M. L. Seghier and C. J. Price, Interpreting and Utilising Intersubject Variability in Brain Function, Trends in Cognitive Sciences, vol.22, issue.6, pp.517-530, 2018.

V. D. Calhoun, S. M. Lawrie, J. Mourao-miranda, and K. E. Stephan, Prediction of individual differences from neuroimaging data, NeuroImage, vol.145, p.135, 2017.

J. Dubois and R. Adolphs, Building a Science of Individual Differences from fMRI, Trends in Cognitive Sciences, vol.20, issue.6, pp.425-443, 2016.

V. D. Calhoun and J. Sui, Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol.1, pp.230-244, 2016.

M. W. Cole, D. S. Bassett, J. D. Power, T. S. Braver, and S. E. Petersen, Intrinsic and Task-Evoked Network Architectures of the Human Brain, Neuron, vol.83, issue.1, pp.238-251, 2014.

J. Zhao, X. Xie, X. Xu, and S. Sun, Multi-view learning overview: Recent progress and new challenges, Information Fusion, vol.38, pp.43-54, 2017.

Y. Li, M. Yang, and Z. M. Zhang, A survey of multi-view representation learning, IEEE Transactions on Knowledge and Data Engineering, 2018.

J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee et al., Multimodal deep learning, Proceedings of the 28th international conference on machine learning, pp.689-696, 2011.

F. Feng, X. Wang, and R. Li, Cross-modal retrieval with correspondence autoencoder, Proceedings of the 22nd ACM international conference on Multimedia, pp.7-16, 2014.

W. Wang, R. Arora, K. Livescu, and J. Bilmes, On deep multiview representation learning, International Conference on Machine Learning, pp.1083-1092, 2015.

M. Kan, S. Shan, and X. Chen, Multi-view deep network for cross-view classification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4847-4855, 2016.

K. Somandepalli, N. Kumar, R. Travadi, and S. Narayanan, Multimodal representation learning using deep multiset canonical correlation, 2019.

T. H. Hsu, W. Weng, W. Boag, M. Mcdermott, and P. Szolovits, Unsupervised multimodal representation learning across medical images and reports, 2018.

A. Virginia, C. Bastien, B. Pascal, and T. Sylvain, A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification, 2019.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, Springer series in statistics, vol.1, 2001.

Z. Zhang, Z. Lai, Y. Xu, L. Shao, J. Wu et al., Discriminative elastic-net regularized linear regression, IEEE Transactions on Image Processing, vol.26, issue.3, pp.1466-1481, 2017.

M. Belkin, P. Niyogi, and V. Sindhwani, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, Journal of machine learning research, vol.7, pp.2399-2434, 2006.

V. Koltchinskii, K. Lounici, and A. B. Tsybakov, Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion, The Annals of Statistics, vol.39, issue.5, pp.2302-2329, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00676868

J. Zhao, L. Niu, and S. Zhan, Trace regression model with simultaneously low rank and row (column) sparse parameter, Computational Statistics & Data Analysis, vol.116, pp.1-18, 2017.

L. Grosenick, B. Klingenberg, K. Katovich, B. Knutson, and J. E. Taylor, Interpretable whole-brain prediction analysis with graphnet, NeuroImage, vol.72, pp.304-321, 2013.

M. Slawski, P. Li, and M. Hein, Regularization-free estimation in trace regression with symmetric positive semidefinite matrices, Advances in Neural Information Processing Systems, pp.2782-2790, 2015.

J. Fan, W. Gong, and Z. Zhu, Generalized high-dimensional trace regression via nuclear norm regularization, Journal of Econometrics, 2019.

A. Beck and M. Teboulle, Gradient-based algorithms with applications to signal recovery, pp.42-88, 2009.

V. Aglieri, R. Watson, C. Pernet, M. Latinus, L. Garrido et al., The glasgow voice memory test: Assessing the ability to memorize and recognize unfamiliar voices, Behavior research methods, vol.49, issue.1, pp.97-110, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01469030

V. Aglieri, B. Cagna, P. Belin, and S. Takerkart, Single-trial fmri activation maps measured during the intertva event-related voice localizer. a data set ready for inter-subject pattern analysis, Data in Brief, p.105170, 2020.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

C. M. Bishop, Pattern recognition and machine learning, 2006.

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural networks, vol.13, issue.4-5, pp.411-430, 2000.

P. Belin, R. J. Zatorre, P. Lafaille, P. Ahad, and B. Ike, Voice-selective areas in human auditory cortex, bibtex: belin voiceselective, vol.403, pp.309-312, 2000.

C. R. Pernet, P. Mcaleer, M. Latinus, K. J. Gorgolewski, I. N. Charest et al., The human voice areas: Spatial organization and inter-individual variability in temporal and extra-te mporal cortices, NeuroImage, vol.119, pp.164-174, 2015.

V. Aglieri, T. Chaminade, S. Takerkart, and P. Belin, Functional connectivity within the voice perception network and its behavioural relevance, NeuroImage, vol.183, pp.356-365, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02335026