B. Biswal, M. Mennes, X. Zuo, S. Gohel, C. Kelly et al., Toward discovery science of human brain function, Proceedings of the National Academy of Sciences, vol.107, issue.10, pp.4734-4743, 2010.
DOI : 10.1073/pnas.0911855107

D. Zhang and M. Raichle, Disease and the brain's dark energy, Nature Reviews Neurology, vol.10, issue.1, pp.15-28, 2009.
DOI : 10.1038/nrneurol.2009.198

M. Cole, D. Bassett, J. Power, T. Braver, and S. Petersen, Intrinsic and Task-Evoked Network Architectures of the Human Brain, Neuron, vol.83, issue.1, pp.238-51, 2014.
DOI : 10.1016/j.neuron.2014.05.014

M. Mennes, C. Kelly, S. Colcombe, F. Castellanos, and M. Milham, The Extrinsic and Intrinsic Functional Architectures of the Human Brain Are Not Equivalent, Cerebral Cortex, vol.23, issue.1, pp.223-232, 2013.
DOI : 10.1093/cercor/bhs010

D. Fair, D. Bathula, K. Mills, T. Dias, M. Blythe et al., Maturing thalamocortical functional connectivity across development. Frontiers in systems neuroscience, p.20514143, 2010.

S. Smith, P. Fox, K. Miller, D. Glahn, P. Fox et al., Correspondence of the brain's functional architecture during activation and rest, Proceedings of the National Academy of Sciences, vol.106, issue.31, pp.13040-13045, 2009.
DOI : 10.1073/pnas.0905267106

A. Laird, P. Fox, S. Eickhoff, J. Turner, K. Ray et al., Behavioral Interpretations of Intrinsic Connectivity Networks, Journal of Cognitive Neuroscience, vol.31, issue.12, pp.4022-4059, 2011.
DOI : 10.1016/j.neuroimage.2009.10.080

V. Doria, C. Beckmann, T. Arichi, N. Merchant, M. Groppo et al., Emergence of resting state networks in the preterm human brain, Proceedings of the National Academy of Sciences, vol.107, issue.46, pp.20015-20035, 2010.
DOI : 10.1073/pnas.1007921107

M. Raichle, A. Macleod, A. Snyder, W. Powers, D. Gusnard et al., A default mode of brain function, Proceedings of the National Academy of Sciences, vol.98, issue.2, pp.676-82, 2001.
DOI : 10.1073/pnas.98.2.676

W. Seeley, V. Menon, A. Schatzberg, J. Keller, G. Glover et al., Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control, Journal of Neuroscience, vol.27, issue.9, pp.2349-56, 2007.
DOI : 10.1523/JNEUROSCI.5587-06.2007

M. Corbetta, G. Patel, and G. Shulman, The Reorienting System of the Human Brain: From Environment to Theory of Mind, Neuron, vol.58, issue.3, pp.306-330, 2008.
DOI : 10.1016/j.neuron.2008.04.017

J. Hipp and M. Siegel, BOLD fMRI Correlation Reflects Frequency-Specific Neuronal Correlation, Current Biology, vol.25, issue.10, pp.1368-74, 2015.
DOI : 10.1016/j.cub.2015.03.049

K. Friston, Modes or models: a critique on independent component analysis for fMRI. Trends in cognitive sciences, pp.373-378, 1998.

M. Anderson, J. Kinnison, and L. Pessoa, Describing functional diversity of brain regions and brain networks, NeuroImage, vol.73, pp.50-58, 2013.
DOI : 10.1016/j.neuroimage.2013.01.071

Z. Shehzad, A. Kelly, P. Reiss, D. Gee, K. Gotimer et al., The Resting Brain: Unconstrained yet Reliable, Cerebral Cortex, vol.19, issue.10, pp.2209-2238, 2009.
DOI : 10.1093/cercor/bhn256

B. Park, J. Kim, D. Lee, S. Jeong, J. Lee et al., Are brain networks stable during a 24-hour period?, NeuroImage, vol.59, issue.1, pp.456-66, 2012.
DOI : 10.1016/j.neuroimage.2011.07.049

E. Allen, E. Damaraju, S. Plis, E. Erhardt, T. Eichele et al., Tracking Whole-Brain Connectivity Dynamics in the Resting State, Cerebral Cortex, vol.24, issue.3, pp.663-76, 2014.
DOI : 10.1093/cercor/bhs352

H. Park and K. Friston, Structural and Functional Brain Networks: From Connections to Cognition, Science, vol.342, issue.6158, p.24179229, 2013.
DOI : 10.1126/science.1238411

G. Shulman, J. Fiez, M. Corbetta, R. Buckner, F. Miezin et al., Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex, Journal of Cognitive Neuroscience, vol.206, issue.5, pp.648-63, 1997.
DOI : 10.1093/cercor/7.3.193

P. Fransson, How default is the default mode of brain function?, Neuropsychologia, vol.44, issue.14, pp.2836-2881, 2006.
DOI : 10.1016/j.neuropsychologia.2006.06.017

D. Sridharan, D. Levitin, and V. Menon, A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks, Proceedings of the National Academy of Sciences, vol.105, issue.34, pp.12569-74, 2008.
DOI : 10.1073/pnas.0800005105

D. Bzdok, M. Eickenberg, O. Grisel, B. Thirion, and G. Varoquaux, Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data, Advances in Neural Information Processing Systems, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01211248

M. Bertolero, B. Yeo, D. Esposito, and M. , The modular and integrative functional architecture of the human brain, Proceedings of the National Academy of Sciences, vol.112, issue.49, pp.6798-807, 2015.
DOI : 10.1073/pnas.1510619112

V. Betti, D. Penna, S. De-pasquale, F. Mantini, D. Marzetti et al., Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain, Neuron, vol.79, issue.4, pp.782-97, 2013.
DOI : 10.1016/j.neuron.2013.06.022

M. Mason, M. Norton, J. Van-horn, D. Wegner, S. Grafton et al., Wandering Minds: The Default Network and Stimulus-Independent Thought, Science, vol.315, issue.5810, pp.393-398, 2007.
DOI : 10.1126/science.1131295

D. Weissman, K. Roberts, K. Visscher, and M. Woldorff, The neural bases of momentary lapses in attention, Nature Neuroscience, vol.2, issue.7, pp.971-979, 2006.
DOI : 10.1038/nn1727

D. Bzdok, R. Langner, L. Schilbach, O. Jakobs, C. Roski et al., Characterization of the temporo-parietal junction by combining data-driven parcellation, complementary connectivity analyses, and functional decoding, NeuroImage, vol.81, pp.381-92, 2013.
DOI : 10.1016/j.neuroimage.2013.05.046

W. Seeley, R. Crawford, J. Zhou, B. Miller, and M. Greicius, Neurodegenerative Diseases Target Large-Scale Human Brain Networks, Neuron, vol.62, issue.1, pp.42-52, 2009.
DOI : 10.1016/j.neuron.2009.03.024

M. Mennes, C. Kelly, X. Zuo, D. Martino, A. Biswal et al., Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity, NeuroImage, vol.50, issue.4, pp.1690-701, 2010.
DOI : 10.1016/j.neuroimage.2010.01.002

D. Kennedy, E. Redcay, and E. Courchesne, Failing to deactivate: Resting functional abnormalities in autism, Proceedings of the National Academy of Sciences, vol.103, issue.21, pp.8275-80, 2006.
DOI : 10.1073/pnas.0600674103

S. Whitfield-gabrieli, H. Thermenos, S. Milanovic, M. Tsuang, S. Faraone et al., Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia, Proceedings of the National Academy of Sciences, vol.106, issue.4, pp.1279-84, 2009.
DOI : 10.1073/pnas.0809141106

J. Hamilton, D. Furman, C. Chang, M. Thomason, E. Dennis et al., Default-Mode and Task-Positive Network Activity in Major Depressive Disorder: Implications for Adaptive and Maladaptive Rumination, Biological Psychiatry, vol.70, issue.4, pp.327-360, 2011.
DOI : 10.1016/j.biopsych.2011.02.003

E. Liddle, C. Hollis, M. Batty, M. Groom, J. Totman et al., Task-related default mode network modulation and inhibitory control in ADHD: effects of motivation and methylphenidate, Journal of Child Psychology and Psychiatry, vol.34, issue.7, pp.761-71, 2011.
DOI : 10.1111/j.1469-7610.2010.02333.x

A. Laird, S. Eickhoff, K. Li, D. Robin, D. Glahn et al., Investigating the Functional Heterogeneity of the Default Mode Network Using Coordinate-Based Meta-Analytic Modeling, Journal of Neuroscience, vol.29, issue.46, pp.14496-505, 2009.
DOI : 10.1523/JNEUROSCI.4004-09.2009

M. Fox, A. Snyder, J. Vincent, M. Corbetta, D. Van-essen et al., From The Cover: The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proceedings of the National Academy of Sciences, vol.102, issue.27, pp.9673-9681, 2005.
DOI : 10.1073/pnas.0504136102

B. Biswal, F. Yetkin, V. Haughton, and J. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar mri, Magnetic Resonance in Medicine, vol.13, issue.4, pp.537-578, 1995.
DOI : 10.1002/mrm.1910340409

O. Sporns, Contributions and challenges for network models in cognitive neuroscience, Nature Neuroscience, vol.57, issue.5, pp.652-60, 2014.
DOI : 10.1038/nn.3690

D. Van-essen, C. Anderson, and D. Felleman, Information processing in the primate visual system: an integrated systems perspective, Science, vol.255, issue.5043, pp.419-442, 1992.
DOI : 10.1126/science.1734518

R. Passingham, K. Stephan, and R. Kotter, The anatomical basis of functional localization in the cortex, Nature Reviews Neuroscience, vol.29, issue.8, pp.606-622, 2002.
DOI : 10.1038/nrn893

N. Kanwisher, Functional specificity in the human brain: A window into the functional architecture of the mind, Proceedings of the National Academy of Sciences, vol.107, issue.25, pp.11163-70, 2010.
DOI : 10.1073/pnas.1005062107

S. Zeki, Functional specialisation in the visual cortex of the rhesus monkey, Nature, vol.40, issue.5670, pp.423-431, 1978.
DOI : 10.1038/225041a0

D. Hubel and T. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, The Journal of Physiology, vol.160, issue.1, pp.106-54, 1962.
DOI : 10.1113/jphysiol.1962.sp006837

G. Iaria, C. Fox, C. Waite, A. I. Barton, and J. , The contribution of the fusiform gyrus and superior temporal sulcus in processing facial attractiveness: Neuropsychological and neuroimaging evidence, Neuroscience, vol.155, issue.2, pp.409-431, 2008.
DOI : 10.1016/j.neuroscience.2008.05.046

B. Yeo, F. Krienen, S. Eickhoff, S. Yaakub, P. Fox et al., Functional Specialization and Flexibility in Human Association Cortex. Cerebral cortex, 2014.

R. Craddock, G. James, P. Holtzheimer, X. Hu, and H. Mayberg, A whole brain fMRI atlas generated via spatially constrained spectral clustering, Human Brain Mapping, vol.22, issue.Pt 1, pp.1914-1942, 2012.
DOI : 10.1002/hbm.21333

C. Beckmann, M. Deluca, J. Devlin, and S. Smith, Investigations into resting-state connectivity using independent component analysis, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.8, issue.2-3, pp.1001-1014, 1457.
DOI : 10.1002/(SICI)1097-0193(1999)8:2/3<151::AID-HBM13>3.0.CO;2-5

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1854918

K. Friston, Modes or models: a critique on independent component analysis for fMRI. Trends in cognitive sciences, pp.373-378, 1998.

M. Mckeown, T. Jung, S. Makeig, G. Brown, S. Kindermann et al., Spatially independent activity patterns in functional MRI data during the Stroop color-naming task, Proceedings of the National Academy of Sciences, vol.95, issue.3, pp.803-813, 1998.
DOI : 10.1073/pnas.95.3.803

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143874

D. Barch, G. Burgess, M. Harms, S. Petersen, B. Schlaggar et al., Function in the human connectome: Task-fMRI and individual differences in behavior, NeuroImage, vol.80, pp.169-89, 2013.
DOI : 10.1016/j.neuroimage.2013.05.033

P. Pinel, B. Thirion, S. Meriaux, A. Jobert, J. Serres et al., Fast reproducible identification and large-scale databasing of individual functional cognitive networks, BMC Neuroscience, vol.8, issue.1, pp.91-17973998, 2007.
DOI : 10.1186/1471-2202-8-91

URL : https://hal.archives-ouvertes.fr/hal-00784462

M. Kuhn and K. Johnson, Applied predictive modeling: Springer; 2013, Network Co-occurrence Models
DOI : 10.1007/978-1-4614-6849-3

D. Bzdok, R. Langner, F. Hoffstaedter, B. Turetsky, K. Zilles et al., The Modular Neuroarchitecture of Social Judgments on Faces, Cerebral Cortex, vol.22, issue.4, pp.951-61, 2012.
DOI : 10.1093/cercor/bhr166

M. Visser, E. Jefferies, and M. Ralph, Semantic Processing in the Anterior Temporal Lobes: A Meta-analysis of the Functional Neuroimaging Literature, Journal of Cognitive Neuroscience, vol.2, issue.6, pp.1083-94, 2010.
DOI : 10.1093/bmb/65.1.95

C. Bishop, Pattern Recognition and Machine Learning, 2006.

R. Spreng, W. Stevens, J. Chamberlain, A. Gilmore, and D. Schacter, Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition, NeuroImage, vol.53, issue.1, pp.303-320, 2010.
DOI : 10.1016/j.neuroimage.2010.06.016

W. Gao, J. Gilmore, S. Alcauter, and W. Lin, The dynamic reorganization of the default-mode network during a visual classification task. Frontiers in systems neuroscience, p.23898240, 2013.

P. Bado, A. Engel, R. De-oliveira-souza, I. Bramati, F. Paiva et al., Functional dissociation of ventral frontal and dorsomedial default mode network components during resting state and emotional autobiographical recall, Human Brain Mapping, vol.5, issue.7, pp.3302-3315, 2014.
DOI : 10.1002/hbm.22403

F. Krienen, P. Tu, and R. Buckner, Clan Mentality: Evidence That the Medial Prefrontal Cortex Responds to Close Others, Journal of Neuroscience, vol.30, issue.41, pp.13906-13921, 2010.
DOI : 10.1523/JNEUROSCI.2180-10.2010

J. Smallwood and J. Schooler, The restless mind., Psychological Bulletin, vol.132, issue.6, pp.946-58, 2006.
DOI : 10.1037/0033-2909.132.6.946

E. Bullmore and O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, vol.10, issue.4, p.312, 2009.
DOI : 10.1038/nrn2618

J. Chumbley and K. Friston, False discovery rate revisited: FDR and topological inference using Gaussian random fields, NeuroImage, vol.44, issue.1, pp.62-70, 2009.
DOI : 10.1016/j.neuroimage.2008.05.021

M. Mesulam, Defining Neurocognitive Networks in the BOLD New World of Computed Connectivity, Neuron, vol.62, issue.1, 2009.
DOI : 10.1016/j.neuron.2009.04.001

M. Anderson, Neural reuse: a fundamental organizational principle of the brain. The Behavioral and brain sciences, pp.245-66, 2010.

S. Dehaene and L. Cohen, Cultural Recycling of Cortical Maps, Neuron, vol.56, issue.2, pp.384-98
DOI : 10.1016/j.neuron.2007.10.004

F. Krienen, B. Yeo, and R. Buckner, Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.12, issue.1, 2014.
DOI : 10.1038/nrn2961

S. Dehaene, M. Kerszberg, and J. Changeux, A neuronal model of a global workspace in effortful cognitive tasks, Proceedings of the National Academy of Sciences, vol.95, issue.24, pp.14529-14563, 1998.
DOI : 10.1073/pnas.95.24.14529

G. Edelman and J. Gally, Degeneracy and complexity in biological systems, Proceedings of the National Academy of Sciences, vol.98, issue.24, pp.13763-13771, 2001.
DOI : 10.1073/pnas.231499798

V. Bertalanffy and L. , AN OUTLINE OF GENERAL SYSTEM THEORY, The British Journal for the Philosophy of Science, vol.I, issue.2, 1950.
DOI : 10.1093/bjps/I.2.134

Y. Golland, S. Bentin, H. Gelbard, Y. Benjamini, R. Heller et al., Extrinsic and Intrinsic Systems in the Posterior Cortex of the Human Brain Revealed during Natural Sensory Stimulation, Cerebral Cortex, vol.17, issue.4, pp.766-77, 2007.
DOI : 10.1093/cercor/bhk030

R. Yuste, From the neuron doctrine to neural networks, Nature Reviews Neuroscience, vol.47, issue.8, pp.487-97, 2015.
DOI : 10.1085/jgp.43.6.129

D. Mantini, A. Gerits, K. Nelissen, J. Durand, O. Joly et al., Default Mode of Brain Function in Monkeys, Journal of Neuroscience, vol.31, issue.36, pp.3112954-62, 2011.
DOI : 10.1523/JNEUROSCI.2318-11.2011

URL : https://hal.archives-ouvertes.fr/hal-00621258

H. Lu, Q. Zou, H. Gu, M. Raichle, E. Stein et al., Rat brains also have a default mode network, Proceedings of the National Academy of Sciences, vol.109, issue.10, pp.3979-84, 2012.
DOI : 10.1073/pnas.1200506109

S. Broyd, C. Demanuele, S. Debener, S. Helps, C. James et al., Default-mode brain dysfunction in mental disorders: A systematic review, Neuroscience & Biobehavioral Reviews, vol.33, issue.3, pp.279-96, 2009.
DOI : 10.1016/j.neubiorev.2008.09.002

S. Whitfield-gabrieli and J. Ford, Default Mode Network Activity and Connectivity in Psychopathology, Annual Review of Clinical Psychology, vol.8, issue.1, pp.49-76, 2012.
DOI : 10.1146/annurev-clinpsy-032511-143049

D. Van-essen, K. Ugurbil, E. Auerbach, D. Barch, T. Behrens et al., The Human Connectome Project: A data acquisition perspective, NeuroImage, vol.62, issue.4, pp.2222-2253, 2012.
DOI : 10.1016/j.neuroimage.2012.02.018

M. Glasser, S. Sotiropoulos, J. Wilson, T. Coalson, B. Fischl et al., The minimal preprocessing pipelines for the Human Connectome Project, NeuroImage, vol.80, pp.105-129, 2013.
DOI : 10.1016/j.neuroimage.2013.04.127

V. Calhoun, T. Adali, G. Pearlson, and J. Pekar, A method for making group inferences from functional MRI data using independent component analysis, Human Brain Mapping, vol.2, issue.3, pp.140-51, 2001.
DOI : 10.1002/hbm.1048

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

F. Pereira, T. Mitchell, and M. Botvinick, Machine learning classifiers and fMRI: A tutorial overview, NeuroImage, vol.45, issue.1, pp.199-209, 2009.
DOI : 10.1016/j.neuroimage.2008.11.007

A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, vol.10, issue.3, pp.626-660, 1999.
DOI : 10.1109/72.761722

J. Shlens, A tutorial on principal component analysis. arXiv preprint

D. Ross, J. Lim, R. Lin, and M. Yang, Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, vol.61, issue.3, pp.1-3125, 2008.
DOI : 10.1007/s11263-007-0075-7

C. Chennubhotla and A. Jepson, Extracting multi-scale structure from data, Sparse PCA ICCV 2001 Proceedings Eighth IEEE International Conference on, 2001.

G. Varoquaux, A. Gramfort, F. Pedregosa, V. Michel, and B. Thirion, Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Information processing in medical imaging: proceedings of the conference, pp.562-73, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00588898

S. Johnson, Hierarchical clustering schemes, Psychometrika, vol.58, issue.4, pp.241-54, 1967.
DOI : 10.1007/BF02289588

B. Thirion, G. Varoquaux, E. Dohmatob, and J. Poline, Which fMRI clustering gives good brain parcellations? Frontiers in neuroscience, p.25071425, 2014.

S. Lloyd, Least squares quantization in PCM. Information Theory, IEEE Transactions on, vol.28, issue.2, pp.129-166, 1982.

L. Nanetti, L. Cerliani, V. Gazzola, R. Renken, and C. Keysers, Group analyses of connectivity-based cortical parcellation using repeated k-means clustering, NeuroImage, vol.47, issue.4, pp.1666-77, 2009.
DOI : 10.1016/j.neuroimage.2009.06.014

S. Eickhoff, B. Thirion, G. Varoquaux, and D. Bzdok, Connectivity-based parcellation: Critique and implications . Human brain mapping, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01184563

C. Beckmann, C. Mackay, N. Filippini, and S. Smith, Group comparison of resting-state FMRI data using multi-subject ICA and dual regression, NeuroImage, vol.47, 2009.
DOI : 10.1016/S1053-8119(09)71511-3

V. Vapnik, The nature of statistical learning theory, 1996.

S. Hanson and Y. Halchenko, Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No ???Face??? Identification Area, Neural Computation, vol.17, issue.11, pp.486-503, 2008.
DOI : 10.1016/S0896-6273(02)00877-2

K. Gorgolewski, C. Burns, C. Madison, D. Clark, Y. Halchenko et al., Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python, Frontiers in Neuroinformatics, vol.5, p.21897815, 2011.
DOI : 10.3389/fninf.2011.00013

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, The Journal of Machine Learning Research, vol.12, pp.2825-2855, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller et al., Machine learning for neuroimaging with scikit-learn, Frontiers in Neuroinformatics, vol.8, p.24600388, 2014.
DOI : 10.3389/fninf.2014.00014

URL : https://hal.archives-ouvertes.fr/hal-01093971

L. Barrett, The Future of Psychology: Connecting Mind to Brain. Perspectives on psychological science, pp.326-365, 2009.

S. Gershman and D. Blei, A tutorial on Bayesian nonparametric models, Journal of Mathematical Psychology, vol.56, issue.1, pp.1-12, 2012.
DOI : 10.1016/j.jmp.2011.08.004

J. Miller and M. Harrison, A simple example of Dirichlet process mixture inconsistency for the number of components Advances in neural information processing systems, 2013.

B. Park, J. Kim, D. Lee, S. Jeong, J. Lee et al., Are brain networks stable during a 24-hour period?, NeuroImage, vol.59, issue.1, pp.456-66, 2012.
DOI : 10.1016/j.neuroimage.2011.07.049

D. Margulies, J. Vincent, C. Kelly, G. Lohmann, L. Uddin et al., Precuneus shares intrinsic functional architecture in humans and monkeys, Proceedings of the National Academy of Sciences, vol.106, issue.47, pp.20069-74, 2009.
DOI : 10.1073/pnas.0905314106

W. Shirer, S. Ryali, E. Rykhlevskaia, V. Menon, and M. Greicius, Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns. Cerebral cortex, 2011.

J. Andrews-hanna, J. Reidler, J. Sepulcre, R. Poulin, and R. Buckner, Functional-Anatomic Fractionation of the Brain's Default Network, Neuron, vol.65, issue.4, pp.550-62, 2010.
DOI : 10.1016/j.neuron.2010.02.005

J. Haxby, E. Hoffman, and M. Gobbini, The distributed human neural system for face perception, Trends in Cognitive Sciences, vol.4, issue.6, pp.223-256, 2000.
DOI : 10.1016/S1364-6613(00)01482-0

D. Sander, J. Grafman, and T. Zalla, The Human Amygdala: An Evolved System for Relevance Detection, Reviews in the Neurosciences, vol.14, issue.4, pp.303-319, 2003.
DOI : 10.1515/REVNEURO.2003.14.4.303

J. Medaglia, M. Lynall, and D. Bassett, Cognitive Network Neuroscience, Journal of Cognitive Neuroscience, vol.27, issue.8, pp.1471-91, 2015.
DOI : 10.1093/cercor/bhr269

D. Bzdok, Classical Statistics and Statistical Learning in Imaging Neuroscience. arXiv preprint arXiv:160301857, 2016.