W. W. Seeley, V. Menon, A. F. Schatzberg, J. Keller, G. H. Glover et al., Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control, Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control, pp.2349-2356, 2007.
DOI : 10.1523/JNEUROSCI.5587-06.2007

N. Simon, J. Friedman, and T. Hastie, A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression, 2013.

G. Varoquaux, A. Gramfort, and B. Thirion, Small-sample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering, Int. Conf. Machine Learning, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00705192

F. De-la-torre, A Least-Squares Framework for Component Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.6, pp.1041-1055, 2012.
DOI : 10.1109/TPAMI.2011.184

L. Floch, E. Guillemot, V. Frouin, V. Pinel, P. Lalanne et al., Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares, NeuroImage, vol.63, issue.1, pp.11-24, 2012.
DOI : 10.1016/j.neuroimage.2012.06.061

URL : https://hal.archives-ouvertes.fr/hal-00750902

D. Macarthur, Methods: Face up to false positives, Nature, vol.487, issue.7408, pp.427-428, 2012.
DOI : 10.1126/science.1210484

A. Javanmard and A. Montanari, Confidence Intervals and Hypothesis Testing for High- Dimensional Regression. arXiv:1306, p.3171, 2013.

T. Nichols and S. Hayasaka, Controlling the familywise error rate in functional neuroimaging: a comparative review, Statistical Methods in Medical Research, vol.12, issue.5, pp.419-446, 2003.
DOI : 10.1191/0962280203sm341ra

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-473, 2010.
DOI : 10.1111/j.1467-9868.2010.00740.x

B. Ng, M. Dresler, G. Varoquaux, J. B. Poline, M. D. Greicius et al., Transport on Riemannian Manifold for Functional Connectivity-Based Classification, MICCAI 2014, pp.405-413, 2014.
DOI : 10.1007/978-3-319-10470-6_51

URL : https://hal.archives-ouvertes.fr/hal-01058521

A. Delaigle, P. Hall, and . Jin, Robustness and Accuracy of Methods for High Dimensional Data Analysis based on Student's t-statistic. arXiv:1001, p.3886, 2010.

H. Tanizaki, Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments, Journal of Applied Statistics, vol.24, issue.5, pp.603-632, 1997.
DOI : 10.1080/02664769723576

G. Schumann, The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology, Molecular Psychiatry, vol.47, issue.12, pp.1128-1139, 2010.
DOI : 10.1016/j.brainres.2006.03.029

S. R. Chamberlain, T. W. Robbins, S. Winder-rhodes, U. Müller, B. J. Sahakian et al., Translational Approaches to Frontostriatal Dysfunction in Attention-Deficit/Hyperactivity Disorder Using a Computerized Neuropsychological Battery, Biological Psychiatry, vol.69, issue.12, pp.1192-1203, 2011.
DOI : 10.1016/j.biopsych.2010.08.019

Y. Behzadi, K. Restom, J. Liau, and T. T. Liu, A component based noise correction method (CompCor) for BOLD and perfusion based fMRI, NeuroImage, vol.37, issue.1, pp.90-101, 2007.
DOI : 10.1016/j.neuroimage.2007.04.042

W. R. Shirer, S. Ryali, E. Rykhlevskaia, V. Menon, and M. D. Greicius, Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns, Cerebral Cortex, vol.22, issue.1, pp.158-165, 2012.
DOI : 10.1093/cercor/bhr099

G. Bush, E. M. Valera, and L. J. Seidman, Functional Neuroimaging of Attention-Deficit/Hyperactivity Disorder: A Review and Suggested Future Directions, Biological Psychiatry, vol.57, issue.11, pp.1273-1284, 2005.
DOI : 10.1016/j.biopsych.2005.01.034

M. Wallentin, E. Weed, L. Østergaard, K. Mouridsen, and A. Roepstorff, Accessing the mental space???Spatial working memory processes for language and vision overlap in precuneus, Human Brain Mapping, vol.24, issue.5, pp.524-532, 2008.
DOI : 10.1002/hbm.20413

R. Whelan, Adolescent impulsivity phenotypes characterized by distinct brain networks, Nature Neuroscience, vol.138, issue.6, pp.920-925, 2012.
DOI : 10.1038/nn.3092

H. Westerberg, T. Hirvikoski, H. Forssberg, and T. Klingberg, Visuo-Spatial Working Memory Span: A Sensitive Measure of Cognitive Deficits in Children With ADHD, Child Neuropsychology, vol.10, issue.3, pp.155-161, 2004.
DOI : 10.1080/09297040409609806

A. Ghanizadeh, Sensory Processing Problems in Children with ADHD, a Systematic Review, Psychiatry Investigation, vol.8, issue.2, pp.89-94, 2011.
DOI : 10.4306/pi.2011.8.2.89

M. D. Fox and M. E. Raichle, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging, Nature Reviews Neuroscience, vol.17, issue.9, pp.700-711, 2007.
DOI : 10.1016/j.neuroimage.2006.02.010

D. Kuonen, -estimates, Journal of Applied Statistics, vol.88, issue.5, pp.443-460, 2005.
DOI : 10.1111/j.1467-9574.1976.tb00272.x

URL : https://hal.archives-ouvertes.fr/hal-01357895