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1. Probabilistic functional tractography of the human cortex revisited.

Author

Lena Trebaul, Pierre Deman, Viateur Tuyisenge, Maciej Jedynak, Etienne Hugues, David Rudrauf, Manik Bhattacharjee, François Tadel, Blandine Chanteloup-Foret, Carole Saubat, Gina Catalina Reyes Mejia, Claude Adam, Anca Nica, Martin Pail, François Dubeau, Sylvain Rheims, Agnès Trébuchon, Haixiang Wang, and Olivier David 0001

Published
2018
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6. A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Author

Jean-Didier, Lemaréchal, Maciej, Jedynak, Lena, Trebaul, Anthony, Boyer, François, Tadel, Manik, Bhattacharjee, Pierre, Deman, Viateur, Tuyisenge, Leila, Ayoubian, Etienne, Hugues, Blandine, Chanteloup-Forêt, Carole, Saubat, Raouf, Zouglech, Gina Catalina, Reyes Mejia, Sébastien, Tourbier, Patric, Hagmann, Claude, Adam, Carmen, Barba, Fabrice, Bartolomei, Thomas, Blauwblomme, Jonathan, Curot, François, Dubeau, Stefano, Francione, Mercedes, Garcés, Edouard, Hirsch, Elizabeth, Landré, Sinclair, Liu, Louis, Maillard, Eeva-Liisa, Metsähonkala, Ioana, Mindruta, Anca, Nica, Martin, Pail, Ana Maria, Petrescu, Sylvain, Rheims, Rodrigo, Rocamora, Andreas, Schulze-Bonhage, William, Szurhaj, Delphine, Taussig, Antonio, Valentin, Haixiang, Wang, Philippe, Kahane, Nathalie, George, Olivier, David, Elisa, Nacci, 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), [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Le Centre de Magnétoencéphalographie et d'Electroencéphalographie [CHU Pitié-Salpêtrière] (MEG-EEG), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-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), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Service de neurochirurgie pédiatrique [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de recherche cerveau et cognition (CERCO), Institut des sciences du cerveau de Toulouse. (ISCT), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Hospital Universitario y Politécnico La Fe [Valencia, Spain], CHU Strasbourg, Centre Hospitalier Sainte Anne [Paris], Jinan University [Guangzhou], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), University Emergency Hospital [Bucharest], Laboratoire Traitement du Signal et de l'Image (LTSI), 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), University Hospital Brno, AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), IMIM-Hospital del Mar, Generalitat de Catalunya, Freiburg University Medical Center, CHU Lille, Institute of Psychiatry, Psychology & Neuroscience, King's College London, King‘s College London, Tsinghua University [Beijing] (THU), 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), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Università degli Studi di Firenze = University of Florence (UniFI), Centre de recherche cerveau et cognition (CERCO UMR5549), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Hospital Universitari i Politècnic La Fe = University and Polytechnic Hospital La Fe, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (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), Universitäts Klinikum Freiburg = University Medical Center Freiburg (Uniklinik), The research leading to these results has received funding from the European Research Councilunder the European Union's Seventh Framework Programme (FP/2007-2013)/ERC GrantAgreement no. 616268 F-TRACT, the European Union’s Horizon 2020 FrameworkProgramme for Research and Innovation under Specific Grant Agreement No. 785907 and945539 (Human Brain Project SGA2 and SGA3), and from the French 'Investissementsd’avenir' programme under grant numbers ANR-11-INBS-0006 and ANR-10-IAIHU-06. PHwas supported by Swiss National Science Foundation grant #CRSII5_170873, ANR-11-INBS-0006,FLI,France Life Imaging(2011), European Project: 616268,EC:FP7:ERC,ERC-2013-CoG,F-TRACT(2014), European Project: 785907,H2020,HBP SGA2(2018), European Project: 945539,H2020,H2020-SGA-FETFLAG-HBP-2019,HBP SGA3(2020), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-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), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Gestionnaire, Hal Sorbonne Université, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, Functional Brain Tractography - F-TRACT - - EC:FP7:ERC2014-08-01 - 2019-07-31 - 616268 - VALID, Human Brain Project Specific Grant Agreement 2 - HBP SGA2 - - H20202018-04-01 - 2020-03-31 - 785907 - VALID, and Human Brain Project Specific Grant Agreement 3 - HBP SGA3 - - H20202020-01-01 - 2023-09-30 - 945539 - VALID

Subjects
neural mass models, 0303 health sciences, Brain Mapping, Epilepsy, cortico-cortical evoked potential, Brain, Bayes Theorem, Electric Stimulation, dynamic causal modelling, 03 medical and health sciences, 0302 clinical medicine, axonal conduction delay, Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], synaptic time constant, Evoked Potentials, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (

Published
2021

7. Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning

Author

Philippe Kahane, Manik Bhattacharjee, Blandine Chanteloup-Foret, Viateur Tuyisenge, Louis Maillard, Olivier David, Sylvain Rheims, Delphine Taussig, Carole Saubat-Guigui, Lena Trebaul, Ioana Mîndruţă, Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Medicine and Pharmacy 'Carol Davila' Bucharest (UMPCD), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Service de neurologie [Rennes], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Fondation Rothschild, Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (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), Université de Rennes (UR), and Fondation Ophtalmologique Adolphe de Rothschild [Paris]

Subjects
0301 basic medicine, Computer science, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Feature extraction, Article, Stereo-EEG, Stereoelectroencephalography, Correlation, 03 medical and health sciences, 0302 clinical medicine, Physiology (medical), Machine learning, Humans, Bad channels, ECoG, electrocorticography, iEEG, intracranial electroencephalography, Epilepsy, Ensemble bagging, Intracranial EEG, business.industry, Brain, Pattern recognition, Ensemble learning, Intracranial eeg, Sensory Systems, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, Neurology, Electrocorticography, Neurology (clinical), Artificial intelligence, SEEG, stereo-electroencephalography, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Classifier (UML), DES, direct electrical stimulation, EEG, electroencephalography, 030217 neurology & neurosurgery, Data selection, Communication channel
Abstract

Highlights • We propose a method that detects automatically bad channels from intracranial EEG (iEEG) datasets. • It computes iEEG features specific to bad channels and uses an ensemble bagging classifier. • The bad channel classification accuracy was demonstrated to be excellent on a large data sample., Objective Intracranial electroencephalographic (iEEG) recordings contain “bad channels”, which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. Methods The features quantified signals’ variance, spatial–temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. Results We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. Conclusions The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. Significance This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals.

Published
2018

8. Validation of cadmium–zinc–telluride camera for measurement of left ventricular systolic performance

Author

Charles Merlin, Lucie Cassagnes, Louis Boyer, Bruno Pereira, Elisabeth Coupez, Viateur Tuyisenge, Jean René Lusson, Laurent Sarry, Unité de soins intensifs [Clermont Ferrand], CHU Clermont-Ferrand-CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand, Institut Pascal (IP), SIGMA Clermont (SIGMA Clermont)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Unité de Biostatistiques [CHU Clermont-Ferrand], Direction de la recherche clinique et de l’innovation [CHU Clermont-Ferrand] (DRCI), CHU Clermont-Ferrand-CHU Clermont-Ferrand, and Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université

Subjects
medicine.medical_specialty, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine, 030204 cardiovascular system & hematology, Sensitivity and Specificity, Single photon emission computed tomography SPECT, Ventricular Function, Left, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Myocardial perfusion imaging, chemistry.chemical_compound, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Internal medicine, medicine, Magnetic resonance imaging MRI, Humans, Gamma Cameras, Radiology, Nuclear Medicine and imaging, Wall motion, Myocardial infarction, cardiovascular diseases, Tomography, Emission-Computed, Single-Photon, Ejection fraction, medicine.diagnostic_test, business.industry, Echo (computing), Reproducibility of Results, Magnetic resonance imaging, medicine.disease, Magnetic Resonance Imaging, 3. Good health, Cadmium zinc telluride, Zinc, chemistry, Echocardiography, Cardiology, cardiovascular system, ST Elevation Myocardial Infarction, Tellurium, Cardiology and Cardiovascular Medicine, Cardiac magnetic resonance, business, Nuclear medicine, Myocardial Function, Cadmium, circulatory and respiratory physiology
Abstract

Erratum : 10.1007/s12350-017-0845-8"As shown in this erratum, the second author’s name needs to be changed from “Merlin Charles” to “Charles Merlin”. His affiliation needs to be changed to “Nuclear Medicine Department, Jean Perrin Cancer Center, Clermont-Ferrand, France”. The original article was corrected."; International audience; BACKGROUND:There are paucity of data comparing measurements of left ventricular systolic performance using cadmium-zinc-telluride (CZT) semiconductor cameras with other imaging modalities. This study compared the new system with echocardiography (echo) and cardiac magnetic resonance (CMR) imaging.METHODS:60 Patients presenting with ST-elevated myocardial infarction (MI) were included. Each patient underwent echo, myocardial perfusion imaging using Spectrum Dynamics D-SPECT(r) (CZT-SPECT), and CMR 6 weeks after MI. The primary endpoint was the agreement between CZT-SPECT and CMR for left ventricular ejection fraction (LVEF) measurement.RESULTS:48 of the 60 patients underwent all 3 studies (echo, CMR, and CZT-SPECT) 40 days after admission. CZT-SPECT and CMR LVEF were well correlated (r = .79, P < .0001), as well as CZT-SPECT vs echo and CMR vs echo (r = .79 and .84, respectively, P < .0001). The segmental LV wall thickening and wall motion also showed good concordance between three techniques.CONCLUSIONS:CZT-SPECT is reliable for LVEF measurement.

Published
2018

9. Probabilistic functional tractography of the human cortex revisited

Author

Thomas Blauwblomme, Martin Pail, M. Jedynak, Caroline Saubat, Pierre Deman, Luca De Palma, Sinclair Liu, Elizabeth Landre, Rodrigo Rocamora, Edouard Hirsch, Andreas Schulze-Bonhage, Manik Bhattacharjee, Delphine Taussig, Blandine Chanteloup-Foret, François Dubeau, Agnès Trébuchon, William Szurhaj, Stefano Francione, Viateur Tuyisenge, Luc Valton, Lena Trebaul, Louis Maillard, Gina Catalina Reyes Mejia, Anca Nica, Eeva-Liisa Metsähonkala, Ana Maria Petrescu, Antonio Valentin, Mercedes Garcés, Claude Adam, Olivier David, Haixiang Wang, Ioana Mindruta, Sylvain Rheims, Etienne Hugues, Philippe Kahane, François Tadel, David Rudrauf, Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Center for Brain and Cognition, Universitat Pompeu Fabra [Barcelona] (UPF), Institut National de la Santé et de la Recherche Médicale (INSERM), Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière (NEMESIS-CRICM), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université 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 Pontchaillou [Rennes], Département de mathématiques [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Institut de Neurobiologie de la Méditerranée [Aix-Marseille Université] (INMED - INSERM U1249), Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU), 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), Computer & Information Sciences Department, Fordham University, Fordham University [New York], INSERM U836, équipe 9, Dynamique des réseaux synchrones épileptiques, Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Neurologie et thérapeutique expérimentale, IFR70-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Service d'explorations neurologiques et épileptologie, CHU Toulouse [Toulouse], Epilepsy Centre, University Hospital Freiburg, Centre « Claudio-Munari » pour la chirurgie de l'épilepsie et du Parkinson, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Service de neurologie [Rennes], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Espaces et Sociétés (ESO), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département Neurologie [CHU Toulouse], Pôle Neurosciences [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM)-Université d'Angers (UA)-AGROCAMPUS OUEST-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Maquin, Didier, Doctoral Programme in Clinical Veterinary Medicine, Clinicum, Lastentautien yksikkö, Children's Hospital, and HUS Children and Adolescents

Subjects
Male, MOTOR SYSTEM, Connectome/methods, Databases, Factual, Computer science, [SDV]Life Sciences [q-bio], [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Connectivity mapping, Intracranial Electroencephalography, 3124 Neurology and psychiatry, Evoked Potentials/physiology, Epilepsy, 0302 clinical medicine, Neural Pathways, TEMPORAL-LOBE EPILEPSY, Focal Epilepsies, Child, Neocortical epilepsy, Evoked Potentials, IN-VIVO, Cerebral Cortex, 05 social sciences, Cerebral Cortex/diagnostic imaging/physiopathology, Human brain, Middle Aged, HUMAN BRAIN, [SDV] Life Sciences [q-bio], medicine.anatomical_structure, Epilepsy/diagnostic imaging/physiopathology, Neurology, Cerebral cortex, Child, Preschool, Female, Cortico-cortical evoked potentials, PULSE ELECTRICAL-STIMULATION, EFFECTIVE CONNECTIVITY, Tractography, Adult, LANGUAGE SYSTEM, Adolescent, Cognitive Neuroscience, NEOCORTICAL EPILEPSY, 050105 experimental psychology, Electrocorticography/methods, Article, 03 medical and health sciences, Databases, Young Adult, Atlases as Topic, Motor system, medicine, Connectome, Humans, Brain atlas, 0501 psychology and cognitive sciences, Preschool, Factual, business.industry, ICTAL ONSET, 3112 Neurosciences, Probabilistic logic, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Pattern recognition, CORTICOCORTICAL EVOKED-POTENTIALS, 3126 Surgery, anesthesiology, intensive care, radiology, medicine.disease, Neural Pathways/diagnostic imaging, Artificial intelligence, Electrocorticography, business, Intracranial electroencephalogram, 030217 neurology & neurosurgery
Abstract

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human. he research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 616268 “F-TRACT”. All the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment.ujf-grenoble.fr), which is supported by the Rhône-Alpes region (GRANT CPER07_13 CIRA: http://www.ci-ra.org). We thank Bruno Bzenik and Romain Cavagna for their help on the CIMENT facility, and Jean-François Mangin and Cyril Poupon from Neurospin, CEA Saclay, for providing us with the ARCHI DTI database.

Published
2018

11. Estimation of Myocardial Strain and Contraction Phase From Cine MRI Using Variational Data Assimilation

Author

Laurent Sarry, Lucie Cassagnes, Thomas Corpetti, Viateur Tuyisenge, Elisabeth Innorta-Coupez, Lemlih Ouchchane, Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Institut Pascal (IP), SIGMA Clermont (SIGMA Clermont)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), CHU Clermont-Ferrand, ANR-11-TECS-0002,3DSTRAIN,Quantification multimodale et validation de la fonction myocardique régionale 3D(2011), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), SIGMA Clermont (SIGMA Clermont)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN)

Subjects
Contraction (grammar), Databases, Factual, Intraclass correlation, Diastole, Magnetic Resonance Imaging, Cine, 02 engineering and technology, cardiac dyskinesia, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Data assimilation, Myocardial motion, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Computer Simulation, Electrical and Electronic Engineering, Cardiac MRI, data assimilation, Simulation, Mathematics, Ground truth, Radiological and Ultrasound Technology, medicine.diagnostic_test, Myocardial strain analysis, Magnetic resonance imaging, Heart, Myocardial Contraction, Computer Science Applications, Cine mri, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], contraction phase, Piecewise, myocardial strain, 020201 artificial intelligence & image processing, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Algorithms, Variational data assimilation methods, MRI
Abstract

International audience; This paper presents a new method to estimate left ventricle deformations using variational data assimilation that combines image observations from cine MRI and a dynamic evolution model of the heart. The main contribution of the model is that it embeds parameters modeling the contraction / relaxation process. It estimates myocardial motion and contraction parameters simultaneously, providing accurate complementary information for diagnosis. The method was applied to synthetic datasets with known ground truth motion and to 47 patients MRI datasets acquired at three slice locations (base, mid-ventricle and apex). Radial and circumferential strain components were compared to those obtained with a reference tag tracking software, exhibiting good agreement with intraclass correlation coefficients (ICC) above 0.8. Results were also evaluated against wall motion score indices used to assess cardiac kinetics in clinical practice. The assimilation process overcame issues caused by temporal artifacts as a result of the dynamic model, compared to using the observation term alone. Moreover we found that the new dynamic model, consisting of a piecewise transport model acting independently on systole and diastole performed better than the standard continuous transport model, which oversmooths temporal variations. Estimated strain and contraction parameters significantly correlated to clinical scores, making them promising features for diagnosing not only hypokinesia but also dyskinesia.

Published
2015

12. Joint Myocardial Motion and Contraction Phase Estimation from Cine MRI Using Variational Data Assimilation

Author

Lucie Cassagnes, Thomas Corpetti, Lemlih Ouchchane, Laurent Sarry, Viateur Tuyisenge, Elisabeth Innorta-Coupez, Institut Pascal (IP), SIGMA Clermont (SIGMA Clermont)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), CHU Clermont-Ferrand, ANR-11-TECS-0002,3DSTRAIN,Quantification multimodale et validation de la fonction myocardique régionale 3D(2011), Laboratoire de Biostatistique, Informatique médicale et Technologies de la communication, CHU Clermont-Ferrand-Université d'Auvergne - Clermont-Ferrand I (UdA), Pharmacochimie et Biologie pour le Développement (PHARMA-DEV), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut de Chimie de Toulouse (ICT-FR 2599), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Chimie du CNRS (INC)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut de Chimie du CNRS (INC)-Institut de Recherche pour le Développement (IRD), SIGMA Clermont (SIGMA Clermont)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Corpetti, Thomas, Institut de Recherche pour le Développement (IRD)-Institut de Chimie de Toulouse (ICT), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)

Subjects
Contraction (grammar), Computer science, Physics::Medical Physics, 0206 medical engineering, 02 engineering and technology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Data assimilation, Software, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Cardiac motion, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Ground truth, Cardiac cycle, Myocardial strain analysis, business.industry, 020601 biomedical engineering, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Dyskinesia, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Myocardial motion, Artificial intelligence, medicine.symptom, business, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Variational data assimilation methods
Abstract

International audience; We present a cardiac motion estimation method with variational data assimilation that combines image observations and a dynamicevolution model. The novelty of the model is that it embeds new parameters modeling heart contraction and relaxation. It was applied to asynthetic dataset with known ground truth motion and to 10 cine-MRI sequences of patients with normal or dyskinetic myocardial zones. Itwas compared to the inTag tagging tracking software for computing the radial motion component, and to the diagnosis for dyskinesia. We foundthat the new dynamic model performed better than the standard transport model, and the contraction parameters are promising features fordiagnosing dyskinesia.

Published
2014

13. Variational myocardial tracking from CINE-MRI with non-linear regularization

Author

Lucie Cassagnes, Charles Merlin, Viateur Tuyisenge, Elisabeth Coupez, P. Windyga, Laurent Sarry, and Adélaïde Albouy-Kissi

Subjects
medicine.diagnostic_test, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Real-time MRI, Classification of discontinuities, Regularization (mathematics), Cine mri, Nonlinear system, Improved performance, Cardiac magnetic resonance imaging, Motion estimation, medicine, Computer vision, Artificial intelligence, business, Mathematics
Abstract

We present a new motion estimation approach for cardiac Magnetic Resonance Imaging (MRI) data from a variational framework. The improved performance of variational approach has been achieved by designing a new regularization term that properly handles motion discontinuities. This approach was applied to both synthetic and real data. The quantitative evaluation revealed the superior performance of the proposed method against reference approaches.

Published
2013

14. Variational Myocardial Tracking from Cine-MRI with Non-linear Regularization: Validation of Radial Displacements vs. Tagged-MRI

Author

Adélaïde Albouy-Kissi, Viateur Tuyisenge, and Laurent Sarry

Subjects
Mathematical optimization, medicine.diagnostic_test, Physics::Medical Physics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Angular error, Classification of discontinuities, Regularization (mathematics), Cine mri, Nonlinear system, Improved performance, Cardiac magnetic resonance imaging, Motion estimation, cardiovascular system, medicine, Algorithm, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics
Abstract

We present a new motion estimation approach for cardiac Magnetic Resonance Imaging (Cine-MRI) data from variational framework. The improved performance of this variational approach has been achieved by designing a new regularization term that properly handles motion discontinuities. This approach was applied to both synthetic and real data. The quantitative evaluation revealed that the results of proposed method on cine-MRI correlates with the results given by inTag, reference approach on tagged-MRI.

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