1 code implementation • 18 Dec 2023 • Ahmad Chamma, Bertrand Thirion, Denis A. Engemann
Furthermore, as handling groups with high cardinality (such as a set of observations of a given modality) are both time-consuming and resource-intensive, we also introduce a new stacking approach extending the DNN architecture with sub-linear layers adapted to the group structure.
no code implementations • 11 Sep 2023 • Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff, Yoni K. Ashar, Tibor Auer, Sylvain Baillet, Shashank Bansal, Leandro Beltrachini, Christian G. Benar, Giacomo Bertazzoli, Suyash Bhogawar, Ross W. Blair, Marta Bortoletto, Mathieu Boudreau, Teon L. Brooks, Vince D. Calhoun, Filippo Maria Castelli, Patricia Clement, Alexander L Cohen, Julien Cohen-Adad, Sasha D'Ambrosio, Gilles de Hollander, María de la iglesia-Vayá, Alejandro de la Vega, Arnaud Delorme, Orrin Devinsky, Dejan Draschkow, Eugene Paul Duff, Elizabeth Dupre, Eric Earl, Oscar Esteban, Franklin W. Feingold, Guillaume Flandin, anthony galassi, Giuseppe Gallitto, Melanie Ganz, Rémi Gau, James Gholam, Satrajit S. Ghosh, Alessio Giacomel, Ashley G Gillman, Padraig Gleeson, Alexandre Gramfort, Samuel Guay, Giacomo Guidali, Yaroslav O. Halchenko, Daniel A. Handwerker, Nell Hardcastle, Peer Herholz, Dora Hermes, Christopher J. Honey, Robert B. Innis, Horea-Ioan Ioanas, Andrew Jahn, Agah Karakuzu, David B. Keator, Gregory Kiar, Balint Kincses, Angela R. Laird, Jonathan C. Lau, Alberto Lazari, Jon Haitz Legarreta, Adam Li, Xiangrui Li, Bradley C. Love, Hanzhang Lu, Camille Maumet, Giacomo Mazzamuto, Steven L. Meisler, Mark Mikkelsen, Henk Mutsaerts, Thomas E. Nichols, Aki Nikolaidis, Gustav Nilsonne, Guiomar Niso, Martin Norgaard, Thomas W Okell, Robert Oostenveld, Eduard Ort, Patrick J. Park, Mateusz Pawlik, Cyril R. Pernet, Franco Pestilli, Jan Petr, Christophe Phillips, Jean-Baptiste Poline, Luca Pollonini, Pradeep Reddy Raamana, Petra Ritter, Gaia Rizzo, Kay A. Robbins, Alexander P. Rockhill, Christine Rogers, Ariel Rokem, Chris Rorden, Alexandre Routier, Jose Manuel Saborit-Torres, Taylor Salo, Michael Schirner, Robert E. Smith, Tamas Spisak, Julia Sprenger, Nicole C. Swann, Martin Szinte, Sylvain Takerkart, Bertrand Thirion, Adam G. Thomas, Sajjad Torabian, Gael Varoquaux, Bradley Voytek, Julius Welzel, Martin Wilson, Tal Yarkoni, Krzysztof J. Gorgolewski
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
no code implementations • 23 May 2023 • Alexandre Pasquiou, Yair Lakretz, Bertrand Thirion, Christophe Pallier
Two fundamental questions in neurolinguistics concerns the brain regions that integrate information beyond the lexical level, and the size of their window of integration.
1 code implementation • 28 Feb 2023 • Alexandre Pasquiou, Yair Lakretz, Bertrand Thirion, Christophe Pallier
A fundamental question in neurolinguistics concerns the brain regions involved in syntactic and semantic processing during speech comprehension, both at the lexical (word processing) and supra-lexical levels (sentence and discourse processing).
no code implementations • 7 Jul 2022 • Alexandre Pasquiou, Yair Lakretz, John Hale, Bertrand Thirion, Christophe Pallier
Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impressive performance on various linguistic tasks.
1 code implementation • 19 Jun 2022 • Alexis Thual, Huy Tran, Tatiana Zemskova, Nicolas Courty, Rémi Flamary, Stanislas Dehaene, Bertrand Thirion
We demonstrate that FUGW is well-suited for whole-brain landmark-free alignment.
no code implementations • 29 May 2022 • Binh T. Nguyen, Bertrand Thirion, Sylvain Arlot
Identifying the relevant variables for a classification model with correct confidence levels is a central but difficult task in high-dimension.
1 code implementation • NeurIPS 2021 • Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.
no code implementations • 12 Oct 2021 • Marc-Andre Schulz, Bertrand Thirion, Alexandre Gramfort, Gaël Varoquaux, Danilo Bzdok
High-quality data accumulation is now becoming ubiquitous in the health domain.
2 code implementations • 11 Jul 2021 • Badr Tajini, Hugo Richard, Bertrand Thirion
Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate.
1 code implementation • 4 Jun 2021 • Jérôme-Alexis Chevalier, Tuan-Binh Nguyen, Bertrand Thirion, Joseph Salmon
This calls for a reformulation of the statistical inference problem, that takes into account the underlying spatial structure: if covariates are locally correlated, it is acceptable to detect them up to a given spatial uncertainty.
no code implementations • 22 Feb 2021 • Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion
By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.
1 code implementation • 8 Dec 2020 • Thomas Bazeille, Elizabeth Dupre, Jean-Baptise Poline, Bertrand Thirion
Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization.
no code implementations • NeurIPS 2020 • Jerome-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion
To deal with this, we adapt the desparsified Lasso estimator ---an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions--- to temporal data corrupted with autocorrelated noise.
1 code implementation • 29 Sep 2020 • Jérôme-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon, Bertrand Thirion
To deal with this, we adapt the desparsified Lasso estimator -- an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions -- to temporal data corrupted with autocorrelated noise.
1 code implementation • NeurIPS 2020 • Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
no code implementations • 5 Mar 2020 • Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann, Bertrand Thirion, Arthur Mensch
We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12, 334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2, 500 individuals, data compression and meta-analysis over more than 15, 000 statistical maps.
2 code implementations • ICML 2020 • Tuan-Binh Nguyen, Jérôme-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot
We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015).
no code implementations • 21 Feb 2020 • Jérôme Dockès, Russell Poldrack, Romain Primet, Hande Gözükan, Tal Yarkoni, Fabian Suchanek, Bertrand Thirion, Gaël Varoquaux
Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms.
no code implementations • 3 Oct 2019 • Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity.
2 code implementations • 27 Sep 2019 • Hugo Richard, Lucas Martin, Ana Luısa Pinho, Jonathan Pillow, Bertrand Thirion
The shared response model provides a simple but effective framework to analyse fMRI data of subjects exposed to naturalistic stimuli.
no code implementations • 13 Feb 2019 • Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
Inferring the location of the current sources that generated these magnetic fields is an ill-posed inverse problem known as source imaging.
no code implementations • 17 Sep 2018 • Andre Manoel, Florent Krzakala, Gaël Varoquaux, Bertrand Thirion, Lenka Zdeborová
We introduce an iterative optimization scheme for convex objectives consisting of a linear loss and a non-separable penalty, based on the expectation-consistent approximation and the vector approximate message-passing (VAMP) algorithm.
1 code implementation • 17 Sep 2018 • Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux
Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework.
no code implementations • 7 Sep 2018 • Hugo Richard, Ana Pinho, Bertrand Thirion, Guillaume Charpiat
The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions.
1 code implementation • 31 Jul 2018 • Sergul Aydore, Bertrand Thirion, Gael Varoquaux
In many applications where collecting data is expensive, for example neuroscience or medical imaging, the sample size is typically small compared to the feature dimension.
no code implementations • 4 Jun 2018 • Jérôme Dockès, Demian Wassermann, Russell Poldrack, Fabian Suchanek, Bertrand Thirion, Gaël Varoquaux
In this paper, we propose to mine brain medical publications to learn the spatial distribution associated with anatomical terms.
1 code implementation • NeurIPS 2017 • Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets.
1 code implementation • 19 Jan 2017 • Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns.
no code implementations • NeurIPS 2016 • Elvis Dohmatob, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
We propose a multivariate online dictionary-learning method for obtaining decompositions of brain images with structured and sparse components (aka atoms).
1 code implementation • 30 Nov 2016 • Arthur Mensch, Julien Mairal, Gaël Varoquaux, Bertrand Thirion
We present a matrix factorization algorithm that scales to input matrices that are large in both dimensions (i. e., that contains morethan 1TB of data).
no code implementations • 18 Nov 2016 • Alexandre Abraham, Michael Milham, Adriana Di Martino, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Gaël Varoquaux
These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas.
1 code implementation • 15 Sep 2016 • Andrés Hoyos-Idrobo, Gaël Varoquaux, Jonas Kahn, Bertrand Thirion
Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis.
no code implementations • 21 Jun 2016 • Gaël Varoquaux, Matthieu Kowalski, Bertrand Thirion
Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions.
1 code implementation • 16 Jun 2016 • Gaël Varoquaux, Pradeep Reddy Raamana, Denis Engemann, Andrés Hoyos-Idrobo, Yannick Schwartz, Bertrand Thirion
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power.
1 code implementation • 3 May 2016 • Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.
Ranked #12 on Recommendation Systems on MovieLens 10M
no code implementations • 8 Feb 2016 • Arthur Mensch, Gaël Varoquaux, Bertrand Thirion
We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects.
1 code implementation • NeurIPS 2015 • Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets.
no code implementations • 16 Nov 2015 • Bertrand Thirion, Andrés Hoyos-Idrobo, Jonas Kahn, Gael Varoquaux
The use of brain images as markers for diseases or behavioral differences is challenged by the small effects size and the ensuing lack of power, an issue that has incited researchers to rely more systematically on large cohorts.
1 code implementation • 12 Dec 2014 • Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Muller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, Gäel Varoquaux
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
no code implementations • 12 Dec 2014 • Alexandre Abraham, Elvis Dohmatob, Bertrand Thirion, Dimitris Samaras, Gael Varoquaux
Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas.
no code implementations • 27 Feb 2014 • Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu, Bertrand Thirion, Alexandre Gramfort
We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.
no code implementations • NeurIPS 2013 • Yannick Schwartz, Bertrand Thirion, Gael Varoquaux
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies.
no code implementations • 15 Nov 2013 • Yannick Schwartz, Bertrand Thirion, Gaël Varoquaux
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies.
no code implementations • 10 Aug 2013 • Michael Eickenberg, Fabian Pedregosa, Senoussi Mehdi, Alexandre Gramfort, Bertrand Thirion
Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations.
no code implementations • 13 May 2013 • Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal.
3 code implementations • 2 Jan 2012 • Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
no code implementations • NeurIPS 2010 • Gael Varoquaux, Alexandre Gramfort, Jean-Baptiste Poline, Bertrand Thirion
We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population.
no code implementations • NeurIPS 2009 • Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-Laure Paillere-Martinot, Catherine Martelli, Jean-Luc Martinot, Jean-Baptiste Poline, Guillermo A. Cecchi
Schizophrenia is a complex psychiatric disorder that has eluded a characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, ``emergent working of the brain.