Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 1A. Mensch.
  Learning representations from functional MRI data, Université Paris-Saclay, September 2018.
  https://tel.archives-ouvertes.fr/tel-01891633

Articles in International Peer-Reviewed Journals

• 2P. A. Ablin, J.-F. Cardoso, A. Gramfort.
  Faster Independent Component Analysis by Preconditioning With Hessian Approximations, in: IEEE Transactions on Signal Processing, August 2018, vol. 66, n^o 15, pp. 4040-4049.
  https://hal.inria.fr/hal-01970746
• 3Y. Bekhti, F. Lucka, J. Salmon, A. Gramfort.
  A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging, in: Inverse Problems, August 2018, vol. 34, n^o 8, 085010 p.
  https://hal.inria.fr/hal-01970744
• 4D. Bzdok, N. Altman, M. Krzywinski.
  Points of Significance: Statistics versus Machine Learning, in: Nature Methods, April 2018, pp. 1-7.
  https://hal.archives-ouvertes.fr/hal-01723223
• 5D. Bzdok, M. Krzywinski, N. Altman.
  Machine learning: Supervised methods, SVM and kNN, in: Nature Methods, January 2018, pp. 1-6.
  https://hal.archives-ouvertes.fr/hal-01657491
• 6D. Bzdok, A. Meyer-Lindenberg.
  Machine learning for precision psychiatry: Opportunites and challenges, in: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, February 2018.
  https://hal.archives-ouvertes.fr/hal-01643933
• 7P. Cerda, G. Varoquaux, B. Kégl.
  Similarity encoding for learning with dirty categorical variables, in: Machine Learning, June 2018, https://arxiv.org/abs/1806.00979. [ DOI : 10.1007/s10994-018-5724-2 ]
  https://hal.inria.fr/hal-01806175
• 8S. Chambon, M. Galtier, P. J. Arnal, G. Wainrib, A. Gramfort.
  A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series, in: IEEE Transactions on Neural Systems and Rehabilitation Engineering, March 2018, vol. 26, n^o 4, 17683810 p, https://arxiv.org/abs/1707.03321. [ DOI : 10.1109/TNSRE.2018.2813138 ]
  https://hal.archives-ouvertes.fr/hal-01810436
• 9C. Cury, J. Glaunès, R. Toro, M. Chupin, G. Schumann, V. Frouin, J.-B. Poline, O. Colliot.
  Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids, in: Frontiers in Neuroscience, November 2018, vol. 12. [ DOI : 10.3389/fnins.2018.00803 ]
  https://hal.inria.fr/hal-01920263
• 10E. Dohmatob, G. Varoquaux, B. Thirion.
  Inter-subject registration of functional images: do we need anatomical images ?, in: Frontiers in Neuroscience, March 2018.
  https://hal.archives-ouvertes.fr/hal-01701619
• 11D. Engemann, F. Raimondo, J.-R. King, B. Rohaut, G. Louppe, F. Faugeras, J. Annen, H. Cassol, O. Gosseries, D. Fernandez-Slezak, S. Laureys, L. Naccache, S. Dehaene, J. Sitt.
  Robust EEG-based cross-site and cross-protocol classification of states of consciousness, in: Brain - A Journal of Neurology , October 2018, vol. 141, n^o 11, pp. 3179–3192. [ DOI : 10.1093/brain/awy251 ]
  https://hal.inria.fr/hal-01887793
• 12P. Filipiak, R. H. Fick, A. Petiet, M. Santin, A.-C. Philippe, S. Lehéricy, P. Ciuciu, R. Deriche, D. Wassermann.
  Reducing the number of samples in spatiotemporal dMRI acquisition design, in: Magnetic Resonance in Medicine, November 2018. [ DOI : 10.1002/mrm.27601 ]
  https://hal.archives-ouvertes.fr/hal-01928734
• 13F. Hadj-Selem, T. Lofstedt, E. Dohmatob, V. Frouin, M. Dubois, V. Guillemot, E. Duchesnay.
  Continuation of Nesterov’s Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging, in: IEEE Transactions on Medical Imaging, 2018, vol. 2018. [ DOI : 10.1109/TMI.2018.2829802 ]
  https://hal-cea.archives-ouvertes.fr/cea-01883286
• 14G. Hartwigsen, D. Bzdok.
  Multivariate single-subject analysis of short-term reorganization in the language network, in: Cortex, July 2018, 4 p. [ DOI : 10.1016/j.cortex.2018.06.013 ]
  https://hal.archives-ouvertes.fr/hal-01824229
• 15Y. Hong, L. J. O'Donnell, P. Savadjiev, F. Zhang, D. Wassermann, O. Pasternak, H. J. Johnson, J. Paulsen, J.-P. Vonsattel, N. Makris, C.-F. Westin, Y. Rathi.
  Genetic load determines atrophy in hand cortico-striatal pathways in presymptomatic Huntington’s disease, in: Human Brain Mapping, 2018. [ DOI : 10.1002/hbm.24217 ]
  https://hal.inria.fr/hal-01787886
• 16M. Jas, E. ​. Larson, D. Engemann, J. Leppäkangas, S. Taulu, M. Hämäläinen, A. Gramfort.
  A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices, in: Frontiers in Neuroscience, August 2018, vol. 12. [ DOI : 10.3389/fnins.2018.00530 ]
  https://hal.archives-ouvertes.fr/hal-01854552
• 17J. M. Kernbach, T. D. Satterthwaite, D. S. Bassett, J. Smallwood, D. Margulies, S. Krall, P. Shaw, G. Varoquaux, B. Thirion, K. Konrad, D. Bzdok.
  Shared Endo-phenotypes of Default Mode Dysfunction in Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder, in: Translational Psychiatry, July 2018.
  https://hal.archives-ouvertes.fr/hal-01790245
• 18J. M. Kernbach, B. T. T. Yeo, J. Smallwood, D. Margulies, M. Thiebaut De Schotten, H. Walter, M. Sabuncu, A. J. Holmes, A. Gramfort, G. Varoquaux, B. Thirion, D. Bzdok.
  Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants, in: Proceedings of the National Academy of Sciences of the United States of America , November 2018. [ DOI : 10.1073/pnas.1804876115 ]
  https://hal.archives-ouvertes.fr/hal-01926796
• 19M. Kowalski, A. Meynard, H.-t. Wu.
  Convex Optimization approach to signals with fast varying instantaneous frequency, in: Applied and Computational Harmonic Analysis, January 2018, vol. 44, n^o 1, pp. 89 - 122, https://arxiv.org/abs/1503.07591. [ DOI : 10.1016/j.acha.2016.03.008 ]
  https://hal.archives-ouvertes.fr/hal-01199615
• 20C. Lazarus, P. Weiss, A. Vignaud, P. Ciuciu.
  An Empirical Study of the Maximum Degree of Undersampling in Compressed Sensing for T2*-weighted MRI, in: Magnetic Resonance Imaging, 2018, pp. 1-31.
  https://hal.inria.fr/hal-01829323
• 21J. Lefort-Besnard, D. S. Bassett, J. Smallwood, D. S. Margulies, B. Derntl, O. Gruber, A. Aleman, R. Jardri, G. Varoquaux, B. Thirion, S. B. Eickhoff, D. Bzdok.
  Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function, in: Human Brain Mapping, January 2018, pp. 1-52.
  https://hal.archives-ouvertes.fr/hal-01620441
• 22J. Lefort-Besnard, G. Varoquaux, B. Derntl, O. Gruber, A. Aleman, R. Jardri, I. Sommer, B. Thirion, D. Bzdok.
  Patterns of Schizophrenia Symptoms: Hidden Structure in the PANSS Questionnaire, in: Translational Psychiatry, 2018.
  https://hal.archives-ouvertes.fr/hal-01888918
• 23L. M. M. Lê, B. Kégl, A. Gramfort, C. Marini, D. Nguyen, M. Cherti, S. Tfaili, A. Tfayli, A. Baillet-Guffroy, P. Prognon, P. Chaminade, E. Caudron.
  Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach, in: Talanta, July 2018, vol. 184, pp. 260-265.
  https://hal.archives-ouvertes.fr/hal-01969999
• 24A. Mensch, J. Mairal, B. Thirion, G. Varoquaux.
  Stochastic Subsampling for Factorizing Huge Matrices, in: IEEE Transactions on Signal Processing, January 2018, vol. 66, n^o 1, pp. 113-128, https://arxiv.org/abs/1701.05363. [ DOI : 10.1109/TSP.2017.2752697 ]
  https://hal.archives-ouvertes.fr/hal-01431618
• 25A. L. Pinho, A. Amadon, T. Ruest, M. Fabre, E. Dohmatob, I. Denghien, C. Ginisty, S. Becuwe-Desmidt, S. Roger, L. Laurier, V. Joly-Testault, G. Médiouni-Cloarec, C. Doublé, B. Martins, P. Pinel, E. Eger, G. Varoquaux, C. Pallier, S. Dehaene, L. Hertz-Pannier, B. Thirion.
  Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping, in: Scientific Data , June 2018, vol. 5, 180105 p. [ DOI : 10.1038/sdata.2018.105 ]
  https://hal.archives-ouvertes.fr/hal-01817528
• 26V. Sydnor, A. M. Rivas-Grajales, A. Lyall, F. Zhang, S. Bouix, S. Karmacharya, M. Shenton, C.-F. Westin, N. Makris, D. Wassermann, L. J. O'Donnell, M. Kubicki.
  A comparison of three fiber tract delineation methods and their impact on white matter analysis, in: NeuroImage, May 2018, vol. 178, pp. 318-331. [ DOI : 10.1016/j.neuroimage.2018.05.044 ]
  https://hal.inria.fr/hal-01807178
• 27G. Varoquaux, R. Poldrack.
  Predictive models avoid excessive reductionism in cognitive neuroimaging, in: Current Opinion in Neurobiology, April 2019, vol. 55. [ DOI : 10.1016/j.conb.2018.11.002 ]
  https://hal.archives-ouvertes.fr/hal-01856412
• 28G. Varoquaux, Y. Schwartz, R. Poldrack, B. Gauthier, D. Bzdok, J.-B. Poline, B. Thirion.
  Atlases of cognition with large-scale brain mapping, in: PLoS Computational Biology, 2018.
  https://hal.inria.fr/hal-01908189
• 29L. Waller, A. Brovkin, L. Dorfschmidt, D. Bzdok, H. Walter, J. D. Kruschwitz.
  GraphVar 2.0: a user-friendly toolbox for machine learning on functional connectivity measures, in: Journal of Neuroscience Methods, January 2018, 40 p.
  https://hal.archives-ouvertes.fr/hal-01828991
• 30H.-T. Wang, D. Bzdok, D. Margulies, C. Craddock, M. Milham, E. Jefferies, J. Smallwood.
  Patterns of thought: population variation in the associations between large-scale network organisation and self-reported experiences at rest, in: NeuroImage, May 2018.
  https://hal.archives-ouvertes.fr/hal-01782292
• 31A. de Pierrefeu, T. Fovet, F. Hadj-Selem, T. Lofstedt, P. Ciuciu, S. Lefebvre, P. Thomas, R. Lopes, R. Jardri, E. Duchesnay.
  Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity, in: Human Brain Mapping, April 2018, vol. 39, n^o 4, pp. 1777 - 1788. [ DOI : 10.1002/hbm.23953 ]
  https://hal-cea.archives-ouvertes.fr/cea-01883271
• 32A. de Pierrefeu, T. Lofstedt, F. Hadj-Selem, M. Dubois, R. Jardri, T. Fovet, P. Ciuciu, V. Frouin, E. Duchesnay.
  Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty, in: IEEE Transactions on Medical Imaging, February 2018, vol. 37, n^o 2, pp. 396 - 407. [ DOI : 10.1109/tmi.2017.2749140 ]
  https://hal-cea.archives-ouvertes.fr/cea-01883278
• 33A. de Pierrefeu, T. Löfstedt, C. Laidi, F. Hadj-Selem, J. Bourgin, T. Hajek, F. Spaniel, M. Kolenic, P. Ciuciu, N. Hamdani, M. Leboyer, T. Fovet, R. Jardri, J. Houenou, E. Duchesnay.
  Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity, in: Acta Psychiatrica Scandinavica, 2018, vol. 2018, pp. 1 - 10. [ DOI : 10.1111/acps.12964 ]
  https://hal-cea.archives-ouvertes.fr/cea-01883283

International Conferences with Proceedings

• 34P. A. Ablin, J.-F. Cardoso, A. Gramfort.
  Accelerating likelihood optimization for ICA on real signals, in: LVA-ICA 2018, Guildford, United Kingdom, July 2018, https://arxiv.org/abs/1806.09390.
  https://hal.inria.fr/hal-01822602
• 35A. Alimi, R. H. Fick, D. Wassermann, R. Deriche.
  Dmipy, a Diffusion Microstructure Imaging toolbox in Python to improve research reproducibility, in: MICCAI 2018 - Workshop on Computational Diffusion MRI, Granada, Spain, September 2018.
  https://hal.inria.fr/hal-01873353
• 36H. Cherkaoui, L. E. Gueddari, C. Lazarus, A. Grigis, F. Poupon, A. Vignaud, S. Farrens, J.-L. Starck, P. Ciuciu.
  Analysis vs Synthesis-based Regularization for combined Compressed Sensing and Parallel MRI Reconstruction at 7 Tesla, in: 26th European Signal Processing Conference (EUSIPCO 2018), Roma, Italy, September 2018.
  https://hal.inria.fr/hal-01800700
• 37J. Dockès, D. Wassermann, R. Poldrack, F. M. Suchanek, B. Thirion, G. Varoquaux.
  Text to brain: predicting the spatial distribution of neuroimaging observations from text reports, in: MICCAI 2018 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Granada, Spain, September 2018, pp. 1-18, https://arxiv.org/abs/1806.01139.
  https://hal.archives-ouvertes.fr/hal-01807295
• 38L. El Gueddari, C. Lazarus, H. Carrié, A. Vignaud, P. Ciuciu.
  Self-calibrating nonlinear reconstruction algorithms for variable density sampling and parallel reception MRI, in: 10th IEEE Sensor Array and Multichannel Signal Processing workshop, Sheffield, United Kingdom, July 2018, pp. 1-5.
  https://hal.inria.fr/hal-01782428
• 39P. Filipiak, R. Fick, A. Petiet, M. Santin, A.-C. Philippe, S. Lehéricy, R. Deriche, D. Wassermann.
  Coarse-Grained Spatiotemporal Acquisition Design for Diffusion MRI, in: ISBI 2019 - Proceedings of The IEEE International Symposium on Biomedical Imaging, Venice, Italy, April 2019.
  https://hal.inria.fr/hal-01973588
• 40G. Gallardo, N. Gayraud, R. Deriche, M. Clerc, S. Deslauriers-Gauthier, D. Wassermann.
  Solving the Cross-Subject Parcel Matching Problem using Optimal Transport, in: International Conference on Medical Image Computing and Computer-Assisted Intervention 2018, Granada, Spain, September 2018.
  https://hal.archives-ouvertes.fr/hal-01935684
• 41D. La Rocca, P. Ciuciu, V. van Wassenhove, H. Wendt, P. Abry, R. Leonarduzzi.
  Scale-free functional connectivity analysis from source reconstructed MEG data, in: EUSIPCO 2018 - 26th European Signal Processing Conference, Roma, Italy, September 2018, pp. 1-5.
  https://hal.inria.fr/hal-01800620
• 42C. Maumet, G. Flandin, M. Perez-Guevara, J.-B. Poline, J. Rajendra, R. Reynolds, B. Thirion, T. E. Nichols.
  A standardised representation for non-parametric fMRI results, in: OHBM 2018 - Annual meeting of the Organization of Human Brain Mapping, Singapore, Singapore, June 2018, pp. 1-4.
  http://www.hal.inserm.fr/inserm-01828914
• 43A. Mensch, M. Blondel.
  Differentiable Dynamic Programming for Structured Prediction and Attention, in: 35th International Conference on Machine Learning, Stockholm, Sweden, Proceedings of the 35th International Conference on Machine Learning, July 2018, vol. 80.
  https://hal.archives-ouvertes.fr/hal-01809550
• 44H. Wendt, P. Abry, P. Ciuciu.
  Spatially regularized wavelet leader scale-free analysis of fMRI data, in: IEEE International Symposium on Biomedical Imaging, Washington, DC, United States, April 2018.
  https://hal.inria.fr/hal-01782332

Conferences without Proceedings

• 45S. Chambon, V. Thorey, P. J. Arnal, E. Mignot, A. Gramfort.
  A deep learning architecture to detect events in EEG signals during sleep, in: IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2018), Aalborg, Denmark, September 2018.
  https://hal.archives-ouvertes.fr/hal-01917529
• 46D. Chyzhyk, G. Varoquaux, B. Thirion, M. Milham.
  Controlling a confound in predictive models with a test set minimizing its effect, in: PRNI 2018 - 8th International Workshop on Pattern Recognition in Neuroimaging, Singapore, Singapore, June 2018, pp. 1-4.
  https://hal.archives-ouvertes.fr/hal-01831701
• 47T. Dupré la Tour, Y. Grenier, A. Gramfort.
  Driver estimation in non-linear autoregressive models, in: 43nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, Canada, April 2018.
  https://hal.archives-ouvertes.fr/hal-01696786
• 48M. Frigo, G. Gallardo, I. Costantini, A. Daducci, D. Wassermann, R. Deriche, S. Deslauriers-Gauthier.
  Reducing false positive connection in tractograms using joint structure-function filtering, in: OHBM 2018 - Organization for Human Brain Mapping, Singapore, Singapore, June 2018, pp. 1-3.
  https://hal.inria.fr/hal-01737434
• 49G. Gallardo, S. Bouix, D. Wassermann.
  Diffusion Driven Label Fusion for White Matter Multi-Atlas Segmentation, in: OHBM 2018 - Organization for Human Brain Mapping, Singapore, Singapore, June 2018, pp. 1-2.
  https://hal.archives-ouvertes.fr/hal-01737422
• 50N. T. H. Gayraud, G. Gallardo, M. Clerc, D. Wassermann.
  Solving the Cross-Subject Parcel Matching Problem: Comparing Four Methods Using Extrinsic Connectivity, in: OHBM 2018, Singapore, Singapore, June 2018.
  https://hal.archives-ouvertes.fr/hal-01737366
• 51T. Kerdreux, F. Pedregosa, A. D'Aspremont.
  Frank-Wolfe with Subsampling Oracle, in: ICML 2018 - 35th International Conference on Machine Learning, Stockholm, Sweden, July 2018, https://arxiv.org/abs/1803.07348.
  https://hal.archives-ouvertes.fr/hal-01927391
• 52T. D. La Tour, T. Moreau, M. Jas, A. Gramfort.
  Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals, in: Advances in Neural Information Processing Systems (NeurIPS), Montréal, Canada, December 2018, https://arxiv.org/abs/1805.09654.
  https://hal.archives-ouvertes.fr/hal-01966685
• 53M. Massias, O. Fercoq, A. Gramfort, J. Salmon.
  Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression, in: 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Spain, April 2018.
  https://hal.archives-ouvertes.fr/hal-01812011
• 54M. Massias, A. Gramfort, J. Salmon.
  Celer: a Fast Solver for the Lasso with Dual Extrapolation, in: ICML 2018 - 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR, July 2018, vol. 80, pp. 3321-3330.
  https://hal.archives-ouvertes.fr/hal-01833398
• 55H. Richard, A. Pinho, B. Thirion, G. Charpiat.
  Optimizing deep video representation to match brain activity, in: CCN 2018 - Conference on Cognitive Computational Neuroscience, Philadelphia, United States, September 2018, https://arxiv.org/abs/1809.02440.
  https://hal.archives-ouvertes.fr/hal-01868735
• 56J.-B. Schiratti, J.-E. Le Douget, M. Le Van Quyen, S. Essid, A. Gramfort.
  An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings, in: International Conference on Acoustics, Speech, and Signal Processing, Calgary, Canada, April 2018.
  https://hal.archives-ouvertes.fr/hal-01724272
• 57A. de Pierrefeu, T. Lofstedt, C. Laidi, F. Hadj-Selem, M. Leboyer, P. Ciuciu, J. Houenou, E. Duchesnay.
  Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity, in: 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), Singapore, Singapore, IEEE, June 2018. [ DOI : 10.1109/PRNI.2018.8423946 ]
  https://hal-cea.archives-ouvertes.fr/cea-01883311

Other Publications

• 58P. A. Ablin, J.-F. Cardoso, A. Gramfort.
  Beyond Pham's algorithm for joint diagonalization, November 2018, https://arxiv.org/abs/1811.11433 - working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01936887
• 59P. A. Ablin, D. Fagot, H. Wendt, A. Gramfort, C. Févotte.
  A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning, November 2018, https://arxiv.org/abs/1811.02225 - working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01912918
• 60F. Almairac, P. Filipiak, L. Slabu, M. Clerc, T. Papadopoulo, D. Fontaine, L. Mondot, S. Chanelet, D. Wassermann, R. Deriche.
  Bridging Brain Structure and Function by Correlating Structural Connectivity and Cortico-Cortical Transmission, June 2018, 2nd C@UCA meeting, Poster.
  https://hal.inria.fr/hal-01852956
• 61D. Bzdok, D. Engemann, O. Grisel, G. Varoquaux, B. Thirion.
  Prediction and inference diverge in biomedicine: Simulations and real-world data, April 2018, working paper or preprint. [ DOI : 10.1101/327437 ]
  https://hal.archives-ouvertes.fr/hal-01848319
• 62D. Bzdok, T. M. Karrer.
  Single-Subject Prediction: A Statistical Paradigm for Precision Psychiatry, February 2018, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01714822
• 63K. Dadi, M. Rahim, A. Abraham, D. Chyzhyk, M. Milham, B. Thirion, G. Varoquaux.
  Benchmarking functional connectome-based predictive models for resting-state fMRI, November 2018, working paper or preprint.
  https://hal.inria.fr/hal-01824205
• 64M. Gabrié, A. Manoel, C. Luneau, J. Barbier, N. Macris, F. Krzakala, L. Zdeborová.
  Entropy and mutual information in models of deep neural networks, November 2018, working paper or preprint.
  https://hal-cea.archives-ouvertes.fr/cea-01930228
• 65J.-R. King, L. ​. Gwilliams, C. ​. Holdgraf, J. ​. Sassenhagen, A. ​. Barachant, D. ​. Engemann, E. ​. Larson, A. Gramfort.
  Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition, July 2018, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01848442
• 66C. Lazarus, P. Weiss, N. Chauffert, F. Mauconduit, L. El Gueddari, C. Destrieux, I. Zemmoura, A. Vignaud, P. Ciuciu.
  Variable-density k-space filling curves for accelerated Magnetic Resonance Imaging, August 2018, working paper or preprint.
  https://hal.inria.fr/hal-01861760
• 67A. Manoel, F. Krzakala, B. Thirion, G. Varoquaux, L. Zdeborová.
  Approximate message-passing for convex optimization with non-separable penalties, November 2018, https://arxiv.org/abs/1809.06304 - working paper or preprint.
  https://hal-cea.archives-ouvertes.fr/cea-01932983
• 68A. Mensch, J. Mairal, B. Thirion, G. Varoquaux.
  Extracting Universal Representations of Cognition across Brain-Imaging Studies, October 2018, https://arxiv.org/abs/1809.06035 - working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01874713
• 69D. Wassermann, D. V. Nguyen, G. Gallardo, J.-R. Li, W. Cai, V. Menon.
  Sensing Von Economo Neurons in the Insula with Multi-shell Diffusion MRI, 2018, International Society for Magnetic Resonance in Medicine, Poster.
  https://hal.inria.fr/hal-01807704

References in notes

• 70K. S. Button, J. P. Ioannidis, C. Mokrysz, B. A. Nosek, J. Flint, E. S. Robinson, M. R. Munafò.
  Power failure: why small sample size undermines the reliability of neuroscience, in: Nature Reviews Neuroscience, 2013, vol. 14, n^o 5, pp. 365–376.
• 71R. A. Poldrack, C. I. Baker, J. Durnez, K. J. Gorgolewski, P. M. Matthews, M. R. Munafò, T. E. Nichols, J.-B. Poline, E. Vul, T. Yarkoni.
  Scanning the horizon: towards transparent and reproducible neuroimaging research, in: Nature Reviews Neuroscience, 2017, vol. 18, n^o 2, pp. 115–126.