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PARIETAL - 2018
PARIETAL - 2018
Project-Team Parietal
Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography


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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

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, no 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, no 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, no 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, no 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, no 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, no 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, no 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, no 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

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, no 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, no 2, pp. 115–126.


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