Members
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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Bibliography

Major publications by the team in recent years
[1]
N. Chauffert, P. Ciuciu, J. Kahn, P. Weiss.
Variable density sampling with continuous trajectories. Application to MRI, in: SIAM Journal of Imaging Sciences, 2014, vol. 7, no 4, pp. 1962–1992.
https://hal.inria.fr/hal-00908486
[2]
B. Da Mota, V. Fritsch, G. Varoquaux, T. Banaschewski, G. J. Barker, A. L. W. Bokde, U. Bromberg, P. J. Conrod, J. Gallinat, H. Garavan, J.-L. Martinot, F. Nees, T. Paus, Z. Pausova, M. Rietschel, M. N. Smolka, A. Ströhle, V. Frouin, J.-B. Poline, B. Thirion.
Randomized parcellation based inference, in: NeuroImage, November 2013, pp. 203 - 215, Digitéo (HiDiNim project and ICoGeN project). [ DOI : 10.1016/j.neuroimage.2013.11.012 ]
http://hal.inria.fr/hal-00915243
[3]
A. Knops, B. Thirion, E. Hubbard, V. Michel, S. Dehaene.
Recruitment of an area involved in eye movements during mental arithmetic, in: Science, Jun 2009, vol. 324, no 5934, pp. 1583–1585.
[4]
V. Michel, A. Gramfort, G. Varoquaux, E. Eger, B. Thirion.
Total variation regularization for fMRI-based prediction of behaviour, in: IEEE Transactions on Medical Imaging, February 2011, vol. 30, no 7, pp. 1328 - 1340. [ DOI : 10.1109/TMI.2011.2113378 ]
http://hal.inria.fr/inria-00563468/en
[5]
F. 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
[6]
Y. 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
[7]
G. Varoquaux, A. Gramfort, F. Pedregosa, V. Michel, B. Thirion.
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity, in: Information Processing in Medical Imaging, Kaufbeuren, Germany, Lecture Notes in Computer Science, Springer, July 2011, vol. 6801, pp. 562-573. [ DOI : 10.1007/978-3-642-22092-0_46 ]
http://hal.inria.fr/inria-00588898/en
[8]
G. Varoquaux, A. Gramfort, J.-B. Poline, B. Thirion.
Brain covariance selection: better individual functional connectivity models using population prior, in: Advances in Neural Information Processing Systems, Canada Vancouver, John Lafferty, Dec 2010.
[9]
G. Varoquaux, A. Gramfort, B. Thirion.
Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering, in: International Conference on Machine Learning, Edimbourg, United Kingdom, L. John, P. Joelle (editors), Andrew McCallum, June 2012.
http://hal.inria.fr/hal-00705192
[10]
G. Varoquaux, Y. Schwartz, P. Pinel, B. Thirion.
Cohort-level brain mapping: learning cognitive atoms to single out specialized regions, in: IPMI - Information Processing in Medical Imaging - 2013, Asilomar, United States, W. M. Wells, S. Joshi, K. M. Pohl (editors), Springer, July 2013, vol. 7917, pp. 438-449. [ DOI : 10.1007/978-3-642-38868-2_37 ]
http://hal.inria.fr/hal-00841502
Publications of the year

Doctoral Dissertations and Habilitation Theses

[11]
F. Pedregosa.
Feature extraction and supervised learning on fMRI: from practice to theory, Université Pierre et Marie Curie, February 2015.
https://tel.archives-ouvertes.fr/tel-01100921
[12]
N. Zilber.
ERF and scale-free analyses of source-reconstructed MEG brain signals during a multisensory learning paradigm, Université Paris Sud - Paris XI, March 2014.
https://tel.archives-ouvertes.fr/tel-00984990

Articles in International Peer-Reviewed Journals

[13]
K. Amunts, M. Hawrylycz, C. Van Essen, J. van Horn., N. Harel, J.-B. Poline, F. De Martino, J. G. Bjaalie, G. Dehaene-Lambertz, S. Dehaene, P. Valdes-Sosa, B. Thirion, K. Zilles, S. Hill, M. Abrams, P. Tass, W. Vanduffel, A. Evans, S. Eickhoff.
Interoperable atlases of the human brain, in: NeuroImage, June 2014, vol. 99, no 1, 8 p. [ DOI : 10.1016/j.neuroimage.2014.06.010 ]
https://hal.inria.fr/hal-01094749
[14]
L. Chaari, P. Ciuciu, S. Mériaux, J.-C. Pesquet.
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI, in: Magnetic Resonance Materials in Physics, Biology and Medicine, March 2014, 41 p. [ DOI : 10.1007/s10334-014-0436-5 ]
https://hal.inria.fr/hal-01084324
[15]
N. Chauffert, P. Ciuciu, J. Kahn, P. Weiss.
Variable density sampling with continuous trajectories. Application to MRI, in: SIAM Journal of Imaging Sciences, 2014, vol. 7, no 4, pp. 1962–1992.
https://hal.inria.fr/hal-00908486
[16]
P. Ciuciu, P. Abry, J. He.
Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks, in: NeuroImage, July 2014, vol. 95, pp. 248 - 263. [ DOI : 10.1016/j.neuroimage.2014.03.047 ]
https://hal.inria.fr/hal-01084242
[17]
B. Da Mota, R. Tudoran, A. Costan, G. Varoquaux, G. Brasche, P. J. Conrod, H. Lemaitre, T. Paus, M. Rietschel, V. Frouin, J.-B. Poline, G. Antoniu, B. Thirion.
Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud, in: Frontiers in Neuroinformatics, April 2014, vol. 8. [ DOI : 10.3389/fninf.2014.00031 ]
https://hal.inria.fr/hal-01057325
[18]
A.-C. Milazzo, B. Ng, H. Jiang, W. Shirer, G. Varoquaux, J.-B. Poline, B. Thirion, M. D. Greicius.
Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm, in: Cerebral Cortex, October 2014, 12 p.
https://hal.inria.fr/hal-01094759
[19]
F. Pedregosa, M. Eickenberg, P. Ciuciu, B. Thirion, A. Gramfort.
Data-driven HRF estimation for encoding and decoding models, in: NeuroImage, January 2015, pp. 209–220.
https://hal.inria.fr/hal-00952554
[20]
S. Takerkart, G. Auzias, B. Thirion, L. Ralaivola.
Graph-based inter-subject pattern analysis of fMRI data, in: PLoS ONE, July 2014. [ DOI : 10.1371/journal.pone.0104586 ]
https://hal.archives-ouvertes.fr/hal-01027769
[21]
B. Thirion, G. Varoquaux, E. Dohmatob, J.-B. Poline.
Which fMRI clustering gives good brain parcellations?, in: Frontiers in neuroscience, May 2014, vol. 8, no 167, 13 p. [ DOI : 10.3389/fnins.2014.00167 ]
https://hal.inria.fr/hal-01015172
[22]
G. Varoquaux, B. Thirion.
How machine learning is shaping cognitive neuroimaging, in: GigaScience, 2014, vol. 3, 28 p. [ DOI : 10.1007/s12021-008-9041-y ]
https://hal.inria.fr/hal-01094737
[23]
T. Vincent, S. Badillo, L. Risser, L. Chaari, C. Bakhous, F. Forbes, P. Ciuciu.
Frontiers in Neuroinformatics Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF, in: Frontiers in Neuroscience, April 2014, vol. 8, no Article 67, 23 p. [ DOI : 10.3389/fnins.2014.00067 ]
https://hal.inria.fr/hal-01084249
[24]
H. Xu, B. Thirion, S. Allassonnière.
Probabilistic Atlas and Geometric Variability Estimation to Drive Tissue Segmentation, in: Statistics in Medicine, April 2014, vol. 33, no 20, 24 p. [ DOI : 10.1002/sim.6156 ]
https://hal.inria.fr/hal-01094739
[25]
N. Zilber, P. Ciuciu, A. Gramfort, L. Azizi, V. Van Wassenhove.
Supramodal processing optimizes visual perceptual learning and plasticity, in: NeuroImage, June 2014, vol. 93, pp. 32 - 46. [ DOI : 10.1016/j.neuroimage.2014.02.017 ]
https://hal.inria.fr/hal-01084251

Invited Conferences

[26]
F. Forbes, A. Frau-Pascual, T. Vincent, J. Sloboda, P. Ciuciu.
Physiologically informed Bayesian analysis of ASL fMRI data, in: Statistical Challenges in Neuroscience workshop, Warwick, United Kingdom, September 2014.
https://hal.inria.fr/hal-01107613

International Conferences with Proceedings

[27]
M. Albughdadi, L. Chaari, F. Forbes, J.-Y. Tourneret, P. Ciuciu.
Model Selection for Hemodynamic Brain Parcellation in fMRI, in: 22nd European Signal Processing Conference, Lisbon, Portugal, September 2014.
https://hal.inria.fr/hal-01107475
[28]
Y. Bekhti, N. Zilber, F. Pedregosa, P. Ciuciu, V. Van Wassenhove, A. Gramfort.
Decoding perceptual thresholds from MEG/EEG, in: Pattern Recoginition in Neuroimaging (PRNI) (2014), Tubingen, Germany, June 2014.
https://hal.archives-ouvertes.fr/hal-01032909
[29]
V. Borghesani, F. Pedregosa, E. Eger, M. Buiatti, M. Piazza.
A perceptual-to-conceptual gradient of word coding along the ventral path, in: Pattern Recognition in Neuroimaging, Tubingen, Germany, IEEE, June 2014.
https://hal.inria.fr/hal-00986606
[30]
E. Dohmatob, A. Gramfort, B. Thirion, G. Varoquaux.
Benchmarking solvers for TV-l1 least-squares and logistic regression in brain imaging, in: Pattern Recoginition in Neuroimaging (PRNI), Tübingen, Germany, IEEE, June 2014.
https://hal.inria.fr/hal-00991743
[31]
A. Frau-Pascual, T. Vincent, F. Forbes, P. Ciuciu.
Hemodynamically informed parcellation of cerebral FMRI data, in: ICASSP - IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, IEEE, May 2014, pp. 2079-2083. [ DOI : 10.1109/ICASSP.2014.6853965 ]
https://hal.inria.fr/hal-01100186
[32]
A. Frau-Pascual, T. Vincent, J. Sloboda, P. Ciuciu, F. Forbes.
Physiologically Informed Bayesian Analysis of ASL fMRI Data, in: BAMBI 2014 - First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, Boston, United States, M. J. Cardoso, I. Simpson, T. Arbel, D. Precup, A. Ribbens (editors), Lecture Notes in Computer Science, Springer International Publishing, September 2014, vol. 8677, pp. 37 - 48. [ DOI : 10.1007/978-3-319-12289-2_4 ]
https://hal.inria.fr/hal-01100266
[33]
D. Keator, S. Ghosh, C. Maumet, G. Flandin, N. Nichols, E. Thomas, G. Burns, R. Bruehl, C. Craddock, B. Frederick, K. Gorgolewski, D. Marcus, M. Hanke, C. Haselgrove, K. Helmer, A. Klein, M. Milham, R. Poldrack, F. Michel, J. Steffener, Y. Schwartz, R. Stoner, J. Turner, N. Kennedy, J.-B. Poline.
Developing and using the Neuroimaging and Data Sharing Data Model: the NIDASH Working Group, in: 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, Germany, June 2014.
http://www.hal.inserm.fr/inserm-01101842
[34]
B. Ng, M. Dressler, G. Varoquaux, J.-B. Poline, M. Greicius, B. Thirion.
Transport on Riemannian Manifold for Functional Connectivity-based Classification, in: MICCAI - 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Boston, United States, Springer, September 2014.
https://hal.inria.fr/hal-01058521
[35]
R. Phlypo, B. Thirion, G. Varoquaux.
Deriving a multi-subject functional-connectivity atlas to inform connectome estimation, in: Medical Image Computing and Computer-Assisted Intervention, Boston, United States, P. Golland, N. Hata, C. Barillot, J. Hornegger, R. Howe (editors), Springer International Publishing, September 2014, vol. 8675, pp. 185-192, MICCAI 2014 preprint. [ DOI : 10.1007/978-3-319-10443-0_24 ]
https://hal.inria.fr/hal-00991124
[36]
B. Thirion, G. Varoquaux, O. Grisel, C. Poupon, P. Pinel.
Principal Component Regression predicts functional responses across individuals, in: MICCAI, Boston, United States, Springer, September 2014.
https://hal.inria.fr/hal-01015173
[37]
F. Yepes, F. Pedregosa, B. Thirion, Y. Wang, N. Lepore.
Automatic pathology classification using a single feature machine learning - support vector machines, in: SPIE Medical Imaging 2014, San Diego, United States, February 2014, vol. 9035, no 1, 24 p. [ DOI : 10.1117/12.2043943 ]
https://hal.archives-ouvertes.fr/hal-00975872

Scientific Books (or Scientific Book chapters)

[38]
B. Ng, M. Toews, S. Durrleman, Y. Shi.
Shape Analysis for Brain Structures: A Review, in: Shape Analysis in Medical Image Analysis, S. Li, J. M. R. S. Tavares (editors), Lecture Notes in Computational Vision and Biomechanics, Springer, 2014, vol. 14.
https://hal.inria.fr/hal-00925536
[39]
X. Pennec, P. Fillard.
Statistical Computing On Non-Linear Spaces For Computational Anatomy, in: Handbook of Biomedical Imaging: Methodologies and Clinical Research, N. Paragios, J. Duncan, N. Ayache (editors), Springer, 2014, forthcoming.
https://hal.inria.fr/inria-00616201

Other Publications

[40]
A. Abraham, E. Dohmatob, B. Thirion, D. Samaras, G. Varoquaux.
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI, September 2014, 8 p, Sparsity Techniques in Medical Imaging.
https://hal.inria.fr/hal-01093944
[41]
N. Chauffert, P. Weiss, J. Kahn, P. Ciuciu.
Gradient waveform design for variable density sampling in Magnetic Resonance Imaging, December 2014.
https://hal.inria.fr/hal-01095320
[42]
S. Medina.
Unsupervised Clustering of Neural Pathways, Universidad de Buenos Aires, February 2014, 75 p.
https://hal.inria.fr/hal-00908433
[43]
F. Pedregosa, F. Bach, A. Gramfort.
On the Consistency of Ordinal Regression Methods, June 2014.
https://hal.inria.fr/hal-01054942
References in notes
[44]
S. Moeller, E. Yacoub, C. A. Olman, E. Auerbach, J. Strupp, N. Harel, K. Uğurbil.
Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI, in: Magn Reson Med, May 2010, vol. 63, no 5, pp. 1144–1153.
http://dx.doi.org/10.1002/mrm.22361