D. A. Alexander, T. B. Dyrby, M. Nilsson, and H. Zhang, Imaging brain microstructure with diffusion MRI: practicality and applications, NMR Biomed, 2017.

J. L. Andersson and S. N. Sotiropoulos, An integrated approach to correction for offresonance effects and subject movement in diffusion MR imaging, Neuroimage, vol.125, pp.1063-1078, 2016.

A. Araque, Gliotransmitters travel in time and space, Neuron, vol.81, issue.4, pp.728-739, 2014.

L. Ben-haim, M. Carrillo-de-sauvage, K. Ceyz-eriat, and C. Escartin, Elusive roles for reactive astrocytes in neurodegenerative diseases, Front. Cell. Neurosci, vol.9, p.278, 2015.
URL : https://hal.archives-ouvertes.fr/cea-02142599

Y. Bito, Diffusion-weighted line-scan echo-planar spectroscopic imaging technique to reduce motion artifacts in metabolite diffusion imaging, Magn. Reson. Med. Sci, vol.14, issue.1, pp.43-50, 2015.

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.247-254, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00349722

P. Basser and C. Pierpaoli, Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI, J. Magn. Reson, vol.111, pp.209-219, 1996.

S. Boretius, R. Tammer, T. Michaelis, J. Brockm?-oller, and J. Frahm, Halogenated volatile anesthetics alter brain metabolism as revealed by proton magnetic resonance spectroscopy of mice in vivo, Neuroimage, vol.69, pp.244-255, 2013.

M. A. Carrillo-de-sauvage, The neuroprotective agent CNTF decreases neuronal metabolites in the rat striatum: an in vivo multimodal magnetic resonance imaging study, J. Cerebr. Blood Flow Metabol, vol.35, issue.6, pp.917-921, 2015.

A. Chvatal, M. Anderova, and F. Kirchhoff, Three-dimensional confocal morphometry -a new approach for studying dynamic changes in cell morphology in brain slices, J. Anat, vol.210, issue.6, pp.671-683, 2007.

A. Colin, Engineered lentiviral vector targeting astrocytes in vivo, Glia, vol.57, issue.6, pp.667-679, 2009.
URL : https://hal.archives-ouvertes.fr/tel-00348988

H. Cuntz, F. Forstner, A. Borst, and M. Hausser, One rule to grow them all: a general theory of neuronal branching and its practical application, PLoS Comput. Biol, vol.6, issue.8, 2010.

L. A. Demetrius, P. J. Magistretti, and L. Pellerin, Alzheimer's disease: the amyloid hypothesis and the Inverse Warburg effect, Front. Physiol, vol.5, p.522, 2014.

C. M. Diaz-garcia, Neuronal stimulation triggers neuronal glycolysis and not lactate uptake, Cell Metabol, vol.26, issue.2, pp.361-374, 2017.

E. Dossi, F. Vasile, and N. Rouach, Human astrocytes in the diseased brain, Brain Res. Bull, vol.136, pp.139-156, 2018.

A. E. Ercan, A. Techawiboonwong, M. J. Versluis, A. G. Webb, and I. Ronen, Diffusionweighted chemical shift imaging of human brain metabolites at 7T, Magn. Reson. Med, vol.73, issue.6, pp.2053-2061, 2014.

E. Ercan, Glial and axonal changes in systemic lupus erythematosus measured with diffusion of intracellular metabolites, Brain, vol.139, pp.1447-1457, 2016.

C. Escartin, Ciliary neurotrophic factor activates astrocytes, redistributes their glutamate transporters GLAST and GLT-1 to raft microdomains, and improves glutamate handling in vivo, J. Neurosci, vol.26, issue.22, pp.5978-5989, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00088896

C. Escartin, Activation of astrocytes by CNTF induces metabolic plasticity and increases resistance to metabolic insults, J. Neurosci, vol.27, issue.27, pp.7094-7104, 2007.

S. K. Fisher, J. E. Novak, and B. W. Agranoff, Inositol and higher inositol phosphates in neural tissues : homeostasis , metabolism and functional significance, J. Neurochem, vol.82, pp.736-754, 2002.

K. Fotso, Diffusion tensor spectroscopic imaging of the human brain in children and adults, Magn. Reson. Med, vol.78, issue.4, pp.1246-1256, 2017.

E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. Van-der-walt et al., Contributors, Dipy, 2014. DIPY, a library for the analysis of diffusion MRI data, Front. Neuroinf, vol.8, issue.8

S. S. Gill, Brain metabolites as 1H NMR markers of neuronal and glial disorders, NMR Biomed, vol.2, issue.5-6, pp.196-200, 1989.

J. L. Griffin, M. Bollard, J. K. Nicholson, and K. Bhakoo, Spectral profiles of cultured neuronal and glial cells derived from HRMAS 1H NMR spectroscopy, NMR Biomed, vol.15, pp.375-384, 2002.

R. A. Harris, Aerobic glycolysis in the frontal cortex correlates with memory performance in wild-type mice but not the APP/PS1 mouse model of cerebral amyloidosis, J. Neurosci, vol.36, issue.6, pp.1871-1878, 2016.

G. Hauser and V. N. Finelli, The biosynthesis of free and phosphatide myo-inositol from glucose by mammalian tissue slices, J. Biol. Chem, vol.238, pp.3224-3228, 1963.

P. G. Haydon and M. Nedergaard, How do astrocytes participate in neural plasticity?, Cold Spring Harb. Perspect. Biol, vol.7, 2015.

L. Hillered, A. Hallstrom, S. Segersvard, L. Persson, and V. Vngerstedt, Dynamics of extracellular metabolites in the striatum after middle cerebral artery occlusion in the rat monitored by intracerebral microdialysis, J. Cerebr. Blood Flow Metabol, vol.9, pp.607-616, 1989.

R. E. Isaacks, A. S. Bender, C. Y. Kim, Y. F. Shi, and M. D. Norenberg, Effect of osmolality and anion channel inhibitors on myo-inositol efflux in cultured astrocytes, J. Neurosci. Res, vol.57, issue.6, pp.866-871, 1999.

J. H. Jensen, J. A. Helpern, A. Ramani, H. Lu, and K. Kaczynski, Diffusional kurtosis imaging: the quantification of non_Gaussian water diffusion by means of magnetic resonance imaging, Magn. Reson. Med, vol.53, pp.1432-1440, 2005.

D. K. Jones, D. C. Alexander, R. Bowtell, M. Cercignani, F. Dell'acqua et al., Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI, Neuroimage, vol.182, pp.8-38, 2018.

E. Kellner, B. Dhital, V. G. Kiselev, and M. Reisert, Gibbs-ringing artifact removal based on local subvoxel-shifts, Magn. Reson. Med, vol.76, issue.5, pp.1574-1581, 2016.

S. A. Liddelow and B. A. Barres, Reactive astrocytes: production, function, and therapeutic potential, Immunity, vol.46, issue.6, pp.957-967, 2017.
DOI : 10.1016/j.immuni.2017.06.006

C. Ligneul, M. Palombo, and J. Valette, Metabolite diffusion up to very high b in the mouse brain in vivo: revisiting the potential correlation between relaxation and diffusion properties, Magn. Reson. Med, vol.77, issue.4, pp.1390-1398, 2017.
URL : https://hal.archives-ouvertes.fr/cea-02155304

P. Linse and O. Soderman, The validity of the short-gradient-pulse approximation in Nmr-studies of restricted diffusion -simulations of molecules diffusing between planes, in cylinders and spheres, J. Magn. Reson., Ser. A, vol.116, issue.1, pp.77-86, 1995.

P. M?-achler, In vivo evidence for a lactate gradient from astrocytes to neurons, Cell Metabol, vol.23, issue.1, pp.94-102, 2016.

P. J. Magistretti, Synaptic plasticity and the Warburg effect, Cell Metabol, vol.19, issue.1, pp.4-5, 2014.

C. Marchadour, E. Brouillet, P. Hantraye, V. Lebon, and J. Valette, Anomalous diffusion of brain metabolites evidenced by diffusion-weighted magnetic resonance spectroscopy in vivo, J. Cerebr. Blood Flow Metabol, vol.32, issue.12, pp.2153-2160, 2012.

C. Najac, C. Marchadour, M. Guillermier, D. Houitte, V. Slavov et al., Intracellular metabolites in the primate brain are primarily localized in long fibers rather than in cell bodies, as shown by diffusion-weighted magnetic resonance spectroscopy, Neuroimage, vol.90, pp.374-380, 2014.

M. Navarrete and A. Araque, The Cajal school and the physiological role of astrocytes: a way of thinking, Front. Neuroanat, vol.8, p.33, 2014.

G. L. Nedjati-gilani, T. Schneider, M. G. Hall, N. Cawley, I. Hill et al., Machine learning based compartment models with permeability for white matter microstructure imaging, Neuroimage, vol.150, pp.119-135, 2017.

L. A. Newman, D. L. Korol, and P. E. Gold, Lactate produced by glycogenolysis in astrocytes regulates memory processing, PLoS One, vol.6, issue.12, p.28427, 2011.

P. Nilsson and U. Ponten, Changes in cortical extracellular levels of energy-related metabolites and amino acids following concussive brain injury in rats, J. Cerebr. Blood Flow Metabol, vol.10, pp.631-637, 1990.

M. Palombo, New paradigm to assess brain cell morphology by diffusionweighted MR spectroscopy in vivo, Proc. Natl. Acad. Sci. U.S.A, vol.113, issue.24, pp.6671-6676, 2016.
URL : https://hal.archives-ouvertes.fr/cea-02155309

M. Palombo, C. Ligneul, and J. Valette, Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion-weighting: diffusion in long fibers (almost) accounts for non-monoexponential attenuation, Magn. Reson. Med, vol.77, issue.1, pp.343-350, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01751423

M. Palombo, C. Ligneul, E. Hernandez-garzon, and J. Valette, Can we detect the effect of spines and leaflets on the diffusion of brain intracellular metabolites?, Neuroimage, vol.182, pp.283-293, 2017.
URL : https://hal.archives-ouvertes.fr/cea-02155338

M. Palombo, N. Shemesh, I. Ronen, and J. Valette, Insights into brain microstructure from in vivo DW-MRS, Neuroimage, vol.182, pp.97-116, 2017.
URL : https://hal.archives-ouvertes.fr/cea-02155345

M. Palombo, N. Shemesh, A. Ianus, D. C. Alexander, and H. Zhang, A compartment based model for non-invasive cell body imaging by diffusion MRI, Proc. Int. Soc. Magn. Reson. Med, vol.27, p.6685, 2018.

M. Palombo, N. Shemesh, A. Ianus, D. C. Alexander, and H. Zhang, Abundance of cell bodies can explain the stick model's failure in grey matter at high b-value, Proc. Int. Soc. Magn. Reson. Med, vol.27, p.6170, 2018.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: machine learning in Python, J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

L. Pellerin and P. J. Magistretti, Glutamate uptake into astrocytes stimulates aerobic glycolysis -a mechanism coupling neuronal-activity to glucose-utilization, Proc. Natl. Acad. Sci. U.S.A, vol.91, issue.22, pp.10625-10629, 1994.

L. Pellerin and P. J. Magistretti, Sweet sixteen for ANLS, J. Cerebr. Blood Flow Metabol, vol.32, issue.7, pp.1152-1166, 2012.

H. Peng, A. Bria, Z. Zhou, G. Iannello, and F. Long, Extensible visualization and analysis for multidimensional images using Vaa3D, Nat. Protoc, vol.9, issue.1, pp.193-208, 2014.
DOI : 10.1038/nprot.2014.011

M. Pitt-a, N. Brunel, and A. Volterra, Astrocytes: orchestrating synaptic plasticity?, Neuroscience, vol.323, pp.43-46, 2016.

J. Pfeuffer, I. Tkac, and R. Gruetter, Extracellular-intracellular distribution of glucose and lactate in the rat brain assessed noninvasively by diffusion-weighted 1H nuclear magnetic resonance spectroscopy in vivo, J. Cerebr. Blood Flow Metabol, vol.20, issue.4, pp.736-746, 2000.

S. W. Provencher, Estimation of metabolite concentrations from localized in vivo proton NMR spectra, Magn. Reson. Med, vol.30, issue.6, pp.672-679, 1993.

J. Schindelin, I. Arganda-carreras, E. Frise, V. Kaynig, M. Longair et al., Fiji: an open-source platform for biological-image analysis, Nat. Methods, vol.9, issue.7, pp.676-682, 2012.

R. Scorcioni, S. Polavaram, and G. A. Ascoli, L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies, Nat. Protoc, vol.3, issue.5, pp.866-876, 2008.

K. Strange, F. Emma, A. Paredes, and R. Morrison, Osmoregulatory changes in myoinositol content and Naþ/myo-inositol cotransport in rat cortical astrocytes, Glia, vol.12, issue.1, pp.35-43, 1994.

A. Suzuki, Astrocyte-neuron lactate transport is required for long-term memory formation, Cell, vol.144, issue.5, pp.810-823, 2011.

C. , NeuroImage, vol.191, pp.457-469, 2019.

A. Tabesh, J. H. Jensen, B. A. Ardekani, and J. A. Helpern, Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging, Magn. Reson. Med, vol.65, issue.3, pp.823-836, 2011.

J. H. Thurston, W. R. Sherman, R. E. Hauhart, and R. F. Kloepper, Myo-inositol: a newly identified nonnitrogenous osmoregulatory molecule in mammalian brain, Pediatr. Res, vol.26, issue.5, pp.482-485, 1989.

G. E. Tyzack, Astrocyte response to motor neuron injury promotes structural synaptic plasticity via STAT3-regulated TSP-1 expression, Nat. Commun, vol.5, p.4294, 2014.
DOI : 10.1038/ncomms5294
URL : https://www.nature.com/articles/ncomms5294.pdf

J. Urenjak, S. R. Williams, D. G. Gadian, and M. Noble, Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types, J. Neurosci, vol.13, pp.981-989, 1993.
DOI : 10.1523/jneurosci.13-03-00981.1993
URL : http://www.jneurosci.org/content/jneuro/13/3/981.full.pdf

J. Veraart, D. H. Poot, W. Van-hecke, I. Blockx, A. Van-der-linden et al., More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging, Magn. Reson. Med, vol.65, issue.1, pp.138-145, 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.

E. T. Wood, Investigating axonal damage in multiple sclerosis by diffusion tensor spectroscopy, J. Neurosci, vol.32, issue.19, pp.6665-6669, 2012.

J. B. Zuchero and B. A. Barres, Glia in mammalian development and disease, Development, vol.142, issue.22, pp.3805-3809, 2015.