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29 results on '"[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]"'

1. Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study

Author: Ali Khatibi, Shahabeddin Vahdat, Ovidiu Lungu, Jurgen Finsterbusch, Christian Büchel, Julien Cohen-Adad, Veronique Marchand-Pauvert, Julien Doyon, McGill University = Université McGill [Montréal, Canada], University of Birmingham [Birmingham], Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), École Polytechnique de Montréal (EPM), Université de Montréal (UdeM), 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), and Marchand-Pauvert, Veronique
Subjects: Brain Mapping, Motor Learning, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cognitive Neuroscience, fMRI, Brain, Magnetic Resonance Imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Neurology, Spinal Cord, networks, Humans, Learning, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract: International audience; The spinal cord is important for sensory guidance and execution of skilled movements. Yet its role in human motor learning is not well understood. Despite evidence revealing an active involvement of spinal circuits in the early phase of motor learning, whether long-term learning engages similar changes in spinal cord activation and functional connectivity remains unknown. Here, we investigated spinal-cerebral functional plasticity associated with learning of a specific sequence of visually-guided joystick movements (sequence task) over six days of training. On the first and last training days, we acquired high-resolution functional images of the brain and cervical cord simultaneously, while participants practiced the sequence or a random task while electromyography was recorded from wrist muscles. After six days of training, the subjects' motor performance improved in the sequence compared to the control condition. These behavioral changes were associated with decreased co-contractions and increased reciprocal activations between antagonist wrist muscles. Importantly, early learning was characterized by activation in the C8 level, whereas a more rostral activation in the C6-C7 was found during the later learning phase. Motor sequence learning was also supported by increased spinal cord functional connectivity with distinct brain networks, including the motor cortex, superior parietal lobule, and the cerebellum at the early stage, and the angular gyrus and cerebellum at a later stage of learning. Our results suggest that the early vs. late shift in spinal activation from caudal to rostral cervical segments synchronized with distinct brain networks, including parietal and cerebellar regions, is related to progressive changes reflecting the increasing fine control of wrist muscles during motor sequence learning.
Published: 2021

2. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Author: Cury, Claire, Durrleman, Stanley, Cash, David, Lorenzi, Marco, Nicholas, Jennifer, Bocchetta, Martina, Van Swieten, John, Borroni, Barbara, Galimberti, Daniela, Masellis, Mario, Tartaglia, Maria Carmela, Rowe, James, Graff, Caroline, Tagliavini, Fabrizio, Frisoni, Giovanni, Laforce, Robert, Finger, Elizabeth, de Mendonça, Alexandre, Sorbi, Sandro, Ourselin, Sébastien, Rohrer, Jonathan, Modat, Marc, Andersson, Christin, Archetti, Silvana, Arighi, Andrea, Benussi, Luisa, Black, Sandra, Cosseddu, Maura, Fallstrm, Marie, Ferreira, Carlos, Fenoglio, Chiara, Fox, Nick, Freedman, Morris, Fumagalli, Giorgio, Gazzina, Stefano, Ghidoni, Robert, Grisoli, Marina, Jelic, Vesna, Jiskoot, Lize, Keren, Ron, Lombardi, Gemma, Maruta, Carolina, Meeter, Lieke, van Minkelen, Rick, Nacmias, Benedetta, Ijerstedt, Linn, Padovani, Alessandro, Panman, Jessica, Pievani, Michela, Polito, Cristina, Premi, Enrico, Prioni, Sara, Rademakers, Rosa, Redaelli, Veronica, Rogaeva, Ekaterina, Rossi, Giacomina, Rossor, Martin, Scarpini, Elio, Tang-Wai, David, Thonberg, Hakan, Tiraboschi, Pietro, Verdelho, Ana, Warren, Jason, Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Dementia Research Centre [London] (DRC), Neuroimagerie: méthodes et applications (Empenn), 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), 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 = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), 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), London School of Hygiene and Tropical Medicine (LSHTM), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Brescia, Centro Dino Ferrari [Milano], Università degli Studi di Milano = University of Milan (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Sunnybrook Research Institute [Toronto] (SRI), Sunnybrook Health Sciences Centre, Tanz Center Research in Neurodegenerative Diseases [Toronto], University of Toronto, University of Cambridge [UK] (CAM), Karolinska Institutet [Stockholm], Karolinska University Hospital [Stockholm], Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Centro San Giovanni di Dio, Fatebenefratelli, Brescia (IRCCS), Università degli Studi di Brescia = University of Brescia (UniBs), Université Laval [Québec] (ULaval), University of Western Ontario (UWO), Faculdade de Medicina [Lisboa], Universidade de Lisboa = University of Lisbon (ULISBOA), Università degli Studi di Firenze = University of Florence (UniFI), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Civic Hospital of Brescia, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, IRCCS Fatebenefratelli - Brescia, Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), UCL, Institute of Neurology [London], Toronto Western Hospital, Neuroimaging and Telemedicine (LENITEM), Mayo Clinic [Jacksonville], Marco Lorenzi received funding from the EPSRC (EP/J020990/1). Jennifer Nicholas is supported by UK Medical Research Council (grant MR/M023664/1). David Cash is supported by grants from the Alzheimer Society(AS-PG-15-025), Alzheimers Research UK (ARUK-PG2014-1946) and Medical Research Council UK (MR/M023664/1). JBR is supported by the Wellcome Trust (103838). Jonathan D. Rohrer is an MRC Clinician Scientist and has received funding from the NIHR Rare Diseases Translational Research Collaboration. Se361 bastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/K005278), the MRC (MR/J01107X/1), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative- BW.mn.BRC10269). Marc Modat is supported by the UCL Leonard Wolfson Experimental Neurology Centre, ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), European Project: 601055,EC:FP7:ICT,FP7-ICT-2011-9,VPH-DARE@IT(2013), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), Cury, Claire, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Idex UCA JEDI - - UCA JEDI2015 - ANR-15-IDEX-0001 - IDEX - VALID, VPH Dementia Research Enabled by IT - VPH-DARE@IT - - EC:FP7:ICT2013-04-01 - 2017-03-31 - 601055 - VALID, Data-driven models for Progression Of Neurological Disease - EuroPOND - - H2020 Pilier Societal Challenges2016-01-01 - 2019-12-31 - 666992 - VALID, Learning spatiotemporal patterns in longitudinal image data sets of the aging brain - LEASP - - H2020 ERC2016-09-01 - 2021-08-31 - 678304 - VALID, Neurology, Empenn, 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), 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 [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)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano [Milano] (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Università degli Studi di Brescia [Brescia], Universidade de Lisboa (ULISBOA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, Prodromal Symptoms, Neuroimaging, Middle Aged, Magnetic Resonance Imaging, Article, Shape analysis, Clustering, Cohort Studies, Computational anatomy, Spatio-Temporal Analysis, Thalamus, Frontotemporal Dementia, ddc:618.97, Parallel transport, Spatiotemporal geodesic regression, Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract: Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease., Graphical abstract Image 1, Highlights • Clustering shape parametrisation allows local shape analysis. • Thalamic shape changes appear 5 years before onset of fronto temporal dementia. • Shape changes seem to occur before volume changes. • Pre-symptomatic shape changes in thalamus are dorsofrontal, where connecting to temporal lobes.
Published: 2019

3. Creativity and the brain: An editorial introduction to the special issue on the neuroscience of creativity

Author: Emmanuelle Volle, Adam E. Green, Lucina Q. Uddin, Manish Saggar, Evangelia G. Chrysikou, Stanford University, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-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), Department of Psychology [Miami], University of Miami [Coral Gables], Drexel University, Georgetown University [Washington] (GU), Gestionnaire, HAL Sorbonne Université 5, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cognitive science, Cognitive Neuroscience, media_common.quotation_subject, 05 social sciences, Neurosciences, MEDLINE, Brain, Neurosciences. Biological psychiatry. Neuropsychiatry, Creativity, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Neurology, Humans, 0501 psychology and cognitive sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, 030217 neurology & neurosurgery, ComputingMilieux_MISCELLANEOUS, media_common, RC321-571
Abstract: International audience
Published: 2021

4. NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Author: Tarek Lajnef, Dmitrii Altukhov, David Meunier, Annalisa Pascarella, Mainak Jas, Golnoush Alamian, Etienne Combrisson, Jordan Alves, Vanessa Hadid, Arthur Dehgan, Anne-Lise Saive, Fanny Barlaam, Daphné Bertrand-Dubois, Karim Jerbi, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Istituto per le Applicazioni del Calcolo 'Mauro Picone' (IAC), Consiglio Nazionale delle Ricerche [Roma] (CNR), Centre for Cognition and Decision Making [HSE, Moscow], Institut of Cognitive Neuroscience [HSE, Moscow] (ICN), Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow] (HSE)-Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow] (HSE), Signal, Statistique et Apprentissage (S2A), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Institut Polytechnique de Paris (IP Paris), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (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), Université de Montréal (UdeM), Université du Québec à Montréal = University of Québec in Montréal (UQAM), University of Montreal, Aarhus University [Aarhus], Canada Research Chairs program and aDiscovery Grant (RGPIN-2015-04854) from the Natural Sciences andEngineering Research Council of Canada, a New Investigators Awardfrom the Fonds de Recherche du Quebec - Nature et Technologies (2018-NC-206005) and an IVADO-Apogee fundamental research project grant.This research is also supported in part by the FRQNT Strategic ClustersProgram (2020-RS4-265502 - Centre UNIQUE - Union Neurosciences&Artificial Intelligence - Quebec), Meunier, David, Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre de recherche en neurosciences de Lyon (CRNL), 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), National Research University Higher School of Economics [Moscow] (HSE)-National Research University Higher School of Economics [Moscow] (HSE), and National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR)
Subjects: Computer science, Source reconstruction, Functional connectivity, 0302 clinical medicine, Software, Magnetoencephalography (MEG), Multi-modality, MATLAB, computer.programming_language, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 05 social sciences, Brain, Magnetoencephalography, Electroencephalography, Magnetic Resonance Imaging, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Toolbox, Electrophysiology, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Algorithms, MNE, MRI, Brain networks, Cognitive Neuroscience, Neuroimaging, Brain imaging, 050105 experimental psychology, Nipype, lcsh:RC321-571, 03 medical and health sciences, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Electroencephalography (EEG), Implementation, MNE Source reconstruction, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, 030304 developmental biology, Pipelines, business.industry, Reproducibility of Results, Reproducible science, Modular design, Python (programming language), Graph theory, Computer architecture, Nerve Net, business, computer, 030217 neurology & neurosurgery, Electrophysiology MRI, Python
Abstract: Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.
Published: 2020

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

Author: Danilo Bzdok, Gaël Varoquaux, Mehdi Rahim, Bertrand Thirion, Irène Buvat, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Imagerie Moléculaire in Vivo (IMIV - U1023 - ERL9218), Service Hospitalier Frédéric Joliot (SHFJ), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), ANR-11-BINF-0004,NiConnect,Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle(2011), ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Rahim, Mehdi, Bio-informatique - Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle - - NiConnect2011 - ANR-11-BINF-0004 - BINF - VALID, and Idex Paris-Saclay - - IPS2011 - ANR-11-IDEX-0003 - IDEX - VALID
Subjects: 0301 basic medicine, Disease status, Cognitive Neuroscience, Schizophrenia (object-oriented programming), Population, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Individuality, Machine learning, computer.software_genre, Brain mapping, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Alzheimer Disease, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Computer Simulation, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], education, Brain Mapping, Brain organization, education.field_of_study, business.industry, Brain, Magnetic Resonance Imaging, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Phenotype, 030104 developmental biology, Neurology, Schizophrenia, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Artificial intelligence, business, Joint (audio engineering), Psychology, computer, Algorithms, 030217 neurology & neurosurgery
Abstract: International audience; To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important: they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimer's Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort: a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.
Published: 2017

6. SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity

Author: Isabelle Merlet, Rémi Gribonval, Hanna Becker, Laurent Albera, Jean-Claude Nunes, Philippe Guillotel, Julien Fleureau, Pierre Comon, Technicolor R & I [Cesson Sévigné], Technicolor, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio (PANAMA), 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), H. Becker was partly supported by Conseil Régional PACA and by CNRS France. The work of P. Comon was partly funded by the European Research Council under the EC 7th framework programme FP7/2007-2013 Grant Agreement no. 320594., European Project: 320594,EC:FP7:ERC,ERC-2012-ADG_20120216,DECODA(2013), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), 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 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), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Albera, Laurent, and Tensor Decomposition for Data Analysis with Applications to Health and Environment - DECODA - - EC:FP7:ERC2013-09-01 - 2018-08-31 - 320594 - VALID
Subjects: Optimization problem, Computer science, Cognitive Neuroscience, Regularization (mathematics), Extended source localization, 030218 nuclear medicine & medical imaging, Domain (software engineering), lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Image Processing, Computer-Assisted, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], EEG, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Cerebral Cortex, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Brain Mapping, Electroencephalography, Signal Processing, Computer-Assisted, Models, Theoretical, Term (time), Neurology, Second-order cone programming, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Focus (optics), ADMM, Algorithm, Sparsity, 030217 neurology & neurosurgery
Abstract: International audience; Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the spatial extent of the distributed sources. In a recent comparison of brain source imaging approaches, the VB-SCCD algorithm has been shown to be one of the most promising algorithms among these methods. However, this technique suffers from several problems: it leads to amplitude-biased source estimates, it has difficulties in separating close sources, and it has a high computational complexity due to its implementation using second order cone programming. To overcome these problems, we propose to include an additional regularization term that imposes sparsity in the original source domain and to solve the resulting optimization problem using the alternating direction method of multipliers. Furthermore, we show that the algorithm yields more robust solutions by taking into account the temporal structure of the data. We also propose a new method to automatically threshold the estimated source distribution, which permits to delineate the active brain regions. The new algorithm, called Source Imaging based on Structured Sparsity (SISSY), is analyzed by means of realistic computer simulations and is validated on the clinical data of four patients.
Published: 2017

7. Functional connectivity is preserved but reorganized across several anesthetic regimes

Author: Guillaume J.-P. C. Becq, Margaux Faucher, Emmanuel L. Barbier, Nora Collomb, Tarik Habet, Sophie Achard, Véronique Coizet, Chantal Delon-Martin, GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes (GIPSA-GAIA), Grenoble Images Parole Signal Automatique (GIPSA-lab), 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), GIPSA-Services (GIPSA-Services), [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), 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 ), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), Becq, Guillaume, and MIAI @ Grenoble Alpes - - MIAI2019 - ANR-19-P3IA-0003 - P3IA - VALID
Subjects: Neurological injury, [SDV.BA] Life Sciences [q-bio]/Animal biology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, resting state functional magnetic resonant imaging (rs-fMRI), Urethane, Cerveau de rat, Correlation, 0302 clinical medicine, Resting state functional magnetic resonance imaging (rs-fMRI), rat, Etomidate, Isoflurane, [SDV.BA]Life Sciences [q-bio]/Animal biology, Functional connectivity, 05 social sciences, Brain, Long evans, Magnetic Resonance Imaging, Neurology, cerebral blood flow (CBF), systemic variables, Cerebrovascular Circulation, Anesthetics, Inhalation, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Anesthetics, Intravenous, medicine.drug, Cognitive Neuroscience, Flux cérébral, anesthesia, Biology, 050105 experimental psychology, lcsh:RC321-571, Anesthésie générale, 03 medical and health sciences, medicine, Animals, Variables systémiques, 0501 psychology and cognitive sciences, Rats, Long-Evans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, functional connectivity (FC), Imagerie fonctionnelle par résonance magnétique en temps réel, Medetomidine, MESH: neurosciences, Rats, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Anesthetic, Nerve Net, Neuroscience, 030217 neurology & neurosurgery
Abstract: International audience; Under anesthesia, systemic variables and CBF are modified. How does this alter the connectivity measures obtained with rs-fMRI? To tackle this question, we explored the effect of four different anesthetics on Long Evans and Wistar rats with multimodal recordings of rs-fMRI, systemic variables and CBF. After multimodal signal processing, we show that the blood-oxygen-level-dependent (BOLD) variations and functional connectivity (FC) evaluated at low frequencies (0.031-0.25 Hz) do not depend on systemic variables and are preserved across a large interval of baseline CBF values. Based on these findings, we found that most brain areas remain functionally active under any anesthetics, i.e. connected to at least one other brain area, as shown by the connectivity graphs. In addition, we quantified the influence of nodes by a measure of functional connectivity strength to show the specific areas targeted by anesthetics and compare correlation values of edges at different levels. These measures enable us to highlight the specific network alterations induced by anesthetics. Altogether, this suggests that changes in connectivity could be evaluated under anesthesia, routinely used in the control of neurological injury.
Published: 2019

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

Author: Anne Baron-Van Evercooren, Anne-Charlotte Philippe, Marco Palombo, Bruno Stankoff, Daniel C. Alexander, Mathieu Santin, Olga Ciccarelli, Ioana Hill, Marie-Stéphane Aigrot, Alexandra Petiet, Hui Zhang, Francesca Branzoli, Stéphane Lehéricy, Ivana Drobnjak, Mehdi Felfli, Demian Wassermann, Dominique Langui, Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), Centre de Neuro-Imagerie de Recherche (CENIR), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neurologie [CHU Saint-Antoine], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Saint-Antoine [AP-HP], Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris] (CENIR), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Gestionnaire, HAL Sorbonne Université 5, and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: Monte Carlo method, Water exchange, Corpus callosum, computer.software_genre, Corpus Callosum, Machine Learning, Mice, 0302 clinical medicine, Image Processing, Computer-Assisted, Physics, Computational model, 05 social sciences, White Matter, Permeability (earth sciences), medicine.anatomical_structure, Neurology, Neurons and Cognition (q-bio.NC), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Monte Carlo Method, Monoamine Oxidase Inhibitors, Cognitive Neuroscience, Splenium, FOS: Physical sciences, Machine learning, 050105 experimental psychology, Permeability, lcsh:RC321-571, White matter, 03 medical and health sciences, Cuprizone, In vivo, medicine, Animals, 0501 psychology and cognitive sciences, Computer Simulation, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, business.industry, Physics - Medical Physics, Axons, Disease Models, Animal, Microscopy, Electron, Diffusion Magnetic Resonance Imaging, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Artificial intelligence, Medical Physics (physics.med-ph), business, computer, 030217 neurology & neurosurgery, Demyelinating Diseases
Abstract: International audience; The intra-axonal water exchange time (i) , a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R 2 = 0.99, i : R 2 = 0.84, intrinsic diffusivity d: R 2 = 0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N = 8) and healthy age-matched wild-type (WT, N = 8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (= 0.98 for f, = 0.82 for i). Finally, we find a statistically significant decrease in i in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (< i > = 310ms/330ms/350ms) compared to the WT group (< i > = 370ms/370ms/380ms). This is in line with our expectations that i is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .
Published: 2019

9. Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain

Author: Sylvie Moritz-Gasser, Hugues Duffau, Guillaume Herbet, Matthew C. Tate, Stefano Merler, Silvio Sarubbo, Alessandro De Benedictis, Division of Neurosurgery, Structural and Functional Connectivity Lab Project, Azienda Provinciale per i Servizi Sanitari (APSS), Feinberg School of Medicine, Northwestern University [Evanston], Bambino Gesù Children’s Hospital [Rome, Italy], NeuroInformatics Laboratory of Bruno Kessler Foundation (NILab), Università degli Studi di Trento (UNITN), Neurochirurgie [Hôpital Gui de Chauliac], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier], Institut des Neurosciences de Montpellier (INM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Salvy-Córdoba, Nathalie, and Institut des Neurosciences de Montpellier - Déficits sensoriels et moteurs (INM)
Subjects: [SDV.MHEP.CHI] Life Sciences [q-bio]/Human health and pathology/Surgery, Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Brain mapping, Neurosurgical Procedures, MESH: Magnetic Resonance Imaging, 0302 clinical medicine, Neural Pathways, MESH: Brain Mapping, Cerebral Cortex, Brain Mapping, medicine.diagnostic_test, 05 social sciences, MESH: Electric Stimulation, Cognition, Neuropsychological test, Human brain, Magnetic Resonance Imaging, White Matter, medicine.anatomical_structure, Neurology, Preoperative Period, Nerve tract, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neuropsychological testing, Atlas, Adult, Cognitive Neuroscience, MESH: Atlases as Topic, Direct electrical stimulation, Subcortical, MESH: Preoperative Period, [SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery, 050105 experimental psychology, Article, lcsh:RC321-571, White matter, 03 medical and health sciences, Atlases as Topic, Neuroimaging, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, MESH: Humans, MESH: Neural Pathways, MESH: Adult, MESH: Neurosurgical Procedures, Neurophysiology, MESH: Cerebral Cortex, Electric Stimulation, Brain functions, MESH: White Matter, Neuroscience, 030217 neurology & neurosurgery
Abstract: Objective The structural and functional organization of brain networks subserving basic daily activities (i.e. language, visuo-spatial cognition, movement, semantics, etc.) are not completely understood to date. Here, we report the first probabilistic cortical and subcortical atlas of critical structures mediating human brain functions based on direct electrical stimulation (DES), a well-validated tool for the exploration of cerebral processing and for performing safe surgical interventions in eloquent areas. Methods We collected 1162 cortical and 659 subcortical DES responses during testing of 16 functional domains in 256 patients undergoing awake surgery. Spatial coordinates for each functional response were calculated, and probability distributions for the entire patient cohort were mapped onto a standardized three-dimensional brain template using a multinomial statistical analysis. In addition, matching analyses were performed against prior established anatomy-based cortical and white matter (WM) atlases. Results The probabilistic maps for each functional domain were provided. The topographical analysis demonstrated a wide spatial distribution of cortical functional responses, while subcortical responses were more restricted, localizing to known WM pathways. These DES-derived data showed reliable matching with existing cortical and WM atlases as well as recent neuroimaging and neurophysiological data. Conclusions We present the first integrated and comprehensive cortical-subcortical atlas of structures essential for humans’ neural functions based on highly-specific DES mapping during real-time neuropsychological testing. This novel atlas can serve as a complementary tool for neuroscientists, along with data obtained from other modalities, to improve and refine our understanding of the functional anatomy of critical brain networks.
Published: 2019

10. Transcranial functional ultrasound imaging of the brain using microbubble-enhanced ultrasensitive Doppler

Author: Olivier Couture, Sophie Pezet, Zsolt Lenkei, Mickael Tanter, Bruno-Felix Osmanski, Claudia Errico, Physique des ondes pour la médecine, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Langevin - Ondes et Images (UMR7587) (IL), Sorbonne Université (SU)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Paris (UP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Plasticité du Cerveau Brain Plasticity (UMR 8249) (PdC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and PEZET, Sophie
Subjects: Male, Somatosensory activation, Ultrasonography, Doppler, Transcranial, Cognitive Neuroscience, medicine.medical_treatment, Contrast Media, Primary sensory cortex, Blood volume, Transcranial, 01 natural sciences, Doppler imaging, Article, Rats, Sprague-Dawley, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Neuroimaging, 0103 physical sciences, Image Processing, Computer-Assisted, medicine, Animals, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Evoked Potentials, 010301 acoustics, ComputingMilieux_MISCELLANEOUS, Craniotomy, Blood Volume, Microbubbles, business.industry, Skull, Ultrasound, Brain, Reproducibility of Results, Functional ultrasound imaging, Somatosensory Cortex, Human brain, Electric Stimulation, Rats, medicine.anatomical_structure, Neurology, symbols, Blood Vessels, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], business, Neuroscience, Doppler effect, 030217 neurology & neurosurgery, Biomedical engineering
Abstract: Functional ultrasound (fUS) is a novel neuroimaging technique, based on high-sensitivity ultrafast Doppler imaging of cerebral blood volume, capable of measuring brain activation and connectivity in rodents with high spatiotemporal resolution (100 μm, 1 ms). However, the skull attenuates acoustic waves, so fUS in rats currently requires craniotomy or a thinned-skull window. Here we propose a non-invasive approach by enhancing the fUS signal with a contrast agent, inert gas microbubbles. Plane-wave illumination of the brain at high frame rate (500 Hz compounded sequence with three tilted plane waves, PRF = 1500Hz with a 128 element 15 MHz linear transducer), yields highly-resolved neurovascular maps. We compared fUS imaging performance through the intact skull bone (transcranial fUS) versus a thinned-skull window in the same animal. First, we show that the vascular network of the adult rat brain can be imaged transcranially only after a bolus intravenous injection of microbubbles, which leads to a 9 dB gain in the contrast-to-tissue ratio. Next, we demonstrate that functional increase in the blood volume of the primary sensory cortex after targeted electrical-evoked stimulations of the sciatic nerve is observable transcranially in presence of contrast agents, with high reproducibility (Pearson's coefficient ρ = 0.7 ± 0.1, p = 0.85). Our work demonstrates that the combination of ultrafast Doppler imaging and injection of contrast agent allows non-invasive functional brain imaging through the intact skull bone in rats. These results should ease non-invasive longitudinal studies in rodents and open a promising perspective for the adoption of highly resolved fUS approaches for the adult human brain., Highlights • We combined ultrafast sensitive Doppler with contrast-enhanced ultrasound imaging. • We retrieved highly-resolved neurovascular transcranial maps with contrast agents. • The presence of microbubbles compensates for the attenuation from the skull. • fUS is sensitive to the local hyperemia in the rat brain through the skull with microbubbles. • Transcranial fUS imaging allows non-invasive functional brain studies in rodents.
Published: 2016

11. Learning and comparing functional connectomes across subjects

Author: R. Cameron Craddock, Gaël Varoquaux, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Smithsonian Institution, Dynamic Diaschisis FRM project DEQ20100318254, Varoquaux, Gaël, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
Subjects: Models, Anatomic, Cognitive Neuroscience, Models, Neurological, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 050105 experimental psychology, Functional connectivity, 03 medical and health sciences, 0302 clinical medicine, Connectome, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Animals, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], medicine.diagnostic_test, Group study, Resting state fMRI, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 05 social sciences, Brain, Cognition, Brain pathologies, Neurology, Quantitative Biology - Neurons and Cognition, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Nerve Net, Functional magnetic resonance imaging, Psychology, Neuroscience, 030217 neurology & neurosurgery
Abstract: International audience; Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes.
Published: 2013

12. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Author: Pierre-Louis Bazin, Pierre Maurel, James Nguyen, Amod Jog, Bram Platel, Heinz Handels, Ariel Birenbaum, Aaron Carass, Oskar Maier, Diana M. Sima, Daniel S. Reich, Devrim Unay, Olga Ciccarelli, Abhijith Chunduru, Jennifer L. Cuzzocreo, Saurabh Jain, Dzung L. Pham, Ciprian M. Crainiceanu, Manuel Jorge Cardoso, Leonardo O. Iheme, Adrian Gherman, Tal Arbel, Snehashis Roy, Olivier Commowick, Ramanathan Muthuganapathy, Xavier Tomas-Fernandez, Dirk Smeets, Suthirth Vaidya, Claudia A. M. Wheeler-Kingshott, Andrew Jesson, Carole H. Sudre, Laurence Catanese, Christian Barillot, Hayit Greenspan, Niamh Cawley, Sebastien Ourselin, Hrishikesh Deshpande, Mohsen Ghafoorian, Peter A. Calabresi, Simon K. Warfield, Elizabeth Magrath, Julia Button, Lotta Maria Ellingsen, Ferran Prados, Jerry L. Prince, Ganapathy Krishnamurthi, Department of Computer Science [Baltimore], Johns Hopkins University (JHU), Department of Electrical and Computer Engineering [Johns Hopkins University] (ECE), Center for Neuroscience and Regenerative Medicine [Bethesda] (CNRM), Henry M. Jackson Foundation for the Advancement of Military Medicine (HJM), Department of Radiology and Radiological Science [Baltimore], Johns Hopkins University School of Medicine [Baltimore], NMR Research Unit [London], Institute of Neurology [London], University College of London [London] (UCL)-University College of London [London] (UCL), Translational imaging group [London] (TIG), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL)-University College of London [London] (UCL)-Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Dementia Research Centre [London] (DRC), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), 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), Computational Radiology Laboratory [Boston] (CRL), Brigham and Women's Hospital [Boston]-Boston Children's Hospital, Department of Engineering Design [Madras], Indian Institute of Technology Madras (IIT Madras), Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], Institute of Medical Informatics [Lübeck], Universität zu Lübeck = University of Lübeck [Lübeck], Biomedical Engineering [Istanbul], Bahcesehir University [Istanbul], Icometrix [Leuven], Institute for Computing and Information Sciences [Nijmegen] (ICIS), Radboud University [Nijmegen], Diagnostic Image Analysis Group [Nijmegen], Radboud University Medical Center [Nijmegen], Department of Electrical Engineering, Tel Aviv University (TAU), Department of Biomedical Engineering [Tel Aviv] (TAU), Department of neurophysics [Leipzig], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Department of Biostatistics [Baltimore], Department of electrical and computer engineering [Reykjavík], University of Iceland [Reykjavik], Translational neuroradiology unit [Bethesda], National Institute of Neurological Disorders and Stroke [Bethesda] (NINDS), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Météo France-Centre National de la Recherche Scientifique (CNRS), 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 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), Boston Children's Hospital-Brigham and Women's Hospital [Boston], McGill University, Universität zu Lübeck [Lübeck] - University of Lübeck [Lübeck], Radboud university [Nijmegen], Tel Aviv University [Tel Aviv], Department of Biomedical Engineering [Tel Aviv], Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL)-University College of London [London] (UCL)-Centre for Medical Image Computing (CMIC), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universität zu Lübeck [Lübeck], Commowick, Olivier, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), 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), and 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)
Subjects: Adult, Male, medicine.medical_specialty, Multiple Sclerosis, Cognitive Neuroscience, computer.software_genre, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Consistency (database systems), 0302 clinical medicine, Resource (project management), Imaging, Three-Dimensional, medicine, Image Processing, Computer-Assisted, Humans, Medical physics, Segmentation, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Longitudinal Studies, Multiple sclerosis lesion, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Observer Variation, Lesion segmentation, Training set, business.industry, Data Science, Middle Aged, Magnetic Resonance Imaging, White Matter, Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], Data set, Neurology, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Data mining, business, computer, 030217 neurology & neurosurgery, Algorithms, Test data
Abstract: Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access) In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
Published: 2016

13. Phase-amplitude coupling at the organism level: The amplitude of spontaneous alpha rhythm fluctuations varies with the phase of the infra-slow gastric basal rhythm

Author: Craig G. Richter, Catherine Tallon-Baudry, Mariana Babo-Rebelo, Denis Schwartz, Laboratoire de Neurosciences Cognitives & Computationnelles (LNC2), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Ernst Strüngmann Institute for Neuroscience in Cooperation (ESI), Max-Planck-Gesellschaft, 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], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), 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), and HAL-UPMC, Gestionnaire
Subjects: 0301 basic medicine, Adult, Male, Brain activity and meditation, Cognitive Neuroscience, Phase (waves), interstitial cells of Cajal, Regulation of gastric function, vagal afferents, Resting-state dynamics, Gastric rhythm, interoception, Article, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Rhythm, Nuclear magnetic resonance, Neural Pathways, Phase-amplitude coupling, medicine, Humans, gastric motility, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Cross-frequency coupling, Physics, General Commentary, Stomach, Brain, Magnetoencephalography, brain gut axis, Coupling (electronics), Alpha Rhythm, 030104 developmental biology, medicine.anatomical_structure, Amplitude, Neurology, motility, Brain-viscera interactions, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, pacemaking, Neuron, Neuroscience, Insula, 030217 neurology & neurosurgery
Abstract: A fundamental feature of the temporal organization of neural activity is phase-amplitude coupling between brain rhythms at different frequencies, where the amplitude of a higher frequency varies according to the phase of a lower frequency. Here, we show that this rule extends to brain-organ interactions. We measured both the infra-slow (~0.05 Hz) rhythm intrinsically generated by the stomach – the gastric basal rhythm – using electrogastrography, and spontaneous brain dynamics with magnetoencephalography during resting-state with eyes open. We found significant phase-amplitude coupling between the infra-slow gastric phase and the amplitude of the cortical alpha rhythm (10–11 Hz), with gastric phase accounting for 8% of the variance of alpha rhythm amplitude fluctuations. Gastric-alpha coupling was localized to the right anterior insula, and bilaterally to occipito-parietal regions. Transfer entropy, a measure of directionality of information transfer, indicates that gastric-alpha coupling is due to an ascending influence from the stomach to both the right anterior insula and occipito-parietal regions. Our results show that phase-amplitude coupling so far only observed within the brain extends to brain-viscera interactions. They further reveal that the temporal structure of spontaneous brain activity depends not only on neuron and network properties endogenous to the brain, but also on the slow electrical rhythm generated by the stomach., Highlights • Spontaneous alpha rhythm fluctuations locked to gastric phase. • Gastric phase explains 8% of alpha variance. • Gastric-alpha coupling mostly to due to ascending influences from stomach to brain. • The stomach as an external oscillator constraining spontaneous brain activity.
Published: 2016

14. Effects of aging on low luminance contrast processing in humans

Author: Kenneth Knoblauch, Emmanuelle Bellot, Jan Warnking, Véronique Coizet, Elena Moro, Michel Dojat, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Institut cellule souche et cerveau (U846 Inserm - UCBL1), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Neurologie : Unité Troubles du mouvement [Hôpital Michallon, Grenoble], Hôpital Michallon, Université Grenoble Alpes. Fondation de l'avenir., ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-11-IDEX-0007,Avenir L.S.E.,PROJET AVENIR LYON SAINT-ETIENNE(2011), Dojat, Michel, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, and PROJET AVENIR LYON SAINT-ETIENNE - - Avenir L.S.E.2011 - ANR-11-IDEX-0007 - IDEX - VALID
Subjects: 0301 basic medicine, Adult, Male, Aging, Superior Colliculi, genetic structures, Cognitive Neuroscience, media_common.quotation_subject, Neuroimaging, Lateral geniculate nucleus, Human Vision, Luminance, Superior Colliculus, Contrast Sensitivity, 03 medical and health sciences, 0302 clinical medicine, medicine, Psychophysics, Contrast (vision), Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], media_common, Aged, Visual Cortex, Brain Mapping, Blood-oxygen-level dependent, Superior colliculus, fMRI, Brain, Geniculate Bodies, Middle Aged, Magnetic Resonance Imaging, 030104 developmental biology, Visual cortex, medicine.anatomical_structure, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Psychology, Neuroscience, 030217 neurology & neurosurgery, Photic Stimulation
Abstract: International audience; Luminance contrast is a fundamental visual cue. Using a dedicated neuroimaging framework, we sought to characterize typical Blood Oxygen Level Dependent (BOLD) responses in two subcortical regions, the superior colliculus (SC) and the lateral geniculate nucleus (LGN), and V1, the primary visual cortex area, and how they change over the lifespan. For imaging subcortical activity related to luminance contrast modulation, specific measurements were introduced to rule out possible signal contamination by cardiovascular activity and vascular alterations with age that could hamper the BOLD signal interpretation. Clearly, BOLD responses increased in these three regions with luminance contrast, with a statistically significant diminution in LGN and V1 for older compared to younger participants, while basal perfusion remained unchanged. Additionally, perceptual responses, as assessed with psychophysical experiments, were highly correlated to BOLD measures in the three studied regions. Taken together, fMRI and psychophysics results indicate an alteration of luminance contrast processing with normal aging. Based on this knowledge we can better recognize when age-related brain changes vary from these expectations especially during neurodegenerative diseases progression where the functioning of subcortical structures is altered. The proposed fMRI-physchophysics methodology allows performing such investigation.
Published: 2016

15. MRI atlas of the human hypothalamus

Author: Didier Dewailly, Serge Blond, Eglantine Balland, Vincent Prevot, Pierre Besson, Patrice Jissendi, Jean Paul Francke, J.-P. Pruvo, Marc Baroncini, Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer - U1172 Inserm - U837 (JPArc), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Lille Nord de France (COMUE)-Université de Lille, Service de neurochirurgie, Hôpital Roger Salengro [Lille]-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Service de neuroradiologie [Lille], Hôpital Roger Salengro [Lille]-Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Laboratoire d'anatomie, Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Service d'endocrinologie, gynécologie et médecine de la reproduction, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer - U837 (JPArc), Université Lille Nord de France (COMUE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, and Prevot, Vincent
Subjects: Male, human hypothalamus, anatomy, Cognitive Neuroscience, Hypothalamus, MESH: Atlases as Topic, Anterior commissure, Biology, Brain mapping, MESH: Magnetic Resonance Imaging, coronal plane, White matter, Young Adult, 03 medical and health sciences, Atlases as Topic, 0302 clinical medicine, medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Anatomy, Artistic, MESH: Brain Mapping, 030304 developmental biology, 2. Zero hunger, Brain Mapping, 0303 health sciences, MESH: Humans, medicine.diagnostic_test, Fornix, Magnetic resonance imaging, Anatomy, Magnetic Resonance Imaging, MESH: Hypothalamus, MESH: Anatomy, Artistic, MESH: Male, 3. Good health, medicine.anatomical_structure, Ventromedial nucleus of the hypothalamus, Neurology, MESH: Young Adult, Coronal plane, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], MESH: Female, Neuroscience, Nucleus, 030217 neurology & neurosurgery, MRI
Abstract: International audience; Gaining new insights into the anatomy of the human hypothalamus is crucial for the development of new treatment strategies involving functional stereotactic neurosurgery. Here, using anatomical comparisons between histology and magnetic resonance images of the human hypothalamus in the coronal plane, we show that discrete gray and white hypothalamic structures are consistently identifiable by MRI. Macroscopic and microscopic images were used to precisely annotate the MRI sequences realized in the coronal plane in twenty healthy volunteers. MRI was performed on a 1.5 T scanner, using a protocol including T1-weighted 3D fast field echo, T1-weighted inversion-recovery, turbo spin echo and T2-weighted 2D fast field echo imaging. For each gray matter structure as well as for white matter bundles, the different MRI sequences were analyzed in comparison to each other. The anterior commissure and the fornix were often identifiable, while the mammillothalamic tract was more difficult to spot. Qualitative analyses showed that MRI could also highlight finer structures such as the paraventricular nucleus, the ventromedial nucleus of the hypothalamus and the infundibular (arcuate) nucleus, brain nuclei that play key roles in the regulation of food intake and energy homeostasis. The posterior hypothalamic area, a target for deep brain stimulation in the treatment of cluster headaches, was readily identified, as was the lateral hypothalamic area, which similar to the aforementioned hypothalamic nuclei, could be a putative target for deep brain stimulation in the treatment of obesity. Finally, each of the identified structures was mapped to Montreal Neurological Institute (MNI) space.
Published: 2012

16. An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling

Author: Camille Maumet, Jean-Christophe Ferré, Christian Barillot, Pierre Maurel, 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)-Télécom Bretagne-CentraleSupélec-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des Solides (LPS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Maurel, Pierre
Subjects: Male, Multivariate statistics, Computer science, Perfusion scanning, searchlight approach, computer.software_genre, 030218 nuclear medicine & medical imaging, Hyper-perfusion, 0302 clinical medicine, a contrario, Neovascularization, Pathologic, Brain Neoplasms, Brain tumour, White noise, Middle Aged, Neurology, Cerebrovascular Circulation, Metric (mathematics), symbols, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Data mining, Perfusion, Blood Flow Velocity, Adult, Cognitive Neuroscience, Gaussian blur, Sensitivity and Specificity, 03 medical and health sciences, symbols.namesake, Arterial Spin Labelling, Image Interpretation, Computer-Assisted, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], General linear model, Hypo-perfusion, business.industry, Univariate, Reproducibility of Results, Pattern recognition, Image Enhancement, Cerebral Angiography, Cerebrovascular Disorders, Spin Labels, Artificial intelligence, Noise (video), business, computer, General Linear Model, 030217 neurology & neurosurgery, Magnetic Resonance Angiography
Abstract: International audience; In this paper, we introduce a new locally multivariate procedure to quantitatively extract voxel-wise patterns of abnormal perfusion in individual patients. This a contrario approach uses a multivariate metric from the computer vision community that is suitable to detect abnormalities even in the presence of closeby hypo- and hyper-perfusions. This method takes into account local information without applying Gaussian smoothing to the data. Furthermore, to improve on the standard a contrario approach, which assumes white noise, we introduce an updated a contrario approach that takes into account the spatial coherency of the noise in the probability estimation.Validation is undertaken on a dataset of 25 patients diagnosed with brain tumors and 61 healthy volunteers. We show how the a contrario approach outperforms the massively univariate General Linear Model usually employed for this type of analysis.
Published: 2015

17. Computational modeling of high-frequency oscillations at the onset of neocortical partial seizures: From ‘altered structure’ to ‘dysfunction’

Author: Pascal Benquet, Fabrice Wendling, Fabrice Bartolomei, Behnam Molaee-Ardekani, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Interactions cellulaires et moléculaires (ICM), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS), Epilepsies, Lesions Cerebrales et Systemes Neuraux de la Cognition, Université de la Méditerranée - Aix-Marseille 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Neurophysiologie Clinique, Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), ICT- FET Project HIVE, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), and Senhadji, Lotfi
Subjects: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Cognitive Neuroscience, Models, Neurological, Neocortex, Electroencephalography, Inhibitory postsynaptic potential, 03 medical and health sciences, Glutamatergic, Epilepsy, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, 030304 developmental biology, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Physics, Epilepsy, Partial, Sensory, 0303 health sciences, [PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], medicine.diagnostic_test, Synaptic Potentials, medicine.disease, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Amplitude, medicine.anatomical_structure, Neurology, Cerebral cortex, Excitatory postsynaptic potential, GABAergic, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neural Networks, Computer, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Neuroscience, 030217 neurology & neurosurgery
Abstract: International audience; In this paper, a neural mass model is proposed to analyze some mechanisms underlying the generation of fast oscillations (80 Hz and beyond) at the onset of seizures. This model includes one sub-population of pyramidal cells and one sub-population of interneurons targeting the perisomatic region of pyramidal cells where fast GABAergic currents are mediated. We identified some conditions for which the model can reproduce the features of high-frequency, chirp-like (from approximately 100 to approximately 70 Hz) signatures observed in real depth-EEG signals recorded in epileptic patients at seizure onset ("fast onset activity"). These conditions included appropriate alterations in (i) the strengths of GABAergic and glutamatergic connections, and (ii) the amplitude of average EPSPs/IPSPs. Results revealed that a subtle balance between excitatory and inhibitory feedbacks is required in the model for reproducing a 'realistic' fast activity, i.e., showing a reduction of frequency with a simultaneous increase in amplitude, as actually observed in epileptogenic cerebral cortex. Results also demonstrated that the number of scenarios (variation, in time, of model parameters) leading to chirp-like signatures was rather limited. First, to produce high-frequency output signals, the model should operate in a "resonance" region, at the frontier between a stable and an unstable region. Second both EPSP and IPSP amplitudes should decrease with time in order to obey the frequency/amplitude constraint. These scenarios obtained through a mathematical analysis of the model show how some alteration in the structure of neural networks can lead to dysfunction. They also provide insights into potentially important mechanisms for high-frequency epileptic activity generation.
Published: 2010

18. Preictal short-term plasticity induced by intracerebral 1 Hz stimulation

Author: Philippe Kahane, Agata Woźniak, Lorella Minotti, Olivier David, Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), AGH University of Science and Technology [Krakow, PL] (AGH UST), Laboratoire de physiopathologie de l'épilepsie (LNPEE), CHU Grenoble, CTRS-IDEE, Hospices Civils de Lyon (HCL), and David, Olivier
Subjects: Male, Nerve net, Electroencephalography, Epilepsy, 0302 clinical medicine, MESH: Neuronal Plasticity, 0303 health sciences, Neuronal Plasticity, MESH: Middle Aged, medicine.diagnostic_test, MESH: Electric Stimulation, Middle Aged, MESH: Seizures, Temporal Lobe, Electrodes, Implanted, medicine.anatomical_structure, Neurology, Data Interpretation, Statistical, MESH: Epilepsy, Temporal Lobe, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, Algorithms, Adult, Adolescent, Cognitive Neuroscience, MESH: Algorithms, Context (language use), Temporal lobe, 03 medical and health sciences, Seizures, MESH: Electroencephalography, Neuroplasticity, medicine, Humans, MESH: Temporal Lobe, Ictal, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030304 developmental biology, MESH: Adolescent, Models, Statistical, MESH: Humans, Dynamic causal modelling, MESH: Adult, medicine.disease, Electric Stimulation, MESH: Male, Epilepsy, Temporal Lobe, MESH: Nerve Net, MESH: Electrodes, Implanted, Nerve Net, MESH: Data Interpretation, Statistical, MESH: Female, Neuroscience, MESH: Models, Statistical, 030217 neurology & neurosurgery
Abstract: International audience; Short-term changes of intrinsic properties of neural networks play a critical role in brain dynamics. In that context, epilepsy is a typical pathology where the fast transition between interictal and ictal states is probably associated to intrinsic modifications of underlying networks. In this study, we focused on the correlates of plastic neural mechanisms in the intracerebral electroencephalogram (iEEG). Data were obtained during 1 Hz electrical stimulation in twenty patients suffering from temporal lobe epilepsy and implanted with intracerebral electrodes for clinical evaluation before resective surgery. First, we developed a procedure of analysis for localisation of the seizure onset zone based on brain excitability and plasticity defined as the average, and as the first-order (linear) modulation respectively, of the standard deviation of iEEG responses over stimulations. Our results suggest that the candidate epileptic focus is particularly prone to exhibiting short-term plasticity. Second, we used Dynamic Causal Modelling (DCM) to model explicitly short-term plasticity as a fast modulation of synaptic efficacies in either intrinsic or extrinsic connections to the focus. We found the two types of modulation both likely. Third, we used DCM to study the fast modulation of synaptic connectivity of long-range connections in neuronal networks restricted to the temporal lobe. Using DCM, we were able to estimate which structures expressed a strong modulatory input to the epileptic focus. Such early changes in interregional connectivity might be important for the initiation of electrically-induced seizures. They may also reflect some aspects of the pathogenesis of epilepsy in those patients.
Published: 2008

19. Functional MRI with active, fully implanted, deep brain stimulation systems: Safety and experimental confounds

Author: Patricia Limousin-Dowsey, Philip J. Allen, Stéphane Thobois, Louis Lemieux, Serge Pinto, David W. Carmichael, Tarek A. Yousry, John S. Thornton, Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), University College of London [London] (UCL), 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), MRI Unit, National Society for Epilepsy, University College London Hospitals (UCLH), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), and Pinto, Serge
Subjects: Deep brain stimulation, Deep Brain Stimulation, Cognitive Neuroscience, medicine.medical_treatment, behavioral disciplines and activities, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Tissue damage, Humans, Medicine, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, medicine.diagnostic_test, Phantoms, Imaging, business.industry, Pulse (signal processing), Induced voltage, Temperature, Magnetic resonance imaging, Magnetic Resonance Imaging, Electrodes, Implanted, nervous system, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], business, Functional magnetic resonance imaging, Echo planar, Neuroscience, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030217 neurology & neurosurgery, Subthalamic nucleus stimulation, Biomedical engineering
Abstract: We investigated safety issues and potential experimental confounds when performing functional magnetic resonance imaging (fMRI) investigations in human subjects with fully implanted, active, deep brain stimulation (DBS) systems. Measurements of temperature and induced voltage were performed in an in vitro arrangement simulating bilateral DBS during magnetic resonance imaging (MRI) using head transmit coils in both 1.5 and 3.0 T MRI systems. For MRI sequences typical of an fMRI study with coil-averaged specific absorption rates (SARs) less than 0.4 W/kg, no MRI-induced temperature change greater than the measurement sensitivity (0.1 degrees C) was detected at 1.5 T, and at 3 T temperature elevations were less than 0.5 degrees C, i.e. within safe limits. For the purposes of demonstration, MRI pulse sequences with SARs of 1.45 W/kg and 2.34 W/kg (at 1.5 T and 3 T, respectively) were prescribed and elicited temperature increases (>1 degrees C) greater than those considered safe for human subjects. Temperature increases were independent of the presence or absence of active stimulator pulsing. At both field strengths during echo planar MRI, the perturbations of DBS equipment performance were sufficiently slight, and temperature increases sufficiently low to suggest that thermal or electromagnetically mediated experimental confounds to fMRI with DBS are unlikely. We conclude that fMRI studies performed in subjects with subcutaneously implanted DBS units can be both safe and free from DBS-specific experimental confounds. Furthermore, fMRI in subjects with fully implanted rather than externalized DBS stimulator units may offer a significant safety advantage. Further studies are required to determine the safety of MRI with DBS for other MRI systems, transmit coil configurations and DBS arrangements.
Published: 2007

20. Brain mapping in stereotactic surgery: A brief overview from the probabilistic targeting to the patient-based anatomic mapping

Author: Jean-Jacques Lemaire, Jean Gabrillargues, Simone Hemm, François Caire, Franck Durif, Christophe Nuti, Vittorio Cristini, Lemlih Ouchchane, Philippe Derost, Jerome Coste, Jean Chazal, Coste, Jérôme, Service de Neurochirurgie A [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand, Equipe de Recherche en Signal et Imagerie Medicale (ERIM-ERI 14), Université d'Auvergne - Clermont-Ferrand I (UdA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Neurochirurgie [CHU Limoges], CHU Limoges, Service de Neurologie [CHU Clermont-Ferrand], CHU Estaing [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand-CHU Gabriel Montpied [Clermont-Ferrand], The University of Texas M.D. Anderson Cancer Center [Houston], Service de Neurochirurgie [CHU Clermont-Ferrand], CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand, and CHU Clermont-Ferrand-CHU Clermont-Ferrand-CHU Estaing [Clermont-Ferrand]
Subjects: [SDV.MHEP.CHI] Life Sciences [q-bio]/Human health and pathology/Surgery, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, medicine.medical_treatment, DBS, Brain mapping, Neurosurgical Procedures, 030218 nuclear medicine & medical imaging, Stereotaxic Techniques, 0302 clinical medicine, Stereotaxy, Computer vision, Brain Mapping, medicine.diagnostic_test, Brain, Commissure, Magnetic Resonance Imaging, Computer aided surgery, medicine.anatomical_structure, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Anatomy, medicine.symptom, Stereotactic imaging, MRI, Tractography, medicine.medical_specialty, Stereotactic surgery, Deep brain stimulation, Cognitive Neuroscience, Models, Neurological, [SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery, Lesion, 03 medical and health sciences, Atlas (anatomy), medicine, Humans, Medical physics, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Computer-aided surgery, book, Targeting, Models, Statistical, business.industry, Magnetic resonance imaging, Mr imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Stereotaxic technique, book.journal, Artificial intelligence, business, 030217 neurology & neurosurgery, Diffusion MRI
Abstract: International audience; In this article, we briefly review the concept of brain mapping in stereotactic surgery taking into account recent advances in stereotactic imaging. The gold standard continues to rely on probabilistic and indirect targeting, relative to a stereotactic reference, i.e., mostly the anterior (AC) and the posterior (PC) commissures. The theoretical position of a target defined on an atlas is transposed into the stereotactic space of a patient's brain; final positioning depends on electrophysiological analysis. The method is also used to analyze final electrode or lesion position for a patient or group of patients, by projection on an atlas. Limitations are precision of definition of the AC–PC line, probabilistic location and reliability of the electrophysiological guidance. Advances in MR imaging, as from 1.5-T machines, make stereotactic references no longer mandatory and allow an anatomic mapping based on an individual patient's brain. Direct targeting is enabled by high-quality images, an advanced anatomic knowledge and dedicated surgical software. Labeling associated with manual segmenta-tion can help for the position analysis along non-conventional, interpolated planes. Analysis of final electrode or lesion position, for a patient or group of patients, could benefit from the concept of membership, the attribution of a weighted membership degree to a contact or a structure according to its level of involvement. In the future, more powerful MRI machines, diffusion tensor imaging, tractography and computational modeling will further the understanding of anatomy and deep brain stimulation effects.
Published: 2007

21. Amygdala activation when one is the target of deceit: Did he lie to you or to someone else?

Author: Richard E. Passingham, Sylvie Berthoz, Julie Grèzes, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Institut Mutualiste de Montsouris (IMM), Wellcome Department of Imaging Neuroscience, University College London, Department of Experimental Psychology, University of Oxford [Oxford], University of Oxford, and Grezes, Julie
Subjects: Adult, Male, Cognitive Neuroscience, Posture, Lie Detection, Amygdala, 050105 experimental psychology, [SCCO]Cognitive science, 03 medical and health sciences, 0302 clinical medicine, Social cognition, Image Processing, Computer-Assisted, medicine, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Anterior cingulate cortex, Behavior, Brain Mapping, Fusiform gyrus, Social perception, 05 social sciences, Social environment, [SCCO] Cognitive science, Superior temporal sulcus, Middle Aged, 16. Peace & justice, Magnetic Resonance Imaging, Biomechanical Phenomena, medicine.anatomical_structure, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Social competence, Psychology, Social psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: International audience; The ability to figure out whether a person is being honest or deceitful is an important part of social competence. Reactions to deceit may however differ depending on whether one is being deceived oneself or observes a deceitful exchange between others. In the present study, we investigated whether personal involvement influenced the neural responses associated with the detection of deceit. Subjects watched videos of actors lifting a box and judged whether the actors had been misled about the real weight of the box. Personal involvement was manipulated by having the participants themselves among the actors. The critical finding was that there was activity in amygdala and fusiform gyrus only for the condition in which participants observed themselves being deceived. In contrast, the superior temporal sulcus and anterior cingulate cortex were activated irrespective of whether the participants detected that the experimenter had deceived themselves or another. These four brain areas are all interconnected and are part of the discrete neural system subserving social cognition. Our results provide direct evidence, using judgments of deceit in a social context, that the crucial factor for amygdala activation is the involvement of the subjects because they are the target of the deceit. We interpret the activation of the amygdala in this situation as reflecting the greater affective reaction when one is deceived oneself. Our results suggest that when one is personally involved, deceit is taken as a potential threat.
Published: 2006

22. Modelling event-related responses in the brain

Author: Olivier David, Lee M. Harrison, Karl J. Friston, David, Olivier, Wellcome Department of Imaging Neuroscience, Functional Imaging Laboratory, Institute of Neurology, and This work was supported by the Wellcome Trust.
Subjects: MESH: Neurons, Neocortex, Electroencephalography, MESH: Signal Processing, Computer-Assisted, Synaptic Transmission, MESH: Magnetic Resonance Imaging, MESH: Neocortex, 0302 clinical medicine, MESH: Animals, Attention, MESH: Neuronal Plasticity, MESH: Oscillometry, Evoked Potentials, Neurons, education.field_of_study, Neuronal Plasticity, medicine.diagnostic_test, Pyramidal Cells, 05 social sciences, Signal Processing, Computer-Assisted, MESH: Interneurons, Magnetic Resonance Imaging, MESH: Nonlinear Dynamics, MESH: Evoked Potentials, Neurology, MESH: Stochastic Processes, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Arousal, Psychology, Cognitive Neuroscience, Population, Stimulus (physiology), 050105 experimental psychology, MESH: Electromyography, MESH: Neural Networks (Computer), 03 medical and health sciences, Superposition principle, Interneurons, Oscillometry, MESH: Electroencephalography, MESH: Synaptic Transmission, medicine, Animals, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Dominance, Cerebral, education, Stochastic Processes, MESH: Attention, MESH: Humans, Electromyography, Stochastic process, business.industry, MESH: Arousal, MESH: Pyramidal Cells, Magnetoencephalography, MESH: Dominance, Cerebral, Phase synchronization, Nonlinear system, Nonlinear Dynamics, Neural Networks, Computer, Artificial intelligence, business, Neuroscience, 030217 neurology & neurosurgery
Abstract: International audience; The aim of this work was to investigate the mechanisms that shape evoked electroencephalographic (EEG) and magneto-encephalographic (MEG) responses. We used a neuronally plausible model to characterise the dependency of response components on the models parameters. This generative model was a neural mass model of hierarchically arranged areas using three kinds of inter-area connections (forward, backward and lateral). We investigated how responses, at each level of a cortical hierarchy, depended on the strength of connections or coupling. Our strategy was to systematically add connections and examine the responses of each successive architecture. We did this in the context of deterministic responses and then with stochastic spontaneous activity. Our aim was to show, in a simple way, how event-related dynamics depend on extrinsic connectivity. To emphasise the importance of nonlinear interactions, we tried to disambiguate the components of event-related potentials (ERPs) or event-related fields (ERFs) that can be explained by a linear superposition of trial-specific responses and those engendered nonlinearly (e.g., by phase-resetting). Our key conclusions were; (i) when forward connections, mediating bottom-up or extrinsic inputs, are sufficiently strong, nonlinear mechanisms cause a saturation of excitatory interneuron responses. This endows the system with an inherent stability that precludes nondissipative population dynamics. (ii) The duration of evoked transients increases with the hierarchical depth or level of processing. (iii) When backward connections are added, evoked transients become more protracted, exhibiting damped oscillations. These are formally identical to late or endogenous components seen empirically. This suggests that late components are mediated by reentrant dynamics within cortical hierarchies. (iv) Bilateral connections produce similar effects to backward connections but can also mediate zero-lag phase-locking among areas. (v) Finally, with spontaneous activity, ERPs/ERFs can arise from two distinct mechanisms: For low levels of (stimulus related and ongoing) activity, the systems response conforms to a quasi-linear superposition of separable responses to the fixed and stochastic inputs. This is consistent with classical assumptions that motivate trial averaging to suppress spontaneous activity and disclose the ERP/ERF. However, when activity is sufficiently high, there are nonlinear interactions between the fixed and stochastic inputs. This interaction is expressed as a phase-resetting and represents a qualitatively different explanation for the ERP/ERF.
Published: 2005

23. Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild Cognitive Impairment

Author: Francis Eustache, Vincent de La Sayette, Marine Fouquet, Renaud La Joie, Florence Mézenge, Brigitte Landeau, Jean-François Mangin, Denis Rivière, Béatrice Desgranges, Gaël Chételat, Jean-Claude Baron, Alice Pélerin, Fausto Viader, Neuropsychologie cognitive et neuroanatomie fonctionnelles de la mémoire humaine, Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Recherches sur la Cognition et l'Apprentissage (CeRCA), Université de Poitiers-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), 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), This work was supported by the Institut National de la Santé Et de la Recherche Médicale U320 and U923, Ministère de la Santé (Programme Hospitalier de Recherche Clinique), Agence Nationale de la Recherche (ANR Longévité et Vieillissement, 2007), Ministère de l'éducation nationale, Association France-Alzheimer, Institut de Recherches Internationales Servier, Conseil Régional de Basse-Normandie and Fondation pour la Recherche Médicale., Eustache, Francis, Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Université de Poitiers, Institut National de la Santé et de la Recherche Médicale ( INSERM ) -École pratique des hautes études ( EPHE ) -Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Normandie Université ( NU ), Laboratoire de Neuroimagerie Assistée par Ordinateur ( LNAO ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Centre de Recherches sur la Cognition et l'Apprentissage ( CeRCA ), Université de Poitiers-Université de Tours-Centre National de la Recherche Scientifique ( CNRS ), Centre de Psychiatrie et Neurosciences ( CPN - U894 ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Paris Descartes - Paris 5 ( UPD5 ), Normandie Université (NU)-Normandie Université (NU)-École Pratique des Hautes Études (EPHE), and Centre de Psychiatrie et Neurosciences (U894)
Subjects: Male, Cognitive Neuroscience, Hippocampus, Grey matter, Hippocampal formation, CA1, White matter, 03 medical and health sciences, 0302 clinical medicine, Atrophy, Encoding (memory), medicine, Humans, Cognitive Dysfunction, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Cognitive impairment, Episodic memory, 030304 developmental biology, Aged, 0303 health sciences, Memory Disorders, amnestic Mild Cognitive Impairment, episodic memory, medicine.disease, encoding, medicine.anatomical_structure, Neurology, nervous system, [ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Psychology, Neuroscience, 030217 neurology & neurosurgery, MRI
Abstract: International audience; Identifying the specific substrates of memory deficits in early Alzheimer's disease would help to develop clinically-relevant therapies. The present study assesses the relationships between encoding versus retrieval deficits in patients with amnestic Mild Cognitive Impairment (aMCI) and atrophy specifically within the hippocampus and throughout the white matter. Twenty-two aMCI patients underwent T1-weighted MRI scans and neuropsychological testing. Grey matter and white matter segments obtained from the MRI images were each entered in correlation analyses, assessed only in the hippocampus for grey matter segments, with encoding and retrieval memory performances. For the grey matter segments, the resulting spmT correlation maps were then superimposed onto a 3D surface view of the hippocampus to identify the relative involvement of the different subfields, a method already used and validated elsewhere. Memory encoding deficits specifically correlated with CA1 subfield atrophy, while no relationship was found with white matter atrophy. In contrast, retrieval deficits were weakly related to hippocampal atrophy and did not involve a particular subfield, while they strongly correlated with loss of white matter, specifically in medial parietal and frontal areas. In aMCI patients, encoding impairment appears specifically related to atrophy of the CA1 hippocampal subfield, consistent with the predominance of encoding deficits and CA1 atrophy in aMCI. In contrast, episodic retrieval deficits seem to be underlain by more distributed tissue losses, consistent with a disruption of a hippocampo-parieto-frontal network.
Published: 2011

24. Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence

Author: Marine Fouquet, Nicolas Villain, Francis Eustache, Alice Pélerin, Florence Mézenge, Katell Mevel, Brigitte Landeau, Renaud La Joie, Béatrice Desgranges, Gaël Chételat, Neuropsychologie cognitive et neuroanatomie fonctionnelles de la mémoire humaine, Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Normandie Université ( NU ) -École pratique des hautes études ( EPHE ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut National de la Santé et de la Recherche Médicale (INSERM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU), Eustache, Francis, Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Male, Aging, genetic structures, High resolution, Hippocampal formation, Neuropsychological Tests, computer.software_genre, Hippocampus, CA1, 0302 clinical medicine, Voxel, Reference Values, Image Processing, Computer-Assisted, Hippocampus (mythology), 10. No inequality, VBM, Sex Characteristics, 05 social sciences, Subiculum, Cognition, Middle Aged, CA3 Region, Hippocampal, Magnetic Resonance Imaging, Neurology, high-resolution MRI, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Psychology, Adult, developmental hypothesis, Cognitive Neuroscience, 050105 experimental psychology, Education, 03 medical and health sciences, Young Adult, Neuroimaging, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], CA1 Region, Hippocampal, Aged, Dentate gyrus, Reproducibility of Results, hippocampal subfields, nervous system, subiculum, [ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neuroscience, computer, 030217 neurology & neurosurgery, Software
Abstract: International audience; Recent advances in neuroimaging have highlighted the interest to differentiate hippocampal subfields for cognitive neurosciences and more notably in assessing the effects of normal and pathological aging. The main goal of the present study is to investigate the effects of normal aging onto the volume of the different hippocampal subfields. For this purpose, we developed a new magnetic resonance sequence together with reliable tracing guidelines to assess the volume of different subfields of the hippocampus using a 3 Tesla scanner, and estimated the validity of a simpler and less time-consuming method based on the widely-used automatic Voxel-Based Morphometry (VBM) technique. Three hippocampal regions of interest were delineated on the right and left hippocampi of 50 healthy subjects between 18 and 68 years old corresponding to the CA1, subiculum and other (including CA2-3-4 and Dentate Gyrus) subfields. A strong effect of age was found on the volume of the subiculum only, with a decrease paralleling that of the global gray matter volume, while CA1 and other subfields seemed relatively spared. Although less precise than the ROI-tracing technique, the VBM-based method appeared as a reliable alternative especially to distinguish CA1 and subiculum subfields. Our findings of a specific effect of age on the subiculum are consistent with the developmental hypothesis ("last-in first-out" theory). This contrasts with the predominant vulnerability of the CA1 subfield to Alzheimer's disease reported in several previous studies, suggesting that the assessment of hippocampal subfields may improve the discrimination between normal and pathological aging.
Published: 2010

25. Two different faces of threat. Comparing the neural systems for recognizing fear and anger in dynamic body expressions

Author: Swann Pichon, Beatrice de Gelder, Julie Grèzes, Laboratoire de Neurosciences Cognitives & Computationnelles (LNC2), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Maastricht University [Maastricht], Cognitive and Affective Neurosciences Laboratory, Tilburg University Tilburg, Grezes, Julie, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Cognitive Neuropsychology
Subjects: Male, Cognitive Neuroscience, media_common.quotation_subject, Temporoparietal junction, Ventromedial prefrontal cortex, ComputingMilieux_LEGALASPECTSOFCOMPUTING, Anger, behavioral disciplines and activities, Amygdala, 050105 experimental psychology, Premotor cortex, [SCCO]Cognitive science, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Perception, medicine, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Nonverbal Communication, ComputingMilieux_MISCELLANEOUS, media_common, Fear processing in the brain, Brain Mapping, medicine.diagnostic_test, 05 social sciences, Brain, [SCCO] Cognitive science, Fear, medicine.anatomical_structure, Neurology, Evoked Potentials, Visual, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Cues, Psychology, Functional magnetic resonance imaging, psychological phenomena and processes, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: Being exposed to fear or anger signals makes us feel threatened and prompts us to prepare an adaptive response. Yet, while fear and anger behaviors are both threat signals, what counts as an adaptive response is often quite different. In contrast with fear, anger is often displayed with the aim of altering the behavior of the agent to which it is addressed. To identify brain responses that are common or specific to the perception of these two types of threat signals, we used functional magnetic resonance imaging and asked subjects to recognize dynamic actions expressing fear, anger and neutral behaviors. As compared with neutral actions, the perception of fear and anger behaviors elicited comparable activity increases in the left amygdala and temporal cortices as well as in the ventrolateral and the dorsomedial prefrontal cortex. Whereas the perception of fear elicited specific activity in the right temporoparietal junction, the perception of anger triggered condition-specific activity in a wider set of regions comprising the anterior temporal lobe, the premotor cortex and the ventromedial prefrontal cortex, consistent with the hypothesis that coping with threat from exposure to anger requires additional contextual information and behavioral adjustments.
Published: 2008

26. The effect of global cerebral vasodilation on focal activation hemodynamics

Author: Karin M. Rylander, Bojana Stefanovic, Jan M. Warnking, G. Bruce Pike, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], and Dojat, Michel
Subjects: Male, genetic structures, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Hemodynamics, MESH: Magnetic Resonance Imaging, 030218 nuclear medicine & medical imaging, Basal (phylogenetics), 0302 clinical medicine, Visual Cortex, Motor Cortex, MESH: Cerebrovascular Circulation, Magnetic Resonance Imaging, Vasodilation, medicine.anatomical_structure, Neurology, Cerebral blood flow, Anesthesia, Cerebrovascular Circulation, Cardiology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, medicine.symptom, Perfusion, Hypercapnia, MESH: Oxygen, Adult, MESH: Hemodynamics, medicine.medical_specialty, Cognitive Neuroscience, Cerebral vasodilation, MESH: Motor Cortex, MESH: Vasodilation, 03 medical and health sciences, Internal medicine, medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], MESH: Humans, business.industry, MESH: Visual Cortex, MESH: Adult, Blood flow, MESH: Male, Oxygen, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Visual cortex, nervous system, business, MESH: Female, 030217 neurology & neurosurgery
Abstract: International audience; In view of the potential of global resting blood flow level to confound the interpretation of blood oxygenation level-dependent (BOLD) fMRI studies, we investigated the effect of pronounced elevation in baseline cerebral blood flow (CBF) on BOLD and CBF responses to functional activation. Twelve healthy volunteers performed bilateral finger apposition while attending to a radial yellow/blue checkerboard. Three levels of global CBF increase were achieved by inhaling 5, 7.5 or 10% CO2. CBF and BOLD signals were simultaneously quantified using interleaved multi-slice pulsed arterial spin labeling (PASL) and T2*-weighted gradient echo sequences. Increasing basal CBF produced a significant decrease in the activation-induced BOLD response, with the slope of the optimal linear fit of activation versus basal BOLD signal changes of -0.32 +/- 0.01%/% for motor and visual cortex regions of interest (ROIs). While the modulation in basal flow level also produced a statistically significant effect on the activation-induced CBF change, the degree of relative attenuation of the flow response was slight, with a slope of -0.18 +/- 0.02%/% in the motor and -0.13 +/- 0.01%/% in the visual cortex ROI. The current findings describe a strong attenuation of the BOLD response at significantly elevated basal flow levels and call for independent quantification of resting CBF in BOLD fMRI studies that involve subjects and/or conditions with markedly elevated global perfusion.
Published: 2005

27. Mechanisms of evoked and induced responses in MEG/EEG

Author: Olivier David, Karl J. Friston, James M. Kilner, Wellcome Department of Imaging Neuroscience, University College of London [London] (UCL)-Institute of Neurology, and David, Olivier
Subjects: MESH: Magnetoencephalography, Cognitive Neuroscience, Models, Neurological, MESH: Algorithms, Electroencephalography, 03 medical and health sciences, 0302 clinical medicine, MESH: Models, Neurological, MESH: Electroencephalography, medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Evoked Potentials, 030304 developmental biology, 0303 health sciences, MESH: Humans, Models, Statistical, medicine.diagnostic_test, Fourier Analysis, Mechanism (biology), Functional connectivity, Magnetoencephalography, Reproducibility of Results, Cortical neurons, MESH: Reproducibility of Results, Coupling (electronics), MESH: Evoked Potentials, Neurology, Data Interpretation, Statistical, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, MESH: Data Interpretation, Statistical, Neuroscience, MESH: Fourier Analysis, MESH: Models, Statistical, 030217 neurology & neurosurgery, Algorithms
Abstract: International audience; Cortical responses, recorded by electroencephalography and magnetoencephalography, can be characterized in the time domain, to study event-related potentials/fields, or in the time-frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced, and on-going oscillations reflect different neuronal processes and mechanisms. In this work, we consider the relationship between the mechanisms generating neuronal transients and how they are expressed in terms of evoked and induced power. This relationship is addressed using a neuronally realistic model of interacting neuronal subpopulations. Neuronal transients were generated by changing neuronal input (a dynamic mechanism) or by perturbing the systems coupling parameters (a structural mechanism) to produce induced responses. By applying conventional time-frequency analyses, we show that, in contradistinction to common conceptions, induced and evoked oscillations are perhaps more related than previously reported. Specifically, structural mechanisms normally associated with induced responses can be expressed in evoked power. Conversely, dynamic mechanisms posited for evoked responses can induce responses, if there is variation in neuronal input. We conclude, it may be better to consider evoked responses as the results of mixed dynamic and structural effects. We introduce adjusted power to complement induced power. Adjusted power is unaffected by trial-to-trial variations in input and can be attributed to structural perturbations without ambiguity.
Published: 2005

28. Inferring false beliefs from the actions of oneself and others: an fMRI study

Author: Chris D. Frith, Richard E. Passingham, Julie Grèzes, Grezes, Julie, Wellcome Department of Imaging Neuroscience, University College London, Department of Experimental Psychology, University of Oxford, and University of Oxford [Oxford]
Subjects: Male, Lifting, Cognitive Neuroscience, media_common.quotation_subject, Self-concept, Gyrus Cinguli, Premotor cortex, Weight-Bearing, [SCCO]Cognitive science, Oxygen Consumption, Perception, Cerebellum, Neural Pathways, medicine, Image Processing, Computer-Assisted, Humans, Weight Perception, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Dominance, Cerebral, media_common, Personal Construct Theory, Expectancy theory, Cerebral Cortex, Brain Mapping, Social perception, [SCCO] Cognitive science, Superior temporal sulcus, Simulated reality, Image Enhancement, Imitative Behavior, Magnetic Resonance Imaging, Self Concept, medicine.anatomical_structure, Personal construct theory, Neurology, Social Perception, Imagination, Visual Perception, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Psychology, Social psychology, Psychomotor Performance
Abstract: International audience; The ability to make judgments about mental states is critical to social interactions. Simulation theory suggests that the observer covertly mimics the activity of the observed person, leading to shared states of mind between the observer and the person observed. We tested this hypothesis by investigating the neural networks activated while subjects watched videos of themselves and of others lifting a box, and judged the beliefs of the actors about the weight of the box. A parietal premotor circuit was recruited during action perception, and the activity started earlier when making judgments about one's own actions as opposed to those of others. This earlier activity in action-related structures can be explained by simulation theory on the basis that when one observes one's own actions, there is a closer match between the simulated and perceived action than there is when one observes the actions of others. When the observers judged the actions to reflect a false belief, there was activation in the superior temporal sulcus, orbitofrontal, paracingulate cortex and cerebellum. We suggest that this reflects a mismatch between the perceived action and the predicted action's outcomes derived from simulation.
Published: 2003

29. Automatic recognition of specific local cortical folding patterns

Author: Borne, Léonie, Rivière, Denis, Cachia, Arnaud, Roca, Pauline, Mellerio, Charles, Oppenheim, Catherine, Mangin, Jean-François, University of Newcastle [Callaghan, Australia] (UoN), Building large instruments for neuroimaging: from population imaging to ultra-high magnetic fields (BAOBAB), 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)-Centre National de la Recherche Scientifique (CNRS), 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), Laboratoire de psychologie du développement et de l'éducation de l'enfant (LaPsyDÉ - UMR 8240), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of Newcastle [Australia] (UoN), Unité Baobab (BAOBAB), 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 la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), and Martinez Rico, Clara
Subjects: Cerebral Cortex, Support Vector Machine, Databases, Factual, [SDV]Life Sciences [q-bio], Neurosciences. Biological psychiatry. Neuropsychiatry, Classification, Magnetic Resonance Imaging, Pattern Recognition, Automated, [SDV] Life Sciences [q-bio], Pattern recognition, Cortical sulci, Machine learning, Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Convolution neural network, Supervised learning, RC321-571
Abstract: International audience; The study of local cortical folding patterns showed links with psychiatric illnesses as well as cognitive functions. Despite the tools now available to visualize cortical folds in 3D, manually classifying local sulcal patterns is a timeconsuming and tedious task. In fact, 3D visualization of folds helps experts to identify different sulcal patterns but fold variability is so high that the distinction between these patterns sometimes requires the definition of complex criteria, making manual classification difficult and not reliable. However, the assessment of the impact of these patterns on the functional organization of the cortex could benefit from the study of large databases, especially when studying rare patterns. In this paper, several algorithms for the automatic classification of fold patterns are proposed to allow morphological studies to be extended and confirmed on such large databases. Three methods are proposed, the first based on a Support Vector Machine (SVM) classifier, the second on the Scoring by Non-local Image Patch Estimator (SNIPE) approach and the third based on a 3D Convolution Neural Network (CNN). These methods are generic enough to be applicable to a wide range of folding patterns. They are tested on two types of patterns for which there is currently no method to automatically identify them: the Anterior Cingulate Cortex (ACC) patterns and the Power Button Sign (PBS). The two ACC patterns are almost equally present whereas PBS is a particularly rare pattern in the general population. The three models proposed achieve balanced accuracies of approximately 80% for ACC patterns classification and 60% for PBS classification. The CNN-based model is more interesting for the classification of ACC patterns thanks to its rapid execution. However, SVM and SNIPE-based models are more effective in managing unbalanced problems such as PBS recognition.
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29 results on '"[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]"'

1. Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study

2. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

3. Creativity and the brain: An editorial introduction to the special issue on the neuroscience of creativity

4. NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

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

6. SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity

7. Functional connectivity is preserved but reorganized across several anesthetic regimes

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

9. Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain

10. Transcranial functional ultrasound imaging of the brain using microbubble-enhanced ultrasensitive Doppler

11. Learning and comparing functional connectomes across subjects

12. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

13. Phase-amplitude coupling at the organism level: The amplitude of spontaneous alpha rhythm fluctuations varies with the phase of the infra-slow gastric basal rhythm

14. Effects of aging on low luminance contrast processing in humans

15. MRI atlas of the human hypothalamus

16. An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling

17. Computational modeling of high-frequency oscillations at the onset of neocortical partial seizures: From ‘altered structure’ to ‘dysfunction’

18. Preictal short-term plasticity induced by intracerebral 1 Hz stimulation

19. Functional MRI with active, fully implanted, deep brain stimulation systems: Safety and experimental confounds

20. Brain mapping in stereotactic surgery: A brief overview from the probabilistic targeting to the patient-based anatomic mapping

21. Amygdala activation when one is the target of deceit: Did he lie to you or to someone else?

22. Modelling event-related responses in the brain

23. Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild Cognitive Impairment

24. Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence

25. Two different faces of threat. Comparing the neural systems for recognizing fear and anger in dynamic body expressions

26. The effect of global cerebral vasodilation on focal activation hemodynamics

27. Mechanisms of evoked and induced responses in MEG/EEG

28. Inferring false beliefs from the actions of oneself and others: an fMRI study

29. Automatic recognition of specific local cortical folding patterns

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29 results on '"[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]"'

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