G. K. Aguirre, E. Zarahn, and M. D'esposito, The variability of human, BOLD hemodynamic responses, Neuroimage, vol.8, issue.4, pp.360-369, 1998.

E. Amico, F. Gomez, C. Di-perri, A. Vanhaudenhuyse, D. Lesenfants et al., Posterior cingulate cortex-related co-activation patterns: a resting state fMRI study in propofol-induced loss of consciousness, PloS One, vol.9, issue.6, pp.1-9, 2014.

R. F. Betzel, M. Fukushima, Y. He, X. Zuo, and O. Sporns, Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks, Neuroimage, vol.127, pp.287-297, 2016.

C. M. Bishop, Bayesian PCA, pp.382-388, 1999.

B. Biswal, F. Zerrin-yetkin, V. M. Haughton, and J. S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar MRI, Magn. Reson. Med, vol.34, issue.4, pp.537-541, 1995.

T. A. Bolton, A. Tarun, V. Sterpenich, S. Schwartz, and D. Van-de-ville, Interactions between large-scale functional brain networks are captured by sparse coupled HMMs, IEEE Trans. Med. Imag, vol.37, issue.1, pp.230-240, 2017.

T. A. Bolton, V. Kebets, E. Glerean, D. Z?-oller, J. Li et al., Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI, Neuroimage, vol.209, p.116433, 2020.

A. Bowring, C. Maumet, and T. E. Nichols, Exploring the impact of analysis software on task fMRI results, Hum. Brain Mapp, vol.40, issue.11, pp.3362-3384, 2019.
URL : https://hal.archives-ouvertes.fr/inserm-01760535

C. Caballero-gaudes and R. C. Reynolds, Methods for cleaning the BOLD fMRI signal, Neuroimage, vol.154, pp.128-149, 2017.

C. Chang and G. H. Glover, Time-frequency dynamics of resting-state brain connectivity measured with fMRI, Neuroimage, vol.50, issue.1, pp.81-98, 2010.

J. E. Chen, C. Chang, M. D. Greicius, and G. H. Glover, Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics, Neuroimage, vol.111, pp.476-488, 2015.

J. E. Chen, J. R. Polimeni, S. Bollmann, and G. H. Glover, On the analysis of rapidly sampled fMRI data, Neuroimage, vol.188, pp.807-820, 2019.

J. S. Damoiseaux, S. A. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam et al., Consistent resting-state networks across healthy subjects, Proc. Natl. Acad. Sci, vol.103, issue.37, pp.13848-13853, 2006.

M. D'esposito, E. Zarahn, G. K. Aguirre, and B. Rypma, The effect of normal aging on the coupling of neural activity to the BOLD hemodynamic response, Neuroimage, vol.10, issue.1, pp.6-14, 1999.

X. Di and B. B. Biswal, Dynamic brain functional connectivity modulated by resting-state networks, Brain Struct. Funct, vol.220, issue.1, pp.37-46, 2015.

D. Perri, C. Amico, E. Heine, L. Annen, J. Martial et al., Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns, Hum. Brain Mapp, vol.39, issue.1, pp.89-103, 2018.

A. H. Fong, K. Yoo, M. D. Rosenberg, S. Zhang, C. R. Li et al., Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies, Neuroimage, vol.188, pp.14-25, 2019.

M. D. Fox and M. Greicius, Clinical applications of resting state functional connectivity, Front. Syst. Neurosci, vol.4, p.19, 2010.

M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van-essen et al., The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proc. Natl. Acad. Sci, vol.102, issue.27, pp.9673-9678, 2005.

L. C. Freeman, Centrality in social networks conceptual clarification in Hawaii nets conferences, Soc. Network, vol.1, issue.3, pp.215-239, 1979.

K. J. Friston, Functional and effective connectivity in neuroimaging: a synthesis, Hum. Brain Mapp, vol.2, issue.1, pp.56-78, 1994.

K. J. Friston, A. P. Holmes, K. J. Worsley, J. Poline, C. D. Frith et al., Statistical parametric maps in functional imaging: a general linear approach, Hum. Brain Mapp, vol.2, issue.4, pp.189-210, 1994.

M. Fukushima, R. F. Betzel, Y. He, M. A. De-reus, M. P. Van-den-heuvel et al., Fluctuations between high-and low-modularity topology in timeresolved functional connectivity, Neuroimage, vol.180, pp.406-416, 2018.

G. H. Glover, T. Li, and D. Ress, Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR, Magn. Reson. Med, vol.44, issue.1, pp.162-167, 2000.

K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01345616

M. Greicius, Resting-state functional connectivity in neuropsychiatric disorders, Curr. Opin. Neurol, vol.21, issue.4, pp.424-430, 2008.

L. Griffanti, G. Salimi-khorshidi, C. F. Beckmann, E. J. Auerbach, G. Douaud et al., ICAbased artefact removal and accelerated fMRI acquisition for improved resting state network imaging, Neuroimage, vol.95, pp.232-247, 2014.

R. C. Gur, J. Richard, P. Hughett, M. E. Calkins, L. Macy et al., A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation, J. Neurosci. Methods, vol.187, issue.2, pp.254-262, 2010.

F. H?-ager, H. Volz, C. Gaser, H. Mentzel, W. A. Kaiser et al., Challenging the anterior attentional system with a continuous performance task: a functional magnetic resonance imaging approach, Eur. Arch. Psychiatr. Clin. Neurosci, vol.248, issue.4, pp.161-170, 1998.

D. A. Handwerker, J. M. Ollinger, and M. Esposito, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, Neuroimage, vol.21, issue.4, pp.1639-1651, 2004.

A. J. Holmes, M. O. Hollinshead, T. M. O'keefe, V. I. Petrov, G. R. Fariello et al., Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures, p.150031, 2015.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun et al., Dynamic functional connectivity: promise, issues, and interpretations, Neuroimage, vol.80, pp.360-378, 2013.

R. H. Kaiser, M. S. Kang, Y. Lew, J. Van-der-feen, B. Aguirre et al., Abnormal frontoinsulardefault network dynamics in adolescent depression and rumination: a preliminary resting-state co-activation pattern analysis, Neuropsychopharmacology, vol.44, pp.1604-1612, 2019.

J. Kang, C. Pae, and H. Park, Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex, PloS One, vol.14, issue.9, p.222161, 2019.

N. Kriegeskorte, W. K. Simmons, P. S. Bellgowan, and C. I. Baker, Circular analysis in systems neuroscience: the dangers of double dipping, Nat. Neurosci, vol.12, issue.5, pp.535-540, 2009.

A. Krishnan, L. J. Williams, A. R. Mcintosh, and H. Abdi, Partial least squares (PLS) methods for neuroimaging: a tutorial and review, Neuroimage, vol.56, issue.2, pp.455-475, 2011.

X. Liu, C. Chang, and J. H. Duyn, Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns, Front. Syst. Neurosci, vol.7, pp.1-11, 2013.

X. Liu and J. H. Duyn, Time-varying functional network information extracted from brief instances of spontaneous brain activity, Proc. Natl. Acad. Sci, vol.110, issue.11, pp.4392-4397, 2013.

X. Liu, N. Zhang, C. Chang, and J. H. Duyn, Co-activation patterns in resting-state fMRI signals, Neuroimage, vol.180, pp.485-494, 2018.

N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, and A. Oeltermann, Neurophysiological investigation of the basis of the fMRI signal, Nature, vol.412, issue.6843, pp.150-157, 2001.

A. R. Mcintosh and N. J. Lobaugh, Partial least squares analysis of neuroimaging data: applications and advances, Neuroimage, vol.23, pp.250-263, 2004.

S. Monti, P. Tamayo, J. Mesirov, and T. Golub, Consensus clustering: a resamplingbased method for class discovery and visualization of gene expression microarray data, Mach. Learn, vol.52, issue.1, pp.91-118, 2003.

K. Murphy and M. D. Fox, Towards a consensus regarding global signal regression for resting state functional connectivity MRI, Neuroimage, vol.154, pp.169-173, 2017.

K. B. Nooner, S. Colcombe, R. Tobe, M. Mennes, M. Benedict et al., The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry, Front. Neurosci, vol.6, p.152, 2012.

R. J. Ogg, P. Zou, D. N. Allen, S. B. Hutchins, R. M. Dutkiewicz et al., Neural correlates of a clinical continuous performance test, Magn. Reson. Imag, vol.26, issue.4, pp.504-512, 2008.

J. D. Power, K. A. Barnes, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, Neuroimage, vol.59, issue.3, pp.2142-2154, 2012.

J. D. Power, A. L. Cohen, S. M. Nelson, G. S. Wig, K. A. Barnes et al., Functional network organization of the human brain, Neuron, vol.72, issue.4, pp.665-678, 2011.

J. D. Power, C. J. Lynch, B. M. Silver, M. J. Dubin, A. Martin et al., Distinctions among real and apparent respiratory motions in human fMRI data, Neuroimage, vol.201, p.116041, 2019.

M. G. Preti, T. A. Bolton, and D. Van-de-ville, The dynamic functional connectome: state-of-the-art and perspectives, Neuroimage, vol.160, pp.41-54, 2017.

R. H. Pruim, M. Mennes, D. Van-rooij, A. Llera, J. K. Buitelaar et al., ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data, Neuroimage, vol.112, pp.267-277, 2015.

D. Rangaprakash, G. Wu, D. Marinazzo, X. Hu, and G. Deshpande, Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity, Magn. Reson. Med, vol.80, issue.4, pp.1697-1713, 2018.

M. D. Rosenberg, E. S. Finn, D. Scheinost, X. Papademetris, X. Shen et al., A neuromarker of sustained attention from whole-brain functional connectivity, Nat. Neurosci, vol.19, issue.1, p.165, 2016.

M. Rubinov and O. Sporns, Complex network measures of brain connectivity: uses and interpretations, Neuroimage, vol.52, issue.3, pp.1059-1069, 2010.

T. D. Satterthwaite, D. H. Wolf, J. Loughead, K. Ruparel, M. A. Elliott et al., Impact of in-scanner head motion on multiple measures of functional connectivity : relevance for studies of neurodevelopment in youth, Neuroimage, vol.60, issue.1, pp.623-632, 2012.

Y. Senbabaoglu, G. Michailidis, and J. Z. Li, Critical limitations of consensus clustering in class discovery, Sci. Rep, vol.4, p.6207, 2014.

W. R. Shirer, S. Ryali, E. Rykhlevskaia, V. Menon, and M. D. Greicius, Decoding subject-driven cognitive states with whole-brain connectivity patterns, Cerebr. Cortex, vol.22, issue.1, pp.158-165, 2012.

S. M. Smith, C. F. Beckmann, J. Andersson, E. J. Auerbach, J. Bijsterbosch et al., Resting-state fMRI in the human connectome project, Neuroimage, vol.80, pp.144-168, 2013.

E. Tagliazucchi, P. Balenzuela, D. Fraiman, and D. R. Chialvo, Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis, Front. Physiol, vol.3, pp.1-12, 2012.

M. G. Tana, E. Montin, S. Cerutti, and A. M. Bianchi, Exploring cortical attentional system by using fMRI during a continuous performance test, Comput. Intell. Neurosci, p.329213, 2010.

C. Tuleasca, T. A. Bolton, J. Najdenovska, E. Witjas, T. Girard et al., Normalization of aberrant pretherapeutic dynamic functional connectivity of extrastriate visual system in patients who underwent thalamotomy with stereotactic radiosurgery for essential tremor: a resting-state functional MRI study, J. Neurosurg, vol.1, pp.1-10, 2019.

M. P. Van-den-heuvel and H. E. Hulshoff-pol, Exploring the brain network: a review on resting-state fMRI functional connectivity, Eur. Neuropsychopharmacol, vol.20, issue.8, pp.519-534, 2010.

K. R. Van-dijk, M. R. Sabuncu, and R. L. Buckner, The influence of head motion on intrinsic functional connectivity MRI, Neuroimage, vol.59, issue.1, pp.431-438, 2012.

D. C. Van-essen, S. M. Smith, D. M. Barch, T. E. Behrens, E. Yacoub et al., The WU-Minn human connectome project: an overview, Neuroimage, vol.80, pp.62-79, 2013.

D. Vidaurre, S. M. Smith, and M. W. Woolrich, Brain network dynamics are hierarchically organized in time, Proc. Natl. Acad. Sci, vol.114, issue.48, 2017.

X. Zhuang, R. R. Walsh, K. Sreenivasan, Z. Yang, V. Mishra et al., Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: an application to Parkinson's disease, Neuroimage, vol.172, pp.64-84, 2018.