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23 results on '"Ile-de-France"'

1. Emergence of β and γ networks following multisensory training

Author

Denis A. Engemann, Virginie van Wassenhove, Philippe Ciuciu, Daria La Rocca, Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), This work was supported by ANR-16-CE33-0020 MultiFracs, France, and the Marie Curie IRG-249222 and the ERC-YStG-263584 to V.vW. We thank Dr Laetitia Grabot, Dr Sophie Herbst, and Dr Tadeusz Kononowicz for their comments on the initial version of the MS., ANR-16-CE33-0020,MULTIFRACS,Théories et méthodes multifractales multivariées pour les systèmes de grande taille - Applications à l'analyse des propriétés d'invariance d'échelle dans la dynamique de l'activité cérébrale(2016), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay

Subjects
Male, Visual perception, [SDV]Life Sciences [q-bio], Motion Perception, Acoustic texture, Motion (physics), Task (project management), Functional connectivity, 0302 clinical medicine, Parietal Lobe, Gamma Rhythm, media_common, Visual Cortex, 0303 health sciences, learning, MEG, medicine.diagnostic_test, 05 social sciences, Brain, Magnetoencephalography, Coherence (statistics), Neurology, [SCCO.PSYC]Cognitive science/Psychology, Auditory Perception, Female, confidence, Psychology, Cognitive psychology, Adult, Oscillations, Adolescent, Cognitive Neuroscience, media_common.quotation_subject, Prefrontal Cortex, Sensory system, 050105 experimental psychology, Article, lcsh:RC321-571, 03 medical and health sciences, Young Adult, Perception, medicine, Humans, 0501 psychology and cognitive sciences, Active listening, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, 030304 developmental biology, audiovisual, [SCCO.NEUR]Cognitive science/Neuroscience, Multisensory, Acoustic Stimulation, Motion coherence, beta, gamma, Beta Rhythm, 030217 neurology & neurosurgery, Photic Stimulation, Acoustic textures
Abstract

Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent perceptual task, and investigated oscillatory activity and functional connectivity as a function of sensory history in training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography (MEG). Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional inter-actions following training. One main finding is the emergence of a large-scale synchronization in the highγ(gamma: 60−120Hz) andβ(beta:15−30Hz) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes.

Published
2020

2. A unified view on beamformers for M/EEG source reconstruction

Author

Britta U. Westner, Sarang S. Dalal, Alexandre Gramfort, Vladimir Litvak, John C. Mosher, Robert Oostenveld, Jan-Mathijs Schoffelen, Donders Institute for Brain, Cognition and Behaviour, Radboud university [Nijmegen], Aarhus University [Aarhus], Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), NMR Research Unit [London], Institute of Neurology [London], University College of London [London] (UCL)-University College of London [London] (UCL), The University of Texas Health Science Center at Houston (UTHealth), Karolinska Institutet [Stockholm], Radboud University [Nijmegen], Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Adult, Cognitive Neuroscience, Source reconstruction, Data analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], 150 000 MR Techniques in Brain Function, 050105 experimental psychology, 310 000 MEG Methods, 03 medical and health sciences, 0302 clinical medicine, Beamforming, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, 0501 psychology and cognitive sciences, EEG, ComputingMilieux_MISCELLANEOUS, Cerebral Cortex, Brain Mapping, [STAT.AP]Statistics [stat]/Applications [stat.AP], Neuro- en revalidatiepsychologie, MEG, Neuropsychology and rehabilitation psychology, 05 social sciences, Magnetoencephalography, Source imaging, Electroencephalography, 180 000 Predictive Brain, Models, Theoretical, Neurology, Source localization, 030217 neurology & neurosurgery, RC321-571
Abstract

Contains fulltext : 246921.pdf (Publisher’s version ) (Open Access) Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging. 11 p.

Published
2022

3. FReM – Scalable and stable decoding with fast regularized ensemble of models

Author

Yannick Schwartz, Andrés Hoyos-Idrobo, Gaël Varoquaux, Bertrand Thirion, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN)

Subjects
decoding, Computer science, Cognitive Neuroscience, Models, Neurological, Datasets as Topic, supervised learning, Regularization (mathematics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, MVPA, Prior probability, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Cluster analysis, Brain Mapping, business.industry, Dimensionality reduction, fMRI, Supervised learning, Brain, Estimator, Pattern recognition, bagging, Magnetic Resonance Imaging, Neurology, Artificial intelligence, business, Algorithms, 030217 neurology & neurosurgery, Decoding methods
Abstract

Brain decoding relates behavior to brain activity through predictive models. These are also used to identify brain regions involved in the cognitive operations related to the observed behavior. Training such multivariate models is a high-dimensional statistical problem that calls for suitable priors. State of the art priors -eg small total-variation- enforce spatial structure on the maps to stabilize them and improve prediction. However, they come with a hefty computational cost. We build upon very fast dimension reduction with spatial structure and model ensembling to achieve decoders that are fast on large datasets and increase the stability of the predictions and the maps. Our approach, fast regularized ensemble of models (FReM), includes an implicit spatial regularization by using a voxel grouping with a fast clustering algorithm. In addition, it aggregates different estimators obtained across splits of a cross-validation loop, each time keeping the best possible model. Experiments on a large number of brain imaging datasets show that our combination of voxel clustering and model ensembling improves decoding maps stability and reduces the variance of prediction accuracy. Importantly, our method requires less samples than state-of-the-art methods to achieve a given level of prediction accuracy. Finally, FreM is much faster than other spatially-regularized methods and, in addition, it can better exploit parallel computing resources.

Published
2018

4. Joint prediction of multiple scores captures better individual traits from brain images

Author

Danilo Bzdok, Gaël Varoquaux, Mehdi Rahim, Bertrand Thirion, Irène Buvat, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Imagerie Moléculaire in Vivo (IMIV - U1023 - ERL9218), Service Hospitalier Frédéric Joliot (SHFJ), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), ANR-11-BINF-0004,NiConnect,Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle(2011), ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Rahim, Mehdi, Bio-informatique - Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle - - NiConnect2011 - ANR-11-BINF-0004 - BINF - VALID, and Idex Paris-Saclay - - IPS2011 - ANR-11-IDEX-0003 - IDEX - VALID

Subjects
0301 basic medicine, Disease status, Cognitive Neuroscience, Schizophrenia (object-oriented programming), Population, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Individuality, Machine learning, computer.software_genre, Brain mapping, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Alzheimer Disease, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Computer Simulation, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], education, Brain Mapping, Brain organization, education.field_of_study, business.industry, Brain, Magnetic Resonance Imaging, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Phenotype, 030104 developmental biology, Neurology, Schizophrenia, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Artificial intelligence, business, Joint (audio engineering), Psychology, computer, Algorithms, 030217 neurology & neurosurgery
Abstract

International audience; To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important: they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimer's Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort: a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.

Published
2017

5. Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

Author

Anne Baron-Van Evercooren, Anne-Charlotte Philippe, Marco Palombo, Bruno Stankoff, Daniel C. Alexander, Mathieu Santin, Olga Ciccarelli, Ioana Hill, Marie-Stéphane Aigrot, Alexandra Petiet, Hui Zhang, Francesca Branzoli, Stéphane Lehéricy, Ivana Drobnjak, Mehdi Felfli, Demian Wassermann, Dominique Langui, Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), Centre de Neuro-Imagerie de Recherche (CENIR), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neurologie [CHU Saint-Antoine], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Saint-Antoine [AP-HP], Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris] (CENIR), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Gestionnaire, HAL Sorbonne Université 5, and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)

Subjects
Monte Carlo method, Water exchange, Corpus callosum, computer.software_genre, Corpus Callosum, Machine Learning, Mice, 0302 clinical medicine, Image Processing, Computer-Assisted, Physics, Computational model, 05 social sciences, White Matter, Permeability (earth sciences), medicine.anatomical_structure, Neurology, Neurons and Cognition (q-bio.NC), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Monte Carlo Method, Monoamine Oxidase Inhibitors, Cognitive Neuroscience, Splenium, FOS: Physical sciences, Machine learning, 050105 experimental psychology, Permeability, lcsh:RC321-571, White matter, 03 medical and health sciences, Cuprizone, In vivo, medicine, Animals, 0501 psychology and cognitive sciences, Computer Simulation, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, business.industry, Physics - Medical Physics, Axons, Disease Models, Animal, Microscopy, Electron, Diffusion Magnetic Resonance Imaging, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Artificial intelligence, Medical Physics (physics.med-ph), business, computer, 030217 neurology & neurosurgery, Demyelinating Diseases
Abstract

International audience; The intra-axonal water exchange time (i) , a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R 2 = 0.99, i : R 2 = 0.84, intrinsic diffusivity d: R 2 = 0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N = 8) and healthy age-matched wild-type (WT, N = 8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (= 0.98 for f, = 0.82 for i). Finally, we find a statistically significant decrease in i in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (< i > = 310ms/330ms/350ms) compared to the WT group (< i > = 370ms/370ms/380ms). This is in line with our expectations that i is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .

Published
2019

6. The functional database of the ARCHI project: Potential and perspectives

Author

Stanislas Dehaene, Philippe Pinel, Thomas Bourgeron, Cyril Poupon, Baudouin Forgeot d’Arc, Bertrand Thirion, Denis Le Bihan, Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Sainte Justine [Montréal], Université de Montréal (UdeM), Chaire Psychologie cognitive expérimentale, Collège de France (CdF (institution)), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Collège de France - Chaire Psychologie cognitive expérimentale, Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Devauchelle, Benjamin

Subjects
Adult, Male, Adolescent, Databases, Factual, Brain activity and meditation, Cognitive Neuroscience, media_common.quotation_subject, Context (language use), computer.software_genre, 050105 experimental psychology, MESH: Magnetic Resonance Imaging, 03 medical and health sciences, MESH: Brain, Young Adult, 0302 clinical medicine, Social cognition, Perception, medicine, Humans, 0501 psychology and cognitive sciences, media_common, MESH: Adolescent, MESH: Humans, Database, medicine.diagnostic_test, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 05 social sciences, Brain atlas, Brain, Cognition, MESH: Adult, Human brain, MESH: Databases, Factual, Magnetic Resonance Imaging, MESH: Male, medicine.anatomical_structure, Neurology, MESH: Young Adult, Female, Psychology, Functional magnetic resonance imaging, computer, MESH: Female, 030217 neurology & neurosurgery
Abstract

International audience; More than two decades of functional magnetic resonance imaging (fMRI) of the human brain have succeeded to identify, with a growing level of precision, the neural basis of multiple cognitive skills within various domains (perception, sensorimotor processes, language, emotion and social cognition …). Progress has been made in the comprehension of the functional organization of localized brain areas. However, the long time required for fMRI acquisition limits the number of experimental conditions performed in a single individual. As a consequence, distinct brain localizations have mostly been studied in separate groups of participants, and their functional relationships at the individual level remain poorly understood. To address this issue, we report here preliminary results on a database of fMRI data acquired on 78 individuals who each performed a total of 29 experimental conditions, grouped in 4 cross-domains functional localizers. This protocol has been designed to efficiently isolate, in a single session, the brain activity associated with language, numerical representation, social perception and reasoning, premotor and visuomotor representations. Analyses are reported at the group and at the individual level, to establish the ability of our protocol to selectively capture distinct regions of interest in a very short time. Test-retest reliability was assessed in a subset of participants. The activity evoked by the different contrasts of the protocol is located in distinct brain networks that, individually, largely replicate previous findings and, taken together, cover a large proportion of the cortical surface. We provide detailed analyses of a subset of regions of relevance: the left frontal, left temporal and middle frontal cortices. These preliminary analyses highlight how combining such a large set of functional contrasts may contribute to establish a finer-grained brain atlas of cognitive functions, especially in regions of high functional overlap. Detailed structural images (structural connectivity, micro-structures, axonal diameter) acquired in the same individuals in the context of the ARCHI database provide a promising situation to explore functional/structural interdependence. Additionally, this protocol might also be used as a way to establish individual neurofunctional signatures in large cohorts.

Published
2018

7. Benchmarking functional connectome-based predictive models for resting-state fMRI

Author

Michael P. Milham, Bertrand Thirion, Alexandre Abraham, Darya Chyzhyk, Kamalaker Dadi, Mehdi Rahim, Gaël Varoquaux, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
Computer science, Cognitive Neuroscience, Rest, Models, Neurological, Machine learning, computer.software_genre, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Connectome, Humans, 0501 psychology and cognitive sciences, Resting-state fMRI, Functional connectomes, Resting state fMRI, business.industry, 05 social sciences, Supervised learning, Brain, Benchmarking, medicine.disease, Classification, Magnetic Resonance Imaging, Predictive modeling, Neurology, Schizophrenia, Benchmark (computing), Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Population study
Abstract

International audience; Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2 000 individuals, encompassing neuro-degenerative (Alzheimer’s, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions –either pre-defined or generated from the rest-fMRI data– 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations inthe populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.

Published
2018

8. Mapping the asynchrony of cortical maturation in the infant brain: A MRI multi-parametric clustering approach

Author

Jessica Dubois, Jean-François Mangin, Ghislaine Dehaene-Lambertz, François Leroy, Cyril Poupon, Lucie Hertz-Pannier, Parvaneh Adibpour, Bertrand Thirion, Jessica Lebenberg, Unité Analyse et Traitement de l'Information (UNATI), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, CATI Multicenter Neuroimaging Platform (CATI), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité d'imagerie par résonance magnétique à très haut champ et de spectroscopie (UNIRS), Unité de recherche en NeuroImagerie Applicative Clinique et Translationnelle (UNIACT), and Dubois, Jessica

Subjects
Male, cortical maturation, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cognitive Neuroscience, Synaptogenesis, Biology, computer.software_genre, 050105 experimental psychology, White matter, 03 medical and health sciences, 0302 clinical medicine, Voxel, diffusion tensor imaging DTI, medicine, Magnetic resonance imaging MRI, Human Brain Project (HBP), Image Processing, Computer-Assisted, Cluster Analysis, Humans, 0501 psychology and cognitive sciences, quantitative T1 and T2 mapping, infancy, Cluster analysis, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Brain Mapping, Artificial neural network, medicine.diagnostic_test, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 05 social sciences, Brain, Infant, Magnetic resonance imaging, Cognition, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Nerve Net, computer, Neuroscience, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Diffusion MRI, clustering
Abstract

International audience; While the main neural networks are in place at term birth, intense changes in cortical microstructure occur during early infancy with the development of dendritic arborization, synaptogenesis and fiber myelination. These maturational processes are thought to relate to behavioral acquisitions and the development of cognitive abilities. Nevertheless, in vivo investigations of such relationships are still lacking in healthy infants. To bridge this gap, we aimed to study the cortical maturation using non-invasive Magnetic Resonance Imaging, over a largely unexplored period (1-5 post-natal months). In a first univariate step, we focused on different quantitative parameters: longitudinal relaxation time (T1), transverse relaxation time (T2), and axial diffusivity from diffusion tensor imaging (λ//) These individual maps, acquired with echo-planar imaging to limit the acquisition time, showed spatial distortions that were first corrected to reliably match the thin cortical ribbon identified on high-resolution T2-weighted images. Averaged maps were also computed over the infants group to summarize the parameter characteristics during early infancy. In a second step, we considered a multi-parametric approach that leverages parameters complementarity, avoids reliance on pre-defined regions of interest, and does not require spatial constraints. Our clustering strategy allowed us to group cortical voxels over all infants in 5 clusters with distinct microstructural T1 and λ// properties The cluster maps over individual cortical surfaces and over the group were in sound agreement with benchmark post mortem studies of sub-cortical white matter myelination, showing a progressive maturation of 1) primary sensori-motor areas, 2) adjacent unimodal associative cortices, and 3) higher-order associative regions. This study thus opens a consistent approach to study cortical maturation in vivo.

Published
2017

9. Seeing it all: Convolutional network layers map the function of the human visual system

Author

Alexandre Gramfort, Bertrand Thirion, Michael Eickenberg, Gaël Varoquaux, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 ('Human Brain Project')France-Berkeley Fund 2012, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
0301 basic medicine, convolutional networks, vision, genetic structures, Computer science, Cognitive Neuroscience, fmri, Models, Neurological, Brain mapping, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Encoding (memory), medicine, Image Processing, Computer-Assisted, Humans, Computer vision, Visual Pathways, Visual Cortex, Brain Mapping, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Signal Processing, Computer-Assisted, Human brain, Net (mathematics), Magnetic Resonance Imaging, 030104 developmental biology, medicine.anatomical_structure, machine learning, Neurology, Retinotopy, Human visual system model, Visual Perception, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, business, 030217 neurology & neurosurgery, Photic Stimulation
Abstract

International audience; Convolutional networks used for computer vision represent candidate models for the computations performed in mammalian visual systems. We use them as a detailed model of human brain activity during the viewing of natural images by constructing predictive models based on their different layers and BOLD fMRI activations. Analyzing the predictive performance across layers yields characteristic fingerprints for each visual brain region: early visual areas are better described by lower level convolutional net layers and later visual areas by higher level net layers, exhibiting a progression across ventral and dorsal streams. Our predictive model generalizes beyond brain responses to natural images. We illustrate this on two experiments, namely retinotopy and face-place oppositions, by synthesizing brain activity and performing classical brain mapping upon it. The synthesis recovers the activations observed in the corresponding fMRI studies, showing that this deep encoding model captures representations of brain function that are universal across experimental paradigms.

Published
2017
Full Text
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10. Randomized parcellation based inference

Author

Vincent Frouin, Tomáš Paus, Jean-Luc Martinot, Gareth J. Barker, Marcella Rietschel, Gaël Varoquaux, Patricia J. Conrod, Jean-Baptiste Poline, Uli Bromberg, Arun L.W. Bokde, Frauke Nees, Jürgen Gallinat, Bertrand Thirion, Virgile Fritsch, Tobias Banaschewski, Andreas Ströhle, Michael N. Smolka, Zdenka Pausova, Hugh Garavan, Benoit Da Mota, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Department of Child and Adolescent Psychiatry and Psychotherapy [Mannheim], Universität Heidelberg [Heidelberg] = Heidelberg University, Institute of Psychiatry, King‘s College London, Institute of Neuroscience, Trinity College Dublin-Trinity College Dublin-Discipline of Psychiatry [Dublin], School of Medicine [Dublin], Trinity College Dublin-Trinity College Dublin-School of Medicine [Dublin], Trinity College Dublin, Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Institute of Psychiatry, Psychology & Neuroscience, King's College London, Department of Psychiatry and Psychotherapy, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], University of Vermont [Burlington], Neuroimagerie en psychiatrie (U1000), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Rotman Research Institute at the Baycrest Centre (RRI), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], School of Psychology [Nottingham], University of Nottingham, UK (UON), SickKids - The Hospital for sick children, The Hospital for sick children [Toronto] (SickKids), Department of Genetic Epidemiology in Psychiatry [Mannhein], Universität Heidelberg [Heidelberg] = Heidelberg University-Central Institute of Mental Health Mannheim, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Digitéo (HiDiNim project and ICoGeN project), Imagen Consortium, European Project: 39513,IMAGEN, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Universität Heidelberg [Heidelberg], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Sud - Paris 11 (UP11), Universität Heidelberg [Heidelberg]-Central Institute of Mental Health Mannheim, Fritsch, Virgile, and Reinforcement-related behaviour in normal brain function and psychopathology - IMAGEN - 39513 - OLD

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, permutations, Cognitive Neuroscience, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Inference, Neuroimaging, Stop signal, Catechol O-Methyltransferase, computer.software_genre, Polymorphism, Single Nucleotide, [STAT.AP] Statistics [stat]/Applications [stat.AP], Voxel, Resampling, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Cluster Analysis, Humans, Computer Simulation, reproducibility, Genetic Association Studies, multiple comparisons, [STAT.AP]Statistics [stat]/Applications [stat.AP], medicine.diagnostic_test, business.industry, Brain, Contrast (statistics), Pattern recognition, Magnetic Resonance Imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Neurology, group analysis, parcellation, Multiple comparisons problem, Data mining, Artificial intelligence, Functional magnetic resonance imaging, Psychology, business, computer
Abstract

International audience; Neuroimaging group analyses are used to relate inter-subject signal differences observed in brain imaging with behavioral or genetic variables and to assess risks factors of brain diseases. The lack of stability and of sensitivity of current voxel-based analysis schemes may however lead to non-reproducible results. We introduce a new approach to overcome the limitations of standard methods, in which active voxels are detected according to a consensus on several random parcellations of the brain images, while a permutation test controls the false positive risk. Both on synthetic and real data, this approach shows higher sensitivity, better accuracy and higher reproducibility than state-of-the-art methods. In a neuroimaging-genetic application, we find that it succeeds in detecting a significant association between a genetic variant next to the COMT gene and the BOLD signal in the left thalamus for a functional Magnetic Resonance Imaging contrast associated with incorrect responses of the subjects from a Stop Signal Task protocol.

Published
2014

11. SpinDoctor: A MATLAB toolbox for diffusion MRI simulation

Author

Van Dang Nguyen, Hoang Trong An Tran, Thi Minh Phuong Nguyen, Try Nguyen Tran, Duc Thach Son Vu, Hoang An Tran, Jan Valdman, Cong-Bang Trang, Khieu Van Nguyen, Jing-Rebecca Li, Shape reconstruction and identification (DeFI ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Division of Computational Science and Technology [Stockholm] (CST), Royal Institute of Technology [Stockholm] (KTH ), University of South Bohemia, Jan Valdman was supportedby the Czech Science Foundation (GACR), through the grant GA17-04301S. Van-Dang Nguyenwas supported by the Swedish Energy Agency, Sweden with the project ID P40435-1 and MSO4SCwith the grant number 731063., Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Diffusion equation, Diffusion magnetic resonance imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cognitive Neuroscience, Finite elements, FOS: Physical sciences, Neuroimaging, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, FOS: Mathematics, Neumann boundary condition, Humans, Effective diffusion coefficient, Computer Simulation, 0501 psychology and cognitive sciences, Mathematics - Numerical Analysis, Diffusion (business), Partial differential equation, Bloch-Torrey equation, 05 social sciences, Mathematical analysis, Brain, Numerical Analysis (math.NA), Models, Theoretical, Computational Physics (physics.comp-ph), [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Finite element method, Apparent diffusion coefficient, Neurology, Ordinary differential equation, Spin echo, Physics - Computational Physics, Software, Simulation, 030217 neurology & neurosurgery
Abstract

The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium can be modeled by the multiple compartment Bloch-Torrey partial differential equation (BTPDE). A mathematical model for the time-dependent apparent diffusion coefficient (ADC), called the H-ADC model, was obtained recently using homogenization techniques on the BTPDE. Under the assumption of negligible water exchange between compartments, the H-ADC model produces the ADC of a diffusion medium from the solution of a diffusion equation (DE) subject to a time-dependent Neumann boundary condition. This paper describes a publicly available Matlab toolbox called SpinDoctor that can be used 1) to solve the BTPDE to obtain the dMRI signal (the toolbox provides a way of robustly fitting the dMRI signal to obtain the fitted ADC); 2) to solve the DE of the H-ADC model to obtain the ADC; 3) a short-time approximation formula for the ADC is also included in the toolbox for comparison with the simulated ADC. The PDEs are solved by P 1 finite elements combined with build-in Matlab routines for solving ordinary differential equations. The finite element mesh generation is performed using an external package called Tetgen that is included in the toolbox. SpinDoctor provides built-in options of including 1) spherical cells with a nucleus; 2) cylindrical cells with a myelin layer; 3) an extra-cellular space (ECS) enclosed either a) in a box or b) in a tight wrapping around the cells; 4) deformation of canonical cells by bending and twisting. 5) permeable membranes for the BT-PDE (the H-ADC assumes negligible permeability). Built-in diffusion-encoding pulse sequences include the Pulsed Gradient Spin Echo and the Oscilating Gradient Spin Echo., 49 pages, 18 figures

Published
2019

12. Learning and comparing functional connectomes across subjects

Author

R. Cameron Craddock, Gaël Varoquaux, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Smithsonian Institution, Dynamic Diaschisis FRM project DEQ20100318254, Varoquaux, Gaël, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Models, Anatomic, Cognitive Neuroscience, Models, Neurological, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 050105 experimental psychology, Functional connectivity, 03 medical and health sciences, 0302 clinical medicine, Connectome, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Animals, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], medicine.diagnostic_test, Group study, Resting state fMRI, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 05 social sciences, Brain, Cognition, Brain pathologies, Neurology, Quantitative Biology - Neurons and Cognition, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Nerve Net, Functional magnetic resonance imaging, Psychology, Neuroscience, 030217 neurology & neurosurgery
Abstract

International audience; Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes.

Published
2013

13. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

Author

Michael P. Milham, Gaël Varoquaux, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Alexandre Abraham, Adriana Di Martino, Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Stony Brook University [SUNY] (SBU), State University of New York (SUNY), NiConnect project from Digiteo, France, SUBSample from Digiteo, France, NIMH grant 1R21MH107045-01A1, Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0301 basic medicine, FOS: Computer and information sciences, Adult, Adolescent, Autism Spectrum Disorder, Cognitive Neuroscience, autism spectrum disorders, data heterogeneity, Datasets as Topic, Machine Learning (stat.ML), computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, data pipelines, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Connectome, Image Processing, Computer-Assisted, Humans, Multicenter Studies as Topic, Biomarker discovery, Child, Cerebral Cortex, medicine.diagnostic_test, Resting state fMRI, [SCCO.NEUR]Cognitive science/Neuroscience, Multi site, Reproducibility of Results, medicine.disease, Pipeline (software), Magnetic Resonance Imaging, 3. Good health, 030104 developmental biology, Neurology, Sample size determination, FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Autism, Neurons and Cognition (q-bio.NC), Data mining, Functional magnetic resonance imaging, Psychology, computer, 030217 neurology & neurosurgery, resting-state fMRI, Biomarkers
Abstract

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks., in NeuroImage, Elsevier, 2016

Published
2016

14. Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas

Author

L. Marrakchi-Kacem, Pierre Fillard, Josselin Houenou, Marion Leboyer, D. Le Bihan, J.-F. Mangin, Pamela Guevara, D. Duclap, Cyril Poupon, Institut d'Imagerie BioMédicale (I2BM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, IFR de Neuroimagerie Fonctionnelle (IFR 49), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Universidad de Concepción [Chile], Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Service NEUROSPIN (NEUROSPIN), National Institutes of Health [Bethesda] (NIH), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Service Hospitalier Frédéric Joliot (SHFJ), Center for Neuroscience and Behavioral Medicine, Children's National Medical Center, Universidad de Concepción - University of Concepcion [Chile], Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Cognitive Neuroscience, Population, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine, Nerve Fibers, Myelinated, 030218 nuclear medicine & medical imaging, White matter, 03 medical and health sciences, Atlases as Topic, Nerve Fibers, 0302 clinical medicine, Neural Pathways, medicine, Humans, Segmentation, Fiber bundle, Anatomy, Artistic, education, Mathematics, education.field_of_study, business.industry, Atlas (topology), Brain, Pattern recognition, Diffusion Tensor Imaging, medicine.anatomical_structure, Neurology, Bundle, Artificial intelligence, business, Nuclear medicine, 030217 neurology & neurosurgery, Diffusion MRI, Tractography
Abstract

International audience; This paper presents a method for automatic segmentation of white matter fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. This atlas is a model of the brain white matter organization, computed for a group of subjects, made up of a set of generic fiber bundles that can be detected in most of the population. Each atlas bundle corresponds to several inter-subject clusters manually labeled to account for subdivisions of the underlying pathways often presenting large variability across subjects. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. The atlas, composed of 36 known deep white matter bundles and 47 superficial white matter bundles in each hemisphere, was inferred from a first database of 12 brains. It was successfully used to segment the deep white matter bundles in a second database of 20 brains and most of the superficial white matter bundles in 10 subjects of the same database.

Published
2012

15. Towards linking diffusion MRI based macro- and microstructure measures with cortico-cortical transmission in brain tumor patients

Author

Filipiak, Patryk, Almairac, Fabien, Papadopoulo, Théodore, Fontaine, Denys, Mondot, Lydiane, Chanalet, Stéphane, Deriche, Rachid, Clerc, Maureen, Wassermann, Demian, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de Recherche en Informatique et en Automatique (Inria), Centre Hospitalier Universitaire de Nice (CHU Nice), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Inria Saclay - Ile de France, and ANR-16-NEUC-0002,NeuroRef,Building Normative Atlases of Diffusion MRI to Identify Subject-Specific Neuroimaging Abnormalities in Brain Trauma and Post-Traumatic Stress(2016)

Subjects
Adult, Male, Hemangioma, Cavernous, Central Nervous System, Pilot Projects, Direct electrical stimulation, Neurosurgical Procedures, lcsh:RC321-571, Young Adult, Neural Pathways, Humans, Brain white matter microstructure, Wakefulness, Effective connectivity, Evoked Potentials, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Aged, Cerebral Cortex, Brain Neoplasms, [SCCO.NEUR]Cognitive science/Neuroscience, Structural connectivity, Glioma, Middle Aged, White Matter, Electric Stimulation, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Electrocorticography, Cortico-cortical evoked potentials, brain white matter microstrucure, Tractography
Abstract

International audience; We aimed to link macro-and microstructure measures of brain white matter obtained from diffusion MRI with effective connectivity measures based on a propagation of cortico-cortical evoked potentials induced with intrasurgical direct electrical stimulation. For this, we compared streamline lengths and log-transformed ratios of streamlines computed from presurgical diffusion-weighted images, and the delays and amplitudes of N1 peaks recorded intrasurgically with electrocorticography electrodes in a pilot study of 9 brain tumor patients. Our results showed positive correlation between these two modalities in the vicinity of the stimulation sites (Pearson coefficient 0.54±0.13 for N1 delays, and 0.47±0.23 for N1 amplitudes), which could correspond to the neural propagation via U-fibers. In addition, we reached high sensitivities (0.78±0.07) and very high specificities (0.93±0.03) in a binary variant of our comparison. Finally, we used the structural connectivity measures to predict the effective connectivity using a multiple linear regression model, and showed a significant role of brain microstructure-related indices in this relation.

Published
2021

16. Multi-subject MEG/EEG source imaging with sparse multi-task regression

Author

Hicham Janati, Marco Cuturi, Thomas Bazeille, Bertrand Thirion, Alexandre Gramfort, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Paris-Saclay, Centre de Recherche en Économie et Statistique (CREST), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-École polytechnique (X)-École Nationale de la Statistique et de l'Administration Économique (ENSAE Paris)-Centre National de la Recherche Scientifique (CNRS), Google Brain, Paris, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
Electromagnetic field, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Cognitive Neuroscience, Pooling, Bayesian probability, Population, Models, Neurological, Machine Learning (stat.ML), Electroencephalography, Signal-To-Noise Ratio, Regularization (mathematics), 050105 experimental psychology, lcsh:RC321-571, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, Linear regression, medicine, Humans, 0501 psychology and cognitive sciences, EEG / MEG source imaging, education, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, education.field_of_study, Brain Mapping, medicine.diagnostic_test, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, Brain, Magnetoencephalography, Pattern recognition, Inverse problem, Regression, Inverse modeling, Neurology, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Multivariate Analysis, Neurons and Cognition (q-bio.NC), Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated these electromagnetic fields is a challenging ill-posed regression problem known as \emph{source imaging}. When considering a group study, a common approach consists in carrying out the regression tasks independently for each subject. An alternative is to jointly localize sources for all subjects taken together, while enforcing some similarity between them. By pooling all measurements in a single multi-task regression, one makes the problem better posed, offering the ability to identify more sources and with greater precision. The Minimum Wasserstein Estimates (MWE) promotes focal activations that do not perfectly overlap for all subjects, thanks to a regularizer based on Optimal Transport (OT) metrics. MWE promotes spatial proximity on the cortical mantel while coping with the varying noise levels across subjects. On realistic simulations, MWE decreases the localization error by up to 4 mm per source compared to individual solutions. Experiments on the Cam-CAN dataset show a considerable improvement in spatial specificity in population imaging. Our analysis of a multimodal dataset shows how multi-subject source localization closes the gap between MEG and fMRI for brain mapping., Comment: version 2. arXiv admin note: text overlap with arXiv:1902.04812

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17. Decoding with confidence: Statistical control on decoder maps.

Author

Chevalier JA, Nguyen TB, Salmon J, Varoquaux G, and Thirion B

Subjects
Algorithms, Humans, Image Processing, Computer-Assisted methods, Psychomotor Performance physiology, Brain diagnostic imaging, Brain physiology, Brain Mapping methods, Data Analysis, Magnetic Resonance Imaging methods, Neural Networks, Computer
Abstract

In brain imaging, decoding is widely used to infer relationships between brain and cognition, or to craft brain-imaging biomarkers of pathologies. Yet, standard decoding procedures do not come with statistical guarantees, and thus do not give confidence bounds to interpret the pattern maps that they produce. Indeed, in whole-brain decoding settings, the number of explanatory variables is much greater than the number of samples, hence classical statistical inference methodology cannot be applied. Specifically, the standard practice that consists in thresholding decoding maps is not a correct inference procedure. We contribute a new statistical-testing framework for this type of inference. To overcome the statistical inefficiency of voxel-level control, we generalize the Family Wise Error Rate (FWER) to account for a spatial tolerance δ, introducing the δ-Family Wise Error Rate (δ-FWER). Then, we present a decoding procedure that can control the δ-FWER: the Ensemble of Clustered Desparsified Lasso (EnCluDL), a procedure for multivariate statistical inference on high-dimensional structured data. We evaluate the statistical properties of EnCluDL with a thorough empirical study, along with three alternative procedures including decoder map thresholding. We show that EnCluDL exhibits the best recovery properties while ensuring the expected statistical control., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2021
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18. Gender identity better than sex explains individual differences in episodic and semantic components of autobiographical memory: An fMRI study.

Author

Compère L, Charron S, Gallarda T, Rari E, Lion S, Nys M, Anssens A, Coussinoux S, Machefaux S, Oppenheim C, and Piolino P

Subjects
Adolescent, Adult, Brain physiology, Female, Functional Neuroimaging, Healthy Volunteers, Humans, Individuality, Magnetic Resonance Imaging, Male, Semantics, Young Adult, Brain diagnostic imaging, Femininity, Gender Identity, Masculinity, Memory, Episodic, Sex
Abstract

Advances in the literature of sex-related differences in autobiographical memory increasingly tend to highlight the importance of psychosocial factors such as gender identity, which may explain these differences better than sex as a biological factor. To date, however, none of these behavioral studies have investigated this hypothesis using neuroimaging. The purpose of this fMRI study is to examine for the first time sex and gender identity-related differences in episodic and semantic autobiographical memory in healthy participants (M=19, W=18). No sex-related differences were found; however, sex-related effects of masculine and feminine gender identity were identified in men and women independently. These results confirm the hypothesis that differences in episodic and semantic autobiographical memory are best explained by gender but are an interaction between biological sex and gender identity and extend these findings to the field of neuroimaging. We discuss the importance of hormonal factors to be taken into consideration in the future., (Copyright © 2020. Published by Elsevier Inc.)

Published
2021
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19. Autoreject: Automated artifact rejection for MEG and EEG data.

Author

Jas M, Engemann DA, Bekhti Y, Raimondo F, and Gramfort A

Subjects
Brain Mapping methods, Humans, Models, Neurological, Signal Processing, Computer-Assisted, Algorithms, Artifacts, Brain physiology, Electroencephalography methods, Magnetoencephalography methods
Abstract

We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Published
2017
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20. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines.

Author

Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, and Thirion B

Subjects
Humans, Brain Diseases diagnostic imaging, Neuroimaging methods, Neuroimaging standards
Abstract

Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within- and across-subject predictions, on multiple datasets -anatomical and functional MRI and MEG- and simulations. Theory and experiments outline that the popular "leave-one-out" strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders' parameters while avoiding circularity bias. However we find that it can be favorable to use sane defaults, in particular for non-sparse decoders., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Published
2017
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21. Group-PCA for very large fMRI datasets.

Author

Smith SM, Hyvärinen A, Varoquaux G, Miller KL, and Beckmann CF

Subjects
Computer Simulation, Humans, Artifacts, Connectome methods, Data Interpretation, Statistical, Magnetic Resonance Imaging methods, Principal Component Analysis methods
Abstract

Increasingly-large datasets (for example, the resting-state fMRI data from the Human Connectome Project) are demanding analyses that are problematic because of the sheer scale of the aggregate data. We present two approaches for applying group-level PCA; both give a close approximation to the output of PCA applied to full concatenation of all individual datasets, while having very low memory requirements regardless of the number of datasets being combined. Across a range of realistic simulations, we find that in most situations, both methods are more accurate than current popular approaches for analysis of multi-subject resting-state fMRI studies. The group-PCA output can be used to feed into a range of further analyses that are then rendered practical, such as the estimation of group-averaged voxelwise connectivity, group-level parcellation, and group-ICA., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Published
2014
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22. Tracking cortical activity from M/EEG using graph cuts with spatiotemporal constraints.

Author

Gramfort A, Papadopoulo T, Baillet S, and Clerc M

Subjects
Algorithms, Artifacts, Data Interpretation, Statistical, Electrophysiological Phenomena, Humans, Image Processing, Computer-Assisted methods, Photic Stimulation, ROC Curve, Somatosensory Cortex physiology, Visual Cortex physiology, Cerebral Cortex physiology, Electroencephalography methods, Magnetoencephalography methods
Abstract

This work proposes to use magnetoencephalography (MEG) and electroencephalography (EEG) source imaging to provide cinematic representations of the temporal dynamics of cortical activations. Cortical activation maps, seen as images of the active brain, are scalar maps defined at the vertices of a triangulated cortical surface. They can be computed from M/EEG data using a linear inverse solver every millisecond. Taking as input these activation maps and exploiting both the graph structure of the cortical mesh and the high sampling rate of M/EEG recordings, neural activations are tracked over time using an efficient graph cut based algorithm. The method estimates the spatiotemporal support of the active brain regions. It consists in computing a minimum cut on a particularly designed weighted graph imposing spatiotemporal regularity constraints on the activation patterns. Each node of the graph is assigned a label (active or non active). The method works globally on the full time-period of interest, can cope with spatially extended active regions and allows the active domain to exhibit topology changes over time. The algorithm is illustrated and validated on synthetic data. Results of the method are provided on two MEG cognitive experiments in the visual and somatosensory cortices, demonstrating the ability of the algorithm to handle various types of data., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Published
2011
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23. A group model for stable multi-subject ICA on fMRI datasets.

Author

Varoquaux G, Sadaghiani S, Pinel P, Kleinschmidt A, Poline JB, and Thirion B

Subjects
Algorithms, Automation, Calibration, Databases, Factual, Humans, Multivariate Analysis, Neural Pathways physiology, Probability, Reproducibility of Results, Brain physiology, Brain Mapping methods, Magnetic Resonance Imaging methods, Models, Statistical, Signal Processing, Computer-Assisted
Abstract

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Published
2010
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