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Bueno A, Bosch I, Rodríguez A, Jiménez A, Carreres J, Fernández M, Marti-Bonmati L, Alberich-Bayarri A. Automated Cervical Spinal Cord Segmentation in Real-World MRI of Multiple Sclerosis Patients by Optimized Hybrid Residual Attention-Aware Convolutional Neural Networks. J Digit Imaging 2022; 35:1131-1142. [PMID: 35789447 PMCID: PMC9582086 DOI: 10.1007/s10278-022-00637-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 03/22/2022] [Accepted: 04/09/2022] [Indexed: 10/17/2022] Open
Abstract
Magnetic resonance (MR) imaging is the most sensitive clinical tool in the diagnosis and monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest in this clinical scenario in recent years, but, unlike the brain, there is a more limited choice of algorithms to assist spinal cord segmentation. Our goal was to investigate and develop an automatic MR cervical cord segmentation method, enabling automated and seamless spinal cord atrophy assessment and setting the stage for the development of an aggregated algorithm for the extraction of lesion-related imaging biomarkers. The algorithm was developed using a real-world MR imaging dataset of 121 MS patients (96 cases used as a training dataset and 25 cases as a validation dataset). Transversal, 3D T1-weighted gradient echo MR images (TE/TR/FA = 1.7-2.7 ms/5.6-8.2 ms/12°) were acquired in a 3 T system (Signa HD, GEHC) as standard of care in our clinical practice. Experienced radiologists supervised the manual labelling, which was considered the ground-truth. The 2D convolutional neural network consisted of a hybrid residual attention-aware segmentation method trained to delineate the cervical spinal cord. The training was conducted using a focal loss function, based on the Tversky index to address label imbalance, and an automatic optimal learning rate finder. Our automated model provided an accurate segmentation, achieving a validation DICE coefficient of 0.904 ± 0.101 compared with the manual delineation. An automatic method for cervical spinal cord segmentation on T1-weighted MR images was successfully implemented. It will have direct implications serving as the first step for accelerating the process for MS staging and follow-up through imaging biomarkers.
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Affiliation(s)
- América Bueno
- Instituto de Tecnologías y Aplicaciones Multimedia, Universitat Politècnica de Valencia, Valencia, Spain.
| | - Ignacio Bosch
- Instituto de Tecnologías y Aplicaciones Multimedia, Universitat Politècnica de Valencia, Valencia, Spain
| | - Alejandro Rodríguez
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Ana Jiménez
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Valencia, Spain
| | - Joan Carreres
- Radiology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Matías Fernández
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Luis Marti-Bonmati
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
| | - Angel Alberich-Bayarri
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Valencia, Spain
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Song Z, Krishnan A, Gaetano L, Tustison NJ, Clayton D, de Crespigny A, Bengtsson T, Jia X, Carano RAD. Deformation-based morphometry identifies deep brain structures protected by ocrelizumab. Neuroimage Clin 2022; 34:102959. [PMID: 35189455 PMCID: PMC8861820 DOI: 10.1016/j.nicl.2022.102959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 02/02/2022] [Accepted: 02/05/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Despite advancements in treatments for multiple sclerosis, insidious disease progression remains an area of unmet medical need, for which atrophy-based biomarkers may help better characterize the progressive biology. METHODS We developed and applied a method of longitudinal deformation-based morphometry to provide voxel-level assessments of brain volume changes and identified brain regions that were significantly impacted by disease-modifying therapy. RESULTS Using brain MRI data from two identically designed pivotal trials of relapsing multiple sclerosis (total N = 1483), we identified multiple deep brain regions, including the thalamus and brainstem, where volume loss over time was reduced by ocrelizumab (p < 0.05), a humanized anti-CD20 + monoclonal antibody approved for the treatment of multiple sclerosis. Additionally, identified brainstem shrinkage, as well as brain ventricle expansion, was associated with a greater risk for confirmed disability progression (p < 0.05). CONCLUSIONS The identification of deep brain structures has a strong implication for developing new biomarkers of brain atrophy reduction to advance drug development for multiple sclerosis, which has an increasing focus on targeting the progressive biology.
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Affiliation(s)
- Zhuang Song
- Personalized Healthcare Imaging, Genentech, Inc., South San Francisco, CA 94080, USA.
| | - Anithapriya Krishnan
- Personalized Healthcare Imaging, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Laura Gaetano
- Product Development Medical Affair, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22904, USA
| | - David Clayton
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Alex de Crespigny
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Thomas Bengtsson
- Personalized Healthcare Imaging, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Xiaoming Jia
- Biomarker Development, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Richard A D Carano
- Personalized Healthcare Imaging, Genentech, Inc., South San Francisco, CA 94080, USA
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Solstrand Dahlberg L, Viessmann O, Linnman C. Heritability of cervical spinal cord structure. Neurol Genet 2020; 6:e401. [PMID: 32185240 PMCID: PMC7061306 DOI: 10.1212/nxg.0000000000000401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 01/13/2020] [Indexed: 12/04/2022]
Abstract
OBJECTIVE Measures of spinal cord structure can be a useful phenotype to track disease severity and development; this observational study measures the hereditability of cervical spinal cord anatomy and its correlates in healthy human beings. METHODS Twin data from the Human Connectome Project were analyzed with semiautomated spinal cord segmentation, evaluating test-retest reliability and broad-sense heritability with an AE model. Relationships between spinal cord metrics, general physical measures, regional brain structural measures, and motor function were assessed. RESULTS We found that the spinal cord C2 cross-sectional area (CSA), left-right width (LRW), and anterior-posterior width (APW) are highly heritable (85%-91%). All measures were highly correlated with the brain volume, and CSA only was positively correlated with thalamic volumes (p = 0.005) but negatively correlated with the occipital cortex area (p = 0.001). LRW was correlated with the participant's height (p = 0.00027). The subjects' sex significantly influenced these metrics. Analyses of a test-retest data set confirmed validity of the approach. CONCLUSIONS This study provides the evidence of genetic influence on spinal cord structure. MRI metrics of cervical spinal cord anatomy are robust and not easily influenced by nonpathological environmental factors, providing a useful metric for monitoring normal development and progression of neurodegenerative disorders affecting the spinal cord, including-but not limited to-spinal cord injury and MS.
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Affiliation(s)
- Linda Solstrand Dahlberg
- Department of Anesthesiology, Perioperative and Pain Medicine (L.S.D., C.L.), Boston Children's Hospital, Harvard Medical School, MA; Departments of Psychiatry and Radiology (L.S.D., C.L.), Massachusetts General Hospital, Harvard Medical School; Department of Neurology and Neurosurgery (L.S.D.), Montreal Neurological Institute, McGill University, Canada; Athinoula A. Martinos Center for Biomedical Imaging (O.V.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Boston; and Spaulding Neuroimaging Lab (C.L.), Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA
| | - Olivia Viessmann
- Department of Anesthesiology, Perioperative and Pain Medicine (L.S.D., C.L.), Boston Children's Hospital, Harvard Medical School, MA; Departments of Psychiatry and Radiology (L.S.D., C.L.), Massachusetts General Hospital, Harvard Medical School; Department of Neurology and Neurosurgery (L.S.D.), Montreal Neurological Institute, McGill University, Canada; Athinoula A. Martinos Center for Biomedical Imaging (O.V.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Boston; and Spaulding Neuroimaging Lab (C.L.), Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA
| | - Clas Linnman
- Department of Anesthesiology, Perioperative and Pain Medicine (L.S.D., C.L.), Boston Children's Hospital, Harvard Medical School, MA; Departments of Psychiatry and Radiology (L.S.D., C.L.), Massachusetts General Hospital, Harvard Medical School; Department of Neurology and Neurosurgery (L.S.D.), Montreal Neurological Institute, McGill University, Canada; Athinoula A. Martinos Center for Biomedical Imaging (O.V.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Boston; and Spaulding Neuroimaging Lab (C.L.), Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA
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6
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Song X, Li D, Qiu Z, Su S, Wu Y, Wang J, Liu Z, Dong H. Correlation between EDSS scores and cervical spinal cord atrophy at 3T MRI in multiple sclerosis: A systematic review and meta-analysis. Mult Scler Relat Disord 2019; 37:101426. [PMID: 32172997 DOI: 10.1016/j.msard.2019.101426] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/28/2019] [Accepted: 09/30/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Cervical spinal cord atrophy (CSCA), which partly reflects the axonal loss in the spinal cord, is increasingly recognized as a valuable predictor of disease outcome. However, inconsistent results have been reported regarding the correlation of CSCA and clinical disability in multiple sclerosis (MS). The aim of this meta-analysis was to synthesize the available data obtained from 3.0-Tesla (3T) MRI scanners and to explore the relationship between CSCA and scores on the Expanded Disability Status Scale (EDSS). METHODS We searched PubMed, Embase, and Web of Science for articles published from the database inception to February 1, 2019. The quality of the articles was assessed according to a quality evaluation checklist which was created based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We conducted a meta-analysis of the correlation between EDSS scores and CSCA at 3T MRI in MS. RESULTS Twenty-two eligible studies involving 1933 participants were incorporated into our meta-analysis. Our results demonstrated that CSCA was negatively and moderately correlated with EDSS scores (rs = -0.42, 95% CI: -0.51 to -0.32; p < 0.0001). Subgroup analyses revealed a weaker correlation in the group of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) (rs = -0.19, 95% CI: -0.31 to -0.07; p = 0.0029). CONCLUSIONS The correlation between CSCA and EDSS scores was significant but moderate. We encourage more studies using reliable and consistent methods to explore whether CSCA is suitable as a predictor for MS progression.
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Affiliation(s)
- Xiaodong Song
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Dawei Li
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Zhandong Qiu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Shengyao Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Yan Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Jingsi Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Zheng Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China.
| | - Huiqing Dong
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China.
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Papinutto N, Asteggiano C, Bischof A, Gundel TJ, Caverzasi E, Stern WA, Bastianello S, Hauser SL, Henry RG. Intersubject Variability and Normalization Strategies for Spinal Cord Total Cross-Sectional and Gray Matter Areas. J Neuroimaging 2019; 30:110-118. [PMID: 31571307 DOI: 10.1111/jon.12666] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/02/2019] [Accepted: 09/16/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE The quantification of spinal cord (SC) atrophy by MRI has assumed an important role in assessment of neuroinflammatory/neurodegenerative diseases and traumatic SC injury. Recent technical advances make possible the quantification of gray matter (GM) and white matter tissues in clinical settings. However, the goal of a reliable diagnostic, prognostic or predictive marker is still elusive, in part due to large intersubject variability of SC areas. Here, we investigated the sources of this variability and explored effective strategies to reduce it. METHODS One hundred twenty-nine healthy subjects (mean age: 41.0 ± 15.9) underwent MRI on a Siemens 3T Skyra scanner. Two-dimensional PSIR at the C2-C3 vertebral level and a sagittal 1 mm3 3D T1-weighted brain acquisition extended to the upper cervical cord were acquired. Total cross-sectional area and GM area were measured at C2-C3, as well as measures of the vertebra, spinal canal and the skull. Correlations between the different metrics were explored using Pearson product-moment coefficients. The most promising metrics were used to normalize cord areas using multiple regression analyses. RESULTS The most effective normalization metrics were the V-scale (from SienaX) and the product of the C2-C3 spinal canal diameters. Normalization methods based on these metrics reduced the intersubject variability of cord areas of up to 17.74%. The measured cord areas had a statistically significant sex difference, while the effect of age was moderate. CONCLUSIONS The present work explored in a large cohort of healthy subjects the source of intersubject variability of SC areas and proposes effective normalization methods for its reduction.
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Affiliation(s)
- Nico Papinutto
- Department of Neurology, University of California, San Francisco, CA
| | - Carlo Asteggiano
- Department of Neurology, University of California, San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Antje Bischof
- Department of Neurology, University of California, San Francisco, CA
| | - Tristan J Gundel
- Department of Neurology, University of California, San Francisco, CA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - William A Stern
- Department of Neurology, University of California, San Francisco, CA
| | - Stefano Bastianello
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, CA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, CA
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Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J, Zhuoquiong R, Liu Y, Granberg T, Ouellette R, Tachibana Y, Hori M, Kamiya K, Chougar L, Stawiarz L, Hillert J, Bannier E, Kerbrat A, Edan G, Labauge P, Callot V, Pelletier J, Audoin B, Rasoanandrianina H, Brisset JC, Valsasina P, Rocca MA, Filippi M, Bakshi R, Tauhid S, Prados F, Yiannakas M, Kearney H, Ciccarelli O, Smith S, Treaba CA, Mainero C, Lefeuvre J, Reich DS, Nair G, Auclair V, McLaren DG, Martin AR, Fehlings MG, Vahdat S, Khatibi A, Doyon J, Shepherd T, Charlson E, Narayanan S, Cohen-Adad J. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 2019; 184:901-915. [PMID: 30300751 PMCID: PMC6759925 DOI: 10.1016/j.neuroimage.2018.09.081] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 09/05/2018] [Accepted: 09/28/2018] [Indexed: 12/12/2022] Open
Abstract
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.
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Affiliation(s)
- Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Atef Badji
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Josefina Maranzano
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Dominique Eden
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Sara M. Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA
| | - Jason Talbott
- Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA
| | - Ren Zhuoquiong
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P. R. China
| | - Yaou Liu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P. R. China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P. R. China
| | - Tobias Granberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | - Russell Ouellette
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | | | | | | | - Lydia Chougar
- Juntendo University Hospital, Tokyo, Japan
- Hospital Cochin, Paris, France
| | - Leszek Stawiarz
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Elise Bannier
- CHU Rennes, Radiology Department
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Visages U1128, France
| | - Anne Kerbrat
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Visages U1128, France
- CHU Rennes, Neurology Department
| | - Gilles Edan
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Visages U1128, France
- CHU Rennes, Neurology Department
| | - Pierre Labauge
- MS Unit. DPT of Neurology. University Hospital of Montpellier
| | - Virginie Callot
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, CHU Timone, CEMEREM, Marseille, France
| | - Jean Pelletier
- APHM, CHU Timone, CEMEREM, Marseille, France
- APHM, Department of Neurology, CHU Timone, APHM, Marseille
| | - Bertrand Audoin
- APHM, CHU Timone, CEMEREM, Marseille, France
- APHM, Department of Neurology, CHU Timone, APHM, Marseille
| | | | - Jean-Christophe Brisset
- Observatoire Français de la Sclérose en Plaques (OFSEP) ; Univ Lyon, Université Claude Bernard Lyon 1 ; Hospices Civils de Lyon ; CREATIS-LRMN, UMR 5220 CNRS & U 1044 INSERM ; Lyon, France
| | - Paola Valsasina
- Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A. Rocca
- Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Rohit Bakshi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
| | - Shahamat Tauhid
- Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
| | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
- Center for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Marios Yiannakas
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
| | - Hugh Kearney
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
| | - Olga Ciccarelli
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
| | | | | | - Caterina Mainero
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | - Jennifer Lefeuvre
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA
| | - Daniel S. Reich
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA
| | | | | | - Allan R. Martin
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Michael G. Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Shahabeddin Vahdat
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Neurology Department, Stanford University, US
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | | | | | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
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