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22 results on '"Francoise Durand-Dubief"'

1. Evaluation of treatment response in adults with relapsing MOG-Ab-associated disease

Author

Alvaro Cobo-Calvo, María Sepúlveda, Fabien Rollot, Thais Armangué, Anne Ruiz, Elisabeth Maillart, Caroline Papeix, Bertrand Audoin, Helene Zephir, Damien Biotti, Jonathan Ciron, Francoise Durand-Dubief, Nicolas Collongues, Xavier Ayrignac, Pierre Labauge, Eric Thouvenot, Bertrand Bourre, Alexis Montcuquet, Mikael Cohen, Romain Deschamps, Nuria Solà-Valls, Sara Llufriu, Jerome De Seze, Yolanda Blanco, Sandra Vukusic, Albert Saiz, and Romain Marignier

Subjects
MOG antibodies, Treatment response, Neuromyelitis optica, Multiple sclerosis, Propensity score, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Myelin oligodendrocyte glycoprotein antibodies (MOG-Ab) are related to several acquired demyelinating syndromes in adults, but the therapeutic approach is currently unclear. We aimed to describe the response to different therapeutic strategies in adult patients with relapsing MOG-Ab-associated disease. Methods This is a retrospective study conducted in France and Spain including 125 relapsing MOG-Ab patients aged ≥ 18 years. First, we performed a survival analysis to investigate the relapse risk between treated and non-treated patients, performing a propensity score method based on the inverse probability of treatment weighting. Second, we assessed the annualised relapse rates (ARR), Expanded Disability Status Scale (EDSS) and visual acuity pre-treatment and on/end-treatment. Results Median age at onset was 34.1 years (range 18.0–67.1), the female to male ratio was 1.2:1, and 96% were Caucasian. At 5 years, 84% (95% confidence interval [CI], 77.1–89.8) patients relapsed. At the last follow-up, 66 (52.8%) received maintenance therapy. Patients initiating immunosuppressants (azathioprine, mycophenolate mophetil [MMF], rituximab) were at lower risk of new relapse in comparison to non-treated patients (HR, 0.41; 95CI%, 0.20–0.82; p = 0.011). Mean ARR (standard deviation) was reduced from 1.05(1.20) to 0.43(0.79) with azathioprine (n = 11; p = 0.041), from 1.20(1.11) to 0.23(0.60) with MMF (n = 11; p = 0.033), and from 1.08(0.98) to 0.43(0.89) with rituximab (n = 26; p = 0.012). Other immunosuppressants (methotrexate/mitoxantrone/cyclophosphamide; n = 5), or multiple sclerosis disease-modifying drugs (MS-DMD; n = 9), were not associated with significantly reduced ARR. Higher rates of freedom of EDSS progression were observed with azathioprine, MMF or rituximab. Conclusion In adults with relapsing MOG-Ab-associated disease, immunosuppressant therapy (azathioprine, MMF and rituximab) is associated with reduced risk of relapse and better disability outcomes. Such an effect was not found in the few patients treated with MS-DMD.

Published
2019
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2. Correlation of diffusion and metabolic alterations in different clinical forms of multiple sclerosis.

Author

Salem Hannoun, Matthieu Bagory, Francoise Durand-Dubief, Danielle Ibarrola, Jean-Christophe Comte, Christian Confavreux, Francois Cotton, and Dominique Sappey-Marinier

Subjects
Medicine, Science
Abstract

Diffusion tensor imaging (DTI) and MR spectroscopic imaging (MRSI) provide greater sensitivity than conventional MRI to detect diffuse alterations in normal appearing white matter (NAWM) of Multiple Sclerosis (MS) patients with different clinical forms. Therefore, the goal of this study is to combine DTI and MRSI measurements to analyze the relation between diffusion and metabolic markers, T2-weighted lesion load (T2-LL) and the patients clinical status. The sensitivity and specificity of both methods were then compared in terms of MS clinical forms differentiation. MR examination was performed on 71 MS patients (27 relapsing remitting (RR), 26 secondary progressive (SP) and 18 primary progressive (PP)) and 24 control subjects. DTI and MRSI measurements were obtained from two identical regions of interest selected in left and right centrum semioval (CSO) WM. DTI metrics and metabolic contents were significantly altered in MS patients with the exception of N-acetyl-aspartate (NAA) and NAA/Choline (Cho) ratio in RR patients. Significant correlations were observed between diffusion and metabolic measures to various degrees in every MS patients group. Most DTI metrics were significantly correlated with the T2-LL while only NAA/Cr ratio was correlated in RR patients. A comparison analysis of MR methods efficiency demonstrated a better sensitivity/specificity of DTI over MRSI. Nevertheless, NAA/Cr ratio could distinguish all MS and SP patients groups from controls, while NAA/Cho ratio differentiated PP patients from controls. This study demonstrated that diffusivity changes related to microstructural alterations were correlated with metabolic changes and provided a better sensitivity to detect early changes, particularly in RR patients who are more subject to inflammatory processes. In contrast, the better specificity of metabolic ratios to detect axonal damage and demyelination may provide a better index for identification of PP patients.

Published
2012
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4. Leptomeningeal enhancement on post-contrast FLAIR images for early diagnosis of Susac syndrome

Author

François Cotton, Damien Biotti, Margaux Roques, Mahmoud Charif, Christophe Cognard, Francoise Durand Dubief, Jean Darcourt, Jérôme De Seze, Thomas Tourdias, Vincent Larrue, Gilles Adam, Fanny Varenne, Guillaume Bellanger, Stéphane Kremer, Alain Viguier, Nicolas Menjot de Champfleur, Pierre Labauge, Fabrice Bonneville, Fleur Lerebours, Jonathan Ciron, Julien Savatovsky, Michael Obadia, Sofia Patsoura, Cécile Dulau, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Pôle d'Imagerie Médecine Nucléaire, Centre Antoine-Lacassagne, Université Côte d'Azur, Centre de Lutte contre le Cancer Antoine Lacassagne [Nice] (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UCA)-UNICANCER-Université Côte d'Azur (UCA), Laboratoire de psychologie clinique, de psychopathologie et de psychanalyse (LPCPP), Aix Marseille Université (AMU), Ingénierie des Matériaux Polymères (IMP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université de Montpellier (UM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Université de Montpellier (UM), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), RMN et optique : De la mesure au biomarqueur, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-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 Claude Bernard Lyon 1 (UCBL), CHU Bordeaux [Bordeaux], Wageningen University and Research [Wageningen] (WUR), CIC Strasbourg (Centre d’Investigation Clinique Plurithématique (CIC - P) ), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Nouvel Hôpital Civil de Strasbourg-Hôpital de Hautepierre [Strasbourg], i-SEP (i-SEP), CRLCC René Huguenin, Institut Curie [Paris], Unité de recherche Pharmacologie-Toxicologie (UPT), and Institut National de la Recherche Agronomique (INRA)

Subjects
medicine.medical_specialty, Susac Syndrome, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, media_common.quotation_subject, Contrast Media, Fluid-attenuated inversion recovery, post-contrast FLAIR, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, leptomeningeal enhancement, medicine, Contrast (vision), Humans, media_common, Retrospective Studies, business.industry, Multiple sclerosis, medicine.disease, Magnetic Resonance Imaging, Multiples sclerosis, Early Diagnosis, Neurology, Feature (computer vision), Neurology (clinical), Radiology, business, 030217 neurology & neurosurgery
Abstract

Background: Leptomeningeal enhancement (LME) is a key feature of Susac syndrome (SuS) but is only occasionally depicted on post-contrast T1-weighted images (T1-WI). Objective: As post-contrast fluid-attenuated inversion recovery (FLAIR) may be more sensitive, our aim was to assess LME in SuS on this sequence. Methods: From 2010 to 2020, 20 patients with definite SuS diagnosis were retrospectively enrolled in this multicentre study. Two radiologists independently assessed the number of LME on post-contrast FLAIR and T1-WI acquisitions performed before any treatment. A chi-square test was used to compare both sequences and the interrater agreement was calculated. Results: Thirty-five magnetic resonance imagings (MRIs) were performed before treatment, including 19 post-contrast FLAIR images in 17 patients and 25 post-contrast T1-WI in 19 patients. In terms of patients, LME was observed on all post-contrast FLAIR, contrary to post-contrast T1-WI (17/17 (100%) vs. 15/19 (79%), p Conclusion: LME was always observed and easily visible on post-contrast FLAIR images prior to SuS treatment. In association with other MRI features, it is highly indicative of SuS.

Published
2021
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5. Aquaporin 4 distribution in the brain and its relevance for the radiological appearance of neuromyelitis optica spectrum disease

Author

Roxana Ameli, Charles R.G. Guttmann, Juan Carlos Prieto, Fabien Rollot, Miklos Palotai, Sandra Vukusic, Romain Marignier, François Cotton, René Anxionnat, Jean-Paul Armspach, Bertrand Audoin, Christian Barillot, Isabelle Berry, Fabrice Bonneville, Claire Boutet, Giovanni Castelnovo, Frédéric Cervenanski, Mikael Cohen, Olivier Commowick, Jerome De Seze, Vincent Dousset, Francoise Durand-Dubief, Gilles Edan, Jean-Christophe Ferre, Damien Galanaud, Tristan Glattard, Sylvie Grand, Justine Guillaumont, Rémy Guillevin, Salem Hannoun, Fabrice Heitz, Alexandre Krainik, Stéphane Kremer, Pierre Labauge, Nicolas Menjot De Champfleur, Jean-Philippe Ranjeva, Jean-Amédée Roch, Dominique Sappey-Marinier, Julien Savatovsky, Bruno Stankoff, Ayman Tourbah, Thomas Tourdias, Bruno Brochet, Michel Clanet, Nathalie Dufay, David Laplaud, Marie-Claire Maze, Thibault Moreau, Cédric Trolliet, Bernard Frangoulis, Javier Olaiz, Jean Pelletier, Bertrand Bourre, David Brassat, Philippe Cabre, Jean-Philippe Camdessanche, William Camu, Olivier Casez, Pierre Clavelou, Nicolas Collongues, Alain Creange, Gilles Defer, Marc Debouverie, Olivier Gout, Christine Lebrun-Frenay, Catherine Lubetzki, Caroline Papeix, Patrick Vermersch, Hélène Zephir, Service de Radiologie [Hôpital Femme Mère Enfant - HCL], Hôpital Femme Mère Enfant [CHU - HCL] (HFME), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Department of Radiology [Boston], Brigham and Women's Hospital [Boston], Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Service de Neurologie [Lyon], CHU Lyon, 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), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), 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), Service de Médecine Nucléaire - Pierre-Paul Riquet [CHU Toulouse], Pôle imagerie médicale [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Pôle Neurosciences [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Service de Radiologie [CHU Saint-Etienne], Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E)-Université Jean Monnet - Saint-Étienne (UJM), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de Neurologie [CHU Nice], Hôpital Pasteur [Nice] (CHU)-Centre Hospitalier Universitaire de Nice (CHU Nice), CHU Strasbourg, Neurocentre Magendie : Physiopathologie de la Plasticité Neuronale (U1215 Inserm - UB), Université de Bordeaux (UB)-Institut François Magendie-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de neuroradiologie [Grenoble], CHU Grenoble, Centre hospitalier universitaire de Poitiers (CHU Poitiers), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université de Montpellier (UM), Institut d’Imagerie Fonctionnelle Humaine [CHU Montpellier] (I2FH), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Hôpital privé Jean Mermoz [Lyon], CHU Rothschild [AP-HP], CHU Tenon [AP-HP], Université de Reims Champagne-Ardenne (URCA), Service de neurologie [Bordeaux], CHU Bordeaux [Bordeaux]-Groupe hospitalier Pellegrin, Neurologie Vasculaire [Toulouse], Service de neurochirurgie [CHU Nantes], Centre hospitalier universitaire de Nantes (CHU Nantes)-Hôpital Guillaume-et-René-Laennec [Saint-Herblain], Service de Neurologie générale, vasculaire et dégénérative (CHU de Dijon), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Fondation Eugène Devic EDMUS, Hôpital Charles Nicolle [Rouen], CHU Rouen, Normandie Université (NU)-Normandie Université (NU), Hôpital Albert Clarac [CHU de la Martinique], CHU de la Martinique [Fort de France], Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département de neurologie, Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble, Service de Neurologie [CHU Clermont-Ferrand], CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand-CHU Estaing [Clermont-Ferrand], CHU Clermont-Ferrand, Hôpital de Hautepierre [Strasbourg], Département de neurosciences (CHU Henri Mondor), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor, Service de Neurologie [CHU Caen], Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Département de neurologie [Lille], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), 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 Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Empenn, 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), Service de Médecine Nucléaire [Toulouse], CHU Toulouse [Toulouse], Pôle neurosciences [Hôpital de Purpan - Toulouse], Service de radiologie [Saint-Etienne], CHU Saint-Etienne-Université Jean Monnet [Saint-Étienne] (UJM), Service de Neuroradiologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier]-Université de Montpellier (UM), Hôpital Rothschild [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse], Service de neurochirurgie [Nantes], Hôpital Nord (Saint Etienne), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], and Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)

Subjects
Male, Pathology, medicine.medical_specialty, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Myelitis, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, Clinical significance, Retrospective Studies, Aquaporin 4, Neuromyelitis optica, Radiological and Ultrasound Technology, business.industry, Incidence (epidemiology), Neuromyelitis Optica, Brain, Human brain, Middle Aged, medicine.disease, Spinal cord, Magnetic Resonance Imaging, medicine.anatomical_structure, Cohort, Female, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

International audience; Background and purposeTo determine the precise incidence of lesions at sites of high Aquaporin-4 expression (hAQP4) and their possible association with known neuromyelitis optica spectrum disease (NMOSD) lesions patterns.Materials and methodsA retrospective analysis of brain and, when available, spinal cord MRI scans of 54 NMOSD patients recruited among the French NMOSD cohort was performed. Brain lesions were annotated as MS-like, non-specific, or evocative of NMOSD. The topography of hAQP4 was reassessed by human brain atlas. The incidence of lesions in hAQP4 and their association with lesions evocative of NMOSD was estimated.ResultsAmong those included (41/54 female, mean age: 45 years) 47/54 (87%) presented brain lesions. Twenty-six/47 (55%) had lesions in hAQP4. Thirty-two/54 patients (60%) had lesions considered evocative of NMOSD. The majority of them also presented lesions in hAQP4 (65%, 21/32). Patients with lesions in hAQP4 and lesions evocative of NMOSD demonstrated more extensive myelitis compared to the other patients (7 [6–10] versus 4 [3–5] vertebral segments, P = 0.009).ConclusionThe coexistence of lesions evocative of NMOSD and in hAQP4 is associated with significantly more extensive myelitis, and might have pathophysiological and clinical significance.

Published
2021
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6. New OFSEP recommendations for MRI assessment of multiple sclerosis patients: Special consideration for gadolinium deposition and frequent acquisitions

Author

Jean-Christophe Brisset, Stephane Kremer, Salem Hannoun, Fabrice Bonneville, Francoise Durand-Dubief, Thomas Tourdias, Christian Barillot, Charles Guttmann, Sandra Vukusic, Vincent Dousset, Francois Cotton, R. Ameli, R. Anxionnat, B. Audoin, A. Attye, E. Bannier, C. Barillot, D. Ben Salem, M.-P. Boncoeur-Martel, G. Bonhomme, F. Bonneville, C. Boutet, J.C. Brisset, F. Cervenanski, B. Claise, O. Commowick, J.-M. Constans, F. Cotton, P. Dardel, H. Desal, V. Dousset, F. Durand-Dubief, J.-C. Ferre, A. Gaultier, E. Gerardin, T. Glattard, S. Grand, T. Grenier, R. Guillevin, C. Guttmann, A. Krainik, S. Kremer, S. Lion, N. Menjot De Champfleur, L. Mondot, O. Outteryck, N. Pyatigorskaya, J.-P. Pruvo, S. Rabaste, J.-P. Ranjeva, J.-A. Roch, J.-C. Sadik, D. Sappey-Marinier, J. Savatovsky, B. Stankoff, J.-Y. Tanguy, A. Tourbah, T. Tourdias, B. Brochet, R. Casey, J. De Sèze, P. Douek, F. Guillemin, D. Laplaud, C. Lebrun-Frenay, L. Mansuy, T. Moreau, J. Olaiz, J. Pelletier, C. Rigaud-Bully, S. Vukusic, M. Debouverie, G. Edan, J. Ciron, C. Lubetzki, P. Vermersch, P. Labauge, G. Defer, E. Berger, P. Clavelou, O. Gout, E. Thouvenot, O. Heinzlef, A. Al-Khedr, B. Bourre, O. Casez, P. Cabre, A. Montcuquet, A. Créange, J.-P. Camdessanché, S. Bakchine, A. Maurousset, I. Patry, T. De Broucker, C. Pottier, J.-P. Neau, C. Labeyrie, C. Nifle, Hôpital de Hautepierre [Strasbourg], Nehme and Therese Tohme Multiple Sclerosis Center [Beyrouth, Liban] (AUBMC), American University of Beirut Medical Center [Beyrouth, Liban] (AUBMC), American University of Beirut [Beyrouth] (AUB)-American University of Beirut [Beyrouth] (AUB), Neuroradiologie Diagnostique et Thérapeutique [Toulouse], Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Hôpital Purpan [Toulouse], CHU Toulouse [Toulouse], Hôpital neurologique et neurochirurgical Pierre Wertheimer [CHU - HCL], Hospices Civils de Lyon (HCL), INSERM, Neurocentre Magendie, U1215, Physiopathologie de la Plasticité Neuronale, F-33000 Bordeaux, France, 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 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Center for Neurological Imaging, Departments of Radiology and Neurology, Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Service de neuroradiologie [Lyon], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Département de neuroradiologie diagnostique et thérapeutique [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Neurologie, maladies neuro-musculaires [Hôpital de la Timone - APHM], Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), Service de neuroradiologie [Grenoble], CHU Grenoble, Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Service de Radiologie et Imagerie Médicale [CHU Limoges], CHU Limoges, Auteur indépendant, Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), CHU Clermont-Ferrand, CHU Amiens-Picardie, Centre hospitalier universitaire de Nantes (CHU Nantes), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN), Hôpital des Charpennes [CHU - HCL], Centre Hospitalier Universitaire [Grenoble] (CHU), Centre hospitalier universitaire de Poitiers (CHU Poitiers), CHU Montpellier, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Hôpital Pasteur [Nice] (CHU), Hôpital Roger Salengro [Lille], CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Aix Marseille Université (AMU), Fondation Ophtalmologique Adolphe de Rothschild [Paris], Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), Hôpital Raymond Poincaré [AP-HP], CHU de Bordeaux Pellegrin [Bordeaux], Biopathologie de la Myéline, Neuroprotection et Stratégies Thérapeutiques (BMNST), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Union pour la lutte contre la sclérose en plaques (UNISEP), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Hôpital de la Timone [CHU - APHM] (TIMONE), Fondation Eugène Devic EDMUS, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Hôpital Gui de Chauliac, Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), CHU Gabriel Montpied [Clermont-Ferrand], Hôpital Universitaire Carémeau [Nîmes] (CHU Nîmes), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), centre hospitalier intercommunal de Poissy/Saint-Germain-en-Laye - CHIPS [Poissy], Hôpital Charles Nicolle [Rouen], Hôpital Pierre Zobda-Quitman [CHU de la Martinique], CHU de la Martinique [Fort de France], Hôpital Dupuytren [CHU Limoges], Hôpital Henri Mondor, Centre Hospitalier Universitaire de Reims (CHU Reims), Hôpital Bretonneau, Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Centre Hospitalier Sud Francilien, CH Evry-Corbeil, Hôpital Delafontaine, Centre Hospitalier de Saint-Denis [Ile-de-France], Centre Hospitalier René Dubos [Pontoise], AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Centre Hospitalier de Versailles André Mignot (CHV), French State, 'Investments for the Future', Eugène Devic EDMUS Foundation, ARSEP Foundation, Service Neuroradiologie Diagnostique et Thérapeutique [CHU Toulouse], Pôle imagerie médicale [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Neurocentre Magendie : Physiopathologie de la Plasticité Neuronale (U1215 Inserm - UB), Université de Bordeaux (UB)-Institut François Magendie-Institut National de la Santé et de la Recherche Médicale (INSERM), Neuroimagerie: méthodes et applications (Empenn), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-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 Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Hôpital Gui de Chauliac [CHU Montpellier], Centre hospitalier intercommunal de Poissy/Saint-Germain-en-Laye - CHIPS [Poissy], CHU Rouen, Normandie Université (NU)-Normandie Université (NU), 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), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM), CCSD, Accord Elsevier, and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
medicine.medical_specialty, Consensus, Multiple Sclerosis, Gadolinium, chemistry.chemical_element, Contrast Media, Fluid-attenuated inversion recovery, 030218 nuclear medicine & medical imaging, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Atrophy, Quality of life, Medicine, Humans, Radiology, Nuclear Medicine and imaging, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Adverse effect, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Progressive multifocal leukoencephalopathy, Multiple sclerosis, Brain, Magnetic resonance imaging, medicine.disease, Image Enhancement, Magnetic Resonance Imaging, 3. Good health, chemistry, OFSEP, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), Radiology, business, 030217 neurology & neurosurgery
Abstract

Purpose New multiple sclerosis (MS) disease-modifying therapies (DMTs), which exert beneficial effects through prevention of relapse, limitation of disability progression, and improvement of patients’ quality of life, have recently emerged. Nonetheless, these DMTs are not without associated complications (severe adverse events like. progressive multifocal leukoencephalopathy). Patient follow-up requires regular clinical evaluations and close monitoring with magnetic resonance imaging (MRI). Detection of new T2 lesions and potential brain atrophy measurements contribute to the evaluation of treatment effectiveness. Current MRI protocols for MS recommend the acquisition of an annual gadolinium (Gd) enhanced MRI, resulting in administration of high volume of contrast agents over time and Gd accumulation in the brain. Methods A consensus report was established by neuroradiologists and neurologists from the French Observatory of MS, which aimed at reducing the number of Gd injections required during MS patient follow-up. Recommendations The French Observatory of MS recommends the use of macrocyclic Gd enhancement at time of diagnosis, when a new DMT is introduced, at 6-month re-baseline, and when previous scans are unavailable for comparison. Gd administration can be performed as an option in case of relapse or suspicion of intercurrent disease such as progressive multifocal leukoencephalopathy. Other follow-up MRIs do not require contrast enhancement, provided current and previous MRI acquisitions follow the same standardized protocol including 3D FLAIR sequences.

Published
2020
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7. Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome

Author

Patrick Vermersch, Robert Thompson Stone, Sunita Venkateswaren, Jean Pelletier, Daniel S. Reich, Christine Lebrun, Ugur Uygunoglu, David Brassat, Mar Tintoré, J. Nicholas Brenton, Francoise Durand Dubief, Megan Langille, Clarisse Carra Dallière, Evangeline Wassmer, Eugene D. Shapiro, Daniel Pelletier, Silvia Tenembaum, Sona Narula, Rinze F. Neuteboom, Orhun H. Kantarci, Jérôme De Seze, Guillaume Mathey, Darin T. Okuda, Philippe Cabre, Veronika Shabanova, Wendy Vargas, Daniela Pohl, Naila Makhani, Matilde Inglese, Observatoire Francophone de la Sclérose en Plaques, Juan Ignacio Rojas, Aksel Siva, Institut Català de la Salut, [Makhani N] Department of Pediatrics, Yale University School of Medicine, USA. Department of Neurology, Yale University School of Medicine, USA. [Lebrun C] CRCSEP Nice Hopital Pasteur, Nice, France. [Siva A] University of Istanbul, Istanbul, Turkey. Cerrahpasa School of Medicine, Istanbul, Turkey. [Narula S] Children's Hospital of Philadelphia, Philadelphia, USA. University of Pennsylvania, Philadelphia, USA. [Wassmer E] Department of Neurology, The Birmingham Children's Hospital NHS Trust, Birmingham, UK. [Brassat D] Centre Hospitalo Universitaire Purpan, Toulouse, France. [Tintoré M] Centre d'Esclerosi Múltiple de Catalunya, Barcelona, Spain., Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Department of Pediatrics, Yale University School of Medicine, Department of Neurology, Yale University School of Medicine, CRCSEP Nice Hopital Pasteur, University of Istanbul, Cerrahpasa School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Department of Neurology, The Birmingham Children's Hospital NHS Trust, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Department of Neurology, University of Virginia, Centre Hospitalo Universitaire Fort de France, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre Hospitalo Universitaire Strasbourg, Centre Hospitalo-Universitaire de Lyon, Department of Neurology and Neuroscience, Icahn School of Medicine, Department of Neurology, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, (DINOGMI) University of Genova and IRCCS, Harbor UCLA Medical Center [Torrance, Ca.], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Department of Pediatric Neurology, Sophia's Children's Hospital, AP-HM, CHU Timone, Pole de Neurosciences Cliniques, Department of Neurology, Marseille, France., Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Division of Neurology, Children's Hospital of Eastern Ontario, Translational Neuroradiology Section, National Institutes of Health, Multiple Sclerosis Center of Buenos Aires, Hospital Italiano de Buenos Aires, Department of Pediatrics, Yale University School of Medicine, USA and Yale School of Public Health, Department of Neurology, University of Rochester Medical Center, Department of Neurology, National Pediatric Hospital Dr Juan P Garrahan, MS Center of Catalunya Cemcat, Department of Pediatric Neurology, Columbia University Medical Center, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Department of Neurology, Mayo Clinic College of Medicine, Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Department of Neurology, Keck School of Medicine of University of Southern California, and Neurology

Subjects
Pediatrics, medicine.medical_specialty, Adolescents, multiple sclerosis, Radiologically isolated syndrome, Esclerosi múltiple - Imatgeria, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, children, 030225 pediatrics, medicine, personas::Grupos de Edad::niño [DENOMINACIONES DE GRUPOS], Amino Acids, Peptides, and Proteins::Proteins::Blood Proteins::Immunoproteins::Immunoglobulins::Antibodies::Oligoclonal Bands [CHEMICALS AND DRUGS], Other subheadings::Other subheadings::/diagnostic imaging [Other subheadings], Radiologically isolated syndrome, children, multiple sclerosis, Personas::Grupos de Edad::Niño [DENOMINACIONES DE GRUPOS], business.industry, Multiple sclerosis, Nervous System Diseases::Autoimmune Diseases of the Nervous System::Demyelinating Autoimmune Diseases, CNS::Multiple Sclerosis [DISEASES], Otros calificadores::Otros calificadores::/diagnóstico por imagen [Otros calificadores], personas::Grupos de Edad::adolescente [DENOMINACIONES DE GRUPOS], Persons::Age Groups::Child [NAMED GROUPS], medicine.disease, Persons::Age Groups::Adolescent [NAMED GROUPS], 3. Good health, Original Research Paper, enfermedades del sistema nervioso::enfermedades autoinmunitarias del sistema nervioso::enfermedades autoinmunes desmielinizantes del SNC::esclerosis múltiple [ENFERMEDADES], aminoácidos, péptidos y proteínas::proteínas::proteínas sanguíneas::inmunoproteínas::inmunoglobulinas::anticuerpos::bandas oligoclonales [COMPUESTOS QUÍMICOS Y DROGAS], Neurology (clinical), business, Immunoglobulines, human activities, Infants, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030217 neurology & neurosurgery
Abstract

Background: Steps towards the development of diagnostic criteria are needed for children with the radiologically isolated syndrome to identify children at risk of clinical demyelination. Objectives: To evaluate the 2005 and 2016 MAGNIMS magnetic resonance imaging criteria for dissemination in space for multiple sclerosis, both alone and with oligoclonal bands in cerebrospinal fluid added, as predictors of a first clinical event consistent with central nervous system demyelination in children with radiologically isolated syndrome. Methods: We analysed an international historical cohort of 61 children with radiologically isolated syndrome (

Published
2019
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8. Additional file 1: of Evaluation of treatment response in adults with relapsing MOG-Ab-associated disease

Author

Cobo-Calvo, Alvaro, Sepúlveda, María, Rollot, Fabien, Thais Armangué, Ruiz, Anne, Maillart, Elisabeth, Papeix, Caroline, Audoin, Bertrand, Zephir, Helene, Biotti, Damien, Ciron, Jonathan, Francoise Durand-Dubief, Collongues, Nicolas, Ayrignac, Xavier, Labauge, Pierre, Thouvenot, Eric, Bourre, Bertrand, Montcuquet, Alexis, Cohen, Mikael, Deschamps, Romain, Solà-Valls, Nuria, Llufriu, Sara, Seze, Jerome De, Blanco, Yolanda, Vukusic, Sandra, Saiz, Albert, and Marignier, Romain

Abstract

Table S1.Treatment regimens and intervals. Table S2 Characteristics in patients with MOG antibodies and multiple sclerosis. Table S3 Comparison of baseline possible confounders between treated and non-treated patients in the original sample. Table S4 Comparison of baseline possible confounders between treated and non-treated patients in the pseudo-population. Table S5 Patients who received combined drugs and reasons for inclusion/exclusion in different analyses. (DOCX 28 kb)

Published
2019
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9. Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Author

Enyi Chen, Berardino Barile, Françoise Durand-Dubief, Thomas Grenier, and Dominique Sappey-Marinier

Subjects
multiple sclerosis, graph convolutional network, CNN, classification, brain morphological connectivity, gray matter thickness, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.

Published
2024
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10. Unusual neurologic presentation of aseptic abscesses syndrome

Author

Philippe, Nicolas, Olivier, Guerrier, Amandine, Benoit, Francoise, Durand-Dubief, Gerald, Raverot, Sebastien, Debarbieux, Clemence, Delteil, Alexandre, Vasiljevic, Emmanuel, Jouanneau, Francois, Cotton, Romain, Marignier, and Sandra, Vukusic

Subjects
Clinical/Scientific Notes
Published
2018

11. Primary Progressive Multiple Sclerosis Evolving from Radiologically Isolated Syndrome

Author

Orhun H, Kantarci, Christine, Lebrun, Aksel, Siva, Mark B, Keegan, Christina J, Azevedo, Matilde, Inglese, Mar, Tintoré, Braeden D, Newton, Francoise, Durand-Dubief, Maria Pia, Amato, Nicola, De Stefano, Maria Pia, Sormani, Daniel, Pelletier, and Darin T, Okuda

Subjects
Adult, Male, Adolescent, Age Factors, Child, Demyelinating Diseases, Female, Follow-Up Studies, Humans, Magnetic Resonance Imaging, Middle Aged, Multiple Sclerosis, Chronic Progressive, Multiple Sclerosis, Relapsing-Remitting, Sex Factors, Young Adult, Disease Progression, Neurology, Neurology (clinical), Multiple Sclerosis, Medicine (all), Relapsing-Remitting, Chronic Progressive
Abstract

The aim of this work was to evaluate the preprogressive phase in subjects with radiologically isolated syndrome (RIS) who evolve to primary progressive multiple sclerosis (PPMS).A multicenter RIS cohort was previously established. Demographic, clinical, and radiological characteristics of subjects with RIS that evolved directly to PPMS were compared to those that developed a relapsing disease course from onset (clinically isolated syndrome [CIS] or relapsing-remitting MS) and were also compared to two other population- and clinic-based PPMS cohorts.Of the 453 subjects with RIS, 128 evolved to symptomatic MS during the follow-up (113 developed a first acute clinical event consistent with CIS/MS, 15 evolved to PPMS). PPMS prevalence (11.7%) and onset age (mean ± standard deviation; 49.1 ± 12.1) in the RIS group were comparable to other PPMS populations (p 0.05). Median time to PPMS was 3.5 years (range, 1.6-5.4). RIS evolved to PPMS more commonly in men (p = 0.005) and at an older age (p 0.001) when compared to CIS/MS, independent of follow-up duration. Subjects who evolved to PPMS had more spinal cord lesions (100%) before symptomatic evolution than those that developed CIS/MS (64%) and those that remained asymptomatic (23%) within the follow-up period (P = 0.005). Other MRI characteristics in the preprogressive phase of PPMS were indistinguishable from CIS/MS.Subjects with RIS evolve to PPMS at the same frequency as expected from general MS populations in an age-dependent manner. Besides age, unequivocal presence of spinal cord lesions and being male predicted evolution to PPMS. Our findings further suggest that RIS is biologically part of the MS spectrum.

Published
2016

12. A meta‐analysis comparing first‐line immunosuppressants in neuromyelitis optica

Author

Jonathan Giovannelli, Jonathan Ciron, Mikael Cohen, Ho‐Jin Kim, Su‐Hyun Kim, Jan‐Patrik Stellmann, Ingo Kleiter, Morgan McCreary, Benjamin M. Greenberg, Romain Deschamps, Bertrand Audoin, Elisabeth Maillart, Caroline Papeix, Nicolas Collongues, Bertrand Bourre, David Laplaud, Xavier Ayrignac, Françoise Durand‐Dubief, Aurélie Ruet, Sandra Vukusic, Romain Marignier, Luc Dauchet, Hélène Zephir, and NEMOS (Neuromyelitis Optica Study Group in Germany), NOMADMUS (Neuromyelitis Optica study Group in France), OFSEP (Observatoire Français de la Sclérose en Plaques) investigators

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objective As phase III trials have shown interest in innovative but expensive drugs in the treatment of neuromyelitis optica spectrum disorder (NMOSD), data are needed to clarify strategies in the treatment of neuromyelitis optica (NMO). This meta‐analysis compares the efficacy of first‐line strategies using rituximab (RTX), mycophenolate mofetil (MMF), or azathioprine (AZA), which are still widely used. Methods Studies identified by the systematic review of Huang et al. (2019) were selected if they considered at least two first‐line immunosuppressants among RTX, MMF, and AZA. We updated this review. The Medline, Cochrane Central Register of Controlled Trials, Embase, and ClinicalTrials databases were queried between November 2018 and April 2020. To be included, the hazard ratio (HR) [95% CI] for the time to first relapse after first‐line immunosuppression had to be available, calculable, or provided by the authors. Results We gathered data from 919 NMO patients (232 RTX‐, 294 MMF‐, and 393 AZA‐treated patients). The risk of first relapse after first‐line immunosuppression was 1.55 [1.04, 2.31] (p = 0.03) for MMF compared with RTX, 1.42 [0.87, 2.30] (p = 0.16) for AZA compared with RTX, and 0.94 [0.58, 1.54] (p = 0.08) for MMF compared with AZA. Interpretation The findings suggest that RTX is more efficient than MMF as a first‐line therapy. Even if the results of our meta‐analysis cannot conclude that RTX has a better efficacy in delaying the first relapse than AZA, the observed effect difference between both treatments combined with the results of previous studies using as outcome the annualized relapse rate may be in favor of RTX.

Published
2021
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13. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome

Author

Berardino Barile, Pooya Ashtari, Claudio Stamile, Aldo Marzullo, Frederik Maes, Françoise Durand-Dubief, Sabine Van Huffel, and Dominique Sappey-Marinier

Subjects
multiple sclerosis, brain connectivity, grey matter, machine learning, artificial intelligence–AI, Mechanical engineering and machinery, TJ1-1570, Electronic computers. Computer science, QA75.5-76.95
Abstract

Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods.Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (Eg), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed.Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8.Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.

Published
2022
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14. Outcome and risk of recurrence in a large cohort of idiopathic longitudinally extensive transverse myelitis without AQP4/MOG antibodies

Author

Elisabeth Maillart, Françoise Durand-Dubief, Céline Louapre, Bertrand Audoin, Bertrand Bourre, Nathalie Derache, Jonathan Ciron, Nicolas Collongues, Jérome de Sèze, Mikael Cohen, Christine Lebrun-Frenay, Nawel Hadhoum, Hélène Zéphir, Romain Deschamps, Clarisse Carra-Dallière, Pierre Labauge, Philippe Kerschen, Alexis Montcuquet, Sandrine Wiertlewski, David Laplaud, Gwenaëlle Runavot, Sandra Vukusic, Caroline Papeix, Romain Marignier, and on behalf of the OFSEP, SFSEP, and NOMADMUS study groups

Subjects
Longitudinally extensive transverse myelitis, Seronegative, Neuromyelitis optica, Outcome, Treatment, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Longitudinally extensive transverse myelitis (LETM) is classically related to aquaporin (AQP4)-antibodies (Ab) neuromyelitis optica spectrum disorders (NMOSD) or more recently to myelin oligodendrocyte glycoprotein (MOG)-Ab associated disease. However, some patients remain negative for any diagnosis, despite a large work-up including AQP4-Ab and MOG-Ab. Data about natural history, disability outcome, and treatment are limited in this group of patients. We aimed to (1) describe clinical, biological, and radiological features of double seronegative LETM patients; (2) assess the clinical course and identify prognostic factors; and (3) assess the risk of recurrence, according to maintenance immunosuppressive therapy. Methods Retrospective evaluation of patients with a first episode of LETM, tested negative for AQP-Ab and MOG-Ab, from the French nationwide observatory study NOMADMUS. Results Fifty-three patients (median age 38 years (range 16–80)) with double seronegative LETM were included. Median nadir EDSS at onset was 6.0 (1–8.5), associated to a median EDSS at last follow-up of 4.0 (0–8). Recurrence was observed in 24.5% of patients in the 18 following months, with a median time to first relapse of 5.7 months. The risk of recurrence was lower in the group of patients treated early with an immunosuppressive drug (2/22, 9%), in comparison with untreated patients (10/31, 32%). Conclusions A first episode of a double seronegative LETM is associated to a severe outcome and a high rate of relapse in the following 18 months, suggesting that an early immunosuppressive treatment may be beneficial in that condition.

Published
2020
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15. Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks

Author

Aldo Marzullo, Gabriel Kocevar, Claudio Stamile, Françoise Durand-Dubief, Giorgio Terracina, Francesco Calimeri, and Dominique Sappey-Marinier

Subjects
multiple sclerosis, graph neural networks, graph-derived metrics, diffusion tensor imaging, connectome, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.

Published
2019
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16. Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Author

Adrian Ion-Mărgineanu, Gabriel Kocevar, Claudio Stamile, Diana M. Sima, Françoise Durand-Dubief, Sabine Van Huffel, and Dominique Sappey-Marinier

Subjects
multiple sclerosis, longitudinal analysis, magnetic resonance spectroscopic imaging, EDSS, lesion load, machine learning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests.Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features.Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

Published
2017
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17. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

Author

Gabriel Kocevar, Claudio Stamile, Salem Hannoun, François Cotton, Sandra Vukusic, Françoise Durand-Dubief, and Dominique Sappey-Marinier

Subjects
Classification, Diffusion Tensor Imaging, Multiple Sclerosis, MRI, SVM, graph theory, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and methods: Sixty-four MS patients (12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)) along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects’ groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8%, 91.8%, 75.6% and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6%, 88.9% and 70.7%) were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients’ clinical profiles.

Published
2016
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18. A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis.

Author

Claudio Stamile, Gabriel Kocevar, François Cotton, Françoise Durand-Dubief, Salem Hannoun, Carole Frindel, Charles R G Guttmann, David Rousseau, and Dominique Sappey-Marinier

Subjects
Medicine, Science
Abstract

Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.

Published
2016
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19. Supplemental Material1 - Supplemental material for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome

Author

Makhani, Naila, Lebrun, Christine, Siva, Aksel, Narula, Sona, Wassmer, Evangeline, Brassat, David, J Nicholas Brenton, Cabre, Philippe, Dallière, Clarisse Carra, Seze, Jérôme De, Francoise Durand Dubief, Inglese, Matilde, Langille, Megan, Mathey, Guillaume, Neuteboom, Rinze F, Pelletier, Jean, Pohl, Daniela, Reich, Daniel S, Rojas, Juan Ignacio, Shabanova, Veronika, Shapiro, Eugene D, Stone, Robert T, Tenembaum, Silvia, Tintoré, Mar, Ugur Uygunoglu, Vargas, Wendy, Venkateswaren, Sunita, Vermersch, Patrick, Kantarci, Orhun, Okuda, Darin T, and Pelletier, Daniel

Subjects
FOS: Clinical medicine, 16. Peace & justice, 3. Good health, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Supplemental Material1 for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome by Naila Makhani, Christine Lebrun, Aksel Siva, Sona Narula, Evangeline Wassmer, David Brassat, J Nicholas Brenton, Philippe Cabre, Clarisse Carra Dallière, Jérôme de Seze, Francoise Durand Dubief, Matilde Inglese, Megan Langille, Guillaume Mathey, Rinze F Neuteboom, Jean Pelletier, Daniela Pohl, Daniel S Reich, Juan Ignacio Rojas, Veronika Shabanova, Eugene D Shapiro, Robert T Stone, Silvia Tenembaum, Mar Tintoré, Ugur Uygunoglu, Wendy Vargas, Sunita Venkateswaren, Patrick Vermersch, Orhun Kantarci, Darin T Okuda, Daniel Pelletier and on behalf of Observatoire Francophone de la Sclérose en Plaques (OFSEP), Société Francophone de la Sclérose en Plaques (SFSEP), the Radiologically Isolated Syndrome Consortium (RISC) and the Pediatric Radiologically Isolated Syndrome Consortium (PARIS) in Multiple Sclerosis Journal – Experimental, Translational and Clinical

20. Supplemental Material2 - Supplemental material for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome

Author

Makhani, Naila, Lebrun, Christine, Siva, Aksel, Narula, Sona, Wassmer, Evangeline, Brassat, David, J Nicholas Brenton, Cabre, Philippe, Dallière, Clarisse Carra, Seze, Jérôme De, Francoise Durand Dubief, Inglese, Matilde, Langille, Megan, Mathey, Guillaume, Neuteboom, Rinze F, Pelletier, Jean, Pohl, Daniela, Reich, Daniel S, Rojas, Juan Ignacio, Shabanova, Veronika, Shapiro, Eugene D, Stone, Robert T, Tenembaum, Silvia, Tintoré, Mar, Ugur Uygunoglu, Vargas, Wendy, Venkateswaren, Sunita, Vermersch, Patrick, Kantarci, Orhun, Okuda, Darin T, and Pelletier, Daniel

Subjects
FOS: Clinical medicine, 16. Peace & justice, 3. Good health, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Supplemental Material2 for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome by Naila Makhani, Christine Lebrun, Aksel Siva, Sona Narula, Evangeline Wassmer, David Brassat, J Nicholas Brenton, Philippe Cabre, Clarisse Carra Dallière, Jérôme de Seze, Francoise Durand Dubief, Matilde Inglese, Megan Langille, Guillaume Mathey, Rinze F Neuteboom, Jean Pelletier, Daniela Pohl, Daniel S Reich, Juan Ignacio Rojas, Veronika Shabanova, Eugene D Shapiro, Robert T Stone, Silvia Tenembaum, Mar Tintoré, Ugur Uygunoglu, Wendy Vargas, Sunita Venkateswaren, Patrick Vermersch, Orhun Kantarci, Darin T Okuda, Daniel Pelletier and on behalf of Observatoire Francophone de la Sclérose en Plaques (OFSEP), Société Francophone de la Sclérose en Plaques (SFSEP), the Radiologically Isolated Syndrome Consortium (RISC) and the Pediatric Radiologically Isolated Syndrome Consortium (PARIS) in Multiple Sclerosis Journal – Experimental, Translational and Clinical

21. Supplemental Material1 - Supplemental material for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome

Author

Makhani, Naila, Lebrun, Christine, Siva, Aksel, Narula, Sona, Wassmer, Evangeline, Brassat, David, J Nicholas Brenton, Cabre, Philippe, Dallière, Clarisse Carra, Seze, Jérôme De, Francoise Durand Dubief, Inglese, Matilde, Langille, Megan, Mathey, Guillaume, Neuteboom, Rinze F, Pelletier, Jean, Pohl, Daniela, Reich, Daniel S, Rojas, Juan Ignacio, Shabanova, Veronika, Shapiro, Eugene D, Stone, Robert T, Tenembaum, Silvia, Tintoré, Mar, Ugur Uygunoglu, Vargas, Wendy, Venkateswaren, Sunita, Vermersch, Patrick, Kantarci, Orhun, Okuda, Darin T, and Pelletier, Daniel

Subjects
FOS: Clinical medicine, 16. Peace & justice, 3. Good health, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Supplemental Material1 for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome by Naila Makhani, Christine Lebrun, Aksel Siva, Sona Narula, Evangeline Wassmer, David Brassat, J Nicholas Brenton, Philippe Cabre, Clarisse Carra Dallière, Jérôme de Seze, Francoise Durand Dubief, Matilde Inglese, Megan Langille, Guillaume Mathey, Rinze F Neuteboom, Jean Pelletier, Daniela Pohl, Daniel S Reich, Juan Ignacio Rojas, Veronika Shabanova, Eugene D Shapiro, Robert T Stone, Silvia Tenembaum, Mar Tintoré, Ugur Uygunoglu, Wendy Vargas, Sunita Venkateswaren, Patrick Vermersch, Orhun Kantarci, Darin T Okuda, Daniel Pelletier and on behalf of Observatoire Francophone de la Sclérose en Plaques (OFSEP), Société Francophone de la Sclérose en Plaques (SFSEP), the Radiologically Isolated Syndrome Consortium (RISC) and the Pediatric Radiologically Isolated Syndrome Consortium (PARIS) in Multiple Sclerosis Journal – Experimental, Translational and Clinical

22. Supplemental Material2 - Supplemental material for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome

Author

Makhani, Naila, Lebrun, Christine, Siva, Aksel, Narula, Sona, Wassmer, Evangeline, Brassat, David, J Nicholas Brenton, Cabre, Philippe, Dallière, Clarisse Carra, Seze, Jérôme De, Francoise Durand Dubief, Inglese, Matilde, Langille, Megan, Mathey, Guillaume, Neuteboom, Rinze F, Pelletier, Jean, Pohl, Daniela, Reich, Daniel S, Rojas, Juan Ignacio, Shabanova, Veronika, Shapiro, Eugene D, Stone, Robert T, Tenembaum, Silvia, Tintoré, Mar, Ugur Uygunoglu, Vargas, Wendy, Venkateswaren, Sunita, Vermersch, Patrick, Kantarci, Orhun, Okuda, Darin T, and Pelletier, Daniel

Subjects
FOS: Clinical medicine, 16. Peace & justice, 3. Good health, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Supplemental Material2 for Oligoclonal bands increase the specificity of MRI criteria to predict multiple sclerosis in children with radiologically isolated syndrome by Naila Makhani, Christine Lebrun, Aksel Siva, Sona Narula, Evangeline Wassmer, David Brassat, J Nicholas Brenton, Philippe Cabre, Clarisse Carra Dallière, Jérôme de Seze, Francoise Durand Dubief, Matilde Inglese, Megan Langille, Guillaume Mathey, Rinze F Neuteboom, Jean Pelletier, Daniela Pohl, Daniel S Reich, Juan Ignacio Rojas, Veronika Shabanova, Eugene D Shapiro, Robert T Stone, Silvia Tenembaum, Mar Tintoré, Ugur Uygunoglu, Wendy Vargas, Sunita Venkateswaren, Patrick Vermersch, Orhun Kantarci, Darin T Okuda, Daniel Pelletier and on behalf of Observatoire Francophone de la Sclérose en Plaques (OFSEP), Société Francophone de la Sclérose en Plaques (SFSEP), the Radiologically Isolated Syndrome Consortium (RISC) and the Pediatric Radiologically Isolated Syndrome Consortium (PARIS) in Multiple Sclerosis Journal – Experimental, Translational and Clinical

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