G. Ojemann, J. Ojemann, E. Lettich, and M. Berger, Cortical language localization in left, dominant hemisphere, Journal of Neurosurgery, vol.71, issue.3, pp.316-326, 1989.
DOI : 10.3171/jns.1989.71.3.0316

L. Steven, E. Bressler, and . Tognoli, Operational principles of neurocognitive networks, International Journal of Psychophysiology, vol.60, issue.2, pp.139-148, 2006.

J. Foucher, S. Vidailhet, and . Chanraud, Functional integration in schizophrenia: too little or too much? Preliminary results on fMRI data, NeuroImage, vol.26, issue.2, 2005.
DOI : 10.1016/j.neuroimage.2005.01.042
URL : https://hal.archives-ouvertes.fr/hal-00331937

J. R. Foucher and D. Luck, Psychosis related to neurological conditions : pro and cons of the dis-/ mis-connectivity models of schizophrenia, Dialogues Clin Neurosci, vol.8, pp.17-27, 2006.

F. Gonzalez-lima and A. Mcintosh, Structural equation modeling and its application to network analysis in functional brain imaging, Hum. Brain Mapp, 1994.

K. J. Friston, L. Harrison, and W. Penny, Dynamic causal modelling, NeuroImage, vol.19, issue.4, 2003.
DOI : 10.1016/S1053-8119(03)00202-7
URL : https://hal.archives-ouvertes.fr/inserm-00388972

S. Denis, Localisation des g¨?¿g¨?¿ 1

V. Noblet, C. Heinrich, F. Heitz, and J. Armspach, Accurate Inversion of 3-D Transformation Fields, IEEE Transactions on Image Processing, vol.17, issue.10, pp.1963-1968, 2008.
DOI : 10.1109/TIP.2008.2002310

K. J. Friston, J. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping : The Analysis of Functional Brain Images, 2007.

J. Chumbley and K. J. 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

C. R. Genovese, N. Lazar, and T. E. Nichols, Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate, NeuroImage, vol.15, issue.4, pp.870-878, 2002.
DOI : 10.1006/nimg.2001.1037

C. F. Beckmann, M. Deluca, J. T. Devlin, and S. M. 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.1001-1013, 2005.
DOI : 10.1002/(SICI)1097-0193(1999)8:2/3<151::AID-HBM13>3.0.CO;2-5

J. S. Damoiseaux, S. A. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam et al., Consistent resting-state networks across healthy subjects, Proceedings of the National Academy of Sciences, vol.103, issue.37, pp.13848-13853, 2006.
DOI : 10.1073/pnas.0601417103

M. De-luca, C. F. Beckmann, N. De-stefano, P. M. Matthews, and S. M. Smith, fMRI resting state networks define distinct modes of long-distance interactions in the human brain, NeuroImage, vol.29, issue.4, pp.1359-1367, 2006.
DOI : 10.1016/j.neuroimage.2005.08.035

P. Williamson, Are Anticorrelated Networks in the Brain Relevant to Schizophrenia?, Schizophrenia Bulletin, vol.33, issue.4, pp.994-1003, 2007.
DOI : 10.1093/schbul/sbm043

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, vol.101, issue.13, pp.4637-4642, 2004.
DOI : 10.1073/pnas.0308627101

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, Biological Psychiatry, vol.62, issue.5, pp.429-437, 2007.
DOI : 10.1016/j.biopsych.2006.09.020

A. G. Garrity, G. D. Pearlson, K. Mckiernan, D. Lloyd, K. A. Kiehl et al., Aberrant ???Default Mode??? Functional Connectivity in Schizophrenia, American Journal of Psychiatry, vol.164, issue.3, pp.450-457, 2007.
DOI : 10.1176/ajp.2007.164.3.450

M. V. Mannell, A. R. Franco, V. D. Calhoun, J. M. Canive, R. J. Thoma et al., Resting state and task-induced deactivation: A methodological comparison in patients with schizophrenia and healthy controls, Human Brain Mapping, vol.97, pp.424-437, 2010.
DOI : 10.1002/hbm.20876

J. Rademacher, A. M. Galaburda, D. N. Kennedy, P. A. Filipek, and V. S. Caviness, Human Cerebral Cortex: Localization, Parcellation, and Morphometry with Magnetic Resonance Imaging, Journal of Cognitive Neuroscience, vol.48, issue.4, pp.352-374, 1992.
DOI : 10.1007/BF00304699

V. S. Caviness, J. Meyer, N. Makris, and D. N. Kennedy, MRI-Based Topographic Parcellation of Human Neocortex: An Anatomically Specified Method with Estimate of Reliability, Journal of Cognitive Neuroscience, vol.41, issue.6, pp.566-587, 1996.
DOI : 10.1007/BF01451766

J. Talairach and P. Tournoux, Co-planar stereotaxic atlas of the human brain, 1988.

J. L. Lancaster, L. H. Rainey, J. L. Summerlin, C. S. Freitas, P. T. Fox et al., Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method, Human Brain Mapping, vol.1, issue.4, pp.238-242, 1997.
DOI : 10.1002/(SICI)1097-0193(1997)5:4<238::AID-HBM6>3.0.CO;2-4

J. Lancaster, M. Woldorff, L. Parsons, C. Liotti, . Freitas et al., Automated Talairach Atlas labels for functional brain mapping, Human Brain Mapping, vol.5, issue.3, pp.120-151, 2000.
DOI : 10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8

D. , L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans, Automatic 3-d modelbased neuroanatomical segmentation, Human Brain Mapping, vol.3, issue.3, pp.190-208, 1995.
DOI : 10.1002/hbm.460030304

N. Tzourio-mazoyer, . Landeau, . Papathanassiou, . Crivello, . Etard et al., Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain, NeuroImage, vol.15, issue.1, pp.273-89, 2002.
DOI : 10.1006/nimg.2001.0978

P. Hagmann, C. Leila, G. Xavier, M. Reto, H. Christopher et al., Mapping the Structural Core of Human Cerebral Cortex, PLoS Biology, vol.87, issue.7, p.159, 2008.
DOI : 10.1371/journal.pbio.0060159.sd004

C. Clouchoux, D. Rivì-ere, J. F. Mangin, G. Operto, J. Régis et al., Modeldriven parameterization of the cortical surface for localization and inter-subject matching, Neuroimage, 2009.

A. Cachia, J. Mangin, D. Rivì-ere, D. Papadopoulos-orfanos, F. Kherif et al., A generic framework for the parcellation of the cortical surface into gyri using geodesic Vorono???? diagrams, Medical Image Computing and Computer Assisted Intervention, pp.403-416, 2003.
DOI : 10.1016/S1361-8415(03)00031-8

C. Clouchoux, . Coulon, . Drivì-ere, J. Cachia, J. Mangin et al., Anatomically Constrained Surface Parameterization for Cortical Localization, 2005.
DOI : 10.1007/11566489_43

C. Clouchoux, O. Coulon, J. L. Anton, J. F. Mangin, and J. Régis, A New Cortical Surface Parcellation Model and Its Automatic Implementation, Med Image Comput Comput Assist Interv, vol.9, pp.193-200, 2006.
DOI : 10.1007/11866763_24

B. Thyreau, B. Thirion, G. Flandin, and J. B. Poline, Anatomo-Functional Description of the Brain : A Probabilistic Approach, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1661474

A. Tucholka, B. Thirion, M. Perrot, P. Pinel, J. Mangin et al., Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?, 11thProc. MIC- CAI, 2008.
DOI : 10.1007/978-3-540-85990-1_48
URL : https://hal.archives-ouvertes.fr/inria-00502805

G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache et al., Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2002, 2002.
DOI : 10.1007/3-540-45786-0_58
URL : https://hal.archives-ouvertes.fr/inria-00615921

S. Fortune, A sweepline algorithm for voronoi diagrams, Algorithmica, vol.2, 1987.

G. Flandin, F. Kherif, X. Pennec, D. Riviere, N. Ayache et al., Parcellation of brain images with anatomical and functional constraints for fMRI data analysis, Proceedings IEEE International Symposium on Biomedical Imaging, pp.907-910, 2002.
DOI : 10.1109/ISBI.2002.1029408
URL : https://hal.archives-ouvertes.fr/inria-00615916

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets, Human Brain Mapping, vol.22, issue.8, pp.678-93, 2006.
DOI : 10.1002/hbm.20210

R. C. Craddock, G. A. James, P. Holtzheimer, X. Hu, and H. Mayberg, Roi atlas generation from whole brain parcellation of resting state fmri data, ISMRM 2010. Annual International Conference of the International Society for Magnetic Resonance in Medicine, 2010.

R. C. Craddock, G. A. James, P. Holtzheimer, X. Hu, and H. Mayberg, A whole brain fmri atlas generated via spatially constrained spectral clustering. part i : Methods, HBM 2010. Annual International Conference of the Organization of Human Brain Mapping, 2010.

P. Bellec, V. Perlbarg, S. Jbabdi, M. Pelegrini-issac, J. Antonand et al., Identification of large-scale networks in the brain using fMRI, NeuroImage, vol.29, issue.4, pp.1231-1243, 2006.
DOI : 10.1016/j.neuroimage.2005.08.044
URL : https://hal.archives-ouvertes.fr/inserm-00164195

J. Shi and J. Malik, Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.8, pp.888-905, 2000.

J. Wang, L. Wang, Y. Zang, H. Yang, H. Tang et al., Parcellation-dependent small-world brain functional networks: A resting-state fMRI study, Human Brain Mapping, vol.393, issue.5, pp.1511-1523, 2009.
DOI : 10.1002/hbm.20623

D. Cordes, V. Haughton, J. D. Carew, K. Arfanakis, and K. Maravilla, Hierarchical clustering to measure connectivity in fMRI resting-state data, Magnetic Resonance Imaging, vol.20, issue.4, pp.305-317, 2002.
DOI : 10.1016/S0730-725X(02)00503-9

P. Filzmoser, R. Baumgartner, and E. Moser, A hierarchical clustering method for analyzing functional MR images, Magnetic Resonance Imaging, vol.17, issue.6, pp.817-826, 1999.
DOI : 10.1016/S0730-725X(99)00014-4

G. N. Lance and W. T. Williams, A Generalized Sorting Strategy for Computer Classifications, Nature, vol.9, issue.5058, p.218, 1966.
DOI : 10.1038/212218a0

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate : a practical and powerful approach to multiple testing, J. Roy. Statist. Soc. Ser. B, vol.57, issue.1, pp.289-300, 1995.

Y. Benjamini and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency, Ann. Statist, vol.29, issue.4, pp.1165-1188, 2001.

T. E. Nichols and A. P. Holmes, Nonparametric permutation tests for functional neuroimaging: A primer with examples, Human Brain Mapping, vol.4, issue.1, pp.1-25, 2001.
DOI : 10.1002/hbm.1058

D. Luck, J. R. Foucher, I. Offerlin-meyer, M. Lepage, and J. M. Danion, Assessment of single and bound features in a working memory task in schizophrenia, Schizophrenia Research, vol.100, issue.1-3, pp.153-160, 2008.
DOI : 10.1016/j.schres.2007.11.004

J. Tanabe, D. Miller, J. Tregellas, R. Freedman, and F. G. Meyer, Comparison of Detrending Methods for Optimal fMRI Preprocessing, NeuroImage, vol.15, issue.4, pp.902-907, 2002.
DOI : 10.1006/nimg.2002.1053

K. J. Friston, O. Josephs, E. Zarahn, A. P. Holmes, S. Rouquette et al., To Smooth or Not to Smooth?, NeuroImage, vol.12, issue.2, pp.196-208, 2000.
DOI : 10.1006/nimg.2000.0609

H. Benali, I. Buvat, F. Bazin, J. P. Frouin, and R. D. Paola, <title>CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion</title>, Mathematical Methods in Medical Imaging III, pp.336-347, 1994.
DOI : 10.1117/12.179264

K. Friston, C. Frith, R. Liddle, and . Frackowiak, Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets, Journal of Cerebral Blood Flow & Metabolism, vol.10, issue.1, 1993.
DOI : 10.1038/jcbfm.1993.4

K. J. Friston, P. Jezzard, and R. Turner, Analysis of functional MRI time-series, Human Brain Mapping, vol.12, issue.2, pp.153-171, 1994.
DOI : 10.1002/hbm.460010207

K. J. Friston, Functional and effective connectivity in neuroimaging: A synthesis, Human Brain Mapping, vol.10, issue.Suppl 2, pp.56-78, 1994.
DOI : 10.1002/hbm.460020107

K. J. Friston, J. Phillips, D. Chawla, and C. Büchel, Revealing interactions among brain systems with nonlinear PCA, Human Brain Mapping, vol.2, issue.2-3, pp.92-97, 1999.
DOI : 10.1002/(SICI)1097-0193(1999)8:2/3<92::AID-HBM4>3.0.CO;2-#

K. J. Friston, J. Phillips, D. Chawla, and C. Büchel, Nonlinear PCA: characterizing interactions between modes of brain activity, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.355, issue.1393, pp.135-146, 2000.
DOI : 10.1098/rstb.2000.0554

F. Esposito, . Formosano, . Seifritz, . Goebel, . Morrone et al., Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?, Human Brain Mapping, vol.36, issue.3, pp.146-157, 2002.
DOI : 10.1002/hbm.10034

E. Formisano, F. Esposito, N. Kriegeskorte, G. Tedeschi, F. D. Salle et al., Spatial independent component analysis of functional magnetic resonance imaging time-series: characterization of the cortical components, Neurocomputing, vol.49, issue.1-4, pp.241-254, 2002.
DOI : 10.1016/S0925-2312(02)00517-9

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, Human Brain Mapping, vol.18, issue.3, pp.160-188, 1998.
DOI : 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1

M. J. Mckeown, Detection of Consistently Task-Related Activations in fMRI Data with Hybrid Independent Component Analysis, NeuroImage, vol.11, issue.1, pp.24-35, 2000.
DOI : 10.1006/nimg.1999.0518

M. Mckeown, Y. J. Hu, and Z. J. Wang, ICA Denoising for Event-Related fMRI Studies, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp.157-161, 2005.
DOI : 10.1109/IEMBS.2005.1616366

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, Human Brain Mapping, vol.2, issue.3, pp.140-151, 2001.
DOI : 10.1002/hbm.1048

V. Perlbarg, G. Marrelec, J. Doyon, M. Pelegrini-issac, S. Lehericy et al., NEDICA: Detection of group functional networks in FMRI using spatial independent component analysis, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1247-1250, 2008.
DOI : 10.1109/ISBI.2008.4541229

]. 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, Magnetic Resonance in Medicine, vol.13, issue.4, pp.537-541, 1995.
DOI : 10.1002/mrm.1910340409

G. Lohmann and S. Bohn, Using replicator dynamics for analyzing fMRI data of the human brain, IEEE Transactions on Medical Imaging, vol.21, issue.5, pp.485-492, 2002.
DOI : 10.1109/TMI.2002.1009384

R. Salvador, J. Suckling, C. Schwarzbauer, and E. Bullmore, Undirected graphs of frequency-dependent functional connectivity in whole brain networks, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.15, issue.4, pp.937-946, 2005.
DOI : 10.1093/cercor/13.4.422

F. Sun, L. Miller, and M. D. Esposito, Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data, NeuroImage, vol.21, issue.2, pp.647-658, 2004.
DOI : 10.1016/j.neuroimage.2003.09.056

B. Thirion, D. Silke, and J. B. Poline, Detection of signal synchronizations in resting-state fMRI datasets, NeuroImage, vol.29, issue.1, pp.321-327, 2006.
DOI : 10.1016/j.neuroimage.2005.06.054

S. Achard, R. Salvador, B. Whitcher, J. Suckling, and E. Bullmore, A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs, Journal of Neuroscience, vol.26, issue.1, pp.63-72, 2006.
DOI : 10.1523/JNEUROSCI.3874-05.2006

G. Marrelec, B. Horwitz, J. Kim, M. Pélégrini-issac, H. Benali et al., Using partial correlation to enhance structural equation modeling of functional MRI data, Magnetic Resonance Imaging, vol.25, issue.8, pp.1181-1189, 2007.
DOI : 10.1016/j.mri.2007.02.012
URL : https://hal.archives-ouvertes.fr/inserm-00140817

P. Golland, Y. Golland, and R. Malach, Detection of Spatial Activation Patterns as Unsupervised Segmentation of fMRI Data, Med Image Comput Comput Assist Interv, vol.10, pp.110-118, 2007.
DOI : 10.1007/978-3-540-75757-3_14

J. Xia, F. Liang, and Y. M. Wang, On clustering fMRI using potts and mixture regression models, Conf Proc IEEE Eng Med Biol Soc, vol.1, pp.4795-4798, 2009.

S. Dodel, J. M. Herrmann, and T. Geisel, Functional connectivity by cross-correlation clustering, Neurocomputing, vol.44, issue.46, pp.44-46, 2002.
DOI : 10.1016/S0925-2312(02)00416-2

M. Victor, D. R. Eguíluz, G. A. Chialvo, M. Cecchi, A. Baliki et al., Scale-free brain functional networks, Phys. Rev. Lett, vol.94, issue.1, p.18102, 2005.

M. P. Van-den-heuvel, C. J. Stam, M. Boersma, and H. E. , Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain, NeuroImage, vol.43, issue.3, pp.528-539, 2008.
DOI : 10.1016/j.neuroimage.2008.08.010

A. Zalesky, A. Fornito, I. H. Harding, L. Cocchi, M. Yücel et al., Whole-brain anatomical networks: Does the choice of nodes matter?, NeuroImage, vol.50, issue.3, pp.970-983, 2010.
DOI : 10.1016/j.neuroimage.2009.12.027

D. S. Bassett, E. Bullmore, B. A. Verchinski, V. S. Mattay, D. R. Weinberger et al., Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia, Journal of Neuroscience, vol.28, issue.37, pp.9239-9248, 2008.
DOI : 10.1523/JNEUROSCI.1929-08.2008

W. Liao, Z. Zhang, Z. Pan, D. Mantini, J. Ding et al., Altered Functional Connectivity and Small-World in Mesial Temporal Lobe Epilepsy, PLoS ONE, vol.7, issue.1, p.8525, 2010.
DOI : 10.1371/journal.pone.0008525.s012

A. Fornito, A. Zalesky, and E. T. Bullmore, Network scaling effects in graph analytic studies of human resting-state fMRI data, Frontiers in Systems Neuroscience, vol.4, p.22, 2010.
DOI : 10.3389/fnsys.2010.00022

J. Cao and K. Worsley, The geometry of correlation fields with an application to functional connectivity of the brain, The Annals of Applied Probability, vol.9, issue.4, pp.1021-1057, 1998.
DOI : 10.1214/aoap/1029962864

M. J. Lowe, B. J. Mock, and J. A. Sorenson, Functional Connectivity in Single and Multislice Echoplanar Imaging Using Resting-State Fluctuations, NeuroImage, vol.7, issue.2, pp.119-132, 1998.
DOI : 10.1006/nimg.1997.0315

D. Cordes, V. M. Haughton, K. Arfanakis, G. J. Wendt, P. A. Turski et al., Mapping functionally related regions of brain with functional connectivity mr imaging, AJNR Am J Neuroradiol, vol.21, issue.9, pp.1636-1644, 2000.

M. Hampson, . Bs-peterson, J. Skudlarski, . Gatenby, and J. Gore, Detection of functional connectivity using temporal correlations in MR images, Human Brain Mapping, vol.8, issue.4, pp.247-262, 2002.
DOI : 10.1002/hbm.10022

M. D. Fox, M. Corbetta, A. Z. Snyder, J. L. Vincent, and M. E. Raichle, Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems, Proceedings of the National Academy of Sciences, vol.103, issue.26, pp.10046-10051, 2006.
DOI : 10.1073/pnas.0604187103

J. Mark, . Lowe, D. Micheal, . Phillips, T. Joseph et al., Multiple sclerosis : low-frequency temporal blood oxygen leveldependent fluctuations indicate reduced functional connectivity initial results, Radiology, vol.224, issue.1, pp.184-192, 2002.

D. J. Watts and S. H. Strogatz, Collective dynamics of 'small-world' networks, Nature, vol.393, issue.6684, pp.440-442, 1998.
DOI : 10.1038/30918

Y. Andrew, M. I. Ng, Y. Jordan, and . Weiss, On spectral clustering : Analysis and an algorithm, Advances in Neural Information Processing Systems 14, pp.849-856, 2001.

J. B. Tenenbaum, V. De-silva, J. C. Langford, S. Kullback, and R. A. Leibler, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 1951.
DOI : 10.1126/science.290.5500.2319

T. Fawcett, Roc graphs : Notes and practical considerations for researchers, Tech. Rep, 2004.

V. Perlbarg, P. Bellec, J. Anton, M. Pélégrini-issac, J. Doyon et al., CORSICA: correction of structured noise in fMRI by automatic identification of ICA components, Magnetic Resonance Imaging, vol.25, issue.1, pp.35-46, 2007.
DOI : 10.1016/j.mri.2006.09.042
URL : https://hal.archives-ouvertes.fr/inserm-00131137

C. G. Thomas, R. A. Harshman, and R. S. Menon, Noise Reduction in BOLD-Based fMRI Using Component Analysis, NeuroImage, vol.17, issue.3, pp.1521-1537, 2002.
DOI : 10.1006/nimg.2002.1200

J. Cohen, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, vol.20, issue.1, p.37, 1960.
DOI : 10.1177/001316446002000104

J. R. Landis and G. G. Koch, The Measurement of Observer Agreement for Categorical Data, Biometrics, vol.33, issue.1, pp.159-174, 1977.
DOI : 10.2307/2529310

R. L. Buckner, A. Z. Snyder, B. J. Shannon, G. Larossa, R. Sachs et al., Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory, Journal of Neuroscience, vol.25, issue.34, pp.7709-7717, 2005.
DOI : 10.1523/JNEUROSCI.2177-05.2005

R. L. Gould, R. G. Brown, A. M. Owen, E. T. Bullmore, and R. J. Howard, Task-induced deactivations during successful paired associates learning: An effect of age but not Alzheimer's disease, NeuroImage, vol.31, issue.2, pp.818-831, 2006.
DOI : 10.1016/j.neuroimage.2005.12.045

M. D. Greicius, B. Krasnow, A. Reiss, and V. Menon, Functional connectivity in the resting brain: A network analysis of the default mode hypothesis, Proceedings of the National Academy of Sciences, vol.100, issue.1, pp.253-258, 2003.
DOI : 10.1073/pnas.0135058100

M. D. Greicius and V. Menon, Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation, Journal of Cognitive Neuroscience, vol.23, issue.9, pp.1484-1492, 2004.
DOI : 10.1162/jocn.1997.9.5.648

D. A. Gusnard, E. Akbudak, G. L. Shulman, and M. E. Raichle, Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function, Proceedings of the National Academy of Sciences, vol.98, issue.7, pp.4259-4264, 2001.
DOI : 10.1073/pnas.071043098

W. Liao, H. Chen, Y. Feng, D. Mantini, C. Gentili et al., Selective aberrant functional connectivity of resting state networks in social anxiety disorder, NeuroImage, vol.52, issue.4, pp.1549-1558, 2010.
DOI : 10.1016/j.neuroimage.2010.05.010

M. Corbetta and G. L. Shulman, CONTROL OF GOAL-DIRECTED AND STIMULUS-DRIVEN ATTENTION IN THE BRAIN, Nature Reviews Neuroscience, vol.3, issue.3, pp.201-215, 2002.
DOI : 10.1038/nrn755

A. W. Macdonald, J. D. Cohen, V. A. Stenger, and C. S. Carter, Dissociating the Role of the Dorsolateral Prefrontal and Anterior Cingulate Cortex in Cognitive Control, Science, vol.288, issue.5472, pp.1835-1838, 2000.
DOI : 10.1126/science.288.5472.1835

R. Cabeza and L. Nyberg, Neural bases of learning and memory: functional neuroimaging evidence, Current Opinion in Neurology, vol.13, issue.4, pp.415-421, 2000.
DOI : 10.1097/00019052-200008000-00008

H. Kim, Dissociating the roles of the default-mode, dorsal, and ventral networks in episodic memory retrieval, NeuroImage, vol.50, issue.4, pp.1648-1657, 2010.
DOI : 10.1016/j.neuroimage.2010.01.051

E. Tulving, S. Kapur, F. I. Craik, M. Moscovitch, and S. Houle, Hemispheric encoding/retrieval asymmetry in episodic memory: positron emission tomography findings., Proceedings of the National Academy of Sciences, vol.91, issue.6, pp.2016-2020, 1994.
DOI : 10.1073/pnas.91.6.2016

R. Habib, L. Nyberg, and E. Tulving, Hemispheric asymmetries of memory: the HERA model revisited, Trends in Cognitive Sciences, vol.7, issue.6, pp.241-245, 2003.
DOI : 10.1016/S1364-6613(03)00110-4

S. Karkar, S. Faisan, L. Thoraval, and J. R. Foucher, Une nouvelle approche de parcellisation multi-niveaux pour l'analyse de la connectivité fonctionnelle cérébrale, XXIIeme Colloque GRETSI, 2009.

S. Karkar, S. Faisan, L. Thoraval, and J. R. Foucher, A multi-level parcellation approach for brain functional connectivity analysis, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3497-3500, 2009.
DOI : 10.1109/IEMBS.2009.5334578

S. Karkar, S. Faisan, L. Thoraval, and J. R. Foucher, Une nouvelle approche de parcellisation multi-niveaux pour l'analyse de la connectivité fonctionnelle cérébrale, XXIIeme Colloque GRETSI, 2009.

S. Karkar, S. Faisan, L. Thoraval, and J. R. Foucher, A multi-level parcellation approach for brain functional connectivity analysis, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3497-3500, 2009.
DOI : 10.1109/IEMBS.2009.5334578

S. Karkar, S. Faisan, L. Thoraval, and J. R. Foucher, About estimating the significance of brain functional networks in fMRI, HBM 2010. Annual International Conference of the Organization of Human Brain Mapping, 2010.