V. Aglieri, B. Cagna, P. Belin, and S. Takerkart, InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification, 2019.

C. Allefeld, K. Grgen, and J. D. Haynes, Valid population inference for information-based imaging: From the second-level t -test to prevalence inference, NeuroImage, vol.141, pp.378-392, 2016.

F. Bach, Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression, The Journal of Machine Learning Research, vol.15, pp.595-627, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00804431

L. Bottou and C. J. Lin, Support vector machine solvers. Large scale kernel machines 3, pp.301-320, 2007.

K. H. Brodersen, J. Daunizeau, C. Mathys, J. R. Chumbley, J. M. Buhmann et al., Variational Bayesian mixedeffects inference for classification studies, NeuroImage, vol.76, pp.345-361, 2013.

A. Capilla, P. Belin, and J. Gross, The Early Spatio-Temporal Correlates and Task Independence of Cerebral Voice Processing Studied with MEG, Cerebral Cortex, vol.23, pp.1388-1395, 2012.

I. Charest, R. A. Kievit, T. W. Schmitz, D. Deca, and N. Kriegeskorte, Unique semantic space in the brain of each beholder predicts perceived similarity, Proceedings of the National Academy of Sciences, vol.111, pp.14565-14570, 2014.

J. Dubois, A. O. De-berker, and D. Y. Tsao, Single-Unit Recordings in the Macaque Face Patch System Reveal Limit ations of fMRI MVPA, Journal of Neuroscience, vol.35, pp.2791-2802, 2015.

J. A. Etzel, MVPA Permutation Schemes: Permutation Testing for the Group Level, 2015 International Workshop on Pattern Recognition in NeuroImaging, pp.65-68, 2015.

J. A. Etzel, N. Valchev, V. Gazzola, and C. Keysers, Is brain activity during action observation modulated by the perceived fairness of the actor?, PLoS One, vol.11, 2016.

T. Fuchigami, Y. Shikauchi, K. Nakae, M. Shikauchi, T. Ogawa et al., Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging, Scientific Reports, vol.8, 2018.

J. Gabrieli, S. Ghosh, and S. Whitfield-gabrieli, Prediction as a Humanitarian and Pragmatic Contribution from Human Cogniti ve Neuroscience, Neuron, vol.85, pp.11-26, 2015.

R. Gilron, J. Rosenblatt, O. Koyejo, R. A. Poldrack, and R. Mukamel, What's in a pattern? examining the type of signal multivariate analysis uncovers at the group level, NeuroImage, vol.146, pp.113-120, 2017.

J. Haxby, J. Guntupalli, A. Connolly, Y. Halchenko, B. Conroy et al., A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex, Neuron, vol.72, pp.404-416, 2011.

J. V. Haxby, A. C. Connolly, and J. S. Guntupalli, Decoding Neural Representational Spaces Using Multivariate Pattern Analysis, Annual Review of Neuroscience, vol.37, pp.435-456, 2014.

J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten et al., Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, Science, vol.293, pp.2425-2430, 2001.

S. M. Helfinstein, T. Schonberg, E. Congdon, K. H. Karlsgodt, J. A. Mumford et al., Predicting risky choices from brain activity patterns, Proceedings of the National Academy of Sciences, vol.111, pp.2470-2475, 2014.

K. Izuma, K. Shibata, K. Matsumoto, and R. Adolphs, Neural predictors of evaluative attitudes toward celebrities, Social cognitive and affective neuroscience, vol.12, pp.382-390, 2017.

J. Jiang, C. Summerfield, and T. Egner, Visual Prediction Error Spreads Across Object Features in Human Visual Cortex, The Journal of Neuroscience, vol.36, pp.12746-12763, 2016.

J. Kim, J. Wang, D. H. Wedell, and S. V. Shinkareva, Identifying core affect in individuals from fmri responses to dynamic naturalistic audiovisual stimuli, PloS one, vol.11, p.161589, 2016.

E. Koechlin and T. Jubault, Broca's Area and the Hierarchical Organization of Human Behavior, Neuron, vol.50, pp.963-974, 2006.

P. A. Kragel, L. Koban, L. F. Barrett, and T. D. Wager, Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging, Neuron, vol.99, pp.257-273, 2018.

N. Kriegeskorte, R. Goebel, and P. Bandettini, Information-based functional brain mapping, Proceedings of the National Academy of Sciences, vol.103, pp.3863-3868, 2006.

M. A. Lindquist, A. Krishnan, M. Lpez-sol, M. E. Jepma, C. W. Woo et al., Group-regularized individual prediction: theory and application to pain, NeuroImage, vol.145, pp.274-287, 2017.

T. Mima, N. Sadato, S. Yazawa, T. Hanakawa, H. Fukuyama et al., Brain structures related to active and passive finger movements in man, Brain, vol.122, pp.1989-1997, 1999.

J. A. Mumford, B. O. Turner, F. G. Ashby, and R. A. Poldrack, Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses, NeuroImage, vol.59, pp.2636-2643, 2012.

T. E. Nichols and A. P. Holmes, Nonparametric permutation tests for functional neuroimaging: a primer with examples, Human brain mapping, vol.15, pp.1-25, 2002.

E. Olivetti, S. Veeramachaneni, and E. Nowakowska, Bayesian hypothesis testing for pattern discrimination in brain decoding, Pattern Recognition, vol.45, pp.2075-2084, 2012.

C. R. Pernet, P. Mcaleer, M. Latinus, K. J. Gorgolewski, I. Charest et al., The human voice areas: Spatial organization and interindividual variability in temporal and extra-temporal cortices, Neuroimage, vol.119, pp.164-174, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01999021

S. Ryali, K. Supekar, D. A. Abrams, and V. Menon, Sparse logistic regression for whole-brain classification of fmri data, NeuroImage, vol.51, pp.752-764, 2010.

J. Stelzer, Y. Chen, and R. Turner, Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (mvpa): random permutations and cluster size control, Neuroimage, vol.65, pp.69-82, 2013.

S. Takerkart, G. Auzias, B. Thirion, and L. Ralaivola, Graphbased inter-subject pattern analysis of fMRI data, PloS ONE, vol.9, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01027769

M. T. Todd, L. E. Nystrom, and J. D. Cohen, Confounds in multivariate pattern analysis: Theory and rule representation case study, NeuroImage, vol.77, pp.157-165, 2013.

G. Varoquaux, Cross-validation failure: Small sample sizes lead to large error bars, NeuroImage, vol.180, pp.68-77, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01545002