, T. S. Garelick, M. E. Brevard, W. Chen, T. L. Messenger et al., Procedure for minimizing stress for fMRI studies in conscious rats, J Neurosci Methods, vol.148, pp.154-60, 2005.

D. Madularu, A. P. Mathieu, C. Kumaragamage, L. M. Reynolds, J. Near et al., A noninvasive restraining system for awake mouse imaging, J Neurosci Methods, vol.287, pp.53-60, 2017.

B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar MRI, Magn Reson Med, vol.34, pp.537-578, 1995.

M. E. Raichle, A. M. Macleod, A. Z. Snyder, W. J. Powers, D. A. Gusnard et al., A default mode of brain function, Proc Natl Acad Sci U S A, vol.98, pp.676-82, 2001.

H. Park and K. Friston, Structural and Functional Brain Networks: From Connections to Cognition, Science, vol.342, pp.1238411-1238411, 2013.

S. M. Smith, K. L. Miller, G. Salimi-khorshidi, M. Webster, C. F. Beckmann et al., Network modelling methods for FMRI, NeuroImage, vol.54, pp.875-91, 2011.

M. P. Van-den-heuvel, H. Pol, and H. E. , Exploring the brain network: A review on resting-state fMRI functional connectivity, Eur Neuropsychopharmacol, vol.20, pp.519-553, 2010.

M. J. Mckeown, S. Makeig, G. G. Brown, T. P. Jung, S. S. Kindermann et al., Analysis of fMRI data by blind separation into independent spatial components, Hum Brain Mapp, vol.6, pp.160-88, 1998.

V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, A method for making group inferences from functional MRI data using independent component analysis, Hum Brain Mapp, vol.14, pp.140-51, 2001.

D. S. Margulies, J. Böttger, X. Long, Y. Lv, C. Kelly et al., Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity, Magma N Y N, vol.23, pp.289-307, 2010.

G. Varoquaux, A. Gramfort, F. Pedregosa, V. Michel, and B. Thirion, Multi-subject dictionary learning to segment an atlas of brain spontaneous activity, Inf Process Med Imaging Proc Conf, vol.22, pp.562-73, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00588898

H. Eavani, R. Filipovych, C. Davatzikos, T. D. Satterthwaite, R. E. Gur et al., Sparse dictionary learning of resting state fMRI networks, Proc. -2012 2nd Int. Workshop Pattern Recognit. NeuroImaging PRNI 2012, pp.73-79, 2012.

M. G. Preti, T. A. Bolton, and D. Van-de-ville, The dynamic functional connectome: State-of-the-art and perspectives, NeuroImage, vol.160, pp.41-54, 2017.

Y. Du, G. D. Pearlson, Q. Yu, H. He, D. Lin et al., Interaction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach, Schizophr Res, vol.170, pp.55-65, 2016.

E. Damaraju, E. A. Allen, A. Belger, J. M. Ford, S. Mcewen et al., Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia, NeuroImage Clin, vol.5, pp.298-308, 2014.

J. Grandjean, M. G. Preti, T. Bolton, M. Buerge, E. Seifritz et al., Dynamic reorganization of intrinsic functional networks in the mouse brain, NeuroImage, vol.152, pp.497-508, 2017.

B. B. Biswal, Resting state fMRI: A personal history, NeuroImage, vol.62, pp.938-982, 2012.

C. F. Beckmann, M. Deluca, J. T. Devlin, and S. M. Smith, Investigations into resting-state connectivity using independent component analysis, Philos Trans R Soc Lond B Biol Sci, vol.360, pp.1001-1014, 2005.

M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. Essen et al., The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proc Natl Acad Sci, vol.102, pp.9673-9681, 2005.

J. S. Damoiseaux, S. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam et al., Consistent resting-state networks across healthy subjects, Proc Natl Acad Sci, vol.103, pp.13848-53, 2006.

. Heuvel-m-van-den, R. Mandl, and H. H. Pol, Normalized Cut Group Clustering of Resting-State fMRI Data, PLOS ONE, vol.3, p.2001, 2008.

M. E. Raichle, The Restless Brain, Brain Connect, vol.1, pp.3-12, 2011.

R. L. Buckner, J. R. Andrews-hanna, and D. L. Schacter, The Brain's Default Network: Anatomy, Function, and Relevance to Disease, Ann N Y Acad Sci, vol.1124, pp.1-38, 2008.

G. L. Poerio, M. Sormaz, H. Wang, D. Margulies, E. Jefferies et al., The role of the default mode network in component processes underlying the wandering mind, Soc Cogn Affect Neurosci, vol.12, pp.1047-62, 2017.

W. Guo, F. Liu, J. Zhang, Z. Zhang, L. Yu et al., Abnormal Default-Mode Network Homogeneity in First-Episode, Drug-Naive Major Depressive Disorder, PLOS ONE, vol.9, p.91102, 2014.

G. Mingoia, G. Wagner, K. Langbein, R. Maitra, S. Smesny et al., Default mode network activity in schizophrenia studied at resting state using probabilistic ICA, Schizophr Res, vol.138, pp.143-152, 2012.

M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI, Proc Natl Acad Sci U S A, vol.101, pp.4637-4642, 2004.

V. Menon and L. Q. Uddin, Saliency, switching, attention and control: a network model of insula function, Brain Struct Funct, vol.214, pp.655-67, 2010.

J. M. Andreano, A. Touroutoglou, B. C. Dickerson, and L. F. Barrett, Resting connectivity between salience nodes predicts recognition memory, Soc Cogn Affect Neurosci, vol.12, pp.948-55, 2017.

M. Balthazar, F. Pereira, T. M. Lopes, E. L. Da-silva, A. C. Coan et al., Neuropsychiatric symptoms in Alzheimer's disease are related to functional connectivity alterations in the salience network, Hum Brain Mapp, vol.35, pp.1237-1283, 2014.

G. B. Chand, J. Wu, I. Hajjar, and D. Qiu, Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment, Brain Connect, vol.7, pp.401-413, 2017.

E. Koechlin and C. Summerfield, An information theoretical approach to prefrontal executive function, Trends Cogn Sci, vol.11, pp.229-264, 2007.

V. Menon, Large-Scale Brain Networks in Cognition: Emerging Principles, p.11, 2010.

E. K. Miller and J. D. Cohen, An integrative theory of prefrontal cortex function, Annu Rev Neurosci, vol.24, pp.167-202, 2001.

L. Wu, R. B. Soder, D. Schoemaker, F. Carbonnell, V. Sziklas et al., Resting State Executive Control Network Adaptations in Amnestic Mild Cognitive Impairment, J Alzheimers Dis, vol.40, pp.993-1004, 2014.

A. L. Hobkirk, R. P. Bell, A. V. Utevsky, S. Huettel, and C. S. Meade, Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users, Drug Alcohol Depend, vol.194, pp.32-41, 2019.

S. Chenji, S. Jha, D. Lee, M. Brown, P. Seres et al., Investigating Default Mode and Sensorimotor Network Connectivity in Amyotrophic Lateral Sclerosis, PLoS ONE, vol.11, 2016.

J. M. Stephen, R. Montaño, C. H. Donahue, J. C. Adair, J. Knoefel et al., Somatosensory responses in normal aging, mild cognitive impairment, and Alzheimer's disease, J Neural Transm Vienna Austria, vol.117, pp.217-242, 1996.

Y. Maatuf, E. A. Stern, and H. Slovin, Abnormal Population Responses in the Somatosensory Cortex of Alzheimer's Disease Model Mice, Sci Rep, vol.6, 2016.

R. L. Albin, A. B. Young, and J. B. Penney, The functional anatomy of basal ganglia disorders, Trends Neurosci, vol.12, pp.366-75, 1989.

A. Cacciola, A. Calamuneri, D. Milardi, E. Mormina, G. Chillemi et al., A Connectomic Analysis of the Human Basal Ganglia Network, Front Neuroanat, vol.11, 2017.

S. Robinson, G. Basso, N. Soldati, U. Sailer, J. Jovicich et al., A resting state network in the motor control circuit of the basal ganglia, BMC Neurosci, vol.10, p.137, 2009.

K. Szewczyk-krolikowski, R. Menke, M. Rolinski, E. Duff, G. Salimi-khorshidi et al., Functional connectivity in the basal ganglia network differentiates PD patients from controls, Neurology, vol.83, pp.208-222, 2014.

C. Rodriguez-sabate, I. Morales, J. N. Lorenzo, and M. Rodriguez, The organization of the basal ganglia functional connectivity network is non-linear in Parkinson's disease, NeuroImage Clin, vol.22, p.101708, 2019.

F. Palesi, G. Castellazzi, L. Casiraghi, E. Sinforiani, P. Vitali et al., Exploring Patterns of Alteration in Alzheimer's Disease Brain Networks: A Combined Structural and Functional Connectomics Analysis, Front Neurosci, vol.10, 2016.

A. Fornito, A. Zalesky, and M. Breakspear, The connectomics of brain disorders, Nat Rev Neurosci, vol.16, pp.159-72, 2015.

V. Zerbi, J. Grandjean, M. Rudin, and N. Wenderoth, Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification, NeuroImage, vol.123, pp.11-21, 2015.

E. Jonckers, J. Van-audekerke, D. Visscher, G. Van-der-linden, A. Verhoye et al., Functional Connectivity fMRI of the Rodent Brain: Comparison of Functional Connectivity Networks in Rat and Mouse, PLoS ONE, vol.6, p.18876, 2011.

F. Sforazzini, A. J. Schwarz, A. Galbusera, A. Bifone, and A. Gozzi, Distributed BOLD and CBVweighted resting-state networks in the mouse brain, NeuroImage, vol.87, pp.403-418, 2014.

J. M. Stafford, B. R. Jarrett, O. Miranda-dominguez, B. D. Mills, N. Cain et al., Largescale topology and the default mode network in the mouse connectome, Proc Natl Acad Sci, vol.111, pp.18745-50, 2014.

F. A. Nasrallah, H. Tay, and K. Chuang, Detection of functional connectivity in the resting mouse brain, NeuroImage, vol.86, pp.417-441, 2014.

A. Gozzi and A. J. Schwarz, Large-scale functional connectivity networks in the rodent brain, NeuroImage, vol.127, pp.496-509, 2016.

I. C. Wright, S. Rabe-hesketh, P. W. Woodruff, A. S. David, R. M. Murray et al., Metaanalysis of regional brain volumes in schizophrenia, Am J Psychiatry, vol.157, pp.16-25, 2000.

J. Ashburner and K. J. Friston, Voxel-based morphometry--the methods, NeuroImage, vol.11, pp.805-826, 2000.

G. Chételat, B. Landeau, F. Eustache, F. Mézenge, F. Viader et al., Using voxelbased morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study, NeuroImage, vol.27, pp.934-980, 2005.

F. Kurth, C. Gaser, and E. Luders, A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM), Nat Protoc, vol.10, pp.293-304, 2015.

R. Bansal, A. J. Gerber, and B. S. Peterson, Brain Morphometry Using Anatomical Magnetic Resonance Imaging, J Am Acad Child Adolesc Psychiatry, vol.47, pp.619-640, 2008.

A. Badea, G. A. Johnson, and J. L. Jankowsky, Remote sites of structural atrophy predict later amyloid formation in a mouse model of Alzheimer's disease, NeuroImage, vol.50, pp.416-443, 2010.

J. M. Redwine, B. Kosofsky, R. E. Jacobs, D. Games, J. F. Reilly et al., Dentate gyrus volume is reduced before onset of plaque formation in PDAPP mice: a magnetic resonance microscopy and stereologic analysis, Proc Natl Acad Sci U S A, vol.100, pp.1381-1387, 2003.

S. Maheswaran, H. Barjat, S. T. Bate, P. Aljabar, D. Hill et al., Analysis of serial magnetic resonance images of mouse brains using image registration, NeuroImage, vol.44, pp.692-700, 2009.

J. C. Lau, J. P. Lerch, J. G. Sled, R. M. Henkelman, A. C. Evans et al., Longitudinal neuroanatomical changes determined by deformation-based morphometry in a mouse model of Alzheimer's disease, NeuroImage, vol.42, pp.19-27, 2008.

A. Badea, N. A. Delpratt, R. J. Anderson, R. Dibb, Y. Qi et al., Multivariate MR biomarkers better predict cognitive dysfunction in mouse models of Alzheimer's disease, Magn Reson Imaging, vol.60, pp.52-67, 2019.

L. Bihan and D. , Looking into the functional architecture of the brain with diffusion MRI, Nat Rev Neurosci, vol.4, pp.469-80, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00349696

L. Bihan, D. Johansen-berg, and H. , Diffusion MRI at 25: exploring brain tissue structure and function, NeuroImage, vol.61, pp.324-365, 2012.

D. L. Bihan, J. Mangin, C. Poupon, C. A. Clark, S. Pappata et al., Diffusion tensor imaging: Concepts and applications, J Magn Reson Imaging, vol.13, pp.534-580, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00349820

L. Bihan and D. , Molecular diffusion, tissue microdynamics and microstructure, NMR Biomed, vol.8, pp.375-86, 1995.
URL : https://hal.archives-ouvertes.fr/hal-00349972

S. Mori and J. Zhang, Principles of diffusion tensor imaging and its applications to basic neuroscience research, Neuron, vol.51, pp.527-566, 2006.

P. J. Basser, J. Mattiello, and D. Lebihan, MR diffusion tensor spectroscopy and imaging, Biophys J, vol.66, pp.259-67, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00349721

P. J. Basser, J. Mattiello, and D. Lebihan, Estimation of the effective self-diffusion tensor from the NMR spin echo, J Magn Reson B, vol.103, pp.247-54, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00349722

P. J. Basser, J. Mattiello, and D. Lebihan, MR diffusion tensor spectroscopy and imaging, Biophys J, vol.66, pp.259-67, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00349721

P. J. Basser and C. Pierpaoli, Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI, J Magn Reson B, vol.111, pp.209-228, 1996.

P. Mukherjee, J. I. Berman, S. W. Chung, C. P. Hess, and R. G. Henry, Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings, AJNR Am J Neuroradiol, vol.29, pp.632-673, 2008.

P. J. Basser and D. K. Jones, Diffusion-tensor MRI: theory, experimental design and data analysis -a technical review, NMR Biomed, vol.15, pp.456-67, 2002.

D. K. Jones, Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI, Imaging Med, vol.2, pp.341-55, 2010.

D. K. Jones, T. R. Knösche, and R. Turner, White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI, NeuroImage, vol.73, pp.239-54, 2013.

D. K. Jones, Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI, IEEE Trans Med Imaging, vol.27, pp.1268-74, 2008.

D. S. Tuch, T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau et al., High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity, Magn Reson Med, vol.48, pp.577-82, 2002.

M. Reisert, I. Mader, C. Anastasopoulos, M. Weigel, S. Schnell et al., Global fiber reconstruction becomes practical, NeuroImage, vol.54, pp.955-62, 2011.

A. Daducci, A. Palú, M. Descoteaux, and J. Thiran, Microstructure Informed Tractography: Pitfalls and Open Challenges, Front Neurosci, vol.10, p.247, 2016.

P. Stämpfli, S. Sommer, A. Manoliu, A. Burrer, A. Schmidt et al., Subtle white matter alterations in schizophrenia identified with a new measure of fiber density, Sci Rep, vol.9, p.4636, 2019.

A. Stadlbauer, O. Ganslandt, E. Salomonowitz, M. Buchfelder, T. Hammen et al., Magnetic resonance fiber density mapping of age-related white matter changes, Eur J Radiol, vol.81, pp.4005-4017, 2012.

T. Roberts, F. Liu, A. Kassner, S. Mori, and A. Guha, Fiber Density Index Correlates with Reduced Fractional Anisotropy in White Matter of Patients with Glioblastoma, p.4, 2005.

G. Prasad, T. M. Nir, A. W. Toga, and P. M. Thompson, TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE, Proc IEEE Int Symp Biomed Imaging, vol.2013, pp.692-697, 2013.

A. L. Alexander, J. E. Lee, M. Lazar, and A. S. Field, Diffusion Tensor Imaging of the Brain, Neurother J Am Soc Exp Neurother, vol.4, pp.316-345, 2007.

P. J. Winklewski, A. Sabisz, P. Naumczyk, K. Jodzio, E. Szurowska et al., Understanding the Physiopathology Behind Axial and Radial Diffusivity Changes-What Do We Know?, Front Neurol, vol.9, 2018.

S. Sun, H. Liang, K. Trinkaus, A. H. Cross, R. C. Armstrong et al., Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum, Magn Reson Med, vol.55, pp.302-310, 2006.

S. Song, S. Sun, W. Ju, S. Lin, A. H. Cross et al., Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia, NeuroImage, vol.20, pp.1714-1736, 2003.

M. A. Lancaster, M. Seidenberg, J. C. Smith, K. A. Nielson, J. L. Woodard et al.,

, Diffusion Tensor Imaging Predictors of Episodic Memory Decline in Healthy Elders at Genetic Risk for Alzheimer's Disease, J Int Neuropsychol Soc JINS, vol.22, pp.1005-1020, 2016.

R. Della-nave, A. Ginestroni, S. Diciotti, S. E. Soricelli, A. Mascalchi et al., Axial diffusivity is increased in the degenerating superior cerebellar peduncles of Friedreich's ataxia, Neuroradiology, vol.53, pp.367-72, 2011.

D. Rosas, H. Lee, S. Y. Bender, A. Zaleta, A. K. Vange et al., Altered White Matter Microstructure in the Corpus Callosum in Huntington's Disease: implications for cortical "disconnection, NeuroImage, vol.49, pp.2995-3004, 2010.

K. Kamagata, Y. Motoi, O. Abe, K. Shimoji, M. Hori et al., White Matter Alteration of the Cingulum in Parkinson Disease with and without Dementia: Evaluation by Diffusion Tensor Tract-Specific Analysis, Am J Neuroradiol, vol.33, pp.890-895, 2012.

P. Kochunov, P. M. Thompson, J. L. Lancaster, G. Bartzokis, S. Smith et al., Relationship between white matter fractional anisotropy and other indices of cerebral health in normal aging: Tract-based spatial statistics study of aging, NeuroImage, vol.35, pp.478-87, 2007.

H. Wakamoto, T. J. Eluvathingal, M. Makki, C. Juhász, and H. T. Chugani, Diffusion tensor imaging of the corticospinal tract following cerebral hemispherectomy, J Child Neurol, vol.21, pp.566-71, 2006.

R. Xie, M. Fang, L. Zhou, S. Fan, J. Liu et al., Diffusion tensor imaging detects Wallerian degeneration of the corticospinal tract early after cerebral infarction, Neural Regen Res, vol.7, pp.900-905, 2012.

G. Thomalla, V. Glauche, M. A. Koch, C. Beaulieu, C. Weiller et al., Diffusion tensor imaging detects early Wallerian degeneration of the pyramidal tract after ischemic stroke, NeuroImage, vol.22, pp.1767-74, 2004.

J. Zhan, T. Lin, J. E. Libbey, P. Sun, Z. Ye et al., Diffusion Basis Spectrum and Diffusion Tensor Imaging Detect Hippocampal Inflammation and Dendritic Injury in a Virus-Induced Mouse Model of Epilepsy, Front Neurosci, vol.12, 2018.

A. Fornito and E. T. Bullmore, Connectomics: a new paradigm for understanding brain disease, Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol, vol.25, pp.733-781, 2015.

O. Sporns, Structure and function of complex brain networks, Dialogues Clin Neurosci, vol.15, pp.247-62, 2013.

A. M. Aertsen, G. L. Gerstein, M. K. Habib, and G. Palm, Dynamics of neuronal firing correlation: modulation of "effective connectivity, J Neurophysiol, vol.61, pp.900-917, 1989.

K. J. Friston, Functional and Effective Connectivity: A Review, Brain Connect, vol.1, pp.13-36, 2011.

O. Sporns, THE HUMAN CONNECTOME: A COMPLEX NETWORK, Schizophr Res, vol.136, p.28, 2012.

O. Sporns, Graph theory methods: applications in brain networks, Dialogues Clin Neurosci, vol.20, pp.111-132, 2018.

M. P. Van-den-heuvel and O. Sporns, Rich-Club Organization of the Human Connectome, J Neurosci, vol.31, pp.15775-86, 2011.

S. Finger, P. J. Koehler, and C. Jagella, The Monakow concept of diaschisis: origins and perspectives, Arch Neurol, vol.61, pp.283-291, 2004.

D. M. Feeney, J. C. Baron, and . Diaschisis, Stroke, vol.17, pp.817-847, 1986.

W. M. Cowan, Anterograde and Retrograde Transneuronal Degeneration in the Central and Peripheral Nervous System, Contemp. Res. Methods Neuroanat, pp.217-51, 1970.

E. Klupp, S. Förster, T. Grimmer, M. Tahmasian, I. Yakushev et al., Alzheimer's Disease, Hypometabolism in Low-Amyloid Brain Regions May Be a Functional Consequence of Pathologies in Connected Brain Regions, Brain Connect, vol.4, pp.371-83, 2014.

J. D. Koen and M. D. Rugg, Neural Dedifferentiation in the Aging Brain, Trends Cogn Sci, 2019.

S. Li, U. Lindenberger, and S. Sikström, Aging cognition: from neuromodulation to representation, Trends Cogn Sci, vol.5, pp.479-86, 2001.

Y. T. Quiroz, A. E. Budson, K. Celone, A. Ruiz, R. Newmark et al., Hippocampal Hyperactivation in Presymptomatic Familial Alzheimer's Disease, Ann Neurol, vol.68, pp.865-75, 2010.

C. L. Grady, A. R. Mcintosh, S. Beig, M. L. Keightley, H. Burian et al., Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer's disease, J Neurosci Off J Soc Neurosci, vol.23, pp.986-93, 2003.

C. R. Jack, D. S. Knopman, W. J. Jagust, R. C. Petersen, M. W. Weiner et al., Update on hypothetical model of Alzheimer's disease biomarkers, Lancet Neurol, vol.12, pp.207-223, 2013.

U. Noppeney, K. J. Friston, and C. J. Price, Degenerate neuronal systems sustaining cognitive functions, J Anat, vol.205, pp.433-475, 2004.

K. Mevel, G. Chételat, F. Eustache, and B. Desgranges, The Default Mode Network in Healthy Aging and Alzheimer's Disease, Int J Alzheimer's Dis, 2011.

A. Burggren and J. Brown, Imaging markers of structural and functional brain changes that precede cognitive symptoms in risk for Alzheimer's disease, Brain Imaging Behav, vol.8, pp.251-61, 2014.

Y. Wang, S. L. Risacher, J. D. West, B. C. Mcdonald, T. R. Magee et al., Altered Default Mode Network Connectivity in Older Adults with Cognitive Complaints and Amnestic Mild Cognitive Impairment, J Alzheimers Dis JAD, vol.35, pp.751-60, 2013.

H. Yao, Y. Liu, B. Zhou, Z. Zhang, N. An et al., Decreased functional connectivity of the amygdala in Alzheimer's disease revealed by resting-state fMRI, Eur J Radiol, vol.82, pp.1531-1539, 2013.

B. Zhang, R. Hua, Z. Qing, L. Ni, X. Zhang et al., Abnormal brain functional connectivity coupled with hypoperfusion measured by Resting-State fMRI: An additional contributing factor for cognitive impairment in patients with Alzheimer's disease, Psychiatry Res Neuroimaging, vol.289, pp.18-25, 2019.

T. Kaneta, O. Katsuse, T. Hirano, M. Ogawa, A. Shihikura-hino et al., Voxel-wise correlations between cognition and cerebral blood flow using arterial spin-labeled perfusion MRI in patients with Alzheimer's disease: a cross-sectional study, BMC Neurol, vol.17, p.91, 2017.

K. Kantarci, M. E. Murray, C. G. Schwarz, R. Reid, S. A. Przybelski et al., White Matter Integrity on DTI and the Pathologic Staging of Alzheimer's Disease, Neurobiol Aging, vol.56, pp.172-181, 2017.

X. Li, H. Wang, Y. Tian, S. Zhou, X. Li et al., Impaired White Matter Connections of the Limbic System Networks Associated with Impaired Emotional Memory in Alzheimer's Disease, Front Aging Neurosci, vol.8, p.250, 2016.

H. Matsuda, MRI morphometry in Alzheimer's disease, Ageing Res Rev, vol.30, pp.17-24, 2016.

K. Nho, S. L. Risacher, P. K. Crane, C. Decarli, M. M. Glymour et al., Voxel and Surface-Based Topography of Memory and Executive Deficits in Mild Cognitive Impairment and Alzheimer's Disease, Brain Imaging Behav, vol.6, pp.551-67, 2012.

P. Coupé, J. V. Manjón, E. Lanuza, and G. Catheline, Lifespan Changes of the Human Brain In Alzheimer's Disease, Sci Rep, vol.9, 2019.

C. Bernard, C. Helmer, B. Dilharreguy, H. Amieva, S. Auriacombe et al., Time course of brain volume changes in the preclinical phase of Alzheimer's disease, Alzheimers Dement, vol.10, pp.143-151, 2014.

R. Sivera, H. Delingette, M. Lorenzi, X. Pennec, and N. Ayache, A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments, NeuroImage, vol.198, pp.255-70, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01948174

A. Bakker, G. L. Krauss, M. S. Albert, C. L. Speck, L. R. Jones et al., Reduction of Hippocampal Hyperactivity Improves Cognition in Amnestic Mild Cognitive Impairment, Neuron, vol.74, pp.467-74, 2012.

K. A. Celone, V. D. Calhoun, B. C. Dickerson, A. Atri, E. F. Chua et al., Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis, J Neurosci, vol.26, pp.10222-10253, 2006.

B. C. Dickerson, D. H. Salat, D. N. Greve, E. F. Chua, E. Rand-giovannetti et al., Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD, Neurology, vol.65, pp.404-415, 2005.

A. Hämäläinen, M. Pihlajamäki, H. Tanila, T. Hänninen, E. Niskanen et al., Increased fMRI responses during encoding in mild cognitive impairment, Neurobiol Aging, vol.28, pp.1889-903, 2007.

J. M. Stephen, R. Montaño, C. H. Donahue, J. C. Adair, J. Knoefel et al., Somatosensory responses in normal aging, mild cognitive impairment, and Alzheimer's disease, J Neural Transm Vienna Austria, vol.117, pp.217-242, 1996.

J. M. Ringman, J. O'neill, D. Geschwind, L. Medina, L. G. Apostolova et al., Diffusion tensor imaging in preclinical and presymptomatic carriers of familial Alzheimer's disease mutations, Brain J Neurol, vol.130, pp.1767-76, 2007.

B. M. Tijms, A. M. Wink, W. De-haan, W. M. Van-der-flier, C. J. Stam et al.,

, Alzheimer's disease: connecting findings from graph theoretical studies of brain networks, Neurobiol Aging, vol.34, pp.2023-2059, 2013.

L. Harsan, D. Paul, S. Schnell, B. W. Kreher, J. Hennig et al., In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain, NMR Biomed, vol.23, pp.884-96, 2010.

A. Liska, A. Galbusera, A. J. Schwarz, and A. Gozzi, Functional connectivity hubs of the mouse brain, NeuroImage, vol.115, pp.281-91, 2015.

D. Shah, J. Praet, L. Hernandez, A. Höfling, C. Anckaerts et al., Early pathologic amyloid induces hypersynchrony of BOLD resting-state networks in transgenic mice and provides an early therapeutic window before amyloid plaque deposition, Alzheimers Dement, vol.12, pp.964-76, 2016.

J. C. Dodart, C. Mathis, J. Saura, K. R. Bales, S. M. Paul et al., Neuroanatomical abnormalities in behaviorally characterized APP(V717F) transgenic mice, Neurobiol Dis, vol.7, pp.71-85, 2000.

F. Gonzalez-lima, J. D. Berndt, J. E. Valla, D. Games, and E. M. Reiman, Reduced corpus callosum, fornix and hippocampus in PDAPP transgenic mouse model of Alzheimer's disease, Neuroreport, vol.12, pp.2375-2384, 2001.

C. Weiss, P. N. Venkatasubramanian, A. S. Aguado, J. M. Power, B. C. Tom et al., Impaired Eyeblink Conditioning and Decreased Hippocampal Volume in PDAPP V717F Mice, Neurobiol Dis, vol.11, pp.425-458, 2002.

J. M. Redwine, B. Kosofsky, R. E. Jacobs, D. Games, J. F. Reilly et al., Dentate gyrus volume is reduced before onset of plaque formation in PDAPP mice: a magnetic resonance microscopy and stereologic analysis, Proc Natl Acad Sci U S A, vol.100, pp.1381-1387, 2003.

M. Dhenain and . Biomarkers, Alzheimer's Disease: Concepts and Applications, Magn Reson Insights, vol.2, 2008.

A. Latif-hernandez, D. Shah, K. Craessaerts, T. Saido, T. Saito et al., Subtle behavioral changes and increased prefrontal-hippocampal network synchronicity in APPNL?G?F mice before prominent plaque deposition, Behav Brain Res, vol.364, pp.431-472, 2019.

J. Grandjean, A. Schroeter, P. He, M. Tanadini, R. Keist et al., Early Alterations in Functional Connectivity and White Matter Structure in a Transgenic Mouse Model of Cerebral Amyloidosis, J Neurosci, vol.34, pp.13780-13789, 2014.

D. Liu, H. Lu, E. Stein, Z. Zhou, Y. Yang et al., Brain regional synchronous activity predicts tauopathy in 3×TgAD mice, Neurobiol Aging, vol.70, pp.160-169, 2018.

K. Govaerts, B. Lechat, T. Struys, A. Kremer, P. Borghgraef et al., Longitudinal assessment of cerebral perfusion and vascular response to hypoventilation in a bigenic mouse model of Alzheimer's disease with amyloid and tau pathology, NMR Biomed, vol.32, p.4037, 2019.

N. El-tannir-el-tayara, B. Delatour, L. Cudennec, C. Guégan, M. Volk et al., Agerelated evolution of amyloid burden, iron load, and MR relaxation times in a transgenic mouse model of Alzheimer's disease, Neurobiol Dis, vol.22, pp.199-208, 2006.

D. Shah, E. Jonckers, J. Praet, G. Vanhoutte, . Palacios-rd-y et al., Resting State fMRI Reveals Diminished Functional Connectivity in a Mouse Model of Amyloidosis, PLOS ONE, vol.8, p.84241, 2013.

M. E. Belloy, D. Shah, A. Abbas, A. Kashyap, S. Roßner et al., Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice, Sci Rep, vol.8, 2018.

V. Zerbi, M. Wiesmann, T. L. Emmerzaal, D. Jansen, M. Van-beek et al., Resting-State Functional Connectivity Changes in Aging apoE4 and apoE-KO Mice, J Neurosci, vol.34, pp.13963-75, 2014.

J. Praet, N. V. Manyakov, L. Muchene, Z. Mai, V. Terzopoulos et al., Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-?-induced pathology, Alzheimers Res Ther, vol.10, 2018.

X. Nie, M. F. Falangola, R. Ward, E. T. Mckinnon, J. A. Helpern et al., Diffusion MRI detects longitudinal white matter changes in the 3xTg-AD mouse model of Alzheimer's disease, Magn Reson Imaging, vol.57, pp.235-277, 2019.

W. M. Snow, R. Dale, Z. O'brien-moran, R. Buist, D. Peirson et al., In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer's Disease with Diffusion Tensor Imaging, J Alzheimers Dis, vol.58, pp.841-53, 2017.

S. R. Kesler, P. Acton, V. Rao, and W. J. Ray, Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer's disease, Netw Neurosci, vol.2, pp.241-58, 2018.

G. Poisnel, A. Hérard, N. El-tannir-el-tayara, E. Bourrin, A. Volk et al., Increased regional cerebral glucose uptake in an APP/PS1 model of Alzheimer's disease, Neurobiol Aging, vol.33, 1995.

E. Micotti, A. Paladini, C. Balducci, D. Tolomeo, A. Frasca et al., Striatum and entorhinal cortex atrophy in AD mouse models: MRI comprehensive analysis, Neurobiol Aging, vol.36, pp.776-88, 2015.

N. Sahara, P. D. Perez, W. Lin, D. W. Dickson, Y. Ren et al., Age-related decline in white matter integrity in a mouse model of tauopathy: an in vivo diffusion tensor magnetic resonance imaging study, Neurobiol Aging, vol.35, pp.1364-74, 2014.

C. Green, A. Sydow, S. Vogel, M. Anglada-huguet, D. Wiedermann et al., Functional networks are impaired by elevated tau-protein but reversible in a regulatable Alzheimer's disease mouse model, Mol Neurodegener, vol.14, 2019.

C. G. Lyketsos, M. C. Carrillo, J. M. Ryan, A. S. Khachaturian, P. Trzepacz et al., Neuropsychiatric symptoms in Alzheimer's disease, Alzheimers Dement J Alzheimers Assoc, vol.7, pp.532-541, 2011.

J. Victoroff, F. V. Lin, K. L. Coburn, S. D. Shillcutt, V. Voon et al., Noncognitive Behavioral Changes Associated With Alzheimer's Disease: Implications of Neuroimaging Findings, J Neuropsychiatry Clin Neurosci, vol.30, pp.14-21, 2018.

R. F. Gariano and P. M. Groves, Burst firing induced in midbrain dopamine neurons by stimulation of the medial prefrontal and anterior cingulate cortices, Brain Res, vol.462, issue.88, pp.90606-90609, 1988.

V. Rajmohan and E. Mohandas, The limbic system, Indian J Psychiatry, vol.49, pp.132-141, 2007.

S. Song, S. Sun, W. Ju, S. Lin, A. H. Cross et al., Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia, NeuroImage, vol.20, pp.1714-1736, 2003.

, A component based noise correction method (CompCor) for BOLD and perfusion based fMRI, July, vol.29, 2019.

J. S. Damoiseaux, R. P. Viviano, P. Yuan, and N. Raz, Differential effect of age on posterior and anterior hippocampal functional connectivity, NeuroImage, vol.133, pp.468-76, 2016.

M. Zarei, C. F. Beckmann, M. Binnewijzend, M. M. Schoonheim, M. A. Oghabian et al., Functional segmentation of the hippocampus in the healthy human brain and in Alzheimer's disease, NeuroImage, vol.66, pp.28-35, 2013.

A. R. Preston and H. Eichenbaum, Interplay of hippocampus and prefrontal cortex in memory, Curr Biol CB, vol.23, pp.764-73, 2013.

R. D'hooge, D. Deyn, and P. P. , Applications of the Morris water maze in the study of learning and memory, Brain Res Rev, vol.36, pp.60-90, 2001.

C. M. Bird and N. Burgess, The Hippocampus Supports Recognition Memory for Familiar Words but Not Unfamiliar Faces, Curr Biol, vol.18, pp.1932-1938, 2008.

T. Van-cauter, J. Camon, A. Alvernhe, C. Elduayen, F. Sargolini et al., Distinct Roles of Medial and Lateral Entorhinal Cortex in Spatial Cognition, Cereb Cortex, vol.23, pp.451-460, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01384091

T. Sasaki, S. Leutgeb, and J. K. Leutgeb, Spatial and memory circuits in the medial entorhinal cortex, Curr Opin Neurobiol, vol.32, pp.16-23, 2015.

E. I. Moser, E. Kropff, and M. Moser, Place Cells, Grid Cells, and the Brain's Spatial Representation System, Annu Rev Neurosci, vol.31, pp.69-89, 2008.

F. L. Assini, M. Duzzioni, and R. N. Takahashi, Object location memory in mice: Pharmacological validation and further evidence of hippocampal CA1 participation, Behav Brain Res, vol.204, pp.206-217, 2009.

J. R. Clarke, M. Cammarota, A. Gruart, I. Izquierdo, and J. M. Delgado-garcía, Plastic modifications induced by object recognition memory processing, Proc Natl Acad Sci U S A, vol.107, pp.2652-2659, 2010.

E. Dere, J. P. Huston, D. Souza-silva, and M. A. , The pharmacology, neuroanatomy and neurogenetics of one-trial object recognition in rodents, Neurosci Biobehav Rev, vol.31, pp.673-704, 2007.

E. A. Murray and B. J. Richmond, Role of perirhinal cortex in object perception, memory, and associations, Curr Opin Neurobiol, vol.11, pp.188-93, 2001.

M. S. Fanselow and H. Dong, Are the Dorsal and Ventral Hippocampus Functionally Distinct Structures?, Neuron, vol.65, pp.7-19, 2010.

I. Lee and F. Solivan, The roles of the medial prefrontal cortex and hippocampus in a spatial paired-association task, Learn Mem, vol.15, pp.357-67, 2008.

G. Barker and E. C. Warburton, Object-in-Place Associative Recognition Memory Depends on Glutamate Receptor Neurotransmission Within Two Defined Hippocampal-Cortical Circuits: A Critical Role for AMPA and NMDA Receptors in the Hippocampus, Perirhinal, and Prefrontal Cortices, Cereb Cortex N Y NY, vol.25, pp.472-81, 2015.

Y. S. Jo and I. Lee, Perirhinal cortex is necessary for acquiring, but not for retrieving object-place paired association, Learn Mem Cold Spring Harb N, vol.17, pp.97-103, 2010.

K. S. Fowler, M. M. Saling, E. L. Conway, J. M. Semple, and W. J. Louis, Paired associate performance in the early detection of DAT, J Int Neuropsychol Soc JINS, vol.8, pp.58-71, 2002.

S. A. Hart, C. M. Smith, and M. Swash, Recognition memory in Alzheimer's disease, Neurobiol Aging, vol.6, pp.287-92, 1985.

A. Van-der-jeugd, D. Blum, S. Raison, S. Eddarkaoui, L. Buée et al., Observations in THY-Tau22 mice that resemble behavioral and psychological signs and symptoms of dementia, Behav Brain Res, vol.242, pp.34-43, 2013.

J. Lewis, E. Mcgowan, J. Rockwood, H. Melrose, P. Nacharaju et al., Neurofibrillary tangles, amyotrophy and progressive motor disturbance in mice expressing mutant (P301L) tau protein, Nat Genet, vol.25, pp.402-407, 2000.

B. Allen, E. Ingram, M. Takao, M. J. Smith, R. Jakes et al., Abundant tau filaments and nonapoptotic neurodegeneration in transgenic mice expressing human P301S tau protein, J Neurosci Off J Soc Neurosci, vol.22, pp.9340-51, 2002.

M. Ikeda, M. Shoji, T. Kawarai, T. Kawarabayashi, E. Matsubara et al., Accumulation of filamentous tau in the cerebral cortex of human tau R406W transgenic mice, Am J Pathol, vol.166, issue.10, pp.62274-62276, 2005.

D. Terwel, R. Lasrado, J. Snauwaert, E. Vandeweert, C. Van-haesendonck et al., Changed conformation of mutant Tau-P301L underlies the moribund tauopathy, absent in progressive, nonlethal axonopathy of Tau-4R/2N transgenic mice, J Biol Chem, vol.280, pp.3963-73, 2005.

M. A. Busche, X. Chen, H. A. Henning, J. Reichwald, M. Staufenbiel et al., Critical role of soluble amyloid-for early hippocampal hyperactivity in a mouse model of Alzheimer's disease, Proc Natl Acad Sci, vol.109, pp.8740-8745, 2012.

N. Takata, Y. Sugiura, K. Yoshida, M. Koizumi, N. Hiroshi et al., Optogenetic astrocyte activation evokes BOLD fMRI response with oxygen consumption without neuronal activity modulation, Glia, vol.66, pp.2013-2036, 2018.

K. V. Kuchibhotla, C. R. Lattarulo, B. T. Hyman, and B. J. Bacskai, Synchronous Hyperactivity and Intercellular Calcium Waves in Astrocytes in Alzheimer Mice, Science, vol.323, pp.1211-1216, 2009.

M. B. Moser and E. I. Moser, Functional differentiation in the hippocampus, Hippocampus, vol.8, pp.608-627, 1998.

A. Tanti and C. Belzung, Neurogenesis along the septo-temporal axis of the hippocampus: are depression and the action of antidepressants region-specific?, Neuroscience, vol.252, pp.234-52, 2013.

A. Maruszak and S. Thuret, Why looking at the whole hippocampus is not enough-a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer's disease diagnosis, Front Cell Neurosci, vol.8, 2014.

Q. Zhong, H. Xu, J. Qin, L. Zeng, D. Hu et al., Functional parcellation of the hippocampus from resting-state dynamic functional connectivity, Brain Res, vol.1715, pp.165-75, 2019.

A. Fuster-matanzo, M. Llorens-martín, E. G. Barreda, . De, J. Ávila et al., Different Susceptibility to Neurodegeneration of Dorsal and Ventral Hippocampal Dentate Gyrus: A Study with Transgenic Mice Overexpressing GSK3?, PLOS ONE, vol.6, 2011.

E. Bellistri, J. Aguilar, J. R. Brotons-mas, G. Foffani, and L. M. De-la-prida, Basic properties of somatosensory-evoked responses in the dorsal hippocampus of the rat, J Physiol, vol.591, pp.2667-86, 2013.

, The Hippocampus as a Cognitive Map n, July, vol.29, 2019.

M. J. Eacott and G. Norman, Integrated memory for object, place, and context in rats: a possible model of episodic-like memory?, J Neurosci Off J Soc Neurosci, vol.24, pp.1948-53, 2004.

X. Long and S. Zhang, A novel somatosensory spatial navigation system outside the hippocampal formation, BioRxiv, p.473090, 2018.

P. Dutar, M. H. Bassant, M. C. Senut, and Y. Lamour, The septohippocampal pathway: structure and function of a central cholinergic system, Physiol Rev, vol.75, pp.393-427, 1995.

E. M. Mcglinchey, A. , and G. , Dorsal Hippocampus Drives Context-Induced Cocaine Seeking via Inputs to Lateral Septum, Neuropsychopharmacology, vol.43, pp.987-1000, 2018.

A. Grinvald and R. Hildesheim, VSDI: a new era in functional imaging of cortical dynamics, Nat Rev Neurosci, vol.5, pp.874-85, 2004.

Y. Maatuf, E. A. Stern, and H. Slovin, Abnormal Population Responses in the Somatosensory Cortex of Alzheimer's Disease Model Mice, Sci Rep, vol.6, 2016.

J. Kim and Y. Jeong, Augmentation of sensory-evoked hemodynamic response in an early Alzheimer's disease mouse model, J Alzheimers Dis JAD, vol.37, pp.857-68, 2013.

A. Pitkänen, M. Pikkarainen, N. Nurminen, and A. Ylinen, Reciprocal Connections between the Amygdala and the Hippocampal Formation, Perirhinal Cortex, and Postrhinal Cortex in Rat: A Review, Ann N Y Acad Sci, vol.911, pp.369-91, 2000.

G. Ritov, Z. Ardi, and G. Richter-levin, Differential activation of amygdala, dorsal and ventral hippocampus following an exposure to a reminder of underwater trauma, Front Behav Neurosci, vol.8, 2014.

R. G. Phillips and J. E. Ledoux, Differential Contribution of Amygdala and Hippocampus to Cued and Contextual Fear Conditioning n.d, p.12

J. Schumacher, L. R. Peraza, M. Firbank, A. J. Thomas, M. Kaiser et al., Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease, NeuroImage Clin, vol.22, p.101812, 2019.

A. J. Mcdonald and D. D. Mott, Functional Neuroanatomy of Amygdalohippocampal Interconnections and Their Role in Learning and Memory, J Neurosci Res, vol.95, pp.797-820, 2017.

D. Paré, D. R. Collins, and J. G. Pelletier, Amygdala oscillations and the consolidation of emotional memories, Trends Cogn Sci, vol.6, pp.306-320, 2002.

M. G. Packard, L. Cahill, and J. L. Mcgaugh, Amygdala modulation of hippocampal-dependent and caudate nucleus-dependent memory processes, Proc Natl Acad Sci U S A, vol.91, pp.8477-81, 1994.

J. A. Hobin, J. J. Maren, and S. , Ventral hippocampal muscimol disrupts context-specific fear memory retrieval after extinction in rats, Hippocampus, vol.16, pp.174-82, 2006.

C. A. Orsini, J. H. Kim, E. Knapska, and S. Maren, Hippocampal and prefrontal projections to the basal amygdala mediate contextual regulation of fear after extinction, J Neurosci Off J Soc Neurosci, vol.31, pp.17269-77, 2011.

M. Ortner, L. Pasquini, M. Barat, P. Alexopoulos, T. Grimmer et al., Progressively Disrupted Intrinsic Functional Connectivity of Basolateral Amygdala in Very Early Alzheimer's Disease, Front Neurol, vol.7, 2016.

M. P. Laakso, H. Soininen, K. Partanen, E. Helkala, P. Hartikainen et al., Volumes of hippocampus, amygdala and frontal lobes in the MRI-based diagnosis of early Alzheimer's disease: Correlation with memory functions, J Neural Transm -Park Dis Dement Sect, vol.9, pp.73-86, 1995.

D. Zanchi, P. Giannakopoulos, S. Borgwardt, C. Rodriguez, and S. Haller, Hippocampal and Amygdala Gray Matter Loss in Elderly Controls with Subtle Cognitive Decline, Front Aging Neurosci, vol.9, 2017.

X. Tang, V. R. Varma, M. I. Miller, and M. C. Carlson, Education is associated with sub-regions of the hippocampus and the amygdala vulnerable to neuropathologies of Alzheimer's disease, Brain Struct Funct, vol.222, pp.1469-79, 2017.

T. Lin, Y. Shih, S. Chen, C. Lien, C. Chang et al., Running exercise delays neurodegeneration in amygdala and hippocampus of Alzheimer's disease (APP/PS1) transgenic mice, Neurobiol Learn Mem, vol.118, pp.189-97, 2015.

L. W. Swanson and W. M. Cowan, The connections of the septal region in the rat, J Comp Neurol, vol.186, pp.621-55, 1979.

T. Chiba, Collateral projection from the amygdalo--hippocampal transition area and CA1 to the hypothalamus and medial prefrontal cortex in the rat, Neurosci Res, vol.38, pp.373-83, 2000.

H. W. Dong, G. D. Petrovich, and L. W. Swanson, Topography of projections from amygdala to bed nuclei of the stria terminalis, Brain Res Brain Res Rev, vol.38, pp.192-246, 2001.

G. D. Petrovich, N. S. Canteras, and L. W. Swanson, Combinatorial amygdalar inputs to hippocampal domains and hypothalamic behavior systems, Brain Res Rev, vol.38, pp.80-87, 2001.

T. Kovács, Mechanisms of olfactory dysfunction in aging and neurodegenerative disorders, Ageing Res Rev, vol.3, pp.215-247, 2004.

J. Djordjevic, M. Jones-gotman, D. Sousa, K. Chertkow, and H. , Olfaction in patients with mild cognitive impairment and Alzheimer's disease, Neurobiol Aging, vol.29, pp.693-706, 2008.

J. K. Olofsson, M. Rönnlund, S. Nordin, L. Nyberg, L. Nilsson et al., Odor Identification Deficit as a Predictor of Five-Year Global Cognitive Change: Interactive Effects with Age and ApoE-?4, Behav Genet, vol.39, pp.496-503, 2009.

R. S. Wilson, S. E. Arnold, J. A. Schneider, P. A. Boyle, A. S. Buchman et al., Olfactory Impairment in Presymptomatic Alzheimer's Disease, Ann N Y Acad Sci, vol.1170, pp.730-735, 2009.

D. P. Devanand, M. H. Tabert, K. Cuasay, J. Manly, N. Schupf et al., Olfactory identification deficits and MCI in a multi-ethnic elderly community sample, Neurobiol Aging, vol.31, pp.1593-600, 2010.

G. Martel, A. Simon, S. Nocera, S. Kalainathan, L. Pidoux et al., Aging, but not tau pathology, impacts olfactory performances and somatostatin systems in THY-Tau22 mice, Neurobiol Aging, vol.36, pp.1013-1041, 2015.

L. Joie, R. Perrotin, A. Barré, L. Hommet, C. Mézenge et al., Region-specific hierarchy between atrophy, hypometabolism, and ?-amyloid (A?) load in Alzheimer's disease dementia, J Neurosci Off J Soc Neurosci, vol.32, pp.16265-73, 2012.

H. Duclos, L. Sayette-v-de, F. Eustache, B. Desgranges, and M. Laisney, La variante frontale de la maladie d'Alzheimer Frontal variant of Alzheimer's disease n.d, p.18

S. Kawakatsu, R. Kobayashi, and H. Hayashi, Typical and atypical appearance of early-onset Alzheimer's disease: A clinical, neuroimaging and neuropathological study, Neuropathology, vol.37, pp.150-73, 2017.

J. K. Johnson, E. Head, R. Kim, A. Starr, and C. W. Cotman, Clinical and pathological evidence for a frontal variant of Alzheimer disease, Arch Neurol, vol.56, pp.1233-1242, 1999.

M. W. Jones and M. A. Wilson, Theta Rhythms Coordinate Hippocampal-Prefrontal Interactions in a Spatial Memory Task, PLoS Biol, vol.3, 2005.

W. Singer, Neuronal synchrony: a versatile code for the definition of relations?, Neuron, vol.24, pp.111-136, 1999.

J. T. Becker, M. A. Mintun, K. Aleva, M. B. Wiseman, T. Nichols et al., Compensatory reallocation of brain resources supporting verbal episodic memory in Alzheimer's disease, Neurology, vol.46, pp.692-700, 1996.

J. L. Woodard, S. T. Grafton, J. R. Votaw, R. C. Green, M. E. Dobraski et al., Compensatory recruitment of neural resources during overt rehearsal of word lists in Alzheimer's disease, Neuropsychology, vol.12, pp.491-504, 1998.

K. D. Dougherty, P. I. Turchin, and T. J. Walsh, Septocingulate and septohippocampal cholinergic pathways: involvement in working/episodic memory, Brain Res, vol.810, issue.98, pp.870-871, 1998.

I. Izquierdo, C. Da-cunha, R. Rosat, D. Jerusalinsky, M. B. Ferreira et al., Neurotransmitter receptors involved in post-training memory processing by the amygdala, medial septum, and hippocampus of the rat, Behav Neural Biol, vol.58, pp.16-26, 1992.

M. Ortner, L. Pasquini, M. Barat, P. Alexopoulos, T. Grimmer et al., Progressively Disrupted Intrinsic Functional Connectivity of Basolateral Amygdala in Very Early Alzheimer's Disease, Front Neurol, vol.7, 2016.

E. K. Hebda-bauer, T. A. Simmons, A. Sugg, E. Ural, J. A. Stewart et al., 3xTg-AD Mice Exhibit an Activated Central Stress Axis during Early-Stage Pathology, J Alzheimers Dis JAD, vol.33, pp.407-429, 2013.

A. S. Fleisher, A. Sherzai, C. Taylor, J. Langbaum, K. Chen et al., Resting-state BOLD networks versus task-associated functional MRI for distinguishing Alzheimer's disease risk groups, NeuroImage, vol.47, pp.1678-90, 2009.

F. Bai, Z. Zhang, H. Yu, Y. Shi, Y. Yuan et al., Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study, Neurosci Lett, vol.438, pp.111-116, 2008.

M. Filippi, E. G. Spinelli, C. Cividini, and F. Agosta, Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings, Front Neurosci, vol.13, 2019.

Z. Fu, A. Caprihan, J. Chen, Y. Du, J. C. Adair et al., Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities, Hum Brain Mapp, vol.40, pp.3203-3224, 2019.

M. Demirta?, C. Tornador, C. Falcón, M. López-solà, R. Hernández-ribas et al., Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder, Hum Brain Mapp, vol.37, pp.2918-2948, 2016.

A. Córdova-palomera, T. Kaufmann, K. Persson, D. Alnaes, N. T. Doan et al., Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer's disease continuum, Sci Rep, vol.7, 2017.

B. Jie, C. Wee, D. Shen, and D. Zhang, Hyper-connectivity of functional networks for brain disease diagnosis, Med Image Anal, vol.32, pp.84-100, 2016.

C. Wee, P. Yap, D. Zhang, K. Denny, J. N. Browndyke et al., Identification of MCI individuals using structural and functional connectivity networks, NeuroImage, vol.59, pp.2045-56, 2012.

E. V. Sullivan, N. M. Zahr, T. Rohlfing, and A. Pfefferbaum, Fiber tracking functionally distinct components of the internal capsule, Neuropsychologia, vol.48, pp.4155-63, 2010.

J. D. Schmahmann, D. L. Rosene, and D. N. Pandya, Motor projections to the basis pontis in rhesus monkey, J Comp Neurol, vol.478, pp.248-68, 2004.

T. H. Ferreira-vieira, I. M. Guimaraes, F. R. Silva, and F. M. Ribeiro, Alzheimer's Disease: Targeting the Cholinergic System, Curr Neuropharmacol, vol.14, pp.101-116, 2016.

H. Cho, D. W. Yang, Y. M. Shon, B. S. Kim, Y. I. Kim et al., Abnormal Integrity of Corticocortical Tracts in Mild Cognitive Impairment: A Diffusion Tensor Imaging Study, J Korean Med Sci, vol.23, pp.477-83, 2008.

S. Thillainadesan, W. Wen, L. Zhuang, J. Crawford, N. Kochan et al., Changes in mild cognitive impairment and its subtypes as seen on diffusion tensor imaging, Int Psychogeriatr, vol.24, pp.1483-93, 2012.

B. R. Copenhaver, L. A. Rabin, A. J. Saykin, R. M. Roth, H. A. Wishart et al., The fornix and mammillary bodies in older adults with Alzheimer's disease, mild cognitive impairment, and cognitive complaints: A volumetric MRI study, Psychiatry Res Neuroimaging, vol.147, pp.93-103, 2006.

S. E. Rose, A. L. Janke, and J. B. Chalk, Gray and white matter changes in Alzheimer's disease: a diffusion tensor imaging study, J Magn Reson Imaging JMRI, vol.27, pp.20-26, 2008.

G. Shim, K. Choi, D. Kim, S. Suh, S. Lee et al., Predicting neurocognitive function with hippocampal volumes and DTI metrics in patients with Alzheimer's dementia and mild cognitive impairment, Brain Behav, vol.7, p.766, 2017.

K. Belarbi, S. Burnouf, F. Fernandez-gomez, J. Desmercières, L. Troquier et al., Loss of medial septum cholinergic neurons in THY-Tau22 mouse model: what links with tau pathology?, Curr Alzheimer Res, vol.8, pp.633-638, 2011.

Y. Hara, Y. Motoi, K. Hikishima, H. Mizuma, H. Onoe et al., Involvement of the Septo-Hippocampal Cholinergic Pathway in Association with Septal Acetylcholinesterase Upregulation in a Mouse Model of Tauopathy, Curr Alzheimer Res, vol.14, pp.94-103, 2017.

S. Mondragón-rodríguez, N. Gu, C. Fasano, F. Peña-ortega, and S. Williams, Functional Connectivity between Hippocampus and Lateral Septum is Affected in Very Young Alzheimer's Transgenic Mouse Model, Neuroscience, vol.401, pp.96-105, 2019.

A. G. Thomas, P. Koumellis, and R. A. Dineen, The Fornix in Health and Disease: An Imaging Review, RadioGraphics, vol.31, pp.1107-1128, 2011.

A. Pereira, S. Ribeiro, M. Wiest, L. C. Moore, J. Pantoja et al., Processing of tactile information by the hippocampus, Proc Natl Acad Sci U S A, vol.104, pp.18286-91, 2007.

P. M. Itskov, E. Vinnik, and M. E. Diamond, Hippocampal representation of touch-guided behavior in rats: persistent and independent traces of stimulus and reward location, PloS One, vol.6, p.16462, 2011.

. Mechling, Fractional Anisotropy (FA) maps were computed by a normalization of diffusion measures from all three axis of the tensor. 2'. From denoised HARDI data, we applied global tractography for each individual mouse brain included in the studies to reconstruct fibers, 2016.

R. D. Adif, F. A. , and F. Avants, an average template was created using the ANTS multivariate template construction function, 2011.

. Yalcin, Figure 2: Pre-processing pipeline of HARDI data 1. Introduction Neuropathic pain is a neurological syndrome that associates both sensory nociceptive and aversive emotional components. It can also lead to anxio-depressive consequences, which increases the pain burden. The existence of neuropathic paininduced affective disorders is further supported by preclinical studies showing that neuropathic pain models can induce anxiety-and/or depression-like behaviors in a time dependent manner, A spatial gaussian smoothing of 0.5mm FWHM was applied to all images, to further performed voxel-based quantification analysis on deformations maps, 2011.

M. N. Baliki, 2015) are involved in the pain chronicity. However, we still do not know how the brain reorganizes functional connectivity when chronic pain comorbid with anxiety and depressive-like behaviors, which may stem from the fact that experiments in humans are generally cross-sectional in nature. Longitudinal experimental designs are thus the unique tool to study how changes occur overtime. Longitudinal neuroimaging studies in rodent neuropathic pain models, Previous imaging studies frequently focused on the transition from an acute to chronic pain state and demonstrated that the nucleus accumbens (ACB), hippocampus (HIP), prefrontal cortex (PFC, 2009.

. Hubbard, 2014) were reported at different time points. However, the main focus of these aforementioned studies once again was nociception, 2015) and an overall reorganization of corticolimbic system functional connectivity highlighting ACB, PFC and hippocampus connectivity changes, 2014.

. Shelton, Affective consequences of pain can be attributed to plasticity changes caused by chronic pain conditions in brain regions processing both pain and emotional/motivational information, Zhuo, 2013) and limbic regions, 2008.

K. H. Abate, Gender Disparity in Prevalence of Depression Among Patient Population: A Systematic Review, Ethiop. J. Health Sci, vol.23, pp.283-288, 2013.

G. Alderton, Sex bias in research animals, Science, vol.364, pp.846-848, 2019.

,

C. Anacker, J. Scholz, K. J. O'donnell, R. Allemang-grand, J. Diorio et al., Neuroanatomic Differences Associated With Stress Susceptibility and Resilience, Biol. Psychiatry, Mechanisms of Resilience to Stress Effects, vol.79, pp.840-849, 2016.

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein et al., A reproducible evaluation of ANTs similarity metric performance in brain image registration, Neuroimage, vol.54, pp.2033-2044, 2011.

M. N. Baliki, P. C. Chang, A. T. Baria, M. V. Centeno, and A. V. Apkarian, Resting-sate functional reorganization of the rat limbic system following neuropathic injury, p.6186, 2014.

M. N. Baliki, P. Y. Geha, R. Jabakhanji, N. Harden, T. J. Schnitzer et al., A Preliminary fMRI Study of Analgesic Treatment in Chronic Back Pain and Knee Osteoarthritis, Mol. Pain, vol.4, pp.1744-8069, 2008.

M. N. Baliki, A. R. Mansour, A. T. Baria, and A. V. Apkarian, Functional reorganization of the default mode network across chronic pain conditions, PloS One, vol.9, 2014.

M. N. Baliki, B. Petre, S. Torbey, K. M. Herrmann, L. Huang et al., Corticostriatal functional connectivity predicts transition to chronic back pain, Nat. Neurosci, vol.15, pp.1117-1119, 2012.

F. Barthas, M. Humo, R. Gilsbach, E. Waltisperger, M. Karatas et al., Cingulate Overexpression of Mitogen-Activated Protein Kinase Phosphatase-1 as a Key Factor for Depression, Biol. Psychiatry, vol.82, pp.370-379, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02106543

F. Barthas, J. Sellmeijer, S. Hugel, E. Waltisperger, M. Barrot et al., The anterior cingulate cortex is a critical hub for pain-induced depression, Biol. Psychiatry, vol.77, pp.236-245, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02349819

M. Benbouzid, V. Pallage, M. Rajalu, E. Waltisperger, S. Doridot et al., Sciatic nerve cuffing in mice: A model of sustained neuropathic pain, Eur. J. Pain, vol.12, pp.591-599, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00343709

M. G. Berman, S. Peltier, D. E. Nee, E. Kross, P. J. Deldin et al., Depression, rumination and the default network, Soc. Cogn. Affect. Neurosci, vol.6, pp.548-555, 2011.

O. Berton, C. A. Mcclung, R. J. Dileone, V. Krishnan, W. Renthal et al., Essential Role of BDNF in the Mesolimbic Dopamine Pathway in Social Defeat Stress, Science, vol.311, pp.864-868, 2006.

A. Bilbao, C. Falfán-melgoza, S. Leixner, R. Becker, S. K. Singaravelu et al., Longitudinal Structural and Functional Brain Network Alterations in a Mouse Model of Neuropathic Pain, 2018.

D. Borsook, C. Linnman, V. Faria, A. M. Strassman, L. Becerra et al., Reward deficiency and anti-reward in pain chronification, Neurosci. Biobehav. Rev, vol.68, pp.282-297, 2016.

S. J. Broyd, C. Demanuele, S. Debener, S. K. Helps, C. J. James et al., Default-mode brain dysfunction in mental disorders: A systematic review, Neurosci. Biobehav. Rev, vol.33, pp.279-296, 2009.

R. L. Buckner, J. R. Andrews-hanna, and D. L. Schacter, The Brain's Default Network: Anatomy, Function, and Relevance to Disease, Ann. N. Y. Acad. Sci, vol.1124, pp.1-38, 2008.

,

D. Buehlmann, J. Grandjeanc, J. Xandryd, and M. Rudin, Longitudinal resting-state fmri in a mouse model of metastatic bone cancer reveals distinct functional reorganizations along a developing chronic pain state, 2018.

F. Cauda, F. D'agata, K. Sacco, S. Duca, D. Cocito et al., Altered resting state attentional networks in diabetic neuropathic pain, J. Neurol. Neurosurg. Psychiatry, vol.81, pp.806-811, 2010.

P. Chang, S. L. Pollema-mays, M. V. Centeno, D. Procissi, M. Contini et al., Role of nucleus accumbens in neuropathic pain: Linked multi-scale evidence in the rat transitioning to neuropathic pain, Pain, vol.155, pp.1128-1139, 2014.

,

C. Clemm-von-hohenberg, W. Weber-fahr, P. Lebhardt, N. Ravi, U. Braun et al., Lateral habenula perturbation reduces default-mode network connectivity in a rat model of depression, Transl. Psychiatry, vol.8, p.68, 2018.

A. F. Dasilva, C. Granziera, D. S. Tuch, J. Snyder, M. Vincent et al., Interictal alterations of the trigeminal somatosensory pathway and PAG in migraine, Neuroreport, vol.18, pp.301-305, 2007.

I. M. Devonshire, J. J. Burston, L. Xu, A. Lillywhite, M. J. Prior et al., Manganese-enhanced magnetic resonance imaging depicts brain activity in models of acute and chronic pain: A new window to study experimental spontaneous pain?, NeuroImage, vol.157, pp.500-510, 2017.

B. Draganski, J. Ashburner, C. Hutton, F. Kherif, R. S. Frackowiak et al., Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ), NeuroImage, vol.55, pp.1423-1434, 2011.

W. C. Drevets, J. L. Price, J. R. Jr, R. D. Todd, T. Reich et al., Subgenual prefrontal cortex abnormalities in mood disorders, Nature, vol.386, pp.824-827, 1997.

,

N. Erpelding, S. Sava, L. E. Simons, A. Lebel, P. Serrano et al., Habenula functional resting-state connectivity in pediatric CRPS, J. Neurophysiol, vol.111, pp.239-247, 2013.

S. Ezzatpanah, V. Babapour, and A. Haghparast, Differential contribution of orexin receptors within the ventral tegmental area to modulation of persistent inflammatory pain, Eur. J. Pain, vol.20, pp.1090-1101, 2016.

M. S. Fanselow and H. Dong, Are the Dorsal and Ventral Hippocampus Functionally Distinct Structures?, Neuron, vol.65, pp.7-19, 2010.

M. A. Farmer, M. N. Baliki, and A. V. Apkarian, A dynamic network perspective of chronic pain, Neurosci. Lett, vol.520, pp.197-203, 2012.

R. B. Fillingim, C. D. King, M. C. Ribeiro-dasilva, B. Rahim-williams, and J. L. Riley, Sex, Gender, and Pain: A Review of Recent Clinical and Experimental Findings, J. Pain Off. J. Am. Pain Soc, vol.10, pp.447-485, 2009.

B. Fu, S. Wen, B. Wang, K. Wang, J. Zhang et al., Gabapentin regulates dopaminergic neuron firing and theta oscillation in the ventral tegmental area to reverse depressionlike behavior in chronic neuropathic pain state, 2018.

, J. Pain Res

L. Garcia-larrea and R. Peyron, Pain matrices and neuropathic pain matrices: a review, Pain, vol.154, 2013.
URL : https://hal.archives-ouvertes.fr/inserm-00877368

N. Gass, R. Becker, A. J. Schwarz, W. Weber-fahr, C. Clemm-von-hohenberg et al., Brain network reorganization differs in response to stress in rats genetically predisposed to depression and stress-resilient rats, Transl. Psychiatry, vol.6, pp.970-970, 2016.

,

N. Gass, D. Cleppien, L. Zheng, A. J. Schwarz, A. Meyer-lindenberg et al., Functionally altered neurocircuits in a rat model of treatment-resistant depression show prominent role of the habenula, Eur. Neuropsychopharmacol, vol.24, pp.381-390, 2014.

P. Y. Geha, M. N. Baliki, R. N. Harden, W. R. Bauer, T. B. Parrish et al., The Brain in Chronic CRPS Pain: Abnormal Gray-White Matter Interactions in Emotional and Autonomic Regions, Neuron, vol.60, pp.570-581, 2008.

L. Gonçalves, R. Silva, F. Pinto-ribeiro, J. M. Pêgo, J. M. Bessa et al., Neuropathic pain is associated with depressive behaviour and induces neuroplasticity in the amygdala of the rat, Exp. Neurol, vol.213, pp.48-56, 2008.

J. Grandjean, D. Azzinnari, A. Seuwen, H. Sigrist, E. Seifritz et al., Chronic psychosocial stress in mice leads to changes in brain functional connectivity and metabolite levels comparable to human depression, NeuroImage, vol.142, pp.544-552, 2016.

,

M. D. Greicius, B. H. Flores, V. Menon, G. H. Glover, H. B. Solvason et al., Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus, Biol. Psychiatry, vol.62, pp.429-437, 2007.

M. D. Greicius, B. Krasnow, A. L. Reiss, and V. Menon, Functional connectivity in the resting brain: a network analysis of the default mode hypothesis, Proc. Natl. Acad. Sci, vol.100, pp.253-258, 2003.

L. Harsan, C. Dávid, M. Reisert, S. Schnell, J. Hennig et al., Mapping remodeling of thalamocortical projections in the living reeler mouse brain by diffusion tractography, Proc. Natl. Acad. Sci. U. S. A, vol.110, pp.1797-1806, 2013.

D. J. Hayes and G. Northoff, Identifying a Network of Brain Regions Involved in Aversion-Related Processing: A Cross-Species Translational Investigation, Front. Integr. Neurosci, vol.5, 2011.

M. J. Henckens, K. Van-der-marel, A. Van-der-toorn, A. G. Pillai, G. Fernández et al., Stress-induced alterations in large-scale functional networks of the rodent brain, NeuroImage, vol.105, pp.312-322, 2015.

L. Hipólito, A. Wilson-poe, Y. Campos-jurado, E. Zhong, J. Gonzalez-romero et al., Inflammatory Pain Promotes Increased Opioid Self-Administration: Role of Dysregulated Ventral Tegmental Area ? Opioid Receptors, J. Neurosci, vol.35, pp.12217-12231, 2015.

,

C. S. Hubbard, S. A. Khan, S. Xu, M. Cha, R. Masri et al., Behavioral, metabolic and functional brain changes in a rat model of chronic neuropathic pain: A longitudinal MRI study, NeuroImage, vol.107, pp.333-344, 2015.

G. D. Iannetti and A. Mouraux, From the neuromatrix to the pain matrix (and back), Exp. Brain Res, vol.205, pp.1-12, 2010.

H. Ikeda, K. Mochizuki, and K. Murase, Astrocytes are involved in long-term facilitation of neuronal excitation in the anterior cingulate cortex of mice with inflammatory pain, Pain, vol.154, pp.2836-2843, 2013.

E. Isingrini, L. Perret, Q. Rainer, B. Amilhon, E. Guma et al., Resilience against Chronic Stress is Mediated by Noradrenergic Regulation of the Ventral Tegmental Area, Biol. Psychiatry, vol.695, p.282, 2017.

N. Ji, J. Kang, R. Hua, and Y. Zhang, Involvement of dopamine system in the regulation of the brain corticotropin-releasing hormone in paraventricular nucleus in a rat model of chronic visceral pain, Neurol. Res, vol.0, pp.1-8, 2018.

M. Y. Ko, E. Y. Jang, J. Y. Lee, S. P. Kim, S. H. Whang et al., The Role of Ventral Tegmental Area Gamma-Aminobutyric Acid in Chronic Neuropathic Pain after Spinal Cord Injury in Rats, J. Neurotrauma, vol.35, pp.1755-1764, 2018.

,

P. Kumar, G. Waiter, T. Ahearn, M. Milders, I. Reid et al., Abnormal temporal difference reward-learning signals in major depression, Brain, vol.131, pp.2084-2093, 2008.

,

S. Lammel, B. K. Lim, and R. C. Malenka, Reward and aversion in a heterogeneous midbrain dopamine system, Neuropharmacology, vol.76, pp.351-359, 2014.

,

A. Lee, J. Kim, E. Cho, M. Kim, and M. Park, Dorsal and Ventral Hippocampus Differentiate in Functional Pathways and Differentially Associate with Neurological Disease-Related Genes during Postnatal Development, Front. Mol. Neurosci, vol.10, 2017.

E. S. Lein, M. J. Hawrylycz, N. Ao, M. Ayres, A. Bensinger et al., Nature, vol.445, pp.168-176, 2007.

J. Li, Y. Li, B. Zhang, X. Shen, and H. Zhao, Why depression and pain often coexist and mutually reinforce: Role of the lateral habenula, Exp. Neurol, vol.284, pp.106-113, 2016.

,

Y. Li, Y. Wang, C. Xuan, Y. Li, L. Piao et al., Role of the Lateral Habenula in Pain-Associated Depression, Front. Behav. Neurosci, vol.11, 2017.

D. Liu, Q. Tang, C. Yin, . Song, . Yu et al., Brain-derived neurotrophic factormediated projection-specific regulation of depressive-like and nociceptive behaviors in the mesolimbic reward circuitry: PAIN 159, vol.175, 2018.

J. Lutz, L. Jäger, D. Quervain, T. Krauseneck, F. Padberg et al., White and gray matter abnormalities in the brain of patients with fibromyalgia: A diffusion-tensor and volumetric imaging study, Arthritis Rheum, vol.58, pp.3960-3969, 2008.

L. D. Mannelli, A. Pacini, L. Bonaccini, M. Zanardelli, T. Mello et al., Morphologic Features and Glial Activation in Rat Oxaliplatin-Dependent Neuropathic Pain, J. Pain, vol.14, pp.1585-1600, 2013.

H. S. Mayberg, Targeted electrode-based modulation of neural circuits for depression, 2009.

, Clin. Invest, vol.119, pp.717-725

V. Mitsi and V. Zachariou, Modulation of pain, nociception, and analgesia by the brain reward center, 2016.

M. Moayedi, I. Weissman-fogel, T. V. Salomons, A. P. Crawley, M. B. Goldberg et al., White matter brain and trigeminal nerve abnormalities in temporomandibular disorder, Pain, vol.153, pp.1467-1477, 2012.

A. A. Mutso, B. Petre, L. Huang, M. N. Baliki, S. Torbey et al., Reorganization of hippocampal functional connectivity with transition to chronic back pain, J. Neurophysiol, vol.111, pp.1065-1076, 2013.

V. Napadow, L. Lacount, K. Park, S. As-sanie, D. J. Clauw et al., Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity, Arthritis Rheum, vol.62, pp.2545-2555, 2011.

N. Minoru, K. Naoko, N. Michiko, K. Chihiro, H. Nana et al., Chronic pain-induced emotional dysfunction is associated with astrogliosis due to cortical ?-opioid receptor dysfunction, J. Neurochem, vol.97, pp.1369-1378, 2006.

C. D. Proulx, O. Hikosaka, and R. Malinow, Reward processing by the lateral habenula in normal and depressive behaviors, Nat. Neurosci, vol.17, pp.1146-1152, 2014.

M. E. Raichle, A. M. Macleod, A. Z. Snyder, W. J. Powers, D. A. Gusnard et al., A default mode of brain function, Proc. Natl. Acad. Sci, vol.98, pp.676-682, 2001.

G. Rajkowska, Postmortem studies in mood disorders indicate altered numbers of neurons and glial cells, Biol. Psychiatry, vol.48, pp.950-951, 2000.

R. Redlich, K. Dohm, D. Grotegerd, N. Opel, P. Zwitserlood et al., Reward Processing in Unipolar and Bipolar Depression: A Functional MRI Study, Neuropsychopharmacology, vol.40, pp.2623-2631, 2015.

M. Reisert, I. Mader, C. Anastasopoulos, M. Weigel, S. Schnell et al., Global fiber reconstruction becomes practical, NeuroImage, vol.54, pp.955-962, 2011.

,

W. Ren, M. V. Centeno, S. Berger, Y. Wu, X. Na et al., The indirect pathway of the nucleus accumbens shell amplifies neuropathic pain, Nat. Neurosci, vol.19, pp.220-222, 2015.

S. J. Russo and E. J. Nestler, The brain reward circuitry in mood disorders, Nat. Rev. Neurosci, vol.14, pp.609-625, 2013.

A. Sartorius, K. L. Kiening, P. Kirsch, C. C. Gall, U. Haberkorn et al., Remission of Major Depression Under Deep Brain Stimulation of the Lateral Habenula in a Therapy-Refractory Patient, Biol. Psychiatry, vol.67, pp.9-11, 2010.

J. Scholz, V. Tomassini, and H. Johansen-berg, Chapter 11 -Individual Differences in White Matter Microstructure in the Healthy Brain, Diffusion MRI, pp.237-249, 2009.

J. Sellmeijer, V. Mathis, S. Hugel, X. Li, Q. Song et al., Hyperactivity of Anterior Cingulate Cortex Areas 24a/24b Drives Chronic Pain-Induced Anxiodepressive-like Consequences, J. Neurosci, vol.38, pp.3102-3115, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02437472

D. A. Seminowicz, A. L. Laferriere, M. Millecamps, J. S. Yu, T. J. Coderre et al., MRI structural brain changes associated with sensory and emotional function in a rat model of long-term neuropathic pain, Brain Body Medicine, vol.47, pp.1007-1014, 2009.

,

R. M. Shansky, Are hormones a "female problem" for animal research?, Science, vol.364, pp.825-826, 2019.

L. Shelton, L. Becerra, and D. Borsook, Unmasking the mysteries of the habenula in pain and analgesia, Prog. Neurobiol, vol.96, pp.208-219, 2012.

L. E. Simons, I. Elman, and D. Borsook, Psychological processing in chronic pain: a neural systems approach, Neurosci. Biobehav. Rev, vol.39, pp.61-78, 2014.

F. Sotres-bayón, E. Torres-lópez, A. López-Ávila, R. Del-Ángel, and F. Pellicer, Lesion and electrical stimulation of the ventral tegmental area modify persistent nociceptive behavior in the rat, Brain Res, vol.898, pp.342-349, 2001.

J. M. Stafford, B. R. Jarrett, O. Miranda-dominguez, B. D. Mills, N. Cain et al., Large-scale topology and the default mode network in the mouse connectome, Proc. Natl. Acad. Sci, vol.111, pp.18745-18750, 2014.

J. Steiner, M. Walter, T. Gos, G. J. Guillemin, H. Bernstein et al., Severe depression is associated with increased microglial quinolinic acid in subregions of the anterior cingulate gyrus: Evidence for an immune-modulated glutamatergic neurotransmission?, J. Neuroinflammation, vol.8, p.94, 2011.

S. A. Stouffer, E. A. Suchman, L. C. Devinney, S. A. Star, and R. M. Williams, Adjustment During Army Life, 1949.

A. Tanti, P. Lutz, J. Kim, L. O'leary, G. Turecki et al., Evidence of decreased gap junction coupling between astrocytes and oligodendrocytes in the anterior cingulate cortex of depressed suicides, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02437465

A. M. Taylor, A. Castonguay, A. J. Taylor, N. P. Murphy, A. Ghogha et al., Microglia Disrupt Mesolimbic Reward Circuitry in Chronic Pain, J. Neurosci, vol.35, pp.8442-8450, 2015.

J. Upadhyay, S. J. Baker, P. Chandran, L. Miller, Y. Lee et al., Default-mode-like network activation in awake rodents, PloS One, vol.6, 2011.

N. A. Uranova, V. M. Vostrikov, D. D. Orlovskaya, and V. I. Rachmanova, Oligodendroglial density in the prefrontal cortex in schizophrenia and mood disorders: a study from the Stanley Neuropathology Consortium, Schizophr. Res, vol.67, pp.181-187, 2004.

J. J. Walsh, A. K. Friedman, H. Sun, E. A. Heller, S. M. Ku et al., Stress and CRF gate neural activation of BDNF in the mesolimbic reward pathway, Nat. Neurosci, vol.17, pp.27-29, 2014.

G. Wang, C. Cen, C. Li, S. Cao, N. Wang et al., Deactivation of excitatory neurons in the prelimbic cortex via Cdk5 promotes pain sensation and anxiety, Nat. Commun, vol.6, p.7660, 2015.

L. Wang, D. F. Hermens, I. B. Hickie, and J. Lagopoulos, A systematic review of resting-state functional-MRI studies in major depression, J. Affect. Disord, vol.142, pp.6-12, 2012.

,

M. Watanabe, . Narita, . Michiko, Y. Hamada, A. Yamashita et al., Activation of ventral tegmental area dopaminergic neurons reverses pathological allodynia resulting from nerve injury or bone cancer, Mol. Pain, vol.14, p.1744806918756406, 2018.

H. Xu, L. Wu, H. Wang, X. Zhang, K. I. Vadakkan et al., Presynaptic and Postsynaptic Amplifications of Neuropathic Pain in the Anterior Cingulate Cortex, J. Neurosci, vol.28, pp.7445-7453, 2008.

I. Yalcin, F. Barthas, and M. Barrot, Emotional consequences of neuropathic pain: Insight from preclinical studies, Neurosci. Biobehav. Rev, vol.47, pp.154-164, 2014.

,

I. Yalcin, Y. Bohren, E. Waltisperger, D. Sage-ciocca, J. C. Yin et al., A time-dependent history of mood disorders in a murine model of neuropathic pain, Biol. Psychiatry, vol.70, pp.946-953, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02390249

I. Yalcin, S. Megat, F. Barthas, E. Waltisperger, M. Kremer et al., The Sciatic Nerve Cuffing Model of Neuropathic Pain in Mice, J. Vis. Exp, 2014.

Y. Yang, H. Wang, J. Hu, and H. Hu, Lateral habenula in the pathophysiology of depression, Neurobiology of Disease, vol.48, pp.90-96, 2018.

E. J. Yoon, Y. K. Kim, H. I. Shin, Y. Lee, and S. E. Kim, Cortical and white matter alterations in patients with neuropathic pain after spinal cord injury, Brain Res, vol.1540, pp.64-73, 2013.

,

Z. Zhang, V. M. Gadotti, L. Chen, I. A. Souza, P. L. Stemkowski et al., Role of Prelimbic GABAergic Circuits in Sensory and Emotional Aspects of Neuropathic Pain, Cell Rep, vol.12, pp.752-759, 2015.

X. Zhu, X. Wang, J. Xiao, J. Liao, M. Zhong et al., Evidence of a Dissociation Pattern in Resting-State Default Mode Network Connectivity in First-Episode, Treatment-Naive Major Depression Patients, Biol. Psychiatry, vol.71, pp.611-617, 2012.

M. Zhuo, Long-term potentiation in the anterior cingulate cortex and chronic pain, Philos. Trans. R. Soc. B Biol. Sci, vol.369, 2013.

, Alzheimer : étude d'un modèle murin de la tauopathie ; Auteurs : Laetitia Degiorgis, Connectivité fonctionnelle au repos au stade prodromal de la maladie d, 2017.

H. Ismrm, E-poster: -Patterns of resting-state functional connectivity in the prodromal phase of Alzheimer's disease: Insights from a tauopathy mouse model (Thy-Tau22) ; Auteurs : Laetitia Degiorgis, 2017.

C. Emim, Poster: -Remodeled functional connectivity in the early phase of Alzheimer's pathology in a tauopathy mouse model (Thy-Tau22) ; Auteurs : Laetitia Degiorgis, 2017.

, Oral presentation: -Mapping functional connectivity alterations in a mouse model of Alzheimer's disease ; Auteurs : Laetitia Degiorgis, FMTS, 2018.

, Oral presentation: -Analyse des réseaux cérébraux par IRM fonctionnelle et structurelle chez un modèle souris de la maladie d'Alzheimer ; Auteurs : Laetitia Degiorgis, IRMGE, 2018.

, Oral presentation: -Longitudinal alterations of resting-state functional connectivity in Alzheimer's disease in a tauopathy mouse model ; Auteurs : Laetitia Degiorgis, ISMRM, 2018.

L. Ad/pd, Poster : -Longitudinal modifications of the connectome of a mouse model of Alzheimer's disease, 2019.

L. Degiorgis, M. Karatas, M. Sourty, T. Bienert, M. Reisert et al.,

B. Thomas, R. Marco, M. Chantal, F. Emilie, B. David et al., Harsan Laura-Adela -Altérations longitudinales du connectome cérébral chez une souris modèle de la maladie d'Alzheimer ; Authors : Degiorgis Laetitia, Karatas Meltem, Sourty Marion, SFRMBM, 2019.