@inproceedings { zaremba-etal:2013,
pdf = {http://hal.inria.fr/hal-00803981/PDF/ipmi2013.pdf},
month = {Mar},
year = {2013},
address = {Asilomar, {\'E}tats-Unis},
booktitle = {International Conference on Information Processing in Medical Imaging 2013},
abstract = {Magneto- and electroencephalography (M/EEG) measure the electromagnetic signals produced by brain activity. In order to address the issue of limited signal-to-noise ratio (SNR) with raw data, acquisitions consist of multiple repetitions of the same experiment. An important challenge arising from such data is the variability of brain activations over the repetitions. It hinders statistical analysis such as prediction performance in a supervised learning setup. One such confounding variability is the time offset of the peak of the activation, which varies across repetitions. We propose to address this misalignment issue by explicitly modeling time shifts of different brain responses in a classification setup. To this end, we use the latent support vector machine (LSVM) formulation, where the latent shifts are inferred while learning the classifier parameters. The inferred shifts are further used to improve the SNR of the M/EEG data, and to infer the chronometry and the sequence of activations across the brain regions that are involved in the experimental task. Results are validated on a long term memory retrieval task, showing significant improvement using the proposed latent discriminative method.},
title = {Learning from M/EEG data with variable brain activation delays},
url = {http://hal.inria.fr/hal-00803981},
author = {Zaremba, Wojciech and M. Pawan, Kumar and Gramfort, Alexandre and Blaschko, Matthew}
}

@article { gramfort-etal:2013,
keywords = {Inverse problem, Magnetoencephalography (MEG), Electroencephalography (EEG), Sparse structured priors, Convex optimization, Time-frequency, Algorithms},
url = {http://www.sciencedirect.com/science/article/pii/S1053811912012372},
doi = {10.1016/j.neuroimage.2012.12.051},
issn = {1053-8119},
note = {},
year = {2013},
pages = {410 - 422},
number = {0},
volume = {70},
journal = {NeuroImage},
title = {Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations},
author = {A. Gramfort and D. Strohmeier and J. Haueisen and M.S. Hämäläinen and M. Kowalski}
}

@article { khan-etal:2013,
journal = {Proceedings of the National Academy of Sciences (PNAS)},
eprint = {http://www.pnas.org/content/early/2013/01/09/1214533110.full.pdf+html},
url = {http://www.pnas.org/content/early/2013/01/09/1214533110.abstract},
abstract = {Long-range cortical functional connectivity is often reduced in autism spectrum disorders (ASD), but the nature of local cortical functional connectivity in ASD has remained elusive. We used magnetoencephalography to measure task-related local functional connectivity, as manifested by coupling between the phase of alpha oscillations and the amplitude of gamma oscillations, in the fusiform face area (FFA) of individuals diagnosed with ASD and typically developing individuals while they viewed neutral faces, emotional faces, and houses. We also measured task-related long-range functional connectivity between the FFA and the rest of the cortex during the same paradigm. In agreement with earlier studies, long-range functional connectivity between the FFA and three distant cortical regions was reduced in the ASD group. However, contrary to the prevailing hypothesis in the field, we found that local functional connectivity within the FFA was also reduced in individuals with ASD when viewing faces. Furthermore, the strength of long-range functional connectivity was directly correlated to the strength of local functional connectivity in both groups; thus, long-range and local connectivity were reduced proportionally in the ASD group. Finally, the magnitude of local functional connectivity correlated with ASD severity, and statistical classification using local and long-range functional connectivity data identified ASD diagnosis with 90% accuracy. These results suggest that failure to entrain neuronal assemblies fully both within and across cortical regions may be characteristic of ASD.},
doi = {10.1073/pnas.1214533110},
year = {2013},
title = {Local and long-range functional connectivity is reduced in concert in autism spectrum disorders},
author = {Khan, Sheraz and Gramfort, Alexandre and Shetty, Nandita R. and Kitzbichler, Manfred G. and Ganesan, Santosh and Moran, Joseph M. and Lee, Su Mei and Gabrieli, John D. E. and Tager-Flusberg, Helen B. and Joseph, Robert M. and Herbert, Martha R. and Hämäläinen, Matti S. and Kenet, Tal}
}

@inproceedings { gramfort:hal-00723897,
month = {Oct},
year = {2012},
audience = {internationale },
address = {Nice, France},
publisher = {Springer},
booktitle = {MICCAI},
affiliation = {PARIETAL - INRIA Saclay - Ile de France , Athinoula A. Martinos Center for Biomedical Imaging , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , Sherbrooke Connectivity Imaging Laboratory - SCIL},
language = {Anglais},
keywords = {diffusion MRI, dictionary learning, diffusion spectral imaging DSI, sparse, brain},
abstract = {Diffusion spectrum imaging (DSI) from multiple diffusion-weighted images (DWI) allows to image the complex geometry of water diffusion in biological tissue. To capture the structure of DSI data, we propose to use sparse coding constrained by physical properties of the signal, namely symmetry and positivity, to learn a dictionary of diffu- sion profiles. Given this estimated model of the signal, we can extract better estimates of the signal from noisy measurements and also speed up acquisition by reducing the number of acquired DWI while giving access to high resolution DSI data. The method learns jointly for all the acquired DWI and scales to full brain data. Working with two sets of 515 DWI images acquired on two different subjects we show that using just half of the data (258 DWI) we can better predict the other 257 DWI than the classic symmetry procedure. The observation holds even if the diffusion profiles are estimated on a different subject dataset from an undersampled q-space of 40 measurements.},
title = {Sparse DSI: Learning DSI structure for denoising and fast imaging},
url = {http://hal.inria.fr/hal-00723897},
hal_id = {hal-00723897},
author = {Gramfort, Alexandre and Cyril, Poupon and Descoteaux, Maxime}
}

@article { varoquaux-etal:2012b,
pdf = {http://hal.inria.fr/hal-00665340/PDF/paper.pdf},
month = {Jan},
year = {2012},
doi = {10.1016/j.jphysparis.2012.01.001 },
audience = {internationale },
journal = {Journal of Physiology - Paris},
pages = {epub ahead of print},
publisher = {Elsevier},
affiliation = {Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , PARIETAL - INRIA Saclay - Ile de France , Neuroimagerie cognitive},
language = {Anglais},
keywords = {fMRI, brain networks; small-world; functional connectivity; Markov models; decomposable graphs},
abstract = {Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems.},
title = {Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?},
url = {http://hal.inria.fr/hal-00665340},
hal_id = {hal-00665340},
author = {Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Poline, Jean Baptiste and Thirion, Bertrand}
}

@inproceedings { varoquaux-etal:2012,
pdf = {http://hal.inria.fr/hal-00705192/PDF/paper.pdf},
month = {Jun},
year = {2012},
audience = {internationale },
editor = {John, Langford and Joelle, Pineau },
organization = {Andrew McCallum},
address = {Edimbourg, Royaume-Uni},
booktitle = {International Conference on Machine Learning},
affiliation = {Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , PARIETAL - INRIA Saclay - Ile de France},
language = {Anglais},
keywords = {Sparse recovery ; correlated design ; clustering ; randomization ; brain imaging ; fMRI},
abstract = {Functional neuroimaging can measure the brain's response to an external stimulus. It is used to perform brain mapping: identifying from these observations the brain regions involved. This problem can be cast into a linear supervised learning task where the neuroimaging data are used as predictors for the stimulus. Brain mapping is then seen as a support recovery problem. On functional MRI (fMRI) data, this problem is particularly challenging as i) the number of samples is small due to lim- ited acquisition time and ii) the variables are strongly correlated. We propose to overcome these difficulties using sparse regression models over new variables obtained by clustering of the original variables. The use of randomization techniques, e.g. bootstrap samples, and clustering of the variables improves the recovery properties of sparse methods. We demonstrate the benefit of our approach on an extensive simulation study as well as two fMRI datasets.},
title = {Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering},
url = {http://hal.inria.fr/hal-00705192},
hal_id = {hal-00705192},
author = {Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Thirion, Bertrand}
}

@inproceedings { gramfort-etal:2012b,
pdf = {http://hal.inria.fr/hal-00730768/PDF/paper.pdf},
month = {Jul},
year = {2012},
doi = {10.1109/PRNI.2012.20 },
audience = {internationale },
address = {Londres, Royaume-Uni},
pages = {13-16},
booktitle = {Pattern Recognition in NeuroImaging (PRNI), 2012 International Workshop on},
affiliation = {PARIETAL - INRIA Saclay - Ile de France , Athinoula A. Martinos Center for Biomedical Imaging , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , Neuroimagerie cognitive},
language = {Anglais},
keywords = {fMRI, classification, machine learning, brain reading, decoding},
abstract = {Word reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs.},
title = {Decoding Visual Percepts Induced by Word Reading with fMRI},
url = {http://hal.inria.fr/hal-00730768},
hal_id = {hal-00730768},
author = {Gramfort, Alexandre and Pallier, Christophe and Varoquaux, Ga{\"e}l and Thirion, Bertrand}
}

@inproceedings { pedregosa-etal:2012b,
pdf = {http://hal.inria.fr/hal-00717954/PDF/paper.pdf},
month = {Jul},
year = {2012},
audience = {internationale },
address = {London, Royaume-Uni},
booktitle = {PRNI 2012 : 2nd International Workshop on Pattern Recognition in NeuroImaging},
affiliation = {SIERRA - INRIA Paris - Rocquencourt , PARIETAL - INRIA Saclay - Ile de France , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , Service NEUROSPIN - NEUROSPIN},
language = {Anglais},
keywords = {fMRI, supervised learning, decoding, ranking},
abstract = {{Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset.},
title = {Improved brain pattern recovery through ranking approaches},
url = {http://hal.inria.fr/hal-00717954},
hal_id = {hal-00717954},
author = {Pedregosa, Fabian and Gramfort, Alexandre and Varoquaux, Ga{\"e}l and Thirion, Bertrand and Pallier, Christophe and Cauvet, Elodie}
}

@inproceedings { pedregosa-etal:2012a,
pdf = {http://hal.inria.fr/hal-00717990/PDF/paper.pdf},
month = {Jul},
year = {2012},
audience = {internationale },
organization = {INRIA},
address = {Nice, France},
booktitle = {MLMI 2012 - 3rd International Workshop on Machine Learning in Medical Imaging},
affiliation = {SIERRA - INRIA Paris - Rocquencourt , PARIETAL - INRIA Saclay - Ile de France , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , Service NEUROSPIN - NEUROSPIN},
language = {Anglais},
keywords = {fMRI, supervised learning, decoding, ranking},
abstract = {Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques.},
title = {Learning to rank from medical imaging data},
url = {http://hal.inria.fr/hal-00717990},
hal_id = {hal-00717990},
author = {Pedregosa, Fabian and Gramfort, Alexandre and Varoquaux, Ga{\"e}l and Cauvet, Elodie and Pallier, Christophe and Thirion, Bertrand}
}

@article { jenatton-etal:2012,
pdf = {http://hal.inria.fr/inria-00589785/PDF/sparse\_hierarchical\_fmri\_mining\_hal.pdf},
month = {Jul},
year = {2012},
doi = {10.1137/110832380 },
audience = {internationale },
number = {3 },
volume = {5},
journal = {SIAM Journal on Imaging Sciences},
pages = {835-856},
publisher = {SIAM},
affiliation = {Laboratoire d'informatique de l'{\'e}cole normale sup{\'e}rieure - LIENS , SIERRA - INRIA Paris - Rocquencourt , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , PARIETAL - INRIA Saclay - Ile de France , Neuroimagerie cognitive},
language = {Anglais},
keywords = {brain reading; structured sparsity; convex optimization; sparse hierarchical models; inter-subject validation; proximal methods},
abstract = {Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference takes into account the multivariate information between voxels and is currently the only way to assess how precisely some cognitive information is encoded by the activity of neural populations within the whole brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, since there are far more features than samples, i.e., more voxels than fMRI volumes. To address this problem, different methods have been proposed, such as, among others, univariate feature selection, feature agglomeration and regularization techniques. In this paper, we consider a sparse hierarchical structured regularization. Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering. This approach encodes the spatial structure of the data at different scales into the regularization, which makes the overall prediction procedure more robust to inter-subject variability. The regularization used induces the selection of spatially coherent predictive brain regions simultaneously at different scales. We test our algorithm on real data acquired to study the mental representation of objects, and we show that the proposed algorithm not only delineates meaningful brain regions but yields as well better prediction accuracy than reference methods.},
title = {Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity},
url = {http://hal.inria.fr/inria-00589785},
hal_id = {inria-00589785},
author = {Jenatton, Rodolphe and Gramfort, Alexandre and Michel, Vincent and Obozinski, Guillaume and Eger, Evelyn and Bach, Francis and Thirion, Bertrand}
}

@article { gramfort-etal:2012a,
month = {Mar},
year = {2012},
doi = {10.1088/0031-9155/57/7/1937 },
number = {7 },
volume = {57},
journal = {Physics in Medicine and Biology},
pages = {1937-1961},
publisher = {IOP Science},
affiliation = {PARIETAL - INRIA Saclay - Ile de France, LNAO CEA Neurospin, Laboratoire des signaux et systemes (L2S) , Athinoula A. Martinos Center for Biomedical Imaging},
language = {Anglais},
abstract = {Magneto- and electroencephalography (M/EEG) measure the electromagnetic fields produced by the neural electrical currents. Given a conductor model for the head, and the distribution of source currents in the brain, Maxwell's equations allow one to compute the ensuing M/EEG signals. Given the actual M/EEG measurements and the solution of this forward problem, one can localize, in space and in time, the brain regions that have produced the recorded data. However, due to the physics of the problem, the limited number of sensors compared to the number of possible source locations, and measurement noise, this inverse problem is ill-posed. Consequently, additional constraints are needed. Classical inverse solvers, often called minimum norm estimates (MNE), promote source estimates with a small ℓ(2) norm. Here, we consider a more general class of priors based on mixed norms. Such norms have the ability to structure the prior in order to incorporate some additional assumptions about the sources. We refer to such solvers as mixed-norm estimates (MxNE). In the context of M/EEG, MxNE can promote spatially focal sources with smooth temporal estimates with a two-level ℓ(1)/ℓ(2) mixed-norm, while a three-level mixed-norm can be used to promote spatially non-overlapping sources between different experimental conditions. In order to efficiently solve the optimization problems of MxNE, we introduce fast first-order iterative schemes that for the ℓ(1)/ℓ(2) norm give solutions in a few seconds making such a prior as convenient as the simple MNE. Furthermore, thanks to the convexity of the optimization problem, we can provide optimality conditions that guarantee global convergence. The utility of the methods is demonstrated both with simulations and experimental MEG data.},
title = {Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods.},
url = {http://hal.inria.fr/hal-00690774},
hal_id = {hal-00690774},
author = {Gramfort, Alexandre and Kowalski, Matthieu and H{\"a}m{\"a}l{\"a}inen, Matti}
}

@inproceedings { gramfort-etal:2011d,
pdf = {http://hal.inria.fr/hal-00704875/PDF/paper.pdf},
month = {Dec},
year = {2011},
audience = {internationale },
address = {Granada, Espagne},
booktitle = {NIPS 2011 MLINI Workshop},
affiliation = {PARIETAL - INRIA Saclay - Ile de France , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO},
language = {Anglais},
keywords = {neuroimaging; sparse; recovery; feature identification; statistics},
abstract = {The prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment.},
title = {Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify},
url = {http://hal.inria.fr/hal-00704875},
hal_id = {hal-00704875},
author = {Gramfort, Alexandre and Varoquaux, Ga{\"e}l and Thirion, Bertrand}
}

@article { pedregosa-etal:2011,
pdf = {http://hal.inria.fr/hal-00650905/PDF/pedregosa11a.pdf},
month = {Oct},
year = {2011},
audience = {internationale },
journal = {Journal of Machine Learning Research},
publisher = {MIT Press},
affiliation = {PARIETAL - INRIA Saclay - Ile de France , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , Nuxeo , Kobe University , Bauhaus-Universit{\"a}t Weimar , Google Inc , Laboratoire de M{\'e}canique et Ing{\'e}nieries - LAMI , University of Washington , Department of Mechanical and Industrial Engineering [UMass] , Enthought Inc , TOTAL},
language = {Anglais},
keywords = {Python; supervised learning; unsupervised learning; model selection},
abstract = {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. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.},
title = {Scikit-learn: Machine Learning in Python},
url = {http://hal.inria.fr/hal-00650905},
hal_id = {hal-00650905},
author = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, {\'E}douard}
}

@incollection { gramfort-etal:2011c,
year = {2011},
doi = {10.1007/978-3-642-22092-0_49},
url = {http://dx.doi.org/10.1007/978-3-642-22092-0_49},
volume = {6801},
pages = {600-611},
isbn = {},
publisher = {Springer Berlin / Heidelberg},
editor = {Székely, Gábor and Hahn, Horst},
series = {Lecture Notes in Computer Science},
booktitle = {Information Processing in Medical Imaging},
title = {Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries},
affiliation = {INRIA, Parietal team, Saclay, France},
author = {Gramfort, Alexandre and Strohmeier, Daniel and Haueisen, Jens and Hamalainen, Matti and Kowalski, Matthieu}
}

@article { michel-etal:2011b,
url = {http://hal.inria.fr/inria-00563468/en/},
issn = {0278-0062},
doi = {10.1109/TMI.2011.2113378},
keywords = {brain mapping; fMRI; multivariate pattern analysis; predictive diagnosis; total variation regularization; image classification},
pages = {1328-1340},
number = {7},
volume = {30},
month = {july},
year = {2011},
title = {Total Variation Regularization for fMRI-Based Prediction of Behavior},
journal = {Medical Imaging, IEEE Transactions on},
author = {Michel, V. and Gramfort, A. and Varoquaux, G. and Eger, E. and Thirion, B.}
}

@inproceedings { varoquaux-etal:2011,
pdf = {http://hal.inria.fr/inria-00588898/PDF/paper.pdf},
month = {Jul},
year = {2011},
organization = {G{\'a}bor Sz\'ekely, Horst Hahn},
address = {Kaufbeuren, Allemagne},
booktitle = {Information Processing in Medical Imaging},
affiliation = {Laboratoire de Neuroimagerie Assist\'ee par Ordinateur - LNAO - CEA : DSV/I2BM/NEUROSPIN - PARIETAL - INRIA Saclay - Ile de France - INRIA - Neuroimagerie cognitive - INSERM : U992 - Universit\'e Paris Sud - Paris XI - CEA : DSV/I2BM/NEUROSPIN},
keywords = {sparse models, atlas, resting state, segmentation},
title = {Multi-subject dictionary learning to segment an atlas of brain spontaneous activity},
url = {http://hal.inria.fr/inria-00588898/en/},
hal_id = {inria-00588898},
author = {Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Pedregosa, Fabian and Michel, Vincent and Thirion, Bertrand}
}

@article { michel-etal:2011,
pdf = {http://hal.inria.fr/inria-00589201/PDF/supervised\_clustering\_vm\_review.pdf},
month = {Apr},
year = {2011},
doi = {10.1016/j.patcog.2011.04.006 },
journal = {Pattern Recognition},
pages = {epub ahead of print},
publisher = {elsevier},
affiliation = {Laboratoire de Neuroimagerie Assist\'ee par Ordinateur - LNAO - CEA : DSV/I2BM/NEUROSPIN - PARIETAL - INRIA Saclay - Ile de France - INRIA - Neuroimagerie cognitive - INSERM : U992 - Universit\'e Paris Sud - Paris XI - CEA : DSV/I2BM/NEUROSPIN - SELECT - INRIA Saclay - Ile de France - INRIA - Universit\'e Paris Sud - Paris XI - CNRS : UMR - Laboratoire de Math\'ematiques d'Orsay - LM-Orsay - CNRS : UMR8628 - Universit\'e Paris Sud - Paris XI},
keywords = {fMRI; brain reading; prediction; hierarchical clustering; dimension reduction; multi-scale analysis; feature agglomeration},
title = {A supervised clustering approach for fMRI-based inference of brain states},
url = {http://hal.inria.fr/inria-00589201/en/},
hal_id = {inria-00589201},
author = {Michel, Vincent and Gramfort, Alexandre and Varoquaux, Ga{\"e}l and Eger, Evelyn and Keribin, Christine and Thirion, Bertrand}
}

@techreport { jenatton-etal:2011,
pdf = {http://hal.inria.fr/inria-00589785/PDF/sparse\_hierarchical\_fmri\_mining\_HAL.pdf},
month = {May},
year = {2011},
type = {Rapport de recherche},
pages = {16},
affiliation = {Laboratoire d'informatique de l'\'ecole normale sup\'erieure - LIENS - CNRS : UMR8548 - Ecole Normale Sup\'erieure de Paris - ENS Paris - SIERRA - INRIA Paris - Rocquencourt - INRIA : PARIS - ROCQUENCOURT - Ecole Normale Sup\'erieure de Paris - ENS Paris - CNRS : UMR8548 - Laboratoire de Neuroimagerie Assist\'ee par Ordinateur - LNAO - CEA : DSV/I2BM/NEUROSPIN - PARIETAL - INRIA Saclay - Ile de France - INRIA - Neuroimagerie cognitive - INSERM : U992 - Universit\'e Paris Sud - Paris XI - CEA : DSV/I2BM/NEUROSPIN},
keywords = {brain reading; structured sparsity; convex optimization; sparse hierarchical models; inter-subject validation; proximal methods},
title = {Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity},
url = {http://hal.inria.fr/inria-00589785/en/},
hal_id = {inria-00589785},
author = {Jenatton, Rodolphe and Gramfort, Alexandre and Michel, Vincent and Obozinski, Guillaume and Eger, Evelyn and Bach, Francis and Thirion, Bertrand}
}

@article { gramfort-etal:2011b,
keywords = {Functional brain imaging, Tracking, Graph Cuts Optimization, Magnetoencephalography (MEG), Electroencephalography (EEG)},
url = {http://www.sciencedirect.com/science/article/B6WNP-5161PBP-4/2/a788648433443badba4516b1d451b049},
doi = {DOI: 10.1016/j.neuroimage.2010.09.087},
issn = {1053-8119},
month = {Feb},
year = {2011},
pages = {1930-1941},
number = {3},
volume = {54},
journal = {NeuroImage},
title = {Tracking cortical activity from M/EEG using graph-cuts with spatiotemporal constraints},
author = {Alexandre Gramfort and Theodore Papadopoulo and Sylvain Baillet and Maureen Clerc}
}

@article { cottereau-etal:2011a,
keywords = {Vision, Retinotopy, Magnetoencephalography (MEG), Steady-State Visual, Evoked Response (SSVER), Source Imaging},
url = {http://www.sciencedirect.com/science/article/B6WNP-516M75G-2/2/69bf20b0381f2b20b6e6b6409d67abe0},
doi = {DOI: 10.1016/j.neuroimage.2010.10.004},
issn = {1053-8119},
note = {},
year = {2011},
pages = {1919 - 1929},
number = {3},
volume = {54},
journal = {NeuroImage},
title = {Phase delays within visual cortex shape the response to steady-state visual stimulation},
author = {Benoit Cottereau and Jean Lorenceau and Alexandre Gramfort and Maureen Clerc and Bertrand Thirion and Sylvain Baillet}
}

@article { gramfort-etal:10b,
keywords = {Boundary Element Method, Electromagnetics, quasistatic regime, Electroencephalography, Magnetoencephalography, Forward modeling, opensource software},
abstract = {BACKGROUND:Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to present OpenMEEG, both from the theoretical and the practical point of view, and to compare its performances with other competing software packages.METHODS:We have run a benchmark study in the field of electro- and magneto-encephalography, in order to compare the accuracy of OpenMEEG with other freely distributed forward solvers. We considered spherical models, for which analytical solutions exist, and we designed randomized meshes to assess the variability of the accuracy. Two measures were used to characterize the accuracy. the Relative Difference Measure and the Magnitude ratio. The comparisons were run, either with a constant number of mesh nodes, or a constant number of unknowns across methods. Computing times were also compared.RESULTS:We observed more pronounced differences in accuracy in electroencephalography than in magnetoencephalography. The methods could be classified in three categories: the linear collocation methods, that run very fast but with low accuracy, the linear collocation methods with isolated skull approach for which the accuracy is improved, and OpenMEEG that clearly outperforms the others. As far as speed is concerned, OpenMEEG is on par with the other methods for a constant number of unknowns, and is hence faster for a prescribed accuracy level.CONCLUSIONS:This study clearly shows that OpenMEEG represents the state of the art for forward computations. Moreover, our software development strategies have made it handy to use and to integrate with other packages. The bioelectromagnetic research community should therefore be able to benefit from OpenMEEG with a limited development effort.},
issn = {1475-925X},
pubmedid = {20819204},
doi = {10.1186/1475-925X-9-45},
url = {http://www.biomedical-engineering-online.com/content/9/1/45},
pages = {45},
number = {1},
year = {2010},
volume = {9},
journal = {BioMedical Engineering OnLine},
title = {OpenMEEG: opensource software for quasistatic bioelectromagnetics},
author = {Gramfort, Alexandre and Papadopoulo, Theodore and Olivi, Emmanuel and Clerc, Maureen}
}

@inproceedings { varoquaux-etal:2010,
year = {2010},
title = {Brain covariance selection: better individual functional connectivity models using population prior.},
publisher = {Curran Associates, Inc.},
pages = {2334-2342},
url = {http://books.nips.cc/papers/files/nips23/NIPS2010_1054.pdf},
editor = {Lafferty, John D. and Williams, Christopher K. I. and Shawe-Taylor, John and Zemel, Richard S. and Culotta, Aron},
booktitle = {NIPS},
author = {Varoquaux, Gael and Gramfort, Alexandre and Poline, Jean-Baptiste and Thirion, Bertrand}
}

@unpublished { kowalski-gramfort:2010,
year = {2010},
url = {http://hal.archives-ouvertes.fr/hal-00473970/en/},
affiliation = {Laboratoire des signaux et systèmes (L2S) - UMR8506 CNRS - SUPELEC - Univ Paris-Sud - PARIETAL - INRIA Saclay - Ile de France - INRIA },
keywords = {normes mixtes ; problème inverse ; opérateurs de proximité ; électroencéphalographie ; magnétoencéphalographie},
title = {A priori par normes mixtes pour les problèmes inverses: Application à la localisation de sources en M/EEG},
author = {Kowalski, Matthieu and Gramfort, Alexandre}
}

@inproceedings { gramfort-etal:10c,
doi = {10.3389/conf.fnins.2010.06.00111},
url = {http://hal.archives-ouvertes.fr/inria-00468592/en/},
note = {Winning Paper of the Young Investigator Award},
booktitle = {Biomag: International Conference on Biomagnetism},
year = {2010},
affiliation = {PARIETAL - INRIA Saclay - Ile de France - INRIA},
keywords = {MEG, EEG, Inverse Problem, Sparse prior, IRLS},
title = {Multi-condition M/EEG inverse modeling with sparsity assumptions: how to estimate what is common and what is specific in multiple experimental conditions},
author = {Gramfort, Alexandre}
}

@inproceedings { gramfort-etal:10d,
doi = {10.3389/conf.fnins.2010.06.00065},
url = {files/poster_openmeeg_biomag_2010.pdf},
booktitle = {Biomag: International Conference on Biomagnetism},
year = {2010},
affiliation = {PARIETAL - INRIA Saclay - Ile de France - INRIA},
keywords = {MEG, EEG, Forward Problem, BEM},
title = {An empirical evaluation of free BEM solvers for accurate M/EEG forward modeling},
author = {Gramfort, Alexandre and Papadopoulo, Th\'eodore and Olivi, Emmanuel and Clerc, Maureen}
}

@article { gramfort-etal:10,
url = {http://www.ncbi.nlm.nih.gov/pubmed/20142163},
keywords = {Electroencephalography, Electroencephalography (EEG), Estimation, Evoked potentials, Graph Cuts Optimization, Graph Laplacian, Latency estimation, Magnetoencephalography (MEG), Manifold learning, Principal component analysis, Single-trial analysis, Time series analysis},
doi = {10.1109/TBME.2009.2037139},
issn = {0018-9294},
pages = {1051 -1061},
number = {5},
volume = {57},
month = {may },
year = {2010},
journal = {Biomedical Engineering, IEEE Transactions on},
title = {Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses},
author = {Gramfort, A. and Keriven, R. and Clerc, M.}
}

@inproceedings { kowalski-gramfort:09,
url = {http://hal.archives-ouvertes.fr/hal-00424039/},
keywords = {Magnetoencephalographie, Electroencephalographie, Problème inverse, Elitist-Lasso, Operateurs de proximité},
month = {sept},
booktitle = {GRETSI},
topic = {Odyssee},
year = {2009},
title = {A priori par normes mixtes pour les problèmes inverses Application à la localisation de sources en M/EEG},
author = {Mathieu Kowalski and Alexandre Gramfort}
}

@inproceedings { cottereau-laurenceau-etal:09,
keywords = {MEG, retinotopy, chronometry, human vision},
month = {jun},
booktitle = {Human Brain Mapping},
topic = {Odyssee},
year = {2009},
title = {Fine chronometric mapping of human visual areas},
author = {B. Cottereau, J. Lorenceau, A. Gramfort, M. Clerc, S. Baillet}
}

@inproceedings { gramfort-kowalski:09,
url = {http://hal.archives-ouvertes.fr/hal-00424029/},
keywords = {Magnetoencephalography, Electroencephalography, Inverse problem, Elitist-Lasso, Proximal iterations},
month = {jun},
booktitle = {IEEE International Symposium on Biomedical Imaging},
topic = {Odyssee},
year = {2009},
title = {Improving M/EEG source localization with an inter-condition sparse prior},
author = {Alexandre Gramfort and Mathieu Kowalski}
}

@inproceedings { gramfort-papadopoulo-etal:08,
url = {http://hal.inria.fr/inria-00336887/fr/},
keywords = {MEG, somatosensory, graph-cuts, tracking},
month = {aug},
booktitle = {Biomag: International Conference on Biomagnetism},
topic = {Odyssee},
year = {2008},
title = {Tracking cortical activity with spatio-temporal constraints using graph-cuts},
author = {Alexandre Gramfort and Théodore Papadopoulo and Benoit Cottereau and Sylvain Baillet and Maureen Clerc}
}

@inproceedings { cottereau-gramfort-etal:08,
owner = { agramfor },
keywords = {MEG, retinotopy},
group = {Odyssee},
month = {jun},
booktitle = {Human Brain Mapping},
topic = {Odyssee},
year = {2008},
title = {Fast retinotopic mapping of visual fields using MEG},
author = {Benoit Cottereau and Alexandre Gramfort and Jean Lorenceau and Bertrand Thirion and Maureen Clerc and Sylvain Baillet}
}

@inproceedings { gramfort-cottereau-etal:07,
owner = { agramfor },
url = {ftp://ftp-sop.inria.fr/odyssee/Publications/2007/gramfort-cottereau-etal:07.pdf},
annote = {Detection of activity from the primary visual cortex is a difficult challenge to magneto-encephalography (MEG) source imaging techniques: the geometry of the visual cortex is intricate, with structured visual field maps extending deeper along the calcarine fissure. This questions the very sensitivity of MEG to the corresponding neural responses of visual stimuli and the usage of MEG source imaging for innovative retinotopic explorations. In this context, we compare two imaging models of MEG generators in realistic simulations of activations within the visual cortex. Localization and spatial extent of neural activity in the visual cortex were extracted from retinotopic maps obtained in fMRI. We prove that the suggested approaches are robust and succeed in accurately recovering the activation patterns with satisfactory match with fMRI results. These results suggest that fast retinotopic exploration of the visual cortex could be obtained from MEG as a complementary alternative to more standard fMRI approaches. The excellent time resolution of MEG imaging further opens interesting perspectives on the temporal and spectral processes sustained by the human visual system.},
keywords = {Retinotopy, MEG, fMRI, Visual Cortex},
group = {Odyssee},
month = {aug},
pages = {4945-4948},
booktitle = {EMBC 2007: IEEE, Engineering in Medicine and Biology Society},
topic = {Odyssee},
year = {2007},
title = {Challenging the estimation of cortical activity from MEG with simulated fMRI-constrained retinotopic maps},
author = {Gramfort, Alexandre and Cottereau, Benoit and Clerc, Maureen and Thirion, Bertrand and Baillet, Sylvain}
}

@inproceedings { gramfort-clerc:07,
owner = { agramfor },
keywords = {EEG, Event-related potentials, Laplacian eigenmaps, P300, dimensionality reduction},
url = {ftp://ftp-sop.inria.fr/odyssee/Publications/2007/gramfort-clerc:07.pdf},
annote = {Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts of data and lead to the manipulations of high dimensional objects like time series or topographies. In the past, essentially in the last decade, various methods for extracting the structure in complex data have been developed and successfully exploited for visualization or classification purposes. Here we propose to use one of these methods, the Laplacian eigenmaps, on EEG data and prove that it provides an powerful approach to visualize and understand the underlying structure of evoked potentials or multitrial time series.},
group = {Odyssee},
month = {oct},
pages = {169-172},
booktitle = {NFSI 2007: Symposium on Noninvasive Functional Source Imaging},
topic = {Misc},
year = {2007},
title = {Low dimensional representations of {MEG/EEG} data using laplacian eigenmaps},
author = {Gramfort, Alexandre and Clerc, Maureen}
}

@inproceedings { clerc-gramfort-etal:07,
owner = { papadop },
keywords = {EEG, MEG, forward modeling, boundary element method},
group = {Odyssee},
booktitle = {Proceedings of Neuromath},
topic = {Odyssee},
year = {2007},
title = {MEG and EEG processing with OpenMEEG},
author = {Clerc, Maureen and Gramfort, Alexandre and Landreau, Perrine and Papadopoulo, Théodore}
}

@inproceedings { cottereau-laurenceau-etal:07,
owner = { papadop },
keywords = {MEG, retinotopy, human vision},
group = {Odyssee},
booktitle = {Proceedings of Neuromath},
topic = {Misc},
year = {2007},
title = {Fast Retinotopic Mapping of Visual Fields using MEG},
author = {Benoit Cottereau and Jean Lorenceau and Alexandre Gramfort and Bertrand Thirion and Maureen Clerc and Sylvain Baillet}
}