J. Acosta-cabronero, G. B. Williams, G. Pengas, and P. J. Nestor, Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease, Brain, vol.133, issue.2, pp.529-539, 2010.
DOI : 10.1093/brain/awp257

M. S. Albert, S. T. Dekosky, D. Dickson, B. Dubois, H. H. Feldman et al., The diagnosis of mild cognitive impairment due to Alzheimer???s disease: Recommendations from the National Institute on Aging-Alzheimer???s Association workgroups on diagnostic guidelines for Alzheimer's disease, Alzheimer's & Dementia, vol.7, issue.3, pp.270-279, 2011.
DOI : 10.1016/j.jalz.2011.03.008

B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and Page, p.64, 2008.

G. Bartzokis, Alzheimer's disease as homeostatic responses to age-related myelin breakdown, Neurobiology of Aging, vol.32, issue.8, pp.1341-1371, 2011.
DOI : 10.1016/j.neurobiolaging.2009.08.007

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3128664

B. Bollobás, Modern graph theory, Graduate texts in mathematics, 1998.

H. Braak and E. Braak, Frequency of Stages of Alzheimer-Related Lesions in Different Age Categories, Neurobiology of Aging, vol.18, issue.4, pp.351-357, 1997.
DOI : 10.1016/S0197-4580(97)00056-0

A. Caroli, A. Prestia, S. Galluzzi, C. Ferrari, W. M. Van-der-flier et al., Mild cognitive impairment with suspected nonamyloid pathology (SNAP): Prediction of progression, Neurology, vol.84, issue.5, pp.508-515
DOI : 10.1212/WNL.0000000000001209

F. Caso, F. Agosta, D. Mattavelli, R. Migliaccio, E. Canu et al., White Matter Degeneration in Atypical Alzheimer Disease, Radiology, vol.277, issue.1, pp.162-172, 2015.
DOI : 10.1148/radiol.2015142766

M. Daianu, E. L. Dennis, N. Jahanshad, T. M. Nir, A. W. Toga et al., Alzheimer's disease disrupts rich club organization in brain connectivity networks, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.266-269, 2013.
DOI : 10.1109/ISBI.2013.6556463

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063983

H. M. Daitz and T. P. Powell, STUDIES OF THE CONNEXIONS OF THE FORNIX SYSTEM, Journal of Neurology, Neurosurgery & Psychiatry, vol.17, issue.1, pp.75-82, 1954.
DOI : 10.1136/jnnp.17.1.75

F. De-vico-fallani, J. Richiardi, M. Chavez, and S. Achard, Graph analysis of functional brain networks: practical issues in translational neuroscience, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.513, issue.5, pp.20130521-20130521, 2014.
DOI : 10.1002/cne.21974

URL : https://hal.archives-ouvertes.fr/hal-01062386

L. Delano-wood, H. Stricker-nikki, S. Scott, F. , N. Daniel et al., Posterior Cingulum White Matter Disruption and Its Associations with Verbal Memory and Stroke Risk in Mild Cognitive Impairment, J. Alzheimeraposs Dis, vol.3233, pp.589-60310, 2012.

B. Dubois, H. H. Feldman, C. Jacova, H. Hampel, J. L. Molinuevo et al., Advancing research diagnostic criteria for Page 37, p.64, 2014.

E. C. Edmonds, L. Delano-wood, D. R. Galasko, D. P. Salmon, and M. W. Bondi, Subtle Cognitive Decline and Biomarker Staging in Preclinical Alzheimer???s Disease, Journal of Alzheimer's Disease, vol.47, issue.1, 2015.
DOI : 10.3233/JAD-150128

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634634

P. Eustache, F. Nemmi, L. Saint-aubert, J. Pariente, and P. Péran, Multimodal Magnetic Resonance Imaging in Alzheimer???s Disease Patients at Prodromal Stage, Journal of Alzheimer's Disease, vol.50, issue.4, 2016.
DOI : 10.3233/JAD-150353

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927932

C. P. Ferri, M. Prince, C. Brayne, H. Brodaty, L. Fratiglioni et al., Global prevalence of dementia: a Delphi consensus study, The Lancet, vol.366, issue.9503, pp.2112-2117, 2005.
DOI : 10.1016/S0140-6736(05)67889-0

F. U. Fischer, D. Wolf, A. Scheurich, and A. Fellgiebel, Altered whole-brain white matter networks in preclinical Alzheimer's disease, NeuroImage: Clinical, vol.8, pp.660-666, 2015.
DOI : 10.1016/j.nicl.2015.06.007

B. Fischl, A. Van-der-kouwe, C. Destrieux, E. Halgren, F. Ségonne et al., Automatically Parcellating the Human Cerebral Cortex, Cerebral Cortex, vol.14, issue.1, 2004.
DOI : 10.1093/cercor/bhg087

B. T. Gold, Z. Zhu, C. A. Brown, A. H. Andersen, M. J. Ladu et al., White matter integrity is associated with cerebrospinal fluid markers of Alzheimer's disease in normal adults, Neurobiology of Aging, vol.35, issue.10, pp.2263-2271, 2014.
DOI : 10.1016/j.neurobiolaging.2014.04.030

P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli, C. J. Honey et al., Mapping the Structural Core of Human Cerebral Cortex, PLoS Biology, vol.87, issue.7, 2008.
DOI : 10.1371/journal.pbio.0060159.sd004

Y. He, Z. Chen, and A. Evans, Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease, Journal of Neuroscience, vol.28, issue.18, 2008.
DOI : 10.1523/JNEUROSCI.0141-08.2008

P. R. Hof, K. Cox, and J. H. Morrison, Quantitative analysis of a vulnerable subset of pyramidal neurons in Alzheimer's disease: I. Superior frontal and inferior temporal cortex, The Journal of Comparative Neurology, vol.86, issue.1, pp.44-54, 1990.
DOI : 10.1002/cne.903010105

P. R. Hof and J. H. Morrison, Neocortical neuronal subpopulations labeled by a monoclonal antibody to calbindin exhibit differential vulnerability in Alzheimer's disease, Experimental Neurology, vol.111, issue.3, pp.293-301, 1991.
DOI : 10.1016/0014-4886(91)90096-U

K. Hua, J. Zhang, S. Wakana, H. Jiang, X. Li et al., Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification, NeuroImage, vol.39, issue.1, pp.336-347, 2008.
DOI : 10.1016/j.neuroimage.2007.07.053

C. R. Jack, PART and SNAP, Acta Neuropathologica, vol.57, issue.6, pp.773-77610, 2014.
DOI : 10.1007/s00401-014-1362-3

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231211

C. R. Jack, D. A. Bennett, K. Blennow, M. C. Carrillo, H. H. Feldman et al., A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers, Neurology, vol.87, issue.5, 2016.
DOI : 10.1212/WNL.0000000000002923

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, The Lancet Neurology, vol.12, issue.2, pp.207-216, 2013.
DOI : 10.1016/S1474-4422(12)70291-0

C. R. Jack, D. S. Knopman, W. J. Jagust, L. M. Shaw, P. S. Aisen et al., Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade, The Lancet Neurology, vol.9, issue.1, pp.119-128, 2010.
DOI : 10.1016/S1474-4422(09)70299-6

C. R. Jack, T. M. Therneau, H. J. Wiste, S. D. Weigand, D. S. Knopman et al., Transition rates between amyloid and neurodegeneration biomarker states and to dementia: a population-based, longitudinal cohort study, The Lancet Neurology, vol.15, issue.1, pp.10-1016, 2015.
DOI : 10.1016/S1474-4422(15)00323-3

C. R. Jack, H. J. Wiste, S. D. Weigand, W. A. Rocca, D. S. Knopman et al., Age-specific population frequencies of cerebral ?-amyloidosis and neurodegeneration among people with normal cognitive function aged, pp.50-89, 2014.

N. Jahanshad, L. Zhan, M. A. Bernstein, B. J. Borowski, C. R. Jack et al., Diffusion tensor imaging in seven minutes: Determining trade-offs between spatial and directional resolution, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1161-1164, 2010.
DOI : 10.1109/ISBI.2010.5490200

B. Jeurissen, J. Tournier, T. Dhollander, A. Connelly, and J. Sijbers, Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data, NeuroImage, vol.103, pp.411-426, 2014.
DOI : 10.1016/j.neuroimage.2014.07.061

D. S. Knopman, C. R. Jack, H. J. Wiste, S. D. Weigand, P. Vemuri et al., Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease, Neurology, vol.78, issue.20, pp.1576-1582, 2012.
DOI : 10.1212/WNL.0b013e3182563bbe

D. S. Knopman, C. R. Jack, H. J. Wiste, S. D. Weigand, P. Vemuri et al., Brain injury biomarkers are not dependent on ??-amyloid in normal elderly, Annals of Neurology, vol.25, issue.4, pp.472-480, 2013.
DOI : 10.1002/ana.23816

W. D. Knowles and P. A. Schwartzkroin, Axonal ramifications of hippocampal Ca1 pyramidal cells, J. Neurosci. Off. J. Soc. Neurosci, vol.1, pp.1236-1241, 1981.

I. O. Korolev, L. L. Symonds, and A. C. Bozoki, Alzheimer's Disease Neuroimaging Initiative, 2016. Predicting Progression from Mild Cognitive Impairment to Alzheimer's Page 41, p.64

S. A. Kozlovskiy, M. M. Pyasik, A. V. Korotkova, A. V. Vartanov, J. M. Glozman et al., Activation of left lingual gyrus related to working memory for schematic faces, International Journal of Psychophysiology, vol.94, issue.2, 2014.
DOI : 10.1016/j.ijpsycho.2014.08.928

A. W. Laxton, D. F. Tang-wai, M. P. Mcandrews, D. Zumsteg, R. Wennberg et al., A phase I trial of deep brain stimulation of memory circuits in Alzheimer's disease, Annals of Neurology, vol.60, issue.suppl 2, pp.521-534, 2010.
DOI : 10.1002/ana.22089

A. Leemans and D. K. Jones, -matrix must be rotated when correcting for subject motion in DTI data, Magnetic Resonance in Medicine, vol.50, issue.6, pp.1336-1349, 2009.
DOI : 10.1002/mrm.21890

URL : https://hal.archives-ouvertes.fr/hal-01442338

A. D. Leow, I. Yanovsky, M. Chiang, A. D. Lee, A. D. Klunder et al., Statistical Properties of Jacobian Maps and the Realization of Unbiased Large-Deformation Nonlinear Image Registration, IEEE Transactions on Medical Imaging, vol.26, issue.6, pp.822-83210892646, 1109.
DOI : 10.1109/TMI.2007.892646

X. Ma, Z. Li, B. Jing, H. Liu, D. Li et al., Alzheimer's Disease Neuroimaging Initiative, 2016. Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis, Front. Aging Neurosci

C. A. Mallio, R. Schmidt, M. A. De-reus, F. Vernieri, L. Quintiliani et al., Epicentral Disruption of Structural Connectivity in Alzheimer's Disease, CNS Neuroscience & Therapeutics, vol.81, issue.10, pp.837-845, 2015.
DOI : 10.1111/cns.12397

C. D. Mayo, E. L. Mazerolle, L. Ritchie, J. D. Fisk, and J. R. Gawryluk, Longitudinal changes in microstructural white matter metrics in Alzheimer's disease, NeuroImage: Clinical, vol.13, pp.330-338, 2017.
DOI : 10.1016/j.nicl.2016.12.012

J. Meng, L. Guo, H. Cheng, Y. Chen, L. Fang et al., Correlation between cognitive function and the association fibers in patients with Alzheimer???s disease using diffusion tensor imaging, Journal of Clinical Neuroscience, vol.19, issue.12, 2012.
DOI : 10.1016/j.jocn.2011.12.031

C. Metzler-baddeley, S. Hunt, D. K. Jones, A. Leemans, J. P. Aggleton et al., Temporal association tracts and the breakdown of episodic memory in mild cognitive impairment, Neurology, vol.79, issue.23, pp.2233-2240, 2012.
DOI : 10.1212/WNL.0b013e31827689e8

J. L. Molinuevo, P. Ripolles, M. Simó, A. Lladó, J. Olives et al., White matter changes in preclinical Alzheimer's disease: a magnetic resonance imaging-diffusion tensor imaging study on cognitively normal older people with positive amyloid ?? protein 42 levels, Neurobiology of Aging, vol.35, issue.12, 2014.
DOI : 10.1016/j.neurobiolaging.2014.05.027

J. H. Morrison and P. R. Hof, Chapter 37 Selective vulnerability of corticocortical and hippocampal circuits in aging and Alzheimer's disease, Prog. Brain Res, vol.136, pp.467-486, 2002.
DOI : 10.1016/S0079-6123(02)36039-4

J. H. Morrison and P. R. Hof, Life and Death of Neurons in the Aging Brain, Science, vol.278, issue.5337, pp.412-419, 1997.
DOI : 10.1126/science.278.5337.412

T. Nir, N. Jahanshad, C. R. Jack, M. W. Weiner, A. W. Toga et al., the Alzheimer's Disease Neuroimaging Initiative (ADNI), 2012. Small world network measures predict white matter degeneration in patients with in patients with earlystage mild cognitive impairment, Proc. IEEE Int. Symp. Biomed. Imaging Nano Macro IEEE Int. Symp. Biomed. Imaging, pp.1405-1408

T. M. Nir, N. Jahanshad, A. W. Toga, M. A. Bernstein, C. R. Jack et al., Connectivity network measures predict volumetric atrophy in mild cognitive impairment, Neurobiology of Aging, vol.36, pp.113-120, 2015.
DOI : 10.1016/j.neurobiolaging.2014.04.038

M. A. Nowrangi and P. B. Rosenberg, The fornix in mild cognitive impairment and Alzheimer's disease. Front, Aging Neurosci, 2015.

S. Oddo, A. Caccamo, L. Tran, M. P. Lambert, C. G. Glabe et al., Model of Alzheimer Disease, Journal of Biological Chemistry, vol.281, issue.3, pp.1599-1604, 2006.
DOI : 10.1074/jbc.M507892200

R. C. Petersen, Mild Cognitive Impairment, New England Journal of Medicine, vol.364, issue.23, pp.2227-2234, 2011.
DOI : 10.1056/NEJMcp0910237

URL : https://hal.archives-ouvertes.fr/inserm-00112038

R. C. Petersen, R. O. Roberts, D. S. Knopman, B. F. Boeve, Y. E. Geda et al., Mild Cognitive Impairment, Archives of Neurology, vol.66, issue.12, pp.1447-1455266, 2009.
DOI : 10.1001/archneurol.2009.266

URL : https://hal.archives-ouvertes.fr/inserm-00112038

S. P. Poulin, R. Dautoff, J. C. Morris, L. F. Barrett, and B. C. Dickerson, Amygdala atrophy is prominent in early Alzheimer's disease and relates to symptom severity, Psychiatry Research: Neuroimaging, vol.194, issue.1, 2011.
DOI : 10.1016/j.pscychresns.2011.06.014

J. W. Prescott, A. Guidon, P. M. Doraiswamy, R. Choudhury, K. Liu et al., The Alzheimer Structural Connectome: Changes in Cortical Network Topology with Increased Amyloid Plaque Burden, Radiology, vol.273, issue.1, pp.175-184
DOI : 10.1148/radiol.14132593

A. Prestia, A. Caroli, W. M. Van-der-flier, R. Ossenkoppele, B. Van-berckel et al., Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease, Neurology, vol.80, issue.11, pp.1048-1056, 2013.
DOI : 10.1212/WNL.0b013e3182872830

M. Rubinov and O. Sporns, Complex network measures of brain connectivity: Uses and interpretations, NeuroImage, vol.52, issue.3, pp.1059-1069, 2010.
DOI : 10.1016/j.neuroimage.2009.10.003

R. E. Smith, J. Tournier, F. Calamante, and A. Connelly, SIFT: Spherical-deconvolution informed filtering of tractograms, NeuroImage, vol.67, pp.298-312, 2013.
DOI : 10.1016/j.neuroimage.2012.11.049

O. Sporns, G. Tononi, and G. M. Edelman, Theoretical Neuroanatomy: Relating Anatomical and Functional Connectivity in Graphs and Cortical Connection Matrices, Cerebral Cortex, vol.10, issue.2, pp.127-141, 1991.
DOI : 10.1093/cercor/10.2.127

N. Tamamaki, K. Abe, and Y. Nojyo, Three-dimensional analysis of the whole axonal arbors originating from single CA2 pyramidal neurons in the rat hippocampus with the aid of a computer graphic technique, Brain Research, vol.452, issue.1-2, pp.255-272, 1988.
DOI : 10.1016/0006-8993(88)90030-3

S. J. Teipel, M. Thomas, W. Maximilian, K. Thomas, B. Katharina et al., White Matter Microstructure in Relation to Education in Aging and Alzheimer's Disease, J. Alzheimers Dis, pp.571-583, 2009.

J. B. Toledo, M. W. Weiner, D. A. Wolk, X. Da, K. Chen et al., Alzheimer's Disease Neuroimaging Initiative, 2014. Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition, Acta Neuropathol, pp.2051-5960

J. Tournier, F. Calamante, and A. Connelly, MRtrix: Diffusion tractography in crossing fiber regions, International Journal of Imaging Systems and Technology, vol.56, issue.1, pp.53-66, 2012.
DOI : 10.1002/ima.22005

J. Tournier, F. Calamante, and A. Connelly, Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved Page 46, p.64, 2007.

N. J. Tustison and B. B. Avants, Explicit B-spline regularization in diffeomorphic image registration, Frontiers in Neuroinformatics, vol.7, 2013.
DOI : 10.3389/fninf.2013.00039

S. J. Vos, C. Xiong, P. J. Visser, M. S. Jasielec, J. Hassenstab et al., Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study, The Lancet Neurology, vol.12, issue.10, pp.957-965, 2013.
DOI : 10.1016/S1474-4422(13)70194-7

S. J. Vos, B. A. Gordon, Y. Su, P. J. Visser, D. M. Holtzman et al., NIA-AA staging of preclinical Alzheimer disease: discordance and concordance of CSF and imaging biomarkers, Neurobiology of Aging, vol.44, 2016.
DOI : 10.1016/j.neurobiolaging.2016.03.025

S. J. Vos, F. Verhey, L. Frölich, J. Kornhuber, J. Wiltfang et al., The Alzheimer's Disease Neuroimaging Initiative, 2015.

S. Wakana, A. Caprihan, M. M. Panzenboeck, J. H. Fallon, M. Perry et al., Reproducibility of quantitative tractography methods applied to cerebral white matter, NeuroImage, vol.36, issue.3, pp.630-644, 2007.
DOI : 10.1016/j.neuroimage.2007.02.049

J. D. Warren, J. D. Rohrer, J. M. Schott, N. C. Fox, J. Hardy et al., Molecular nexopathies: a new paradigm of neurodegenerative disease, Trends in Neurosciences, vol.36, issue.10, pp.561-569, 2013.
DOI : 10.1016/j.tins.2013.06.007

M. Wirth, S. Villeneuve, C. M. Haase, C. M. Madison, H. Oh et al., Associations Between Alzheimer Disease Biomarkers, Neurodegeneration, and Cognition in Cognitively Normal Older People, JAMA Neurology, vol.70, pp.1512-1519, 2013.
DOI : 10.1001/jamaneurol.2013.4013

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4962545

L. E. Wisse, N. Butala, S. R. Das, C. Davatzikos, B. C. Dickerson et al., Suspected non-AD pathology in mild cognitive impairment, Neurobiology of Aging, vol.36, issue.12, pp.3152-3162, 2015.
DOI : 10.1016/j.neurobiolaging.2015.08.029

Z. Yao, Y. Zhang, L. Lin, Y. Zhou, C. Xu et al., Alzheimer's Disease Neuroimaging Initiative Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease, PLoS Comput. Biol, 2010.

A. Zalesky, L. Cocchi, A. Fornito, M. M. Murray, and E. Bullmore, Connectivity differences in brain networks, Connectivity differences in brain networks, pp.1055-1062, 2012.
DOI : 10.1016/j.neuroimage.2012.01.068

A. Zalesky, A. Fornito, and E. T. Bullmore, Network-based statistic: Identifying differences in brain networks, NeuroImage, vol.53, issue.4, pp.1197-1207, 2010.
DOI : 10.1016/j.neuroimage.2010.06.041

L. Zhan, N. Jahanshad, D. B. Ennis, Y. Jin, M. A. Bernstein et al., Angular versus spatial resolution trade-offs for diffusion imaging under time constraints, Human Brain Mapping, vol.49, issue.10, pp.2688-2706, 2013.
DOI : 10.1002/hbm.22094

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3468661

Q. Zhu, S. Bi, X. Yao, Z. Ni, Y. Li et al., Disruption of thalamic connectivity in Alzheimer???s disease: a diffusion tensor imaging study, Metabolic Brain Disease, vol.54, issue.Suppl 1, pp.1295-1308, 2015.
DOI : 10.1007/s11011-015-9708-7

S. Supplementary-figure, Mean MD in anatomical white matter tracks. Mean MD was computed over the 48 tracts of the DTI-81 white-matter atlas to assess white matter microstructure in each tract. The bar graph shows mean values (and standard deviation) for each group. *p<0.05 for Mann Whitney U test. The correspondence between label numbers and white matter tracts are given in the supplementary Table S1

S. Supplementary-figure, Mean AxD in anatomical white matter tracks Mean AxD was computed over the 48 tracts of the DTI-81 white-matter atlas to assess white matter microstructure in each tract. The bar graph shows mean values (and standard deviation) for each group. *p<0.05 for Mann Whitney U test. The correspondence between label numbers and white matter tracts are given in the supplementary Table S1

S. Supplementary-figure, Mean RD in anatomical white matter tracks. Mean RD was computed over the 48 tracts of the DTI-81 white-matter atlas to assess white matter microstructure in each tract. The bar graph shows mean values (and standard deviation) for each group. *p<0.05 for Mann Whitney U test. The correspondence between label numbers and white matter tracts are given in the supplementary Table S1

S. Supplementary-figure, Nodes normalized efficiency Significant differences were found in patients with MCI A+N+ compared to MCI A-N-in the left hippocampus. Bar height represents the mean metric value, and error bar represents one standard deviation from the mean

S. Figure and S. , Location of the fornix ROIs from the ICBM-DTI-81 whitematter atlas. A) Coronal slice dorsal view. B) Coronal slice ventral view. C) Sagittal slice left view. Pink, Column and body of fornix; Green, Right fornix cres and stria terminalis

S. Figure and S. , Location of the tapetum ROIs and neighboring tracks from the ICBM-DTI-81 white-matter atlas. A) From the left to the right: axial slice cranial view, axial slice caudal view and coronal slice ventral view. B) Same as A) but with neighboring tracks. Pink, Right tapetum; Ligth blue, Left tapetum; Red, Splenium of corpus callosum

S. Figure and S. Suvr, Bar height represents the mean AV45 SUVr value, and error bar represents one standard deviation from the mean

S. Table and S. , List of the 48 white matter tracts segmented in the ICBM- DTI-81 white-matter labels atlas, pp.62-64

S. Table and S. , Characteristics of impaired module in MCI subgroups and CN A-N-compared to MCI A+N-. We indicate the number of nodes in the module in order to assess the extent of the module compared to that of the whole network

. Key, . Cb, and G. G. Cn, H, Hemisphere; L, Left; MCI, mild cognitive impairment