O. Sporns, Discovering the human connectome, 2012.

S. Achard, A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs". en, The Journal of Neuroscience, vol.26, pp.1529-2401, 2006.

V. Fabrizio-de and . Fallani, Graph analysis of functional brain networks: practical issues in translational neuroscience, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.369, p.20130521, 2014.

E. Petra and . Vértes, Simple models of human brain functional networks, Proceedings of the National Academy of Sciences, vol.109, pp.5868-5873, 2012.

J. Richiardi, Machine learning with brain graphs: Predictive modeling approaches for functional imaging in systems neuroscience, IEEE Signal Processing Magazine, vol.30, pp.58-70, 2013.

E. Bullmore and O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat Rev Neurosci, vol.10, pp.186-198, 2009.

J. Duncan, . Watts, H. Steven, and . Strogatz, Collective dynamics of 'small-world' networks, vol.393, p.440, 1998.

A. Zalesky, A. Fornito, and E. Bullmore, On the use of correlation as a measure of network connectivity, NeuroImage, vol.60, pp.2096-2106, 2012.

M. Newman and . Networks, , 2018.

T. Nikola and . Markov, Cortical high-density counterstream architectures, Science, vol.342, p.1238406, 2013.

S. Danielle, E. T. Bassett, and . Bullmore, Small-world brain networks revisited, The Neuroscientist, vol.23, pp.499-516, 2017.

X. Ameera, E. T. Patel, and . Bullmore, A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs, p.NeuroImage, 2015.

K. Ashurbekova, S. Achard, and F. Forbes, Structure Learning via Hadamard Product of Correlation and Partial Correlation Matrices, EUSIPCO 2019 -27th European Signal Processing Conference, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02290847

F. Vá?a, T. Edward, A. Bullmore, and . Patel, Probabilistic thresholding of functional connectomes: Application to schizophrenia, Neuroimage, vol.172, pp.326-340, 2018.

M. Termenon, Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project, Neuroimage, vol.142, pp.172-187, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01330966

A. Hero and B. Rajaratnam, Large-scale correlation screening, Journal of the American Statistical Association, vol.106, pp.1540-1552, 2011.

Q. Niu, Robustness of centrality measures against network manipulation, Physica A: Statistical Mechanics and its Applications, vol.438, pp.124-131, 2015.

S. Epskamp, D. Borsboom, and E. Fried, Estimating psychological networks and their accuracy: A tutorial paper, Behavior Research Methods, vol.50, pp.195-212, 2018.

H. Lv, Resting-state functional MRI: everything that nonexperts have always wanted to know, American Journal of Neuroradiology, vol.39, pp.1390-1399, 2018.

J. Karl, . Field, J. William, C. White, and . Lang, Anaesthetic effects of chloral hydrate, pentobarbitone and urethane in adult male rats, Laboratory animals, vol.27, pp.258-269, 1993.

L. Ciobanu, Effects of anesthetic agents on brain blood oxygenation level revealed with ultra-high field MRI, PloS One, vol.7, issue.3, 2012.
URL : https://hal.archives-ouvertes.fr/cea-00842892

K. Wang, Temporal scaling properties and spatial synchronization of spontaneous blood oxygenation level-dependent (BOLD) signal fluctuations in rat sensorimotor network at different levels of isoflurane anesthesia, NMR Biomed. 24, vol.1, pp.1099-1492, 2011.

X. Jennifer, A. Haensel, C. Spain, and . Martin, A systematic review of physiological methods in rodent pharmacological MRI studies, Psychopharmacology, vol.232, pp.489-499, 2015.

M. Marija and . Petrinovic, A novel anesthesia regime enables neurofunctional studies and imaging genetics across mouse strains, In: Sci. Rep, vol.6, pp.2045-2322, 2016.

P. Hernandez, An in vivo MRI template set for morphometry, tissue segmentation, and fMRI localization in rats, In: Front. Neuroinf, vol.5, p.26, 2011.

A. Eszter and . Papp, Waxholm Space atlas of the Sprague Dawley rat brain, NeuroImage, vol.97, pp.1053-8119, 2014.

J. Ashburner, SPM12 manual, Wellcome Trust Centre for Neuroimaging, 2014.

J. Guillaume and . Becq, Functional connectivity is preserved but reorganized across several anesthetic regimes, p.Neurogimage, 2020.

D. Jonathan and . Power, Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, Neuroimage, vol.59, pp.2142-2154, 2012.

. Chao-gan-yan, A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics, Neuroimage, vol.76, pp.183-201, 2013.

J. Brandon and . Whitcher, Assessing nonstationary time series using wavelets, 1998.

B. Whitcher, P. Guttorp, and D. Percival, Wavelet analysis of covariance with application to atmospheric time series, Journal of Geophysical Research: Atmospheres, vol.105, pp.14941-14962, 2000.

J. Robb and . Muirhead, Aspects of multivariate statistical theory, vol.197, 2009.

S. Achard and E. Bullmore, Efficiency and cost of economical brain functional networks, PLoS computational biology, vol.3, 2007.

M. W. Cole, S. Pathak, and W. Schneider, Identifying the brain's most globally connected regions, NeuroImage, vol.49, pp.1053-8119, 2010.

V. Dany and . Souza, Preserved modular network organization in the sedated rat brain, PLoS One, vol.9, 2014.

R. and M. Hutchison, Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain's functional architecture, Human Brain Mapping, vol.35, pp.1097-0193, 2014.

X. Liang, Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study, PloS one, vol.7, 2012.

A. Liska, Functional connectivity hubs of the mouse brain, Neuroimage, vol.115, pp.281-291, 2015.

H. Lu, Rat brains also have a default mode network, Proceedings of the National Academy of Sciences, vol.109, pp.3979-3984, 2012.

Z. Ma, Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration, p.Neuroimage, 2016.

M. Magnuson, W. Majeed, and S. D. Keilholz, Functional connectivity in blood oxygenation level-dependent and cerebral blood volumeweighted resting state functional magnetic resonance imaging in the rat brain, Journal of Magnetic Resonance Imaging, vol.32, pp.584-592, 2010.

. Matthew-evan-magnuson, Time-dependent effects of isoflurane and dexmedetomidine on functional connectivity, spectral characteristics, and spatial distribution of spontaneous BOLD fluctuations, NMR in biomedicine, vol.27, pp.291-303, 2014.

M. Rubinov and O. Sporns, Complex network measures of brain connectivity: uses and interpretations, Neuroimage, vol.52, pp.1059-1069, 2010.

B. Sinclair, Heritability of the network architecture of intrinsic brain functional connectivity, NeuroImage, vol.121, pp.1053-8119, 2015.

A. Aric, D. A. Hagberg, P. J. Schult, and . Swart, Exploring Network Structure, Dynamics, and Function using NetworkX, Proceedings of the 7th Python in Science Conference, pp.11-15, 2008.

E. Jones, T. Oliphant, and P. Peterson, Open source scientific tools for Python, 2001.

A. Barabási and R. Albert, Emergence of scaling in random networks". In: science 286, vol.5439, pp.509-512, 1999.

P. Erdös and A. Rényi, On random graphs, Publicationes mathematicae, vol.6, pp.290-297, 1959.

G. Csardi and T. Nepusz, The igraph software package for complex network research, InterJournal Complex Systems, p.1695, 2006.

K. Dadi, Benchmarking functional connectome-based predictive models for resting-state fMRI, NeuroImage, vol.192, pp.115-134, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01824205

X. Liu, The change of functional connectivity specificity in rats under various anesthesia levels and its neural origin, Brain topography, vol.26, pp.363-377, 2013.

J. Grandjean, Optimization of anesthesia protocol for resting-state fMRI in mice based on differential effects of anesthetics on functional connectivity patterns, NeuroImage 102.Part, vol.2, pp.838-847, 2014.

E. Jonckers, Different anesthesia regimes modulate the functional connectivity outcome in mice, Magn. Reson. Med, vol.72, pp.1103-1112, 2014.

G. Ruggero and . Bettinardi, Gradual emergence of spontaneous correlated brain activity during fading of general anesthesia in rats: Evidences from fMRI and local field potentials, NeuroImage, vol.114, pp.185-198, 2015.

J. Paasonen, Functional connectivity under six anesthesia protocols and the awake condition in rat brain, NeuroImage, vol.172, pp.9-20, 2018.

A. Fornito, A. Zalesky, and M. Breakspear, Graph analysis of the human connectome: promise, progress, and pitfalls, Neuroimage, vol.80, pp.426-444, 2013.

T. Xu, Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI, NeuroImage: clinical, vol.11, pp.302-315, 2016.

D. Ribeiro-de-paula, A method for independent component graph analysis of resting-state fMRI, Brain and behavior, vol.7, p.626, 2017.

Z. Liang, J. King, and N. Zhang, Intrinsic organization of the anesthetized brain, Journal of Neuroscience, vol.32, pp.10183-10191, 2012.

A. Alexander-bloch, The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia, Neuroimage, vol.59, pp.3889-3900, 2012.

C. E. Ginestet, P. Arnaud, A. Fournel, and . Simmons, Statistical network analysis for functional MRI: summary networks and group comparisons, Frontiers in computational neuroscience, vol.8, p.51, 2014.

A. Kathleen and . Williams, Comparison of ?-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat, Magn. Reson. Imaging, vol.28, pp.995-1003, 2010.

E. Jonckers, Functional connectivity fMRI of the rodent brain: comparison of functional connectivity networks in rat and mouse, PLoS One, vol.6, p.18876, 2011.

D. Mark, K. Humphries, and . Gurney, Network 'small-world-ness': a quantitative method for determining canonical network equivalence, PloS one, vol.3, p.2051, 2008.