Bibliography

Major publications by the team in recent years

• 1D. Bzdok, M. Eickenberg, O. Grisel, B. Thirion, G. Varoquaux.
  
  Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data, in: Neural Information Processing Systems, December 2015.
  
  https://hal.archives-ouvertes.fr/hal-01211248
• 2N. Chauffert, P. Ciuciu, J. Kahn, P. Weiss.
  
  A Projection Method on Measures Sets, in: Constructive Approximation, February 2017, vol. 45, n^o 1, pp. 83 - 111. [ DOI : 10.1007/s00365-016-9346-2 ]
  
  https://hal.inria.fr/hal-01432720
• 3M. Eickenberg, E. Dohmatob, B. Thirion, G. Varoquaux.
  
  Total Variation meets Sparsity: statistical learning with segmenting penalties, in: Medical Image Computing and Computer Aided Intervention (MICCAI), München, Germany, Proceedings of MICCAI 2015, October 2015.
  
  https://hal.inria.fr/hal-01170619
• 4A. A. Hoyos-Idrobo, G. Varoquaux, Y. Schwartz, B. Thirion.
  
  FReM – scalable and stable decoding with fast regularized ensemble of models, in: NeuroImage, 2017, pp. 1-16. [ DOI : 10.1016/j.neuroimage.2017.10.005 ]
  
  https://hal.archives-ouvertes.fr/hal-01615015
• 5M. Jas, D. A. Engemann, Y. Bekhti, F. A. Raimondo, A. Gramfort.
  
  Autoreject: Automated artifact rejection for MEG and EEG data, in: NeuroImage, 2017. [ DOI : 10.1016/j.neuroimage.2017.06.030 ]
  
  https://hal.inria.fr/hal-01562403
• 6A. Mensch, J. Mairal, D. Bzdok, B. Thirion, G. Varoquaux.
  
  Learning Neural Representations of Human Cognition across Many fMRI Studies, in: Neural Information Processing Systems, Long Beach, United States, December 2017.
  
  https://hal.archives-ouvertes.fr/hal-01626823
• 7A. 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. [ DOI : 10.1109/TSP.2017.2752697 ]
  
  https://hal.archives-ouvertes.fr/hal-01431618
• 8B. Ng, G. Varoquaux, J.-B. Poline, M. D. Greicius, B. Thirion.
  
  Transport on Riemannian Manifold for Connectivity-based Brain Decoding, in: IEEE Transactions on Medical Imaging, 2015, vol. PP, n^o 99, 9 p. [ DOI : 10.1109/TMI.2015.2463723 ]
  
  https://hal.inria.fr/hal-01185200
• 9F. Pedregosa, M. Eickenberg, P. Ciuciu, B. Thirion, A. Gramfort.
  
  Data-driven HRF estimation for encoding and decoding models, in: NeuroImage, November 2014, pp. 209–220.
  
  https://hal.inria.fr/hal-00952554
• 10Y. Schwartz, B. Thirion, G. Varoquaux.
  
  Mapping cognitive ontologies to and from the brain, in: NIPS (Neural Information Processing Systems), United States, November 2013.
  
  http://hal.inria.fr/hal-00904763

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 11E. Dohmatob.
  
  Enhancement of functional brain connectome analysis by the use of deformable models in the estimation of spatial decompositions of the brain images, Université Paris-Saclay, September 2017.
  
  https://tel.archives-ouvertes.fr/tel-01630295

Articles in International Peer-Reviewed Journals

• 12M. Albughdadi, L. Chaari, J.-Y. Tourneret, F. Forbes, P. Ciuciu.
  
  A Bayesian Non-Parametric Hidden Markov Random Model for Hemodynamic Brain Parcellation, in: Signal Processing, June 2017, vol. 135, pp. 132–146. [ DOI : 10.1016/j.sigpro.2017.01.005 ]
  
  https://hal.archives-ouvertes.fr/hal-01426385
• 13D. T. Alcalá-López, J. Smallwood, E. Jefferies, F. B. Van Overwalle, K. Vogeley, R. B. Mars, B. B. Turetsky, A. R. Laird, P. T. Fox, S. B. Eickhoff, D. Bzdok.
  
  Computing the Social Brain Connectome Across Systems and States, in: Cerebral Cortex, 2017, pp. 1 - 26. [ DOI : 10.1093/cercor/bhx121 ]
  
  https://hal.archives-ouvertes.fr/hal-01519450
• 14D. Bzdok.
  
  Classical Statistics and Statistical Learning in Imaging Neuroscience: Two Statistical Cultures in Neuroimaging, in: Frontiers in Human Neuroscience, November 2017.
  
  https://hal.archives-ouvertes.fr/hal-01583175
• 15D. Bzdok, M. Krzywinski, N. Altman.
  
  Machine learning: A primer, in: Nature Methods, November 2017.
  
  https://hal.archives-ouvertes.fr/hal-01598285
• 16D. 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
• 17D. 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
• 18D. Bzdok, B. T. T. Yeo.
  
  Inference in the age of big data: Future perspectives on neuroscience, in: NeuroImage, April 2017. [ DOI : 10.1016/j.neuroimage.2017.04.061 ]
  
  https://hal.archives-ouvertes.fr/hal-01516891
• 19N. Chauffert, P. Ciuciu, J. Kahn, P. Weiss.
  
  A Projection Method on Measures Sets, in: Constructive Approximation, February 2017, vol. 45, n^o 1, pp. 83 - 111. [ DOI : 10.1007/s00365-016-9346-2 ]
  
  https://hal.inria.fr/hal-01432720
• 20M. Dubol, C. Trichard, A. Larisa Sandu, C. Leroy, M. Rahim, J.-L. Martinot, E. Artiges.
  
  286. Dopamine Transporter and Reward Anticipation in Psychiatric Patients: A Positron Emission Tomography and Functional Magnetic Resonance Imaging Study, in: Biological Psychiatry, May 2017, vol. 81, n^o 10, pp. S117 - S118. [ DOI : 10.1016/j.biopsych.2017.02.300 ]
  
  https://hal.inria.fr/hal-01593040
• 21M. Dubol, C. Trichard, C. Leroy, A.-L. Sandu, M. Rahim, B. Granger, E. T. Tzavara, L. Karila, J.-L. Martinot, E. Artiges.
  
  Dopamine Transporter and Reward Anticipation in a Dimensional Perspective: A Multimodal Brain Imaging Study, in: Neuropsychopharmacology, August 2017. [ DOI : 10.1038/npp.2017.183 ]
  
  https://hal.inria.fr/hal-01576551
• 22T. Dupré La Tour, L. Tallot, L. Grabot, V. Doyère, V. Van Wassenhove, Y. Grenier, A. Gramfort.
  
  Non-linear auto-regressive models for cross-frequency coupling in neural time series, in: PLoS Computational Biology, December 2017, vol. 13, n^o 12, e1005893. [ DOI : 10.1371/journal.pcbi.1005893 ]
  
  https://hal.archives-ouvertes.fr/hal-01679078
• 23A. A. Hoyos-Idrobo, G. Varoquaux, Y. Schwartz, B. Thirion.
  
  FReM – scalable and stable decoding with fast regularized ensemble of models, in: NeuroImage, 2017, pp. 1-16. [ DOI : 10.1016/j.neuroimage.2017.10.005 ]
  
  https://hal.archives-ouvertes.fr/hal-01615015
• 24M. Jas, D. A. Engemann, Y. Bekhti, F. A. Raimondo, A. Gramfort.
  
  Autoreject: Automated artifact rejection for MEG and EEG data, in: NeuroImage, 2017, https://arxiv.org/abs/1612.08194. [ DOI : 10.1016/j.neuroimage.2017.06.030 ]
  
  https://hal.inria.fr/hal-01562403
• 25Y. Le Guen, G. Auzias, F. Leroy, M. Noulhiane, G. Dehaene-Lambertz, E. Duchesnay, J.-F. Mangin, O. Coulon, V. Frouin.
  
  Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds, in: Cerebral Cortex, April 2017, pp. 1 - 12. [ DOI : 10.1093/cercor/bhx098 ]
  
  https://hal-amu.archives-ouvertes.fr/hal-01527005
• 26J. 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
• 27G. Lemaitre, F. Nogueira, C. K. Aridas.
  
  Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning, in: Journal of Machine Learning Research, 2017, vol. 18, pp. 1 - 5.
  
  https://hal.inria.fr/hal-01516244
• 28J. Loula, G. Varoquaux, B. Thirion.
  
  Decoding fMRI activity in the time domain improves classification performance, in: NeuroImage, August 2017. [ DOI : 10.1016/j.neuroimage.2017.08.018 ]
  
  https://hal.inria.fr/hal-01576641
• 29A. 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
• 30B. Ng, G. Varoquaux, J. Baptiste Poline, B. Thirion, M. D. Greicius, K. L. Poston.
  
  Distinct alterations in Parkinson's Medication-state and Disease-state Connectivity Running Title: PD altered connectivity, in: Neuroimage-Clinical, 2017.
  
  https://hal.archives-ouvertes.fr/hal-01614971
• 31T. E. Nichols​, S. Das​, S. B. Eickhoff​ 3​, A. C. Evans​, T. Glatard​, M. Hanke​, N. Kriegeskorte​, M. P. Milham​, R. A. Poldrack​, J.-B. P. Poline​, E. P. Proal​, B. Thirion​, D. Van Essen, T. White​, B. T. Yeo​.
  
  Best Practices in Data Analysis and Sharing in Neuroimaging using MRI, in: Nature Neuroscience, February 2017, vol. 20, pp. 299-303. [ DOI : 10.1038/nn.4500 ]
  
  https://hal.inria.fr/hal-01577319
• 32M. Rahim, B. Thirion, D. Bzdok, I. Buvat, G. Varoquaux.
  
  Joint prediction of multiple scores captures better individual traits from brain images, in: NeuroImage, June 2017. [ DOI : 10.1016/j.neuroimage.2017.06.072 ]
  
  https://hal.inria.fr/hal-01547524
• 33G. Varoquaux.
  
  Cross-validation failure: small sample sizes lead to large error bars, in: NeuroImage, June 2017, https://arxiv.org/abs/1706.07581. [ DOI : 10.1016/j.neuroimage.2017.06.061 ]
  
  https://hal.inria.fr/hal-01545002
• 34D. Vatansever, D. Bzdok, H.-T. Wang, G. Mollo, M. Sormaz, C. Murphy, T. Karapanagiotidis, J. Smallwood, E. Jefferies.
  
  Varieties of semantic cognition revealed through simultaneous decomposition of intrinsic brain connectivity and behaviour, in: NeuroImage, July 2017, 34 p. [ DOI : 10.1016/j.neuroimage.2017.06.067 ]
  
  https://hal.archives-ouvertes.fr/hal-01546394
• 35H.-T. Wang, P. Guilia, C. Murphy, D. Bzdok, E. Jefferies, J. Smallwood.
  
  Dimensions of Experience: Exploring the Heterogeneity of the Wandering Mind, in: Psychological Science, August 2017.
  
  https://hal.archives-ouvertes.fr/hal-01562681

International Conferences with Proceedings

• 36J. Arias, P. Ciuciu, M. Dojat, F. Forbes, A. Frau-Pascual, T. Perret, J. M. Warnking.
  
  PyHRF: A Python Library for the Analysis of fMRI Data Based on Local Estimation of the Hemodynamic Response Function, in: 16th Python in Science Conference (SciPy 2017), Austin, TX, United States, July 2017. [ DOI : 10.25080/shinma-7f4c6e7-006 ]
  
  https://hal.archives-ouvertes.fr/hal-01566457
• 37E. B. Belilovsky, K. Kastner, G. Varoquaux, M. B. Blaschko.
  
  Learning to Discover Sparse Graphical Models, in: International Conference on Machine Learning, Sydney, Australia, August 2017, https://arxiv.org/abs/1605.06359.
  
  https://hal.inria.fr/hal-01306491
• 38P. Ciuciu, H. Wendt, S. Combrexelle, P. Abry.
  
  Spatially regularized multifractal analysis for fMRI Data, in: EMBC’17 - 39th International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, South Korea, Kwang Suk Park, July 2017, 4 p.
  
  https://hal.inria.fr/hal-01574216
• 39M. Clerc, A. Gramfort, E. Olivi, T. Papadopoulo.
  
  OpenMEEG software for forward problems handling non-nested geometries, in: BaCI Conference 2017: International Conference on Basic and Clinical Multimodal Imaging, Bern, Switzerland, August 2017.
  
  https://hal.inria.fr/hal-01581710
• 40A. Hoyos-Idrobo, G. Varoquaux, B. Thirion.
  
  Towards a Faster Randomized Parcellation Based Inference, in: PRNI 2017 - 7th International Workshop on Pattern Recognition in NeuroImaging, Toronto, Canada, June 2017.
  
  https://hal.inria.fr/hal-01552237
• 41M. Jas, T. Dupré La Tour, U. Şimşekli, A. Gramfort.
  
  Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding, in: Advances in neural information processing systems, Long Beach, United States, December 2017, https://arxiv.org/abs/1705.08006.
  
  https://hal.archives-ouvertes.fr/hal-01590988
• 42C. Lazarus, P. Weiss, N. Chauffert, F. Mauconduit, M. Bottlaender, A. Vignaud, P. Ciuciu.
  
  SPARKLING: Novel Non-Cartesian Sampling Schemes for Accelerated 2D Anatomical Imaging at 7T Using Compressed Sensing, in: 25th annua meeting of the International Society for Magnetic Resonance Imaging, Honolulu, United States, April 2017.
  
  https://hal.inria.fr/hal-01577200
• 43C. Lazarus, P. Weiss, N. Chauffert, A. Vignaud, P. Ciuciu.
  
  SPARKLING: nouveaux schémas d’échantillonnage compressif prospectif pour l’IRM haute résolution, in: GRETSI, Juan les Pins, France, September 2017.
  
  https://hal.inria.fr/hal-01577207
• 44G. Lemaitre, R. Martí, M. Rastgoo, F. Mériaudeau.
  
  Computer-Aided Detection for Prostate Cancer Detection based on Multi-Parametric Magnetic Resonance Imaging, in: EMBC 2017 : 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, South Korea, July 2017.
  
  https://hal.inria.fr/hal-01516245
• 45A. Mensch, J. Mairal, D. Bzdok, B. Thirion, G. Varoquaux.
  
  Learning Neural Representations of Human Cognition across Many fMRI Studies, in: Neural Information Processing Systems, Long Beach, United States, December 2017, https://arxiv.org/abs/1710.11438.
  
  https://hal.archives-ouvertes.fr/hal-01626823
• 46M. Rahim, B. Thirion, G. Varoquaux.
  
  Multi-output predictions from neuroimaging: assessing reduced-rank linear models, in: PRNI 2017 - The 7th International Workshop on Pattern Recognition in Neuroimaging, Toronto, Canada, June 2017, pp. 1 - 4. [ DOI : 10.1109/PRNI.2017.7981504 ]
  
  https://hal.inria.fr/hal-01547572
• 47M. Rahim, B. Thirion, G. Varoquaux.
  
  Population-shrinkage of covariance to estimate better brain functional connectivity, in: Medical Image Computing and Computer Assisted Interventions, Quebec city, Canada, September 2017.
  
  https://hal.inria.fr/hal-01547612

Conferences without Proceedings

• 48D. Bzdok, M. Eickenberg, G. Varoquaux, B. Thirion.
  
  Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging, in: International conference on Information Processing in Medical Imaging (IPMI) 2017, Boone, North Carolina, USA, June 2017.
  
  https://hal.archives-ouvertes.fr/hal-01480885
• 49C. Laroche, H. Papadopoulos, M. Kowalski, G. Richard.
  
  Drum extraction in single channel audio signals using multi-layer non negative matrix factor deconvolution, in: ICASSP, Nouvelle Orleans, United States, March 2017.
  
  https://hal.archives-ouvertes.fr/hal-01438851
• 50M.-A. Schulz, G. Varoquaux, A. Gramfort, B. Thirion, D. Bzdok.
  
  Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction, in: Neural Information Processing Systems, Machine Learning in Health Workshop, Long Beach, United States, December 2017.
  
  https://hal.archives-ouvertes.fr/hal-01633096

Other Publications

• 51P. Ablin, J.-F. Cardoso, A. Gramfort.
  
  Faster ICA under orthogonal constraint, November 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01651842
• 52P. Ablin, J.-F. Cardoso, A. Gramfort.
  
  Faster independent component analysis by preconditioning with Hessian approximations, June 2017, https://arxiv.org/abs/1706.08171 - working paper or preprint.
  
  https://hal.inria.fr/hal-01552340
• 53G. Julia Guiomar Niso, K. J. Gorgolewski, E. Bock, T. Brooks, G. Flandin, A. Gramfort, R. N. Henson, M. Jas, V. Litvak, J. Moreau, R. Oostenveld, J.-M. Schoffelen, F. Tadel, J. Wexler, S. Baillet.
  
  MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography, September 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01591080

References in notes

• 54K. 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.
• 55R. 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.