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1,463 results on '"Service NEUROSPIN (NEUROSPIN)"'

201. Multivariate single-subject analysis of short-term reorganization in the language network

Author: Gesa Hartwigsen, Danilo Bzdok, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, 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), Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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), and Rheinisch-Westfälische Technische Hochschule Aachen (RWTH)
Subjects: 0301 basic medicine, Multivariate statistics, Cognitive Neuroscience, Experimental and Cognitive Psychology, Angular gyrus, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Functional neuroimaging, Humans, Language, Brain Mapping, Artificial neural network, [SCCO.NEUR]Cognitive science/Neuroscience, Univariate, Brain, Cognition, Precision medicine, Term (time), 030104 developmental biology, Neuropsychology and Physiological Psychology, Multivariate Analysis, Nerve Net, Psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: International audience; Numerous functional neuroimaging studies in humans used univariate group analyses to identify brain areas that show increased activity across participants during specific tasks. However, there is increasing evidence that group-level analyses may obscure important parts of the signal response (Margulies, 2017; Poldrack, 2017). Here, we re-analyzed data from our recent study (Hartwigsen et al., 2017) that investigated functional reorganization in the language network. We show that across-voxel pattern-learning approaches are useful to isolate plastic changes in neural networks underlying cognitive functions at the individual subject level. Using predictive machine-learning tools to identify and exploit subject-specific neural activity patterns have been argued to become an important cornerstone of precision medicine in psychiatry and neurology.
Published: 2018

202. Differentiable Dynamic Programming for Structured Prediction and Attention

Author: Mensch , Arthur, Blondel , Mathieu, 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), NTT Communication Science Laboratories, NTT Corporation, 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), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-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 )
Subjects: FOS: Computer and information sciences, structured prediction, Viterbi, Machine Learning (stat.ML), smoothing, Dynamic programming, Machine Learning (cs.LG), attention, Computer Science - Learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, NER, DTW, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]
Abstract: International audience; Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, many DP algorithms are non-differentiable, which hampers their use as a layer in neural networks trained by back-propagation. To address this issue, we propose to smooth the max operator in the dynamic programming recursion, using a strongly convex regularizer. This allows to relax both the optimal value and solution of the original combina-torial problem, and turns a broad class of DP algorithms into differentiable operators. Theoretically , we provide a new probabilistic perspective on backpropagating through these DP operators , and relate them to inference in graphical models. We derive two particular instantiations of our framework, a smoothed Viterbi algorithm for sequence prediction and a smoothed DTW algorithm for time-series alignment. We show-case these instantiations on structured prediction (audio-to-score alignment, NER) and on structured and sparse attention for translation.
Published: 2018

203. Accelerating likelihood optimization for ICA on real signals

Author: Pierre Ablin, Jean-François Cardoso, 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), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), 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: Hessian matrix, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Maximum likelihood, 020206 networking & telecommunications, Machine Learning (stat.ML), maximum likelihood estimation, 02 engineering and technology, Independent component analysis, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], 16. Peace & justice, Machine Learning (cs.LG), symbols.namesake, Rate of convergence, preconditioning, Statistics - Machine Learning, Simulated data, 0202 electrical engineering, electronic engineering, information engineering, symbols, Optimization methods, 020201 artificial intelligence & image processing, Algorithm, optimization
Abstract: International audience; We study optimization methods for solving the maximum likelihood formulation of independent component analysis (ICA). We consider both the the problem constrained to white signals and the unconstrained problem. The Hessian of the objective function is costly to compute, which renders Newton's method impractical for large data sets. Many algorithms proposed in the literature can be rewritten as quasi-Newton methods, for which the Hessian approximation is cheap to compute. These algorithms are very fast on simulated data where the linear mixture assumption really holds. However, on real signals, we observe that their rate of convergence can be severely impaired. In this paper, we investigate the origins of this behavior, and show that the recently proposed Preconditioned ICA for Real Data (Picard) algorithm overcomes this issue on both constrained and unconstrained problems.
Published: 2018

204. 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

205. Diffusion Driven Label Fusion for White Matter Multi-Atlas Segmentation

Author: Gallardo, Guillermo, Bouix, Sylvain, Wassermann, Demian, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Psychiatry Neuroimaging Laboratory (PNL), Brigham and Women's Hospital [Boston], 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, 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), European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016), Université Côte d'Azur ( UCA ), Psychiatry Neuroimaging Laboratory ( PNL ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ), ANR : NeuroRef,Subject-Specific Diffusion MRI Profiles of Injury in TBI and PTSD, European Project : 694665,H2020 ERC,ERC-2015-AdG,CoBCoM ( 2016 ), 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, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: health services administration, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [ SCCO.NEUR ] Cognitive science/Neuroscience, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, education, humanities, health care economics and organizations
Abstract: International audience; White matter pathologies such as tumors or traumatic brain injury disrupt the structure of white matter. These disruptions hamper the inference of affected pathways using tractography. A way to overcome this is to use a label fusion technique. Label fusion aims to infer the localization of the brain structure of a subject from its localization in a group of control subjects. The most common technique is known as the voting rule, where a structure is said to be present in a voxel if it's present in the majority of the voting subjects. Furthermore, this can be improved by weighting each vote by the similarity between the T1 of each voting subject and the subject to be inferred. However, these techniques only relay in the spatial localization of the structures. In this work, we introduce a way to weight the vote of each subject based on how the voted pathway is supported by the test subject's diffusion data. This is, if the diffusion data of the test subject is consistent with the direction of the voted pathway, the vote has a higher weight. We show that adding dMRI to the label fusion process achieves a similar number of true positives than the voting technique, with a 60% less of false positives. However, this incurs in a trade-off of a 40% false negatives.
Published: 2018

206. Reducing false positive connection in tractograms using joint structure-function filtering

Author: Frigo , Matteo, Gallardo , Guillermo, Costantini , Isa, Daducci , Alessandro, Wassermann , Demian, Deriche , Rachid, Deslauriers-Gauthier , Samuel, 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), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Department of Computer Science [Verona] (UNIVR | DI), Università degli studi di Verona = University of Verona (UNIVR), 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, European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016), 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 ), Université Côte d'Azur ( UCA ), Dipartimento di Informatica [Verona], Università degli Studi di Verona, Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, University of Verona (UNIVR), 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), Frigo, Matteo, and Computational Brain Connectivity Mapping - CoBCoM - - H2020 ERC2016-09-01 - 2021-08-31 - 694665 - VALID
Subjects: [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, [ SCCO.NEUR ] Cognitive science/Neuroscience, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Due to its ill-posed nature, tractography generates a significant number of false positive connections between brain regions [3]. To reduce the number of false positives, Daducci et al. [1] proposed the COMMIT framework, which has the goal of re-establishing the link between tractography and tissue microstructure. In this framework, the diffusion MRI signal is modeled as a linear combination of local models associated with streamlines where the weights are identified by solving a convex optimization problem. Streamlines with a weight of zero do not contribute to the diffusion MRI data and are assumed to be false positives. Removing these false positives yields a subset of streamlines supporting the anatomical data. However, COMMIT does not make use of the link between structure and function and thus weights all bundles equally. In this work, we propose a new strategy that enhances the COMMIT framework by injecting the functional information provided by functional MRI. The result is an enhanced tractogram filtering strategy that considers both functional and structural data.
Published: 2018

207. Sulcal Neuroanatomy and Relative Relationships using Spatial Gradients

Author: Machlouzarides-Shalit, Antonia, Gallardo, Guillermo, Wassermann, Demian, 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), 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), 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: stomatognathic diseases, nervous system, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]
Abstract: International audience; Objective: To characterise sulcal traits and relationships by means of spatial gradients. Our use of spatial gradients allows for the investigation of sulcal associations on structural MRI images. We localise, identify and relate sulci to examine inter-sulcal relationships, providing additional insight into cortical mapping.
Published: 2018

208. Solving the Cross-Subject Parcel Matching Problem: Comparing Four Methods Using Extrinsic Connectivity

Author: Gayraud, Nathalie, Gallardo, Guillermo, Clerc, Maureen, Wassermann, Demian, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), 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), 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), European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016), European Project: 757672,H2020 Pilier ERC,NeuroLang(2018), 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, Université Côte d'Azur ( UCA ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), 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 ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, European Project : 694665,H2020 ERC,ERC-2015-AdG,CoBCoM ( 2016 ), European Project : 757672,NeuroLang, 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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, Gallardo, Guillermo, Computational Brain Connectivity Mapping - CoBCoM - - H2020 ERC2016-09-01 - 2021-08-31 - 694665 - VALID, and Accelerating Neuroscience Research by Unifying Knowledge Representation and Analysis Through a Domain Specific Language - NeuroLang - - H2020 Pilier ERC2018-03-01 - 2023-02-28 - 757672 - VALID
Subjects: [SCCO.COMP] Cognitive science/Computer science, [ SCCO.COMP ] Cognitive science/Computer science, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, [ SCCO.NEUR ] Cognitive science/Neuroscience, [SCCO.COMP]Cognitive science/Computer science
Abstract: International audience; Matching parcels across different subjects is an open problem in neuroscience. Even when produced by the same technique, parcellations tend to differ in the number, shape, and spatial localization of parcels across subject. To the best of our knowledge, no technique has been able to tackle this problem. In this work, we propose and compare four different methods to match parcels across subjects based on their extrinsic connectivity fingerprint. We test their performance by matching parcels of the Desikan atlas [4] across subjects, as its parcels have been shown to have consistent connectivity profiles across subjects [6]. Then, we repeat the experiment for a pre-existing groupwise parcellation based on structural connectivity [5], which has been show to be consistent across groups of subjects. The outline of our work can be seen in Figure 1. Our results show that our methods based on Optimal Transport (OT) and Kullback Leibler (KL) divergence achieve more than 99% of correct matches.
Published: 2018

209. Similarity encoding for learning with dirty categorical variables

Author: Gaël Varoquaux, Balázs Kégl, Patricio Cerda, 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, Laboratoire de l'Accélérateur Linéaire (LAL), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Centre National de la Recherche Scientifique (CNRS), ANR-17-CE23-0018,DirtyData,Intégration et nettoyage de données pour l'analyse statistique(2017), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), 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 ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Laboratoire de l'Accélérateur Linéaire ( LAL ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Centre National de la Recherche Scientifique ( CNRS ), ANR-17-CE23-0018,DirtyData,Data integration and cleaning for statistical analysis, 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 Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Science - Artificial Intelligence, Computer science, Feature vector, Machine Learning (stat.ML), 02 engineering and technology, [ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG], Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Redundancy (information theory), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Statistics - Machine Learning, Artificial Intelligence, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Data deduplication, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Categorical variable, Statistical learning, business.industry, Dimensionality reduction, Pattern recognition, Computer Science - Learning, Artificial Intelligence (cs.AI), 020201 artificial intelligence & image processing, Artificial intelligence, business, Software
Abstract: International audience; For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. "Dirty" non-curated data gives rise to categorical variables with a very high cardinality but redundancy: several categories reflect the same entity. In databases, this issue is typically solved with a deduplication step. We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains. We study a generalization of one-hot encoding, similarity encoding, that builds feature vectors from similarities across categories. We perform a thorough empirical validation on non-curated tables, a problem seldom studied in machine learning. Results on seven real-world datasets show that similarity encoding brings significant gains in prediction in comparison with known encoding methods for categories or strings, notably one-hot encoding and bag of character n-grams. We draw practical recommendations for encoding dirty categories: 3-gram similarity appears to be a good choice to capture morphological resemblance. For very high-cardinality, dimensionality reduction significantly reduces the computational cost with little loss in performance: random projections or choosing a subset of prototype categories still outperforms classic encoding approaches.
Published: 2018

210. Controlling a confound in predictive models with a test set minimizing its effect

Author: Michael P. Milham, Bertrand Thirion, Gaël Varoquaux, Darya Chyzhyk, 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), 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: 0301 basic medicine, Computer science, phenotype, subsampling, Machine learning, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], statistical testing, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], confound, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Healthy aging, Statistical hypothesis testing, business.industry, Linear model, biomarkers, Mutual information, Variance (accounting), predictive models, Confounding effect, 030104 developmental biology, Test set, Biomarker (medicine), Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Predictive models applied on brain images can extract imaging biomarkers of pathologies or psychological traits. Yet, a successful prediction may be driven by a confounding effect that is correlated with the effect of interest. For instance fluid intelligence is strongly impacted by age; age is well predicted from brain images; hence successful prediction of fluid intelligence from brain images might have captured nothing more than a biomarker of aging. Here we introduce a non-parametric approach to control for a confounding effect in a predictive model. It is based on crafting a test set on which the effect of interest is independent from the confounding effect. We name this strategy " anti mutual-information subsampling ". We demonstrate the approach with a large sample resting-state fMRI and psychometric data of healthy aging subjects (n = 608). We show that using a linear model to remove the effect of age on the brain signals (" deconfounding ") leads to pessimistic scores, as previously reported. Anti mutual-information subsampling does not require to remove from the brain signals the shared variance between aging and fluid intelligence, and hence does not display this pessimistic behavior. In addition, it is non-parametric and hence robust to violations of the linear hypothesis.
Published: 2018

211. Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity

Author: Amicie de Pierrefeu, Marion Leboyer, Philippe Ciuciu, Fouad Hadj-Selem, Edouard Duchesnay, Josselin Houenou, Tommy Löfstedt, Charles Laidi, 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, Umeå University, 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), Fondation FondaMental [Créteil], Pôle de Psychiatrie [Hôpital Henri Mondor], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital H. Mondor - A. Chenevier, VEhicule DEcarboné et COmmuniquant et sa Mobilité (VeDeCom), 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)), 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: Training set, business.industry, Computer science, Feature extraction, Feature selection, Overfitting, Machine learning, computer.software_genre, Regularization (mathematics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030227 psychiatry, Support vector machine, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Neuroimaging, Resampling, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; The use of machine-learning (ML) in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Indeed, ML algorithms can jointly examine all brain features to capture complex relationships in the data in order to make inferences at a single-subject level. To deal with such high dimensional input and the associated risk of overfitting on the training data, a proper regularization (or feature selection) is required. Standard $l_2$-regularized predictors, such as Support Vector Machine, provide dense patterns of predictors. However, in the context of predictive disease signature discovery, it is now essential to understand the brain pattern that underpins the prediction. Despite $l_1$-regularized (sparse) has often been advocated as leading to more interpretable models, they generally lead to scattered and unstable patterns. We hypothesize that the integration of prior knowledge regarding the structure of the input images should improve the relevance and the stability of the predictive signature. Such structured sparsity can be obtained by combining together $l_1$ (possibly $l_2$) and Total variation (TV) penalties. We demonstrated the relevance of using ML with structured sparsity on a large multisite dataset of schizophrenia patients and controls. Using 3D maps of grey matter density , we obtained promising inter-site prediction performances. More importantly, we have uncovered a predictive signature of schizophrenia that is clinically interpretable and stable across resampling. This suggests that structured sparsity provides a major breakthrough over 'off-the-shelf' algorithms to perform a robust selection of important brain regions in the context of biomarkers discovery.
Published: 2018

212. Neuroimaging Research: From Null-Hypothesis Falsification to Out-of-Sample Generalization

Author: Gaël Varoquaux, Bertrand Thirion, Danilo Bzdok, Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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), Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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), and Bzdok, Danilo
Subjects: 0301 basic medicine, Generalization, Inference, computer.software_genre, Mental operations, Article, Education, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Developmental and Educational Psychology, Statistical inference, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Applied Psychology, Statistical hypothesis testing, Cognitive science, Applied Mathematics, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], 030104 developmental biology, Out of sample, MESH: neuroscience, statistical inference, epistemology, hypothesis testing, cross-validation, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Psychology, Null hypothesis, computer, 030217 neurology & neurosurgery
Abstract: Brain-imaging technology has boosted the quantification of neurobiological phenomena underlying human mental operations and their disturbances. Since its inception, drawing inference on neurophysiological effects hinged on classical statistical methods, especially, the general linear model. The tens of thousands of variables per brain scan were routinely tackled by independent statistical tests on each voxel. This circumvented the curse of dimensionality in exchange for neurobiologically imperfect observation units, a challenging multiple comparisons problem, and limited scaling to currently growing data repositories. Yet, the always bigger information granularity of neuroimaging data repositories has lunched a rapidly increasing adoption of statistical learning algorithms. These scale naturally to high-dimensional data, extract models from data rather than prespecifying them, and are empirically evaluated for extrapolation to unseen data. The present article portrays commonalities and differences between long-standing classical inference and upcoming generalization inference relevant for conducting neuroimaging research.
Published: 2018

213. Patterns of thought: population variation in the associations between large-scale network organisation and self-reported experiences at rest

Author: Michael P. Milham, Elizabeth Jefferies, Jonathan Smallwood, R. Cameron Craddock, Danilo Bzdok, Hao-Ting Wang, Daniel S. Margulies, Department of Psychology [York, UK], University of York [York, UK], Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, EJ was supported by ERC (SEMBIND -283530). JS was supported by ERC (WANDERINGMINDS -646927), Volkswagen Foundation (Wandering Minds -89440 and 89439) and the John Templeton Foundation, 'Prospective Psychology Stage 2: A Research Competition'. DB was supported by the German Research Foundation (DFG, BZ2/2-1, BZ2/3-1, and BZ2/4-1), International Research Training Group (IRTG2150), Amazon AWS Research Grant (2016 and 2017), the German National Academic Foundation, and the START-Program of the Faculty of Medicineand an Exploratory Research Space Seed Fund, RWTH Aachen University., Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Bzdok, Danilo
Subjects: Adult, Male, Adolescent, Cognitive Neuroscience, Rest, Cognitive neuroscience, 050105 experimental psychology, Session (web analytics), Machine Learning, Thinking, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Limbic system, medicine, Connectome, Limbic System, Humans, 0501 psychology and cognitive sciences, Attention, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Association (psychology), Default mode network, Cerebral Cortex, medicine.diagnostic_test, 05 social sciences, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognition, Middle Aged, Magnetic Resonance Imaging, medicine.anatomical_structure, Variation (linguistics), Neurology, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Nerve Net, Functional magnetic resonance imaging, Psychology, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: International audience; Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whether dimensions of population variation in different modes of unconstrained processing can be described by the associations between patterns of neural activity and self-reports of experience during the same period. We selected 258 individuals from a publicly available data set who had measures of resting-state functional magnetic resonance imaging, and self-reports of experience during the scan. We used machine learning to determine patterns of association between the neural and self-reported data, finding variation along four dimensions. ‘Purposeful’ experiences were associated with lower connectivity -in particular default mode and limbic networks were less correlated with attention and sensorimotor networks. ‘Emotional’ experiences were associated with higher connectivity, especially between limbic and ventral attention networks. Experiences focused on themes of ‘personal importance’ were associated with reduced functional connectivity within attention and control systems. Finally, visual experiences were associated with stronger connectivity between visual and other networks, in particular the limbic system. Some of these patterns had contrasting links with cognitive function as assessed in a separate laboratory session -purposeful thinking was linked to greater intelligence and better abstract reasoning, while a focus on personal importance had the opposite relationship. Together these findings are consistent with an emerging literature on unconstrained states and also underlines that these states are heterogeneous, with distinct modes of population variation reflecting the interplay of different large-scale networks.
Published: 2018

214. A comparison of three fiber tract delineation methods and their impact on white matter analysis

Author: Amanda E. Lyall, Lauren J. O'Donnell, Ana Maria Rivas-Grajales, Sylvain Bouix, Martha E. Shenton, Fan Zhang, Carl-Fredrik Westin, Sarina Karmacharya, Demian Wassermann, Marek Kubicki, Nikos Makris, Valerie J. Sydnor, Psychiatry Neuroimaging Laboratory (PNL), Brigham and Women's Hospital [Boston], Department of Psychiatry Massachusetts General Hospital (MGH), Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine (BCM), Baylor University-Baylor University, Laboratory of Mathematics in Imaging [Boston], Department of Radiology [Boston], Departments of Psychiatry and Radiology [Boston], Center for Morphometric Analysis (CMA), Massachusetts General Hospital [Boston], 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), 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, European Project: 757672,H2020 Pilier ERC,NeuroLang(2018), College of Information and Electrical Engineering [Beijing] ( CIEE ), China Agricultural University ( CAU ), Psychiatry Neuroimaging Laboratory ( PNL ), Center for Morphometric Analysis ( CMA ), Massachusetts General Hospital [Boston] ( MGH ), 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 ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, Brogham Women's Hospital, European Project : 757672,NeuroLang, 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: Male, Computer science, Cognitive Neuroscience, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Fiber tract, Uncinate fasciculus, Brain mapping, Article, 030218 nuclear medicine & medical imaging, Diffusion MRI, White matter, 03 medical and health sciences, 0302 clinical medicine, [ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Region of interest, Fractional anisotropy, Fasciculus, Image Processing, Computer-Assisted, medicine, Humans, Arcuate fasciculus, ComputingMilieux_MISCELLANEOUS, Brain Mapping, medicine.diagnostic_test, biology, Automatic classification of white matter tracts, Brain, Magnetic resonance imaging, biology.organism_classification, Diffusion Tensor Imaging, medicine.anatomical_structure, Neurology, Cartography, Tractography, 030217 neurology & neurosurgery
Abstract: Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.
Published: 2018

215. Prediction and inference diverge in biomedicine: Simulations and real-world data

Author: Gaël Varoquaux, Danilo Bzdok, Bertrand Thirion, Denis A. Engemann, Olivier Grisel, Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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), Nuxeo, nuxeo, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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: Value (ethics), Computer science, Inference, MESH: data science, Machine learning, computer.software_genre, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistical inference, 0101 mathematics, Clinical care, Biomedicine, 030304 developmental biology, 0303 health sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], business.industry, Linear model, MESH: scientific discovery, Precision medicine, 3. Good health, Variable (computer science), MESH: intelligent algorithms, Pattern recognition (psychology), [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Artificial intelligence, business, Real world data, computer, MESH: variable importance, MESH: reproducibility
Abstract: In the 20thcentury many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21stcentury, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional methods used to infer statistically significant group differences and burgeoning machine-learning tools suited to forecast an individual’s future. This comparison applies the linear model for identifyingsignificantcontributing variables and for finding the mostpredictivevariable sets. In systematic data simulations and common medical datasets, we explored how statistical inference and pattern recognition can agree and diverge. Across analysis scenarios, even small predictive performances typically coincided with finding underlying significant statistical relationships. However, even statistically strong findings with very low p-values shed little light on their value for achieving accurate prediction in the same dataset. More complete understanding of different ways to define ‘important’ associations is a prerequisite for reproducible research findings that can serve to personalize clinical care.
Published: 2018

216. An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings

Author: Jean-Baptiste Schiratti, Jean-Eudes Le Douget, Alexandre Gramfort, Michel Le Van Quyen, Slim Essid, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-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)-Centre National de la Recherche Scientifique (CNRS), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), 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), 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)-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), 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, [SDV]Life Sciences [q-bio], 0206 medical engineering, Medically intractable epilepsy, Feature extraction, 02 engineering and technology, Electroencephalography, 03 medical and health sciences, Epilepsy, 0302 clinical medicine, Machine learning, medicine, [STAT.AP]Statistics [stat]/Applications [stat.AP], Intracranial EEG, medicine.diagnostic_test, business.industry, Probabilistic logic, Pattern recognition, medicine.disease, 020601 biomedical engineering, Ensemble learning, Intracranial eeg, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Epilepsy and seizures, Support vector machine, Artificial intelligence, business, Classifier (UML), 030217 neurology & neurosurgery
Abstract: International audience; This paper proposes a patient-specific supervised classification algorithm to detect seizures in long offline intracranial electroencephalographic (iEEG) recordings. The main idea of the proposed algorithm is to combine a set of probabilistic classifiers, trained on a dataset of 1 s epochs, into a weighted ensemble classifier which can be used to analyze longer 5 s data segments. The method is trained and evaluated on 24 patients , all suffering from focal medically intractable epilepsy, from the Epilepsiae database. The evaluation of the method, conducted using an average of 113 hours (min: 32 h, max: 229 h) of iEEG data per patient, shows that the proposed algorithm improves upon existing methods for seizure detection with iEEG.
Published: 2018

217. Driver estimation in non-linear autoregressive models

Author: Yves Grenier, Alexandre Gramfort, Tom Dupré la Tour, Télécom ParisTech, 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), 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, and Dupré la Tour, Tom
Subjects: spectrum estimation, Polynomial, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 020206 networking & telecommunications, cross-frequency coupling, 02 engineering and technology, non-linear autoregressive models, electrophysiology, Data modeling, Nonlinear system, Autoregressive model, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Gradient descent, Parametrization, Algorithm, Linear filter, Parametric statistics
Abstract: International audience; In non-linear autoregressive models, the time dependency of coefficients is often driven by a particular time-series which is not given and thus has to be estimated from the data. To allow model evaluation on a validation set, we describe a parametric approach for such driver estimation. After estimating the driver as a weighted sum of potential drivers, we use it in a non-linear autoregressive model with a polynomial parametrization. Using gradient descent, we optimize the linear filter extracting the driver, outperforming a typical grid-search on predefined filters.
Published: 2018

218. Spatially regularized wavelet leader scale-free analysis of fMRI data

Author: Herwig Wendt, Philippe Ciuciu, Patrice Abry, CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, 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, 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 Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-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, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Computer science, Working memory, business.industry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Bayesian inference, fMRI, Univariate, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Pattern recognition, computer.software_genre, Wavelet, multivariate, Neuroimaging, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Voxel, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Artificial intelligence, business, computer, Default mode network, spatial regularization, scale-free, ACM: I.: Computing Methodologies
Abstract: International audience; Slow brain dynamics has received considerable interest in the recent years, with the scale-free paradigm playing a crucial role for analysis of various neuroimaging modalities. However , assessing the role of slow arrhythmic fluctuations requires the use of a large continuum of time scales and thus of long time series, hence raising concerns regarding the use of scale-free tools on fMRI data. Further, scale-free analysis remained so far mostly univariate, that is, voxels are analyzed independently, hence neglecting their spatial organization. The present contribution aims to propose a spatially regularized estimation of the self-similarity parameter, based on a recently formalized formalism combining wavelet leaders and Bayesian models. The spatially regularized estimates permit to quantify the modulations of the scale-free dynamics from rest to a working memory task from fMRI data collected for 21 healthy volunteers. These modulations are significant in the default mode network and in some regions involved in task performance such as primary visual regions or the supplementary motor area.
Published: 2018

219. Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity

Author: Tommy Löfstedt, Amicie de Pierrefeu, Fouad Hadj-Selem, Stéphanie Lefebvre, Renaud Jardri, Philippe Ciuciu, Renaud Lopes, Pierre Thomas, Thomas Fovet, Edouard Duchesnay, 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), Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), VEhicule DEcarboné et COmmuniquant et sa Mobilité (VeDeCom), Umeå University, 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), Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), This study was supported by the Programme Hospitalier de Recherche Clinique(PHRC-N2011 19-02 MULTIMODHAL awarded to RJ) and the Agence Nationale de la Recherche (ANR-16-CE37-0015 INTRUDEawarded to RJ)., ANR-16-CE37-0015,INTRUDE,Pensées intrusives: du décodage de l'activité fonctionnelle per-hallucinatoire en temps réel à l'innovation thérapeutique(2016), Université de Lille, CNRS, CHU Lille, Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193, Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab], Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV], 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, Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), 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), Troubles cognitifs dégénératifs et vasculaires - U 1171 (TCDV), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)
Subjects: Adult, Male, Hallucinations, Computer science, Schizophrenia (object-oriented programming), Variation (game tree), Machine learning, computer.software_genre, Brain mapping, Field (computer science), Pattern Recognition, Automated, 03 medical and health sciences, 0302 clinical medicine, Stimulus modality, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Neural Pathways, Humans, Radiology, Nuclear Medicine and imaging, Research Articles, Brain Mapping, Principal Component Analysis, Radiological and Ultrasound Technology, business.industry, Brain, Neurofeedback, Magnetic Resonance Imaging, 030227 psychiatry, Support vector machine, Neurology, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Principal component analysis, Auditory Perception, Schizophrenia, Real-time fMRI, Female, Neurology (clinical), Artificial intelligence, Anatomy, business, computer, 030217 neurology & neurosurgery, Resting-state networks
Abstract: International audience; Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods , such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the data-set. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback.
Published: 2018

220. Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation

Author: Bertrand Thirion, 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), ANR-11-BINF-0004,NiConnect,Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle(2011), European Project: 785907,H2020,HBP SGA2(2018), 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: Computer science, Rest, Population, population models, Twins, Health Informatics, Positive-definite matrix, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Estimation of covariance matrices, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Connectome, Image Processing, Computer-Assisted, Statistics::Methodology, Humans, Radiology, Nuclear Medicine and imaging, education, Shrinkage, education.field_of_study, Models, Statistical, Radiological and Ultrasound Technology, Covariance, [SCCO.NEUR]Cognitive science/Neuroscience, Siblings, functional connectivity, Probabilistic logic, Age Factors, Estimator, Reproducibility of Results, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, [STAT]Statistics [stat], shrinkage, Phenotype, Population model, Computer Vision and Pattern Recognition, Algorithm, 030217 neurology & neurosurgery, Algorithms
Abstract: International audience; Estimating covariances from functional Magnetic Resonance Imaging at rest (r-fMRI) can quantify interactions between brain regions. Also known as brain functional connectivity, it reflects inter-subject variations in behavior and cognition, and characterizes neuropathologies. Yet, with noisy and short time-series, as in r-fMRI, covariance estimation is challenging and calls for penalization, as with shrinkage approaches. We introduce population shrinkage of covariance estimator (PoSCE) : a covariance estimator that integrates prior knowledge of covariance distribution over a large population, leading to a non-isotropic shrinkage. The shrinkage is tailored to the Riemannian geometry of symmetric positive definite matrices. It is coupled with a probabilistic modeling of the individual and population covariance distributions. Experiments on two large r-fMRI datasets (HCP n=815, Cam-CAN n=626) show that PoSCE has a better bias-variance trade-off than existing covariance estimates: this estimator relates better functional-connectivity measures to cognition while capturing well intra-subject functional connectivity.
Published: 2018

221. Inter-subject registration of functional images: do we need anatomical images ?

Author: Dohmatob, Elvis, Varoquaux, Gael, Thirion, Bertrand, 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), 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), and Dohmatob, Elvis
Subjects: [STAT.AP]Statistics [stat]/Applications [stat.AP], B0 inhomogeneity, General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, human brain-mapping, high-resolution EPI, distortions, lcsh:RC321-571, inter-subject registration, [STAT.AP] Statistics [stat]/Applications [stat.AP], Methods, functional MRI, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Neuroscience
Abstract: International audience; In Echo-Planar Imaging (EPI)-based MRI (Magnetic Resonance Imaging), inter-subject registration typically uses the subject's T1-weighted (T1w) anatomical image to learn deformations of the subject's brain onto a template. The estimated deformation fields are then applied to the subject's EPI scans (functional or diffusion-weighted images) to warp the latter to a template space. Historically, such indirect T1w-based registration was motivated by the lack of clear anatomical details in low-resolution EPI images: a direct registration of the EPI scans to template space would be futile. A central prerequisite in such indirect methods is that the anatomical (aka the T1w) image of each subject is well aligned with their EPI images via rigid coregistration. We provide experimental evidence that things have changed: nowadays, there is a decent amount of anatomical contrast in high-resolution EPI data. That notwithstanding, EPI distortions due to B0 inhomogeneities cannot be fully corrected. Residual uncorrected distortions induce non-rigid deformations between the EPI scans and the same subject's anatomical scan. In this manuscript, we contribute a computationally cheap pipeline that leverages the high spatial resolution of modern EPI scans for direct inter-subject matching. Our pipeline is direct and does not rely on the T1w scan to estimate the inter-subject deformation. Results on a large dataset show that this new pipeline outperforms the classical indirect T1w-based registration scheme, across a variety of post-registration quality-assessment metrics including: Normalized Mutual Information, relative variance (variance-to-mean ratio), and to a lesser extent, improved peaks of group-level General Linear Model (GLM) activation maps.
Published: 2018

222. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series

Author: Gilles Wainrib, Pierrick J. Arnal, Mathieu Galtier, Alexandre Gramfort, Stanislas Chambon, Laboratoire Traitement et Communication de l'Information ( LTCI ), Télécom ParisTech-Institut Mines-Télécom [Paris]-Université Paris-Saclay, School of Engineering and Science [Bremen] ( JU-SES ), Jacobs University [Bremen], Département d'informatique de l'École normale supérieure ( DI-ENS ), École normale supérieure - Paris ( ENS Paris ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ), ANRT Grant 2015/1005, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Rythm [Paris], Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), 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), École normale supérieure - Paris (ENS 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)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, and Département d'informatique de l'École normale supérieure (DI-ENS)
Subjects: FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Polysomnography, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], transfer learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, EMG, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, EEG, [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], multivariate time se, Sleep Stages, Signal processing, medicine.diagnostic_test, General Neuroscience, Rehabilitation, Electroencephalography, Signal Processing, Computer-Assisted, ries, Softmax function, 020201 artificial intelligence & image processing, Neurons and Cognition (q-bio.NC), Algorithms, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Feature extraction, Biomedical Engineering, Expert Systems, Machine Learning (stat.ML), EOG, 03 medical and health sciences, Computer Systems, Internal Medicine, medicine, Humans, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], business.industry, Electromyography, Deep learning, Decision Trees, deep learning, Pattern recognition, Sleep stage classification, Data segment, Electrooculography, ComputingMethodologies_PATTERNRECOGNITION, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, spatio-temporal data, Multivariate Analysis, Spectrogram, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract: International audience; Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can exploit the temporal context of each 30 s window of data. For each modality the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields state-of-the-art performance. Our study reveals a number of insights on the spatio-temporal distribution of the signal of interest: a good trade-off for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting one minute of data before and after each data segment offers the strongest improvement when a limited number of channels is available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver state-of-the-art classification performance with a small computational cost.
Published: 2018

223. Single-Subject Prediction: A Statistical Paradigm for Precision Psychiatry

Author: Bzdok, Danilo, Karrer, Teresa, Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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, Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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 Bzdok, Danilo
Subjects: neuroimaging, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, precision medicine, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, prediction-inference distinction, genetics, health analytics
Published: 2018

224. Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty

Author: Vincent Frouin, Tommy Löfstedt, Renaud Jardri, Amicie de Pierrefeu, Philippe Ciuciu, Fouad Hadj-Selem, Mathieu Dubois, Thomas Fovet, Edouard Duchesnay, 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, VEhicule DEcarboné et COmmuniquant et sa Mobilité (VeDeCom), Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), 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)), 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), Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab), 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: Elastic net regularization, Population, Neuroimaging, 01 natural sciences, Regularization (mathematics), 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, 0101 mathematics, Electrical and Electronic Engineering, education, Interpretability, Principal Component Analysis, education.field_of_study, PCA, Radiological and Ultrasound Technology, business.industry, Sparse PCA, Brain, Pattern recognition, Magnetic Resonance Imaging, Thresholding, Computer Science Applications, Data set, total variation, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Principal component analysis, Artificial intelligence, business, unsupervised machine learning, Algorithms, 030217 neurology & neurosurgery, Software, MRI
Abstract: International audience; Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's inter-pretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as Sparse PCA, have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining e.g., $l$1 and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured sparse PCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as e.g., N-dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as Sparse PCA and ElasticNet PCA) or structured approach (such as GraphNet PCA) are significant, since SPCA-TV reveals the variability within a data set in the form of intelligible brain patterns that are easier to interpret and more stable across different samples.
Published: 2018

225. Machine learning for precision psychiatry: Opportunites and challenges

Author: Bzdok, Danilo, Meyer-Lindenberg, Andreas, Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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), Central Institute of Mental Health [Mannheim], University Hospital Mannheim | Universitätsmedizin Mannheim, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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), Medical Faculty [Mannheim], and Bzdok, Danilo
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], predictive analytics, single-subject prediction, machine learning, null-hypothesis testing, Research Domain Criteria (RDoC), [INFO]Computer Science [cs], personalized medicine, [INFO] Computer Science [cs], artificial intelligence, endophenotypes, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; The nature of mental illness remains a conundrum. Traditional disease categories are increasinglysuspected to misrepresent the causes underlying mental disturbance. Yet, psychiatrists andinvestigators now have an unprecedented opportunity to benefit from complex patterns in brain,behavior, and genes using methods from machine learning (e.g., support vector machines, modernneural-network algorithms, cross-validation procedures). Combining these analysis techniques with awealth of data from consortia and repositories has the potential to advance a biologically groundedredefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroupsof psychiatric patient can better predict treatment outcomes than DSM/ICD diagnoses. In a new eraof evidence-based psychiatry tailored to single patients, objectively measurable endophenotypescould allow for early disease detection, individualized treatment selection and dosage adjustment toreduce the burden of disease. This primer aims to introduce clinicians and researchers to theopportunities and challenges in bringing machine intelligence into psychiatric practice.
Published: 2018

226. GraphVar 2.0: a user-friendly toolbox for machine learning on functional connectivity measures

Author: Waller, Lea, Brovkin, Anastasia, Dorfschmidt, Lena, Bzdok, Danilo, Walter, Henrik, Kruschwitz, Johann Daniel, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Department of Computational Neuroscience [Hamburg], Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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), Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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), and Bzdok, Danilo
Subjects: FOS: Computer and information sciences, Computational Neuroscience, MATLAB, Linear SV, Elastic Net, Decoding, [SDV]Life Sciences [q-bio], Precision Psychiatry, Statistics - Applications, Reproducibility, Functional Connectivity, [SDV] Life Sciences [q-bio], Machine Learning, Model Performance, Graph Theory, Encoding, Applications (stat.AP), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Toolbox, Nested Cross Validation
Abstract: Background: We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. New Method: GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of network measures and additional variables, allowing for a flexibility in neuroimaging applications. Results: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, network metrics and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. Comparison with existing methods: Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated cueing for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. Conclusions: GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators., MATLAB, GUI, Toolbox, 11 pages, first two authors contributed equally, , uncorrected proof Journal of Neuroscience Methods, Accepted 1 July 2018
Published: 2018

227. Optimizing deep video representation to match brain activity

Author: Hugo Richard, Guillaume Charpiat, Bertrand Thirion, Ana Luísa Pinho, 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, TAckling the Underspecified (TAU), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-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)-Inria Saclay - Ile de France, and Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, genetic structures, Brain activity and meditation, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Optical flow, 02 engineering and technology, Brain mapping, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Foveal, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing (cs.NE), Association (psychology), business.industry, Deep learning, Representation (systemics), Computer Science - Neural and Evolutionary Computing, deep learning, Image content, Pattern recognition, eye diseases, video encoding, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, brain mapping, Neurons and Cognition (q-bio.NC), 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract: International audience; The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions. Here we study fMRI activity in ten subjects watching color natural movies and compute deep representations of these movies with an architecture that relies on optical flow and image content. The association of activity in visual areas with the different layers of the deep architecture displays complexity-related contrasts across visual areas and reveals a striking foveal/peripheral dichotomy.
Published: 2018

228. Statistical Inference with Ensemble of Clustered Desparsified Lasso

Author: Chevalier, Jérôme-Alexis, Salmon, Joseph, Thirion, Bertrand, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), Télécom ParisTech, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Paris-Saclay, 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: FOS: Computer and information sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], Applications (stat.AP), Statistics - Applications
Abstract: International audience; Medical imaging involves high-dimensional data, yet their acquisition is obtained for limited samples. Multivariate predictive models have become popular in the last decades to fit some external variables from imaging data, and standard algorithms yield point estimates of the model parameters. It is however challenging to attribute confidence to these parameter estimates, which makes solutions hardly trustworthy. In this paper we present a new algorithm that assesses parameters statistical significance and that can scale even when the number of predictors p ≥ 10^5 is much higher than the number of samples n ≤ 10^3 , by lever-aging structure among features. Our algorithm combines three main ingredients: a powerful inference procedure for linear models –the so-called Desparsified Lasso– feature clustering and an ensembling step. We first establish that Desparsified Lasso alone cannot handle n p regimes; then we demonstrate that the combination of clustering and ensembling provides an accurate solution, whose specificity is controlled. We also demonstrate stability improvements on two neuroimaging datasets.
Published: 2018

229. Sensing Von Economo Neurons in the Insula with Multi-shell Diffusion MRI

Author: Wassermann, Demian, Nguyen, Dang Van, Gallardo, Guillermo, Li, Jing-Rebecca, Cai, Weidong, Menon, Vinod, 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), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Royal Institute of Technology [Stockholm] (KTH ), Shape reconstruction and identification (DeFI ), 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, Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, LargeBrainNets, LargeSmallBrainNets, European Project: 757672,H2020 Pilier ERC,NeuroLang(2018), 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, 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), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-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 ), Computational Imaging of the Central Nervous System ( ATHENA ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Royal Institute of Technology in Stockholm, Shape reconstruction and identification ( DeFI ), 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, Stanford University School of Medicine [Stanford], Stanford University [Stanford], and European Project : 757672,NeuroLang
Subjects: [ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

230. Convex Optimization approach to signals with fast varying instantaneous frequency

Author: Hau-Tieng Wu, Matthieu Kowalski, Adrien Meynard, 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 des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics [University of Toronto], University of Toronto, Hau-tieng Wu’s work is partially supported by Sloan Research Fellow FR-2015-65363. Matthieu Kowalski benefited from the support of the 'FMJH Program Gaspard Monge in optimization and operation research', and from the support to this program from EDF., 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: FOS: Computer and information sciences, Instantaneous frequency, Chirp factor, 010103 numerical & computational mathematics, 02 engineering and technology, 01 natural sciences, Instantaneous phase, Signal, Methodology (stat.ME), Time–frequency representation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Mathematics - Numerical Analysis, 0101 mathematics, Representation (mathematics), Statistics - Methodology, Mathematics, FISTA, Ideal (set theory), Signal reconstruction, Applied Mathematics, 020206 networking & telecommunications, Numerical Analysis (math.NA), Time–frequency analysis, Convex optimization, Time-frequency analysis, Algorithm, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract: International audience; Motivated by the limitation of analyzing oscillatory signals composed of multiple components with fast-varying instantaneous frequency, we approach the time-frequency analysis problem by optimization. Based on the proposed adaptive harmonic model, the time-frequency representation of a signal is obtained by directly minimizing a functional, which involves few properties an "ideal time-frequency representation" should satisfy, for example, the signal reconstruction and concentrative time frequency representation. FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) is applied to achieve an efficient numerical approximation of the functional. We coin the algorithm as {\it Time-frequency bY COnvex OptimizatioN} (Tycoon). The numerical results confirm the potential of the Tycoon algorithm.
Published: 2018

231. Celer: a Fast Solver for the Lasso with Dual Extrapolation

Author: Massias, Mathurin, Gramfort, Alexandre, Salmon, Joseph, 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), Université Paris-Saclay, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Télécom ParisTech, 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: FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
Abstract: International audience; Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of regression, this can be achieved either by discarding irrelevant features (screening techniques) or by prioritizing features likely to be included in the support of the solution (working set techniques). Duality comes into play at several steps in these techniques. Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of improved dual points. This enables a tighter control of op-timality as used in stopping criterion, as well as better screening performance of Gap Safe rules. Finally, we propose a working set strategy based on an aggressive use of Gap Safe screening rules. Thanks to our new dual point construction, we show significant computational speedups on multiple real-world problems.
Published: 2018
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232. An Empirical Study of the Maximum Degree of Undersampling in Compressed Sensing for T2*-weighted MRI

Author: Lazarus, Carole, Weiss, Pierre, Vignaud, Alexandre, Ciuciu, Philippe, 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), Institut des Technologies Avancées en sciences du Vivant (ITAV), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), 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, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)
Subjects: Compressed Sensing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, maximum undersampling factor, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, image quality, MRI
Abstract: International audience; Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging modalities used in clinical routine today. Yet, one major limitation to this technique resides in its long acquisition times. Over the last decade, Compressed Sensing (CS) has been increasingly used to address this issue and offers to shorten MR scans by reconstructing images from undersampled Fourier data. Nevertheless, a quantitative guide on the degree of acceleration applicable to a given acquisition scenario is still lacking today, leading in practice to a trial-and-error approach in the selection of the appropriate undersampling factor. In this study, we shortly point out the existing theoretical sampling results in CS and their limitations which motivate the focus of this work: an empirical and quantitative analysis of the maximum degree of undersampling allowed by CS in the specific context of T2*-weighted MRI. We make use of a generic method based on retrospective undersampling to quantitatively deduce the maximum acceleration factor R max which preserves a desired image quality as a function of the image resolution and the available signal-to-noise ratio (SNR). Our results quantify how larger acceleration factors can be applied to higher resolution images as long as a minimum SNR is guaranteed. In practice however, the maximum acceleration factor for a given resolution appears to be constrained by the available SNR inherent to the considered acquisition. Our analysis enables to take this a priori knowledge into account, allowing to derive a sequence-specific maximum acceleration factor adapted to the intrinsic SNR of any MR pipeline. These results obtained on an analytical T2*-weighted phantom image were corroborated by prospective experiments performed on MR data collected with radial trajectories on a 7 Tesla scanner with the same contrast. The proposed framework allows to study other sequence weightings and therefore better optimize sequences when accelerated using CS.
Published: 2018

233. Atlases of cognition with large-scale brain mapping

Author: Varoquaux, Gaël, Schwartz, Yannick, Poldrack, Russell, Gauthier, Baptiste, Bzdok, Danilo, Poline, Jean-Baptiste, Thirion, Bertrand, 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), Stanford University, 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, 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), 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 (COmUE)
Subjects: [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract: International audience; To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.
Published: 2018

234. Machine learning: Supervised methods, SVM and kNN

Author: Bzdok, Danilo, Krzywinski, Martin, Altman, Naomi, 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), Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), Canada's Michael Smith Genome Sciences Centre (CMSGSC), BC Cancer Agency (BCCRC), Pennsylvania State University (Penn State), Penn State System, 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), Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), and Bzdok, Danilo
Subjects: [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract: International audience; In supervised learning, a set of input variables, such as bloodmetabolite or gene expression levels, are used to predict aquantitative response variable like hormone level or a qualitative onesuch as healthy versus diseased individuals. We have previouslydiscussed several supervised learning algorithms, including logisticregression and random forests, and their typical behaviors withdifferent sample sizes and numbers of predictor variables. Thismonth, we look at two very common supervised methods in thecontext of machine learning: linear support vector machines (SVM)and k-nearest neighbors (kNN). Both have been successfully appliedto challenging pattern-recognition problems in biology and medicine.
Published: 2018

235. Offline A/B testing for Recommender Systems

Author: Simon Dollé, Thomas Nedelec, Alexandre Gilotte, Alexandre Abraham, Clément Calauzènes, Criteo [Paris], 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), 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: Counterfactual thinking, FOS: Computer and information sciences, Importance sampling, Computer science, Machine Learning (stat.ML), 02 engineering and technology, Recommender system, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Machine learning, computer.software_genre, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), 010104 statistics & probability, Statistics - Machine Learning, Order (exchange), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Off-policy evaluation, 0101 mathematics, A/B testing, business.industry, Estimator, Product (business), Computer Science - Learning, Benchmark (computing), Artificial intelligence, business, computer, Counterfactual estimation
Abstract: International audience; Online A/B testing evaluates the impact of a new technology byrunning it in a real production environment and testing its performance on a subset of the users of the platform. It is a well-known practice to run a preliminary offline evaluation on historical data to iterate faster on new ideas, and to detect poor policies in order to avoid losing money or breaking the system. For such offline evaluations, we are interested in methods that can compute offline an estimate of the potential uplift of performance generated by anew technology. Offline performance can be measured using estimators known as counterfactual or off-policy estimators. Traditional counterfactual estimators, such as capped importance sampling or normalised importance sampling, exhibit unsatisfying bias-variance compromises when experimenting on personalized product recommendation systems. To overcome this issue, we model the bias incurred by these estimators rather than bound it in the worst case, which leads us to propose a new counterfactual estimator. We provide a benchmark of the different estimators showing their correlation with business metrics observed by running online A/B tests on a large-scale commercial recommender system.
Published: 2018
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236. Statistics versus Machine Learning

Author: Danilo Bzdok, Martin Krzywinski, Naomi Altman, Bzdok, Danilo, 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), Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), Pennsylvania State University (Penn State), Penn State System, Canada's Michael Smith Genome Sciences Centre (CMSGSC), BC Cancer Agency (BCCRC), 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), and Rheinisch-Westfälische Technische Hochschule Aachen (RWTH)
Subjects: 0301 basic medicine, Computer science, Population, MEDLINE, Sample (statistics), [INFO] Computer Science [cs], Machine learning, computer.software_genre, Biochemistry, Article, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Text mining, Statistics, [INFO]Computer Science [cs], education, Molecular Biology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], education.field_of_study, Models, Genetic, business.industry, Data interpretation, Cell Biology, 030104 developmental biology, Gene Expression Regulation, Data Interpretation, Statistical, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 030217 neurology & neurosurgery, Biotechnology
Abstract: International audience; Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will have a disease. Prediction makesit possible to identify best courses of action (e.g. treatment choice)without requiring understanding of the underlying mechanisms. In atypical research project, both inference and prediction are of value—we want to know how biological processes work and what will happennext. For example, we might want to infer which biological processesare associated with the dysregulation of a gene in a disease as well asclassify whether a subject has the disease and predict the besttherapy, such as drug intervention or invasive surgery.
Published: 2018

237. Extracting representations of cognition across neuroimaging studies improves brain decoding

Author: Mensch, Arthur, Mairal, Julien, Thirion, Bertrand, Varoquaux, Gaël, 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), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), ANR-14-CE23-0003,MACARON,Apprentissage statistique à grande échelle et applications(2014), European Project: 714381,H2020,SOLARIS(2017), 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: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Social Sciences, Quantitative Biology - Quantitative Methods, Diagnostic Radiology, Machine Learning (cs.LG), Cognition, Learning and Memory, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, Functional Magnetic Resonance Imaging, Task Performance and Analysis, Medicine and Health Sciences, Psychology, Biology (General), Quantitative Methods (q-bio.QM), Statistical Data, Brain Mapping, Radiology and Imaging, Statistics, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Brain, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Magnetic Resonance Imaging, Physical Sciences, Research Article, Computer and Information Sciences, Neural Networks, Imaging Techniques, QH301-705.5, Models, Neurological, Neuroimaging, Machine Learning (stat.ML), Models, Psychological, Research and Analysis Methods, Diagnostic Medicine, Humans, Learning, Stochastic Processes, Functional Neuroimaging, Cognitive Psychology, Computational Biology, Biology and Life Sciences, Mathematical Concepts, FOS: Biological sciences, Linear Models, Cognitive Science, Nerve Net, Mathematics, Neuroscience
Abstract: Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data., Author summary Brain-imaging findings in cognitive neuroscience often have low statistical power, despite the availability of functional imaging data across hundreds of studies. Yet, with current analytic frameworks, combining data across studies that map responses to different tasks discards the nuances of the cognitive questions they ask. In this paper, we propose a new approach for fMRI analysis, where a predictive model is used to extract the shared information from many studies together, while respecting their original paradigms. Our method extracts cognitive representations that associate a wide variety of functions to specific brain structures. This provides quantitative improvements and cognitive insights when analyzing together 35 task-fMRI studies; the breadth of the functional data we consider is much higher than in previous work. Reusing the representations learned by our approach also improves statistical power in studies outside the training corpus.
Published: 2018
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238. Patterns of Schizophrenia Symptoms: Hidden Structure in the PANSS Questionnaire

Author: Oliver Gruber, André Aleman, Gaël Varoquaux, Bertrand Thirion, Danilo Bzdok, Renaud Jardri, Iris E. C. Sommer, Jérémy Lefort-Besnard, Birgit Derntl, Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), 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, sans affiliation, Department of Psychiatry [Heidelberg], University of Heidelberg, Medical Faculty, University of Groningen [Groningen], Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Brain Centre Rudolf Magnus [Utrecht], University Medical Center [Utrecht], Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), 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), Sans affiliation, Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab), Perceptual and Cognitive Neuroscience (PCN), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Neuropsychology, Guided Treatment in Optimal Selected Cancer Patients (GUTS), Clinical Cognitive Neuropsychiatry Research Program (CCNP), and Movement Disorder (MD)
Subjects: Adult, Male, Psychometrics, Schizophrenia (object-oriented programming), MEDLINE, behavioral disciplines and activities, Article, PREDICT, lcsh:RC321-571, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Cluster Analysis, Humans, In patient, Patient group, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, ComputingMilieux_MISCELLANEOUS, Psychiatric Status Rating Scales, Negative symptom, Principal Component Analysis, INSTRUMENT, [SCCO.NEUR]Cognitive science/Neuroscience, Cognition, Regression, 3. Good health, 030227 psychiatry, MODEL, Psychiatry and Mental health, NEGATIVE SYNDROME SCALE, POSITIVE SYMPTOMS, Clinical diagnosis, Schizophrenia, Female, Psychology, 030217 neurology & neurosurgery, Clinical psychology
Abstract: The clinical presentation of patients with schizophrenia has long been described to be very heterogeneous. Coherent symptom profiles can probably be directly derived from behavioral manifestations quantified in medical questionnaires. The combination of machine learning algorithms and an international multi-site dataset (n = 218 patients) identified distinctive patterns underlying schizophrenia from the widespread PANSS questionnaire. Our clustering approach revealed a negative symptom patient group as well as a moderate and a severe group, giving further support for the existence of schizophrenia subtypes. Additionally, emerging regression analyses uncovered the most clinically predictive questionnaire items. Small subsets of PANSS items showed convincing forecasting performance in single patients. These item subsets encompassed the entire symptom spectrum confirming that the different facets of schizophrenia can be shown to enable improved clinical diagnosis and medical action in patients. Finally, we did not find evidence for complicated relationships among the PANSS items in our sample. Our collective results suggest that identifying best treatment for a given individual may be grounded in subtle item combinations that transcend the long-trusted positive, negative, and cognitive categories.
Published: 2018

239. Bridging Brain Structure and Function by Correlating Structural Connectivity and Cortico-Cortical Transmission

Author: Almairac, Fabien, Filipiak, Patryk, Slabu, Lavinia, Clerc, Maureen, Papadopoulo, Théodore, Fontaine, Denys, Mondot, Lydiane, Chanelet, Stéphan, Wassermann, Demian, Deriche, Rachid, Filipiak, Patryk, Building Normative Atlases of Diffusion MRI to Identify Subject-Specific Neuroimaging Abnormalities in Brain Trauma and Post-Traumatic Stress - - NeuroRef2016 - ANR-16-NEUC-0002 - CRCNS - VALID, Hôpital Pasteur [Nice] (CHU), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), 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), 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, 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), 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: [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

240. Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Author: R. Jardri, Josselin Houenou, Charles Laidi, Filip Spaniel, Nora Hamdani, Thomas Fovet, Marion Leboyer, Edouard Duchesnay, Marian Kolenic, Philippe Ciuciu, Julie Bourgin, Tommy Löfstedt, A. de Pierrefeu, Fouad Hadj-Selem, Tomas Hajek, 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, Umeå University, 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), Fondation FondaMental [Créteil], VEhicule DEcarboné et COmmuniquant et sa Mobilité (VeDeCom), Hopital Louis Mourier - AP-HP [Colombes], Institut de psychiatrie et neurosciences (U894 / UMS 1266), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Dalhousie University [Halifax], National Institute of Mental Health [Topolova] (NUDZ), 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)-Université Paris-Saclay-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), Pôle de Psychiatrie [Hôpital Henri Mondor], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital H. Mondor - A. Chenevier, Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), ANR-11-IDEX-0004-02/11-LABX-0035,BIOPSY,Laboratoire de Psychiatrie Biologique(2011), ANR-10-BINF-04-01/10-BINF-0004,Brainomics,Brainomics(2010), ANR-08-MNPS-0041,VIP,Viro-immunologie, Imagerie cérébrale et Psychoses(2008), 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), Hôpital Louis Mourier - AP-HP [Colombes], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Psychiatrie et Neurosciences (U894), 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, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital H. Mondor - A. Chenevier, Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab), ANR-11-IDEX-0004,SUPER,Sorbonne Universités à Paris pour l'Enseignement et la Recherche(2011), ANR-10-BINF-0004,Brainomics,Solutions méthodologiques et logicielles pour l'intégration des données de neuro-imagerie et de génomique(2010), 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: Adult, Male, Schizophrenia (object-oriented programming), Machine learning, computer.software_genre, Sensitivity and Specificity, Severity of Illness Index, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], First episode psychosis, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Gray Matter, business.industry, Reproducibility of Results, Psychoradiology, Middle Aged, First-episode psychosis, Classification, Magnetic Resonance Imaging, Signature (logic), 3. Good health, 030227 psychiatry, Psychiatry and Mental health, Structural MRI, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Schizophrenia, Female, Artificial intelligence, business, Psychology, computer, 030217 neurology & neurosurgery
Abstract: International audience; Objective: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings' reproducibility. Method: We propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross-site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first-episode patients. Results: Machine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first-episode psychosis patients (73% accuracy). Conclusion: These results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.
Published: 2018

241. A standardised representation for non-parametric fMRI results

Author: Camille Maumet, Guillaume Flandin, Martin Perez-Guevara, Jean-Baptiste Poline, Justin Rajendra, Richard Reynolds, Bertrand Thirion, Nichols, Thomas E., Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Criteo [Paris], McGill University = Université McGill [Montréal, Canada], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), National Institutes of Health [Bethesda] (NIH), 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), University of Oxford, Maumet, Camille, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), 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, and University of Oxford [Oxford]
Subjects: [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMethodologies_GENERAL
Abstract: International audience; Introduction : Reuse of data collected and analysed at another site is becoming more prevalent in the neuroimagingcommunity (cf. for example (Milham et al. 2017)) but this process usually relies on intensive data andmetadata curation. Given the ever-increasing number of research datasets produced and shared, it isdesirable to rely on standards that will enable automatic data and metadata retrieval for large-scaleanalyses (Wilkinson et al. 2016).We recently introduced NIDM-Results (Maumet et al. 2016), a data model to represent and publishdata and metadata created as part of a mass univariate neuroimaging study (typically functionalmagnetic resonance imaging). Here we extend this model to allow for the representation ofnon-parametric analyses and we introduce a JSON API that will facilitate export into NIDM-Results.
Published: 2018
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242. A deep learning architecture to detect events in EEG signals during sleep

Author: Stanislas Chambon, Alexandre Gramfort, Valentin Thorey, Emmanuel Mignot, Pierrick J. Arnal, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Division of Sleep Medicine [Stanford], Department of Psychiatry and Behavioral Sciences [Stanford], Stanford Medicine, Stanford University-Stanford University-Stanford Medicine, Stanford University-Stanford University, Dreem SAS, 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), 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: 0301 basic medicine, Signal Processing (eess.SP), FOS: Computer and information sciences, medicine.medical_specialty, Computer Science - Machine Learning, Time series, Computer science, Machine Learning (stat.ML), Electroencephalography, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Convolutional neural network, Sleep medicine, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), EEG-patterns, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, medicine, FOS: Electrical engineering, electronic engineering, information engineering, EEG, Electrical Engineering and Systems Science - Signal Processing, Sleep Stages, medicine.diagnostic_test, Event (computing), business.industry, Deep learning, Pattern recognition, 3. Good health, 030104 developmental biology, Feature (computer vision), Sleep (system call), Artificial intelligence, Event detection, business, Sleep, 030217 neurology & neurosurgery
Abstract: International audience; Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (≥ 10 s) such as sleep stages, and micro-events (≤ 2 s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time consuming and tedious process with a large inter-scorer variability. Automatic algorithms have been developed to detect various types of events but these are event-specific. We propose a deep learning method that jointly predicts locations, durations and types of events in EEG time series. It relies on a convolutional neural network that builds a feature representation from raw EEG signals. Numerical experiments demonstrate efficiency of this new approach on various event detection tasks compared to current state-of-the-art, event specific, algorithms.
Published: 2018
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243. Beyond Pham's algorithm for joint diagonalization

Author: Ablin, Pierre, Cardoso, Jean-François, Gramfort, Alexandre, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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, Centre National de la Recherche Scientifique (CNRS), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-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, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Numerical Analysis, MathematicsofComputing_NUMERICALANALYSIS, FOS: Mathematics, Machine Learning (stat.ML), Numerical Analysis (math.NA), [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Machine Learning (cs.LG)
Abstract: International audience; The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible. This problem naturally appears in several statistical learning tasks such as blind signal separation. We consider the diagonalization criterion studied in a seminal paper by Pham (2001), and propose a new quasi-Newton method for its optimization. Through numerical experiments on simulated and real datasets, we show that the proposed method outper-forms Pham's algorithm. An open source Python package is released.
Published: 2018
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244. Non-linear auto-regressive models for cross-frequency coupling in neural time series

Author: Virginie van Wassenhove, Yves Grenier, Alexandre Gramfort, Laetitia Grabot, Lucille Tallot, Tom Dupré la Tour, Valérie Doyère, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Neuroscience Paris Seine (NPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Neurosciences de Paris-Saclay (Neuro-PSI), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie cognitive (LCogn), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Neuroimagerie cognitive (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Sud - Paris 11 (UP11), 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), Signal, Statistique et Apprentissage (S2A), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Département Images, Données, Signal (IDS), Télécom ParisTech, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-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)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), 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 Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut des Neurosciences Paris-Saclay (NeuroPSI), 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, ANR-16-CE37-0004,AutoTime,Du codage automatique à la perception consciente du temps dans le système nerveux central: un déficit fondamental dans la schizophrénie?(2016), Neurosciences Paris Seine (NPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Saclay (COmUE), 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), 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, Physiology, Computer science, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, MESH: Neurons, Action Potentials, computer.software_genre, Mathematical and Statistical Techniques, 0302 clinical medicine, Goodness of fit, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Medicine and Health Sciences, Feature (machine learning), Bandwidth (Signal Processing), lcsh:QH301-705.5, MESH: Action Potentials, Parametric statistics, Neurons, Mammals, 0303 health sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], Ecology, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Physics, Brain, Eukaryota, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Signal Filtering, Electrophysiology, Computational Theory and Mathematics, Autoregressive model, Modeling and Simulation, Vertebrates, Physical Sciences, Parametric model, symbols, Engineering and Technology, Algorithm, Research Article, Models, Neurological, Neurophysiology, Research and Analysis Methods, Machine learning, Rodents, 03 medical and health sciences, Cellular and Molecular Neuroscience, symbols.namesake, MESH: Brain, MESH: Models, Neurological, Acoustic Signals, Sine Waves, Genetics, Animals, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, business.industry, Model selection, [SCCO.NEUR]Cognitive science/Neuroscience, Organisms, Probabilistic logic, Biology and Life Sciences, Acoustics, Filter (signal processing), Bandpass Filters, Nonlinear system, 030104 developmental biology, lcsh:Biology (General), Speech Signal Processing, Signal Processing, Amniotes, Artificial intelligence, Hilbert transform, business, Mathematical Functions, computer, 030217 neurology & neurosurgery, Neuroscience
Abstract: We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling., Author summary Neural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation.
Published: 2017

245. An analysis of psychoacoustically-inspired matching pursuit decompositions of speech signals

Author: Daoudi, Khalid, Vinuesa, Nicolas, Geometry and Statistics in acquisition data (GeoStat), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Groupe d'Imagerie Neurofonctionnelle (GIN - UMR 5296), 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)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-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: Perceptual models, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Matching pursuit, Time-frequency decompositions, Sparse representation, Gammatones
Abstract: International audience; Matching pursuit (MP), particularly using the Gammatones dictionary , has become a popular tool in sparse representations of speech/audio signals. The classical MP algorithm does not however take into account psychoacoustical aspects of the auditory system. Recently two algorithms, called PAMP and PMP have been introduced in order to select only perceptually relevant atoms during MP decomposition. In this paper we compare this two algorithms on few speech sentences. The results suggest that PMP, which also has the strong advantage of including an implicit stop criterion, always outperforms PAMP as well as classical MP. We then raise the question of whether the Gam-matones dictionary is the best choice when using PMP. We thus compare it to the popular Gabor and damped-Sinusoids dictionaries. The results suggest that Gammatones always outperform damped-Sinusoids, and that Gabor yield better reconstruction quality but with higher atoms rate.
Published: 2017

246. The challenge of mapping the human connectome based on diffusion tractography

Author: Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean Christophe, Côté, Marc Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang Cheng, Lin, Ying Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, Renjie, H., Li, Qiang, Westin, Carl Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M.W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean Philippe, Cetingul, H. Ertan, Odry, Benjamin L., Mailhe, Boris, Nadar, Mariappan S., Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E., Galvis, Justin, Thompson, Paul M., Requejo, Francisco De Santiago, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell'Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B., Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, Descoteaux, Maxime, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Sherbrooke Connectivity Imaging Lab [Sherbrooke] (SCIL), Département d'informatique [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS)-Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Krembil Research Institute, University Health Network, University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), IMT Institute for Advanced Studies [Lucca], Department of Diagnostic Imaging [Memphis], St Jude Children's Research Hospital, Institute of Medical Science [Toronto], University of Toronto, Zhejiang University of Technology, Shanghai United Imaging Healthcare Co Ltd, Laboratory of Mathematics in Imaging [Boston], Brigham and Women's Hospital [Boston], Centro de Investigación en Matemáticas (CIMAT), Consejo Nacional de Ciencia y Tecnología [Mexico] (CONACYT), 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), Image sciences institute - University of Utrecht (ISI), University Medical Center [Utrecht], Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), Université de Montréal (UdeM), Laboratoire d'Imagerie Biomédicale (LIB), 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), Center for advanced research in sleep medicine, Montreal, Institute of Bioimaging and Molecular Physiology [Germaneto], National Research Council [Italy] (CNR), Department of Speech and Hearing Sciences [Washington], University of Washington [Seattle], Schulich School of Medicine and Dentistry, University of Western Ontario (UWO), Synaptive Medical Inc, Signal Processing Laboratory [Lausanne] (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Biomedical Image Technologies Lab, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Siemens Healthcare, Keck School of Medicine [Los Angeles], University of Southern California (USC), Institute of Psychiatry, Psychology & Neuroscience, King's College London, King‘s College London, Groupe d'Imagerie Neurofonctionnelle (GIN - UMR 5296), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-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, Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Danish Research Centre for Magnetic Resonance (DRCMR), Hvidovre Hospital, Department of Applied Mathematics and Computer Science [Lyngby] (DTU Compute), Technical University of Denmark [Lyngby] (DTU), Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), University Hospital Basel [Basel], Université de Montréal. Faculté des arts et des sciences. Département de psychologie, Laboratoire d'Imagerie Biomédicale [Paris] (LIB), 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 de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Université de Sherbrooke [Sherbrooke], Université de Montréal [Montréal], Groupe d'imagerie neurofonctionnelle (GIN), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Caruyer, Emmanuel
Subjects: 0301 basic medicine, Databases, Factual, Chemistry(all), Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, General Physics and Astronomy, computer.software_genre, Biochemistry, 0302 clinical medicine, Image Processing, Computer-Assisted, Diffusion Tractography, lcsh:Science, IN-VIVO, Reliability (statistics), Telecomunicaciones, Ground truth, BRAIN FIBER PATHWAYS, PHANTOMS, Multidisciplinary, Orientation (computer vision), Brain, Human Connectome, Diffusion Tensor Imaging, Mapping, Connectome, Electrónica, WHITE-MATTER, Algorithms, Tractography, Nervous system, GLOBAL TRACTOGRAPHY, Medicina, Science, Physics and Astronomy(all), Machine learning, LINEAR REGISTRATION, VALIDATION, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Medical research, Journal Article, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, MRI TRACTOGRAPHY, Author Correction, MICROSTRUCTURE INFORMED TRACTOGRAPHY, OCCIPITAL FASCICULUS, human connectome, business.industry, Biochemistry, Genetics and Molecular Biology(all), Reproducibility of Results, General Chemistry, Computational biology and bioinformatics, Data set, 030104 developmental biology, lcsh:Q, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Genetics and Molecular Biology(all), Neuroscience
Abstract: Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations., Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.
Published: 2017

247. Cognitive and imaging markers in non-demented subjects attending a memory clinic: study design and baseline findings of the MEMENTO cohort

Author: Dufouil , Carole, Dubois , Bruno, Vellas , Bruno, Pasquier , Florence, Blanc , Frédéric, Hugon , Hugo, Hanon , Olivier, Dartigues , Jean-François, Harston , Sandrine, Gabelle , Audrey, Ceccaldi , Mathieu, Beauchet , Olivier, Krolak-Salmon , Pierre, David , David, Rouaud , Olivier, Godefroy , Olivier, Belin , Catherine, Rouch , Isabelle, Auguste , Nicolas, Wallon , David, Benetos , Athanase, Pariente , Jeremie, Paccalin , Marc, Moreaud , Olivier, Hommet , Caroline, Sellal , François, Boutoleau-Bretonnière , Claire, Jalenques , Isabelle, Gentric , Armelle, Vandel , Pierre, Azouani , Chabha, Fillon , Ludovic, Fischer , Clara, Savarieau , Helen, Operto , Grégory, Bertin , Hugo, Chupin , Marie, Bouteloup , Vincent, Habert , Marie-Odile, Mangin , Jean-François, Chêne , Geneviève, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, 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 [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Epidémiologie et analyses en santé publique : risques, maladies chroniques et handicaps (LEASP), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Hématopoïèse normale et pathologique, Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Gustave Roussy (IGR)-Université Paris-Sud - Paris 11 (UP11), Laboratoire de physique de la matière condensée (LPMC), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP), Groupe hospitalier Broca, CHU Bordeaux [Bordeaux], Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier]-Université de Montpellier (UM), Service de neurologie et de neuropsychologie, Université de la Méditerranée - Aix-Marseille 2-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), Laboratoire de Psychologie des Pays de la Loire (LPPL), Université d'Angers (UA)-Université de Nantes - UFR Lettres et Langages (UFRLL), Université de Nantes (UN)-Université de Nantes (UN), Hospices Civils de Lyon (HCL), Centre Mémoire de Ressources et de Recherche [Nice] (CMRR Nice), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Mines Nantes (Mines Nantes)-Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Centre National de la Recherche Scientifique (CNRS), CHU Amiens-Picardie, Système Nerveux Autonome - Epidémiologie, Physiologie, Ingénierie, Santé (SNA-EPIS), Université Jean Monnet [Saint-Étienne] (UJM)-Centre Hospitalier Universitaire de Saint-Etienne, Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), CHU Rouen, Normandie Université (NU), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Toulouse Neuro Imaging Center (ToNIC), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Hôpital Purpan [Toulouse], CHU Toulouse [Toulouse]-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre hospitalier universitaire de Poitiers (CHU Poitiers), Laboratoire de Psychologie et NeuroCognition (LPNC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Imagerie et cerveau, Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Neurologie [Strasbourg], CHU Strasbourg-Hopital Civil, Service de neurologie [Nantes], Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Hôpital Guillaume-et-René-Laennec [Saint-Herblain], Neuro-Psycho Pharmacologie des Systèmes Dopimanégiques sous-corticaux - Clermont Auvergne (NPsy-Sydo), CHU Clermont-Ferrand-Université Clermont Auvergne (UCA), Centre Hospitalier Régional Universitaire [Besançon] (CHRU Besançon), CATI Multicenter Neuroimaging Platform (CATI), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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, Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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 [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université de Bordeaux (UB), Pôle de Santé publique [CHU Bordeaux], CHU de bordeaux, Université Pierre et Marie Curie - Paris 6 (UPMC)-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)-Centre National de la Recherche Scientifique (CNRS), Fédération des Centres Mémoire de Ressource et de Recherche du Sud de la France [CHU Toulouse] (F-CMRR-SF), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Troubles cognitifs dégénératifs et vasculaires - U 1171 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Centre Mémoire de Ressources et de Recherche [Lille-Bailleul] (CMRR), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Hôpital Roger Salengro [Lille], Centre Mémoire de Ressources et de Recherches [Bordeaux], Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre de Mémoire de Ressources et de Recherche [Paris Nord], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Sorbonne Paris Cité (USPC), Maladie d'Alzheimer : marqueurs génétiques et vasculaires, neuropsychologies (EA 4468), Université Paris Descartes - Paris 5 (UPD5)-Groupe hospitalier Broca, Centre de Mémoire de Ressources et de Recherche [Paris Broca], AP-HP - Hôpital Broca [Paris], Centre de Mémoire de Ressources et de Recherche [Hôpital Pellegrin, Bordeaux], CHU Bordeaux [Bordeaux]-Groupe hospitalier Pellegrin [Bordeaux], Centre de Mémoire de Ressources et de Recherche [Hôpital Xavier Arnozan, Bordeaux], Hôpital Xavier Arnozan - CHU de Bordeaux, Centres Mémoire de Ressources et de Recherche [Hôpital Gui de Chauliac, Montpellier] (CMRR), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac, Centre de Mémoire de Ressources et de Recherche [Hôpital La Timone, Marseille], Hôpital de la Timone [CHU - APHM] (TIMONE)-CHU Marseille, Centre Mémoire de Ressources et de Recherche [CHU Angers], Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Nantes Angers Le Mans (UNAM), Centre Mémoire de Ressources et de Recherche [Lyon] (CMRR), Hospices Civils de Lyon (HCL)-Hôpital des Charpennes [CHU - HCL], Cognition Behaviour Technology (CobTek), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Université Côte d'Azur (UCA), Centre Mémoire Ressources et Recherche [Dijon] (CMRR Dijon), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Centre Mémoire de Ressources et de Recherche [CHU Amiens], Clinique de la Mémoire [Hôpitaux Universitaires Paris-Seine-Saint-Denis], Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Mémoire de Ressources et de Recherche [CHU de Saint-Etienne Hôpital Nord], CHU de Saint-Étienne Hôpital Nord [Saint Etienne], Centre Mémoire de Ressources et de Recherche [CHU de Saint-Etienne Hôpital de la Charité], CHU de Saint-Étienne Hôpital la Charite, Service de neurologie [Rouen], Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Centre Mémoire de Ressources et de Recherche [CHU de Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Centre Mémoire [CHU de Toulouse], Centre Mémoire de Ressources et de Recherche [CHRU de Poitiers La Miletrie], CHRU de Poitiers La Miletrie [Poitiers], Centre Mémoire de Ressources et de Recherche [CHU Grenoble Alpes-Site NORD, La Tronche], CHU Grenoble Alpes - Site NORD [La Tronche], Centre Mémoire de Ressources et de Recherche [CHRU de Tours Hôpital Bretonneau], Centre Hospitalier Régional Universitaire de Tours (CHRU Tours)-Hôpital Bretonneau [Tours], Mécanismes Centraux et Périphériques de la Neurodégénérescence, Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Mémoire de Ressources et de Recherche [Hôpitaux Civils de Colmar, Strasbourg], Hôpitaux Civils de Colmar, Centre Mémoire de Ressources et de Recherche [CHU de Nantes], Centre hospitalier universitaire de Nantes (CHU Nantes), Centre Mémoire de Ressources et de Recherche [CHU Clermont-Ferrand] (CMRR), CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche [CHRU de Brest], Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Centre Mémoire de Ressources et de Recherche [CHRU de Besançon] (CMRR), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Hôpital Saint-Jacques [CHRU de Besançon], Centre pour l’Acquisition et le Traitement des Images [Saclay] (CATI), 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)-Institut d'imagerie biomédicale (I²BM), Sorbonne Université (SU), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), 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, The MEMENTO cohort was sponsored by the Fondation Plan Alzheimer (Alzheimer Plan 2008–2012). This work was also supported by the following : CIC 1401-EC, Bordeaux University Hospital, Inserm and the University of Bordeaux., on behalf of the MEMENTO cohort Study Group, BMC, BMC, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre Mémoire de Ressources et de Recherche [CHU de Saint-Etienne] (CMRR 42), Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E)-Université Jean Monnet - Saint-Étienne (UJM), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université Paris-Sud - Paris 11 (UP11)-Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur (UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA), Mines Nantes (Mines Nantes)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hôpital Purpan [Toulouse], CHU Toulouse [Toulouse]-CHU Toulouse [Toulouse], Laboratoire de Psychologie et NeuroCognition (LPNC ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Imagerie et cerveau (iBrain - Inserm U1253 - UNIV Tours ), Neuro-Psycho Pharmacologie des Systèmes Dopimanégiques sous-corticaux (NPsy-Sydo), CHU Clermont-Ferrand-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut d'imagerie biomédicale (I²BM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Centre hospitalier universitaire de Toulouse - CHU Toulouse-Hôpital La Grave-Casselardit [Toulouse], Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7), Hôpital Gui de Chauliac-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Hôpital des Charpennes [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice), Centre Mémoire de Ressources et de Recherche [CHU de Toulouse, Hôpital Purpan], Hôpital Purpan [Toulouse], Hôpital Bretonneau [Tours]-Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), INSERM Unit 708-Neuroepidemiology, Paris, France, Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] ( IM2A ), Université Pierre et Marie Curie - Paris 6 ( UPMC ), Epidémiologie et anlyses en santé publique: risques, maladies chroniques et handicaps, Université Paul Sabatier - Toulouse 3 ( UPS ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Service de Neurologie [Lille], Hôpital Roger Salengro-Centre Hospitalier Régional Universitaire [Lille] ( CHRU Lille ), Kalray, Service de gériatrie, Assistance publique - Hôpitaux de Paris (AP-HP)-Université Paris Descartes - Paris 5 ( UPD5 ) -AP-HP - Hôpital Cochin Broca Hôtel Dieu [Paris], Epidémiologie, santé publique et développement, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -IFR99-ISPED, Institut de recherche en biothérapie ( IRB ), Université Montpellier 1 ( UM1 ) -Centre Hospitalier Régional Universitaire [Montpellier] ( CHRU Montpellier ) -Université de Montpellier ( UM ), Université de la Méditerranée - Aix-Marseille 2-Assistance Publique - Hôpitaux de Marseille ( APHM ) - Hôpital de la Timone [CHU - APHM] ( TIMONE ), Département de Médecine Interne et Gérontologie clinique, CHU Angers, Centre de recherche en neurosciences de Lyon ( CRNL ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de génie des procédés - environnement - agroalimentaire ( GEPEA ), Centre National de la Recherche Scientifique ( CNRS ) -Université de Nantes ( UN ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université Bretagne Loire ( UBL ) -Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique ( ONIRIS ), Laboratoire de Neurosciences Fonctionnelles et Pathologies ( LNFP ), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique ( CNRS ), AP-HP Hôpital Avicenne [Bobigny], Centre Hospitalier Universitaire de Saint-Etienne ( CHU de Saint-Etienne ), Risque cardiovasculaire, rigidité-fibrose et hypercoagulabilité ( RCV ), Université Henri Poincaré - Nancy 1 ( UHP ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Imagerie cérébrale et handicaps neurologiques, Centre Mémoire de Ressources et de Recherche ( CM2R ), CHU de Poitiers-Hôpital Jean Bernard, Unité de Neuropsychologie, CHU Grenoble, Université de Tours-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université de Nantes ( UN ) -Centre hospitalier universitaire de Nantes ( CHU Nantes ) -Hôpital Nord Laënnec, Neuro-Psycho Pharmacologie des Systèmes Dopimanégiques sous-corticaux - Clermont Auvergne ( NPsy-Sydo ), CHU Clermont-Ferrand-Université Clermont Auvergne ( UCA ), Service de Médecine Interne Gériatrique ( BREST - SMIG ), Centre Hospitalier Régional Universitaire de Brest ( CHRU Brest ), Cognition, Action, et Plasticité Sensorimotrice [Dijon - U1093] ( CAPS ), Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Centre d'Acquisition et de Traitement des Images [Paris], Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Bordeaux population health ( BPH ), Université de Bordeaux ( UB ) -Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Laboratoire Jacques-Louis Lions ( LJLL ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Epidemiologie-Biostatistique [Bordeaux], Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université de Bordeaux Ségalen [Bordeaux 2], Service de médecine nucléaire [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Pitié-Salpêtrière [APHP], Laboratoire de Neuroimagerie Assistée par Ordinateur ( LNAO ), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Université de Bordeaux (UB), Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Strasbourg (UNISTRA), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice), Centre Hospitalier Régional Universitaire de Tours (CHRU TOURS)-Hôpital Bretonneau [Tours], Centre Mémoire de Ressources et de Recherche [CHU de Clermont-Ferrand], CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche [CHRU de Besançon], Centre Hospitalier Régional Universitaire [Besançon] (CHRU Besançon)-Hôpital Jean Minjoz-Hôpital Saint-Jacques [CHRU de Besançon], Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut d'imagerie biomédicale (I²BM), Sorbonne Université, Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Université Côte d'Azur (UCA), 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)-Institut d'imagerie biomédicale (I²BM), Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), 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)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Université de Strasbourg (UNISTRA)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Université de Strasbourg (UNISTRA), Groupe hospitalier Pellegrin [Bordeaux] -CHU Bordeaux [Bordeaux], COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA), CHU Lille, CNRS, Inserm, Université de Lille, Troubles cognitifs dégénératifs et vasculaires - U1171, Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ), Fédération des Centres Mémoire de Ressource et de Recherche du Sud de la France [CHU Toulouse] ( F-CMRR-SF ), Troubles cognitifs dégénératifs et vasculaires ( DN2M ), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] ( CHRU Lille ) -INSERM, Centre Mémoire de Ressources et de Recherche [Lille-Bailleul] ( CMRR ), Centre Hospitalier Régional Universitaire [Lille] ( CHRU Lille ) -Hôpital Roger Salengro [Lille], Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie ( ICube ), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg ( ENGEES ) -Université de Strasbourg ( UNISTRA ) -Institut National des Sciences Appliquées - Strasbourg ( INSA Strasbourg ), Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ) -Matériaux et nanosciences d'Alsace, Université de Strasbourg ( UNISTRA ) -Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Strasbourg ( UNISTRA ) -Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Réseau nanophotonique et optique, Université de Strasbourg ( UNISTRA ) -Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Strasbourg ( UNISTRA ), Assistance publique - Hôpitaux de Paris (AP-HP)-Université Paris Diderot - Paris 7 ( UPD7 ) -Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Université Sorbonne Paris Cité ( USPC ), Maladie d'Alzheimer : marqueurs génétiques et vasculaires, neuropsychologie ( EA 4468 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Groupe hospitalier Broca, Centre Hospitalier Universitaire de Bordeaux-Groupe hospitalier Pellegrin [Bordeaux], Centres Mémoire de Ressources et de Recherche [Hôpital Gui de Chauliac, Montpellier] ( CMRR ), Centre Hospitalier Régional Universitaire [Montpellier] ( CHRU Montpellier ) -Hôpital Gui de Chauliac, Hôpital de la Timone [CHU - APHM] ( TIMONE ) -CHU Marseille, Centre Mémoire de Ressources et de Recherche [Lyon] ( CMRR ), Hospices Civils de Lyon ( HCL ) -Hôpital des Charpennes [CHU - HCL], Hospices Civils de Lyon ( HCL ), Cognition Behaviour Technology ( CobTek ), Université Nice Sophia Antipolis ( UNS ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -CHU Nice-Institut Claude Pompidou [Nice] ( ICP - Nice ), Centre Mémoire de Ressources et de Recherche [Nice] ( CMRR Nice ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -CHU Nice, Centre Mémoire Ressources et Recherche [Dijon] ( CMRR Dijon ), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand ( CHU Dijon ), CHU Amiens Picardie - Site Nord (Hôpital Nord) [Amiens], CHU Rouen-Université de Rouen Normandie ( UNIROUEN ), Normandie Université ( NU ) -Normandie Université ( NU ), Centre Hospitalier Régional Universitaire de Nancy ( CHRU Nancy ), Centre Hospitalier Régional Universitaire de Tours ( CHRU TOURS ) -Hôpital Bretonneau [Tours], Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Centre hospitalier universitaire de Nantes ( CHU Nantes ), Centre Hospitalier Régional Universitaire [Besançon] ( CHRU Besançon ) -Hôpital Jean-Minjoz [CHRU de Besançon]-Hôpital Saint-Jacques [CHRU de Besançon], Centre pour l’Acquisition et le Traitement des Images [Saclay] ( CATI ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -NeuroSpin-Institut d'imagerie biomédicale (I²BM), Sorbonne Universités, Département de Médecine Nucléaire [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [APHP], Laboratoire d'Imagerie Biomédicale ( LIB ), and 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 )
Subjects: Male, [SDV]Life Sciences [q-bio], Neuroimaging, Neuropsychological Tests, Spinal Puncture, Pattern Recognition, Automated, [ SDV.NEU.PC ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Diagnostic Self Evaluation, [ SDV.NEU.SC ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognitive aging, Fluorodeoxyglucose F18, Image Processing, Computer-Assisted, Humans, Cognitive Dysfunction, Longitudinal Studies, ComputingMilieux_MISCELLANEOUS, Aged, [ SDV ] Life Sciences [q-bio], Cohort studies, Natural history studies (prognosis), Alzheimer''s disease, Research, Brain, Organ Size, Magnetic Resonance Imaging, [SDV] Life Sciences [q-bio], Research Design, Positron-Emission Tomography, [ SDV.NEU.NB ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [ SCCO.NEUR ] Cognitive science/Neuroscience, Female, France, Radiopharmaceuticals, Alzheimer’s disease, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Follow-Up Studies
Abstract: Background The natural history and disease mechanisms of Alzheimer’s disease and related disorders (ADRD) are still poorly understood. Very few resources are available to scrutinise patients as early as needed and to use integrative approaches combining standardised, repeated clinical investigations and cutting-edge biomarker measurements. Methods In the nationwide French MEMENTO cohort study, participants were recruited in memory clinics and screened for either isolated subjective cognitive complaints (SCCs) or mild cognitive impairment (MCI; defined as test performance 1.5 SD below age, sex and education-level norms) while not demented (Clinical Dementia Rating [CDR]
Published: 2017

248. Variation in homotopic areas’ activity and inter-hemispheric intrinsic connectivity with type of language lateralization: an FMRI study of covert sentence generation in 297 healthy volunteers

Author: Marc Joliot, Damien Marie, Bernard Mazoyer, Nathalie Tzourio-Mazoyer, Groupe d'Imagerie Neurofonctionnelle (GIN - UMR 5296), Université de Bordeaux (UB)-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)-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), Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Adult, Male, 0301 basic medicine, medicine.medical_specialty, Histology, Adolescent, Asymmetries, Corpus callosum, Audiology, Functional Laterality, Lateralization of brain function, Developmental psychology, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Neural Pathways, Healthy volunteers, medicine, Humans, Resting state, Language lateralization, Handedness, Language, Brain Mapping, Language production, Resting state fMRI, [SCCO.NEUR]Cognitive science/Neuroscience, General Neuroscience, Production, Brain, Middle Aged, Magnetic Resonance Imaging, Hemispheric specialization, 030104 developmental biology, Variation (linguistics), Covert, Inter-hemispheric interactions, Female, Anatomy, Psychology, 030217 neurology & neurosurgery
Abstract: International audience; We investigated the regional correlates of differences in hemispheric lateralization in 297 healthy volunteers [including 153 left-handers (LH)] previously classified into three types of language lateralization according to their hemispheric functional lateralization index measured with fMRI during covert sentence production versus word list production (PRODSENT-LIST): 250 leftward asymmetrical Typicals, 10 rightward asymmetrical Strong-atypicals (only LH), and 37 Ambilaterals with weak lateralization. Using a functionally driven homotopic atlas (AICHA), we compared patterns of regional asymmetry during PRODSENT-LIST in these three groups. Among the 192 homotopic regions of interest (hROIs) of the AICHA atlas, 58 exhibited a significant effect of the type of lateralization on their BOLD signal variation during PRODSENT-LIST. The analyses of patterns of asymmetry of these 58 hROIs showed that (1) hROIs asymmetries in Strong-atypicals were significantly negatively correlated with those observed in Typicals, which indicates that their regional pattern of rightward asymmetries was comparable to the regional pattern of leftward language asymmetries of Typicals; (2) right- and left-handed Typicals had identical profiles, whereas left-handed Ambilaterals exhibited reduced leftward asymmetry as compared either to right-handed Ambilaterals or to Typicals. Moreover, left-handed Ambilaterals pattern of hROIs asymmetries significantly positively correlated with those of both Typicals and Strong-atypicals. In 291 of the participants, we tested the hypothesis that differences in language lateralization were associated with differences in inter-hemispheric connectivity during resting state by measuring their regional homotopic inter-hemispheric intrinsic connectivity coefficient (rHIICC) in 36 of the 58 hROIs known to be connected via the corpus callosum. Mean rHIICCs were negatively correlated with task-induced functional asymmetries, suggesting that enhanced inter-hemispheric cooperation at rest translates into increased inter-hemispheric cooperation during language production. In addition, the left-handed Ambilaterals exhibited a significantly larger rHIICC compared with right-handed Ambilaterals and Typicals, confirming a difference in inter-hemispheric organization in this group.
Published: 2015

249. Neurodevelopmental disorders (NDD) without boundaries: research and interventions beyond classifications

Author: Cécile Louveau, Pierre Ellul, Anton Iftimovici, Julien Dubreucq, Charles Laidi, Quentin Leyrolle, Diane Purper-Ouakil, Sebastien Jacquemont, Stanislas Lyonnet, Catherine Barthélémy, Marie-Odile Krebs, Jing Bai, Paul Olivier, Boris Chaumette, GHU Paris Psychiatrie et Neurosciences, AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Immunologie - Immunopathologie - Immunothérapie [CHU Pitié Salpêtrière] (I3), CHU Charles Foix [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)-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), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), 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), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), CHU Saint-Etienne, Child Mind Institute, Nutrition et Neurobiologie intégrée (NutriNeuro), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Ecole nationale supérieure de chimie, biologie et physique-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Université de Montpellier (UM), Hôpital Saint Eloi (CHRU Montpellier), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Centre de recherche du CHU Sainte-Justine / Research Center of the Sainte-Justine University Hospital [Montreal, Canada], Université de Montréal (UdeM)-CHU Sainte Justine [Montréal], Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), CHU Necker - Enfants Malades [AP-HP], GIS autisme et troubles du neuro-développement (GIS-Autisme et TND), Université de Tours (UT), McGill University = Université McGill [Montréal, Canada], Martinez Rico, Clara, Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), and Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E)
Subjects: Psychiatry and Mental health, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), Biological Psychiatry
Abstract: International audience; On June 2022, the 2nd Webinar "Neurodevelopmental Disorders (NDD) without boundaries took place at the Imagine Institute in Paris and was broadcasted live and in replay. The aim of this webinar is to address NDD in a dimensional rather than in a categorical approach. Several speakers were invited to present their researches on the subject. Classifications in NDD were discussed: irritability in NDD, involvement of the immune system in neurodevelopment, nutrition and gut microbiota modulate brain inflammation and neurodevelopment, co-occurring conditions in autistic adolescents and adults without intellectual disability. Classifications in psychiatric disorders were asked: mapping the effect of genes on cognition and autism risk, epigenetics and symptomatic trajectory in neurodevelopmental disorders, the autism-schizophrenia continuum in two examples: minor neurological signs and EEG microstates, the cerebellum in schizophrenia and autism: from imaging to intervention perspectives. Both genetic and environmental factors, along with clinical and imaging features, argue toward a continnum between NDD but also with adult psychiatric presentations. This new paradigm could modify the therapeutic strategy, with the development of large-spectrum treatments or new psychotherapies addressing co-occuring symptoms. The complexity and the heterogeneity of NDD apply well to the next scientific and political challenges: developing international convergence to push back the frontiers of our knowledge. This article is a summary of the 2nd webinar "Neurodevelopmental Disorders (NDD) without boundaries: research and interventions beyond classifications" sponsored by the French National Academy of Medicine, the autism and neurodevelopmental disorders scientific interest group (GIS), the International Research Network Dev-O-Psy and the French Institute of Psychiatry (GDR3557). Oral presentations are available as a replay on the following website (in French): https://autisme-neurodev.org/evenements/2022/04/12/tnd-sans-frontieres-la-recherche-et-les-interventions-au-dela-des-classifications/ .
Published: 2023

250. Survival and consciousness recovery are better in the minimally conscious state than in the vegetative state

Author: Lionel Naccache, Federico Raimondo, Mélanie Valente, Sophie Demeret, Marion Quirins, Jacobo D. Sitt, Athena Demertzi, Francis Bolgert, Nicolas Weiss, Marie-Odile Habert, Alexandra Grinea, Frédéric Faugeras, Louis Puybasset, Denis A. Engemann, Clémence Marois, Damien Galanaud, Benjamin Rohaut, Service de Neurophysiologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), 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)-CHU Pitié-Salpêtrière [AP-HP], Service de Neuroradiologie [CHU Pitié-Salpêtrière], UPMC - Faculté de médecine Pitié Salpétrière, Université Pierre et Marie Curie - Paris 6 (UPMC)-CHU Pitié-Salpêtrière [AP-HP], Service de médecine nucléaire [CHU Pitié-Salpétrière], Laboratoire d'Imagerie Biomédicale (LIB), 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 d'Anesthésie réanimation [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de neurologie 1 [CHU Pitié-Salpétrière], 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), Université Pierre et Marie Curie - Paris 6 (UPMC)-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)-Centre National de la Recherche Scientifique (CNRS), 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), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), 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, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Adult, Male, Adolescent, media_common.quotation_subject, Neuroscience (miscellaneous), outcomes, Severity of Illness Index, 03 medical and health sciences, Young Adult, Clinical, 0302 clinical medicine, Developmental and Educational Psychology, medicine, Humans, 030212 general & internal medicine, DOC patients, media_common, Aged, Aged, 80 and over, Persistent Vegetative State, [SCCO.NEUR]Cognitive science/Neuroscience, Minimally conscious state, Recovery of Function, Middle Aged, medicine.disease, Clinical diagnosis, Consciousness Disorders, Female, Neurology (clinical), State (computer science), prognosis, Consciousness, Psychology, Value (mathematics), 030217 neurology & neurosurgery, Cognitive psychology
Abstract: The prognosis value of early clinical diagnosis of consciousness impairment is documented by an extremely limited number of studies, whereas it may convey important information to guide medical decisions.We aimed at determining if patients diagnosed at an early stage (90 days after brain injury) as being in the minimally conscious state (MCS) have a better prognosis than patients in the vegetative state/Unresponsive Wakefulness syndrome (VS/UWS), independent of care limitations or withdrawal decisions.Patients hospitalized in ICUs of the Pitié-Salpêtrière Hospital (Paris, France) from November 2008 to January 2011 were included and evaluated behaviourally with standardized assessment and with the Coma Recovery Scale-Revised as being either in the VS/UWS or in the MCS. They were then prospectively followed until 1July 2011 to evaluate their outcome with the GOSE. We compared survival function and outcomes of these two groups.Both survival function and outcomes, including consciousness recovery, were significantly better in the MCS group. This difference of outcome still holds when considering only patients still alive at the end of the study.Early accurate clinical diagnosis of VS/UWS or MCS conveys a strong prognostic value of survival and of consciousness recovery.
Published: 2017

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