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Labounek R, Bondy MT, Paulson AL, Bédard S, Abramovic M, Alonso-Ortiz E, Atcheson NT, Barlow LR, Barry RL, Barth M, Battiston M, Büchel C, Budde MD, Callot V, Combes A, Leener BD, Descoteaux M, Loureiro de Sousa P, Dostál M, Doyon J, Dvorak AV, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers JM, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler H, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Laganà MM, Laule C, Law CSW, Leutritz T, Liu Y, Llufriu S, Mackey S, Martin AR, Martinez-Heras E, Mattera L, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley GW, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber KA, Weiskopf N, Wise RG, Wyss PO, Xu J, Cohen-Adad J, Lenglet C, Nestrašil I. Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study. bioRxiv 2024:2024.04.29.591421. [PMID: 38746371 PMCID: PMC11092490 DOI: 10.1101/2024.04.29.591421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.
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Wesselink EO, Pool-Goudzwaard A, De Leener B, Law CSW, Fenyo MB, Ello GM, Coppieters MW, Elliott JM, Mackey S, Weber KA. Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: A UK Biobank study. Spine J 2024:S1529-9430(24)00080-9. [PMID: 38417587 DOI: 10.1016/j.spinee.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/06/2024] [Accepted: 02/18/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND CONTEXT The role of lumbar paraspinal muscle health in back pain (BP) is not straightforward. Challenges in this field have included the lack of tools and large, heterogenous datasets to interrogate the association between muscle health and BP. Computer-vision models have been transformative in this space, enabling the automated quantification of muscle health and the processing of large datasets. PURPOSE To investigate the associations between lumbar paraspinal muscle health and age, sex, BMI, physical activity, and BP in a large, heterogenous dataset using an automated computer-vision model. DESIGN Cross-sectional study. PATIENT SAMPLE Participants from the UK Biobank with abdominal Dixon fat-water MRI (N=9,564) were included (41.8% women, mean [SD] age: 63.5 [7.6] years, BMI: 26.4 [4.1] kg/m2) of whom 6,953 reported no pain, 930 acute BP, and 1,681 chronic BP. OUTCOME MEASURES Intramuscular fat (IMF) and average cross-sectional area (aCSA) were automatically derived using a computer-vision model for the left and right lumbar multifidus (LM), erector spinae (ES), and psoas major (PM) from the L1 to L5 vertebral levels. METHODS Two-tailed partial Pearson correlations were generated for each muscle to assess the relationships between the muscle measures (IMF and aCSA) and age (controlling for BMI, sex, and physical activity), BMI (controlling for age, sex, and physical activity), and physical activity (controlling for age, sex, and BMI). One-way ANCOVA was used to identify sex differences in IMF and aCSA for each muscle while controlling for age, BMI, and physical activity. Similarly, one-way ANCOVA was used to identify between-group differences (no pain, acute BP, and chronic BP) for each muscle and along the superior-inferior expanse of the lumbar spine while controlling for age, BMI, sex, and physical activity (α=0.05). RESULTS Females had higher IMF (LM mean difference [MD]=11.1%, ES MD=10.2%, PM MD=0.3%, p<.001) and lower aCSA (LM MD=47.6 mm2, ES MD=350.0 mm2, PM MD=321.5 mm2, p<.001) for all muscles. Higher age was associated with higher IMF and lower aCSA for all muscles (r≥0.232, p<.001) except for LM and aCSA (r≤0.013, p≥.267). Higher BMI was associated with higher IMF and aCSA for all muscles (r≥0.174, p<.001). Higher physical activity was associated with lower IMF and higher aCSA for all muscles (r≥0.036, p≤.002) except for LM and aCSA (r≤0.010, p≥.405). People with chronic BP had higher IMF and lower aCSA than people with no pain (IMF MD≤1.6%, aCSA MD≤27.4 mm2, p<.001) and higher IMF compared to acute BP (IMF MD≤1.1%, p≤.044). The differences between people with BP and people with no pain were not spatially localized to the inferior lumbar levels but broadly distributed across the lumbar spine. CONCLUSIONS Paraspinal muscle health is associated with age, BMI, sex, and physical activity with the exception of the association between LM aCSA and age and physical activity. People with BP (chronic>acute) have higher IMF and lower aCSA than people reporting no pain. The differences were not localized but broadly distributed across the lumbar spine. When interpreting measures of paraspinal muscle health in the research or clinical setting, the associations with age, BMI, sex, and physical activity should be considered.
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Affiliation(s)
- Evert Onno Wesselink
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands; Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA.
| | - Annelies Pool-Goudzwaard
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands; SOMT University of Physiotherapy, Softwareweg 5, 3821 BN Amersfoort, the Netherlands
| | - Benjamin De Leener
- Department of Computer Engineering and Software Engineering, Polytechnique Montreal, 2900 Edouard Montpetit Blvd, Quebec H3T 1J4, Canada
| | - Christine Sze Wan Law
- Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA
| | - Meredith Blair Fenyo
- Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA
| | - Gabriella Marie Ello
- Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA
| | - Michel Willem Coppieters
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands; School of Health Sciences and Social Work, Menzies Health Institute Queensland, Griffith University, Brisbane and Gold Coast, 170 Kessels Road, 4111 Brisbane, Australia
| | - James Matthew Elliott
- The University of Sydney, Faculty of Medicine and Health and the Northern Sydney Local Health District, The Kolling Institute, Reserve Road, St Leonards NSW Sydney 2065, Australia
| | - Sean Mackey
- Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA
| | - Kenneth Arnold Weber
- Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA
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Vahdat S, Landelle C, Lungu O, De Leener B, Doyon J, Baniasad F. FASB: an integrated processing pipeline for Functional Analysis of simultaneous Spinal cord-Brain fMRI. RESEARCH SQUARE 2024:rs.3.rs-3889284. [PMID: 38352433 PMCID: PMC10862948 DOI: 10.21203/rs.3.rs-3889284/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Simultaneous functional magnetic resonance imaging (fMRI) of the spinal cord and brain represents a powerful method for examining both ascending sensory and descending motor pathways in humans in vivo . However, its image acquisition protocols, and processing pipeline are less well established. This limitation is mainly due to technical difficulties related to spinal cord fMRI, and problems with the logistics stemming from a large field of view covering both brain and cervical cord. Here, we propose an acquisition protocol optimized for both anatomical and functional images, as well as an optimized integrated image processing pipeline, which consists of a novel approach for automatic modeling and mitigating the negative impact of spinal voxels with low temporal signal to noise ratio (tSNR). We validate our integrated pipeline, named FASB, using simultaneous fMRI data acquired during the performance of a motor task, as well as during resting-state conditions. We demonstrate that FASB outperforms the current spinal fMRI processing methods in three domains, including motion correction, registration to the spinal cord template, and improved detection power of the group-level analysis by removing the effects of participant-specific low tSNR voxels, typically observed at the disk level. Using FASB, we identify significant task-based activations in the expected sensorimotor network associated with a unilateral handgrip force production task across the entire central nervous system, including the contralateral sensorimotor cortex, thalamus, striatum, cerebellum, brainstem, as well as ipsilateral ventral horn at C5-C8 cervical levels. Additionally, our results show significant task-based functional connectivity between the key sensory and motor brain areas and the dorsal and ventral horns of the cervical cord. Overall, our proposed acquisition protocol and processing pipeline provide a robust method for characterizing the activation and functional connectivity of distinct cortical, subcortical, brainstem and spinal cord regions in humans.
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Taherzadeh M, Zhang E, Londono I, De Leener B, Wang S, Cooper JD, Kennedy TE, Morales CR, Chen Z, Lodygensky GA, Pshezhetsky AV. Severe central nervous system demyelination in Sanfilippo disease. Front Mol Neurosci 2023; 16:1323449. [PMID: 38163061 PMCID: PMC10756675 DOI: 10.3389/fnmol.2023.1323449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Chronic progressive neuroinflammation is a hallmark of neurological lysosomal storage diseases, including mucopolysaccharidosis III (MPS III or Sanfilippo disease). Since neuroinflammation is linked to white matter tract pathology, we analyzed axonal myelination and white matter density in the mouse model of MPS IIIC HgsnatP304L and post-mortem brain samples of MPS III patients. Methods Brain and spinal cord tissues of human MPS III patients, 6-month-old HgsnatP304L mice and age- and sex-matching wild type mice were analyzed by immunofluorescence to assess levels of myelin-associated proteins, primary and secondary storage materials, and levels of microgliosis. Corpus callosum (CC) region was studied by transmission electron microscopy to analyze axon myelination and morphology of oligodendrocytes and microglia. Mouse brains were analyzed ex vivo by high-filed MRI using Diffusion Basis Spectrum Imaging in Python-Diffusion tensor imaging algorithms. Results Analyses of CC and spinal cord tissues by immunohistochemistry revealed substantially reduced levels of myelin-associated proteins including Myelin Basic Protein, Myelin Associated Glycoprotein, and Myelin Oligodendrocyte Glycoprotein. Furthermore, ultrastructural analyses revealed disruption of myelin sheath organization and reduced myelin thickness in the brains of MPS IIIC mice and human MPS IIIC patients compared to healthy controls. Oligodendrocytes (OLs) in the CC of MPS IIIC mice were scarce, while examination of the remaining cells revealed numerous enlarged lysosomes containing heparan sulfate, GM3 ganglioside or "zebra bodies" consistent with accumulation of lipids and myelin fragments. In addition, OLs contained swollen mitochondria with largely dissolved cristae, resembling those previously identified in the dysfunctional neurons of MPS IIIC mice. Ex vivo Diffusion Basis Spectrum Imaging revealed compelling signs of demyelination (26% increase in radial diffusivity) and tissue loss (76% increase in hindered diffusivity) in CC of MPS IIIC mice. Discussion Our findings demonstrate an important role for white matter injury in the pathophysiology of MPS III. This study also defines specific parameters and brain regions for MRI analysis and suggests that it may become a crucial non-invasive method to evaluate disease progression and therapeutic response.
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Affiliation(s)
- Mahsa Taherzadeh
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
- Department of Anatomy and Cell Biology, McGill University, Montreal, QC, Canada
| | - Erjun Zhang
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
| | - Irene Londono
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
| | - Benjamin De Leener
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
- NeuroPoly Lab, Institute of Biomedical Engineering, Department of Computer Engineering and Software Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Sophie Wang
- Pediatric Storage Disorders Laboratory (PSDL), Departments of Pediatrics, Genetics and Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jonathan D. Cooper
- Pediatric Storage Disorders Laboratory (PSDL), Departments of Pediatrics, Genetics and Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Timothy E. Kennedy
- Department of Anatomy and Cell Biology, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Carlos R. Morales
- Department of Anatomy and Cell Biology, McGill University, Montreal, QC, Canada
| | - Zesheng Chen
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
| | - Gregory A. Lodygensky
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
| | - Alexey V. Pshezhetsky
- Department of Pediatrics, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, University of Montreal, Montreal, QC, Canada
- Department of Anatomy and Cell Biology, McGill University, Montreal, QC, Canada
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Landelle C, Dahlberg LS, Lungu O, Misic B, De Leener B, Doyon J. Altered Spinal Cord Functional Connectivity Associated with Parkinson's Disease Progression. Mov Disord 2023; 38:636-645. [PMID: 36802374 DOI: 10.1002/mds.29354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/13/2023] [Accepted: 01/30/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) has traditionally been viewed as an α-synucleinopathy brain pathology. Yet evidence based on postmortem human and animal experimental models indicates that the spinal cord may also be affected. OBJECTIVE Functional magnetic resonance imaging (fMRI) seems to be a promising candidate to better characterize spinal cord functional organization in PD patients. METHODS Resting-state spinal fMRI was performed in 70 PD patients and 24 age-matched healthy controls, the patients being divided into three groups based on their motor symptom severity: PDlow (n = 24), PDmed (n = 22), and PDadv (n = 24) groups. A combination of independent component analysis (ICA) and a seed-based approach was applied. RESULTS When pooling all participants, the ICA revealed distinct ventral and dorsal components distributed along the rostro-caudal axis. This organization was highly reproducible within subgroups of patients and controls. PD severity, assessed by Unified Parkinson's Disease Rating Scale (UPDRS) scores, was associated with a decrease in spinal functional connectivity (FC). Notably, we observed a reduced intersegmental correlation in PD as compared to controls, the latter being negatively associated with patients' upper-limb UPDRS scores (P = 0.0085). This negative association between FC and upper-limb UPDRS scores was significant between adjacent C4-C5 (P = 0.015) and C5-C6 (P = 0.20) cervical segments, levels associated with upper-limb functions. CONCLUSIONS The present study provides the first evidence of spinal cord FC changes in PD and opens new avenues for the effective diagnosis and therapeutic strategies in PD. This underscores how spinal cord fMRI can serve as a powerful tool to characterize, in vivo, spinal circuits for a variety of neurological diseases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Caroline Landelle
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Linda Solstrand Dahlberg
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ovidiu Lungu
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Bratislav Misic
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Benjamin De Leener
- Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.,CHU Sainte-Justine Research Centre, Montreal, Quebec, Canada
| | - Julien Doyon
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Abbott NJ, Anderson JAE, Gagana B, Bleile M, Bloomfield PS, Bottom V, Bourque J, Boyle R, Brynildsen JK, Calarco N, Castrellon JJ, Chaku N, Chen B, Chopra S, Coffey EBJ, Colenbier N, Cox DJ, Crippen JE, Crouse JJ, David S, Leener BD, Delap G, Deng ZD, Dugre JR, Eklund A, Ellis K, Ered A, Farmer H, Faskowitz J, Finch JE, Flandin G, Flounders MW, Fonville L, Frandsen SB, Garic D, Garrido-Vásquez P, Gonzalez-Escamilla G, Grogans SE, Grotheer M, Gruskin DC, Guberman GI, Haggerty EB, Hahn Y, Hall EH, Hanson JL, Harel Y, Vieira BH, Hettwer MD, Hobday H, Horien C, Huang F, Huque ZM, James AR, Kahhale I, Kamhout SLH, Keller AS, Khera HS, Kiar G, Kirk PA, Kohl SH, Korenic SA, Korponay C, Kozlowski AK, Kraljevic N, Lazari A, Leavitt MJ, Li Z, Liberati G, Lorenc ES, Lossin AJ, Lotter LD, Lydon-Staley DM, Madan CR, Magielse N, Marusak HA, Mayor J, McGowan AL, Mehta KP, Meisler SL, Michael C, Mitchell ME, Morand-Beaulieu S, Newman BT, Nielsen JA, O’Mara SM, Ojha A, Omary A, Özarslan E, Parkes L, Peterson M, Pines AR, Pisanu C, Rich RR, Sahoo AK, Samara A, Sayed F, Schneider JT, Shaffer LS, Shatalina E, Sims SA, Sinclair S, Song JW, Hogrogian GS, Tamnes CK, Tooley UA, Tripathi V, Turker HB, Valk SL, Wall MB, Walther CK, Wang Y, Wegmann B, Welton T, Wiesman AI, Wiesman AG, Wiesman M, Winters DE, Yuan R, Zacharek SJ, Zajner C, Zakharov I, Zammarchi G, Zhou D, Zimmerman B, Zoner K, Satterthwaite TD, Rokem A. Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:709. [PMID: 36396653 PMCID: PMC9671885 DOI: 10.1038/s41597-022-01816-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Pierre WC, Zhang E, Londono I, De Leener B, Lesage F, Lodygensky GA. Non-invasive in vivo MRI detects long-term microstructural brain alterations related to learning and memory impairments in a model of inflammation-induced white matter injury. Behav Brain Res 2022; 428:113884. [DOI: 10.1016/j.bbr.2022.113884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/18/2022] [Accepted: 04/03/2022] [Indexed: 11/28/2022]
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Landelle C, Lungu O, Vahdat S, Kavounoudias A, Marchand-Pauvert V, De Leener B, Doyon J. Investigating the human spinal sensorimotor pathways through functional magnetic resonance imaging. Neuroimage 2021; 245:118684. [PMID: 34732324 DOI: 10.1016/j.neuroimage.2021.118684] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 01/29/2023] Open
Abstract
Most of our knowledge about the human spinal ascending (sensory) and descending (motor) pathways comes from non-invasive electrophysiological investigations. However, recent methodological advances in acquisition and analyses of functional magnetic resonance imaging (fMRI) data from the spinal cord, either alone or in combination with the brain, have allowed us to gain further insights into the organization of this structure. In the current review, we conducted a systematic search to produced somatotopic maps of the spinal fMRI activity observed through different somatosensory, motor and resting-state paradigms. By cross-referencing these human neuroimaging findings with knowledge acquired through neurophysiological recordings, our review demonstrates that spinal fMRI is a powerful tool for exploring, in vivo, the human spinal cord pathways. We report strong cross-validation between task-related and resting-state fMRI in accordance with well-known hemicord, postero-anterior and rostro-caudal organization of these pathways. We also highlight the specific advantages of using spinal fMRI in clinical settings to characterize better spinal-related impairments, predict disease progression, and guide the implementation of therapeutic interventions.
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Affiliation(s)
- Caroline Landelle
- McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Ovidiu Lungu
- McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Anne Kavounoudias
- CNRS, UMR7291, Laboratory of Cognitive Neurosciences, Aix-Marseille University, Marseille, France
| | | | - Benjamin De Leener
- Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada; CHU Sainte-Justine Research Centre, Montreal, QC, Canada
| | - Julien Doyon
- McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Cohen-Adad J, Alonso-Ortiz E, Abramovic M, Arneitz C, Atcheson N, Barlow L, Barry RL, Barth M, Battiston M, Büchel C, Budde M, Callot V, Combes AJE, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak A, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Gros C, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers J, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler HH, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Labounek R, Laganà MM, Laule C, Law CS, Lenglet C, Leutritz T, Liu Y, Llufriu S, Mackey S, Martinez-Heras E, Mattera L, Nestrasil I, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley G, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber Ii KA, Weiskopf N, Wise RG, Wyss PO, Xu J. Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 2021; 8:251. [PMID: 34556662 PMCID: PMC8460649 DOI: 10.1038/s41597-021-01044-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada. .,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada. .,Mila - Quebec AI Institute, Montreal, QC, Canada.
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Carina Arneitz
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Nicole Atcheson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Laura Barlow
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA
| | - Markus Barth
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Canada.,CHU Sainte-Justine Research Centre, Montreal, QC, Canada
| | - Maxime Descoteaux
- Centre de Recherche CHUS, CIMS, Sherbrooke, Canada.,Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Marek Dostál
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karla R Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin S Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandru Foias
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Michela Fratini
- Institute of Nanotechnology, CNR, Rome, Italy.,IRCCS Santa Lucia Foundation, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Rome, Italy.,CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.,Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Miloš Keřkovský
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Joo-Won Kim
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawal Kinany
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Department Of Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Petr Kudlička
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Slawomir Kusmia
- CUBRIC, Cardiff University, Wales, UK.,Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK.,Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.,Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic
| | | | - Cornelia Laule
- Departments of Radiology, Pathology & Laboratory Medicine, Physics & Astronomy; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Christine S Law
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sean Mackey
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Genève, 1202, Geneva, Switzerland
| | - Igor Nestrasil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.,Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Papp
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deborah Pareto
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Todd B Parrish
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK.,E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Rebecca S Samson
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Giovanni Savini
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alan C Seifert
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex K Smith
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary A Smith
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Y Suzuki
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | | | - Alexandra Tinnermann
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Valošek
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Kenneth A Weber Ii
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Richard G Wise
- CUBRIC, Cardiff University, Wales, UK.,Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti-Pescara, Italy
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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10
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Levitis E, van Praag CDG, Gau R, Heunis S, DuPre E, Kiar G, Bottenhorn KL, Glatard T, Nikolaidis A, Whitaker KJ, Mancini M, Niso G, Afyouni S, Alonso-Ortiz E, Appelhoff S, Arnatkeviciute A, Atay SM, Auer T, Baracchini G, Bayer JMM, Beauvais MJS, Bijsterbosch JD, Bilgin IP, Bollmann S, Bollmann S, Botvinik-Nezer R, Bright MG, Calhoun VD, Chen X, Chopra S, Chuan-Peng H, Close TG, Cookson SL, Craddock RC, De La Vega A, De Leener B, Demeter DV, Di Maio P, Dickie EW, Eickhoff SB, Esteban O, Finc K, Frigo M, Ganesan S, Ganz M, Garner KG, Garza-Villarreal EA, Gonzalez-Escamilla G, Goswami R, Griffiths JD, Grootswagers T, Guay S, Guest O, Handwerker DA, Herholz P, Heuer K, Huijser DC, Iacovella V, Joseph MJE, Karakuzu A, Keator DB, Kobeleva X, Kumar M, Laird AR, Larson-Prior LJ, Lautarescu A, Lazari A, Legarreta JH, Li XY, Lv J, Mansour L S, Meunier D, Moraczewski D, Nandi T, Nastase SA, Nau M, Noble S, Norgaard M, Obungoloch J, Oostenveld R, Orchard ER, Pinho AL, Poldrack RA, Qiu A, Raamana PR, Rokem A, Rutherford S, Sharan M, Shaw TB, Syeda WT, Testerman MM, Toro R, Valk SL, Van Den Bossche S, Varoquaux G, Váša F, Veldsman M, Vohryzek J, Wagner AS, Walsh RJ, White T, Wong FT, Xie X, Yan CG, Yang YF, Yee Y, Zanitti GE, Van Gulick AE, Duff E, Maumet C. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective. Gigascience 2021; 10:6355274. [PMID: 34414422 DOI: 10.1093/gigascience/giab051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
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Affiliation(s)
- Elizabeth Levitis
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Cassandra D Gould van Praag
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Rémi Gau
- Institute of Psychology, Université Catholique de Louvain, Louvain la Neuve 1348, Belgium
| | - Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
| | - Elizabeth DuPre
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, QC, H3A 2B4, Canada.,Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
| | - Aki Nikolaidis
- Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, BN1 9RR, UK.,Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK.,NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada
| | - Guiomar Niso
- Departement of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA.,ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, 28040 Madrid, Spain
| | - Soroosh Afyouni
- Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.,Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Eva Alonso-Ortiz
- Department of Electrical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1J4, Canada
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Selim Melvin Atay
- Neuroscience and Neurotechnology, Middle East Technical University, Ankara 06800, Turkey
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford GU2 7XH, UK
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, H3A 2B4, Canada.,Montréal Neurological Institute, Montréal, QC, H3A 2B4, Canada
| | - Johanna M M Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, 3010, Parkville, Melbourne, Australia.,Orygen Youth Health, Melbourne, VIC, 3052, Royal Park, Melbourne, Australia
| | | | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Isil P Bilgin
- Department of Biomedical Engineering, Cybernetics, The School of Biological Sciences, The University of Reading, Reading, RG6 6AH, UK
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Steffen Bollmann
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.,Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, 100101, Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, Beijing, China
| | - Sidhant Chopra
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing 210024, China
| | - Thomas G Close
- Department of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.,National Imaging Facility, The University of Sydney, Sydney, NSW 2006, Australia
| | - Savannah L Cookson
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX 78712, USA
| | - Alejandro De La Vega
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.,Research Centre, Sainte-Justine University Hospital Center, Montreal, QC, H3T 1C5, Canada
| | - Damion V Demeter
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Paola Di Maio
- Center for Systems, Knowledge Representation and Neuroscience, Edinburgh and Taipei, UK and Taiwan.,Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Erin W Dickie
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne 1003, Switzerland
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń 87-100, Poland
| | - Matteo Frigo
- Athena Project Team, Université Côte D'Azur, Inria, 06103 Nice, France
| | - Saampras Ganesan
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen DK-2100, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen DK-2100, Denmark
| | - Kelly G Garner
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD 4072, Australia.,School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.,School of Psychology, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Eduardo A Garza-Villarreal
- Laboratorio Nacional de Imagenología por Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Qro 76230, Mexico
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Rohit Goswami
- Faculty of Physical Sciences, University of Iceland, 102 Reykjavík, Iceland.,Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - John D Griffiths
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Tijl Grootswagers
- The MARCS Institute for Brain, Behaviour & Development, Western Sydney University, Sydney 2751, NSW, Australia
| | - Samuel Guay
- Department of Psychology, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Olivia Guest
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, 6525 EN, Netherlands
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA
| | - Peer Herholz
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorien C Huijser
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, Rotterdam 3062, the Netherlands.,Developmental and Educational Psychology, Leiden University, Leiden 2333, the Netherlands
| | - Vittorio Iacovella
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto 38068, Italy
| | - Michael J E Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1N8, Canada.,Montréal Heart Institute, University of Montréal, Montréal, QC, H1T 1C8, Canada
| | - David B Keator
- Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Xenia Kobeleva
- Department of Neurology, University Hospital Bonn, 53127 Bonn, Germany.,Clinical Research, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany
| | - Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL 33199, USA
| | - Linda J Larson-Prior
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.,Arkansas Children's Nutrition Center, Little Rock, AR, USA.,Department of Neurology, Pediatrics, Neuroscience & Developmental Sciences, Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Alexandra Lautarescu
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 7EH, UK
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Jon Haitz Legarreta
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Xue-Ying Li
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing101408, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.,Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China.,CFIN and PET Center, Aarhus University, 8000 Aarhus, Denmark
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, NSW 2006, Australia
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - David Meunier
- Aix Marseille Univ, CNRS, INT, Institut de Neurosciences de la Timone, 13005 Marseille, France
| | | | - Tulika Nandi
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 7LF, UK
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Matthias Nau
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA.,Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Stephanie Noble
- Radiology & Biomedical Imaging, Yale University, New Haven, CT 06519, USA
| | - Martin Norgaard
- Center for Reproducible Neuroscience, Department of Psychology, Stanford University, Stanford, CA 94305Ci, USA.,Neurobiology Research Unit, Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Johnes Obungoloch
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara City, Uganda
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6500 GL, The Netherlands.,NatMEG, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Edwina R Orchard
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France
| | | | - Anqi Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, Smart Systems Institute, National University of Singapore, Singapore 117583, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Ariel Rokem
- Department of Psychology & eScience Institute, University of Washington, Seattle, WA 98195, USA
| | - Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands.,Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Thomas B Shaw
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Warda T Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, VIC 3053, Australia
| | | | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Neuroscience Department, Institut Pasteur, 75015 Paris, France
| | - Sofie L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany.,Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France.,Montreal Neurological Institute, McGill, Montreal, QC, H3A 2B4, Canada
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry Psychology & Neuroscience, King's College London SE5 8AF, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxfordshire, OX2 6GG, Oxford, UK
| | - Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark
| | - Adina S Wagner
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Reubs J Walsh
- Department of Clinical, Neuro-, and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam,1081BT, The Netherlands.,Center for Applied Transgender Studies , Chicago, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, 3000CB, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam 3000CB, The Netherlands
| | - Fu-Te Wong
- Institute of Linguistics, Academia Sinica, Taipei, Taiwan.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School, New York City, NY 10065, USA
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, 100101 Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yu-Fang Yang
- Department of Psychology, University of Würzburg, Würzburg 97074, Germany
| | - Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.,Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
| | | | - Ana E Van Gulick
- Figshare, Cambridge, MA 02139, USA.,University Libraries, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Paediatrics, University of Oxford, Oxford, OX3 9DU, UK
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, 35042 Rennes, France
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11
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Cohen-Adad J, Alonso-Ortiz E, Abramovic M, Arneitz C, Atcheson N, Barlow L, Barry RL, Barth M, Battiston M, Büchel C, Budde M, Callot V, Combes AJE, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak A, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Gros C, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers J, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler HH, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Labounek R, Laganà MM, Laule C, Law CS, Lenglet C, Leutritz T, Liu Y, Llufriu S, Mackey S, Martinez-Heras E, Mattera L, Nestrasil I, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley G, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber Ii KA, Weiskopf N, Wise RG, Wyss PO, Xu J. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 2021; 8:219. [PMID: 34400655 PMCID: PMC8368310 DOI: 10.1038/s41597-021-00941-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/21/2022] Open
Abstract
In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.
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Affiliation(s)
- Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
- Mila - Quebec AI Institute, Montreal, QC, Canada.
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Carina Arneitz
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Nicole Atcheson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Laura Barlow
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA
| | - Markus Barth
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Canada
- CHU Sainte-Justine Research Centre, Montreal, QC, Canada
| | - Maxime Descoteaux
- Centre de Recherche CHUS, CIMS, Sherbrooke, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Marek Dostál
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karla R Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin S Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandru Foias
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Michela Fratini
- Institute of Nanotechnology, CNR, Rome, Italy
- IRCCS Santa Lucia Foundation, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Rome, Italy
- CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Miloš Keřkovský
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Joo-Won Kim
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawal Kinany
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department Of Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Petr Kudlička
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Slawomir Kusmia
- CUBRIC, Cardiff University, Wales, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic
| | | | - Cornelia Laule
- Departments of Radiology, Pathology & Laboratory Medicine, Physics & Astronomy; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Christine S Law
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sean Mackey
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Genève, 1202, Geneva, Switzerland
| | - Igor Nestrasil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Papp
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deborah Pareto
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Todd B Parrish
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Rebecca S Samson
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Giovanni Savini
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alan C Seifert
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex K Smith
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary A Smith
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Y Suzuki
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | | | - Alexandra Tinnermann
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Valošek
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Kenneth A Weber Ii
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Richard G Wise
- CUBRIC, Cardiff University, Wales, UK
- Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti-Pescara, Italy
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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12
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Ouellette R, Treaba CA, Granberg T, Herranz E, Barletta V, Mehndiratta A, De Leener B, Tauhid S, Yousuf F, Dupont SM, Klawiter EC, Sloane JA, Bakshi R, Cohen-Adad J, Mainero C. 7 T imaging reveals a gradient in spinal cord lesion distribution in multiple sclerosis. Brain 2021; 143:2973-2987. [PMID: 32935834 DOI: 10.1093/brain/awaa249] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 06/03/2020] [Accepted: 06/29/2020] [Indexed: 12/11/2022] Open
Abstract
We used 7 T MRI to: (i) characterize the grey and white matter pathology in the cervical spinal cord of patients with early relapsing-remitting and secondary progressive multiple sclerosis; (ii) assess the spinal cord lesion spatial distribution and the hypothesis of an outside-in pathological process possibly driven by CSF-mediated immune cytotoxic factors; and (iii) evaluate the association of spinal cord pathology with brain burden and its contribution to neurological disability. We prospectively recruited 20 relapsing-remitting, 15 secondary progressive multiple sclerosis participants and 11 age-matched healthy control subjects to undergo 7 T imaging of the cervical spinal cord and brain as well as conventional 3 T brain acquisition. Cervical spinal cord imaging at 7 T was used to segment grey and white matter, including lesions therein. Brain imaging at 7 T was used to segment cortical and white matter lesions and 3 T imaging for cortical thickness estimation. Cervical spinal cord lesions were mapped voxel-wise as a function of distance from the inner central canal CSF pool to the outer subpial surface. Similarly, brain white matter lesions were mapped voxel-wise as a function of distance from the ventricular system. Subjects with relapsing-remitting multiple sclerosis showed a greater predominance of spinal cord lesions nearer the outer subpial surface compared to secondary progressive cases. Inversely, secondary progressive participants presented with more centrally located lesions. Within the brain, there was a strong gradient of lesion formation nearest the ventricular system that was most evident in participants with secondary progressive multiple sclerosis. Lesion fractions within the spinal cord grey and white matter were related to the lesion fraction in cerebral white matter. Cortical thinning was the primary determinant of the Expanded Disability Status Scale, white matter lesion fractions in the spinal cord and brain of the 9-Hole Peg Test and cortical thickness and spinal cord grey matter cross-sectional area of the Timed 25-Foot Walk. Spinal cord lesions were localized nearest the subpial surfaces for those with relapsing-remitting and the central canal CSF surface in progressive disease, possibly implying CSF-mediated pathogenic mechanisms in lesion development that may differ between multiple sclerosis subtypes. These findings show that spinal cord lesions involve both grey and white matter from the early multiple sclerosis stages and occur mostly independent from brain pathology. Despite the prevalence of cervical spinal cord lesions and atrophy, brain pathology seems more strongly related to physical disability as measured by the Expanded Disability Status Scale.
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Affiliation(s)
- Russell Ouellette
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Tobias Granberg
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.,Harvard Medical School, Boston, MA, USA
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Valeria Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Benjamin De Leener
- Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Shahamat Tauhid
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Fawad Yousuf
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah M Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Eric C Klawiter
- Harvard Medical School, Boston, MA, USA.,Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jacob A Sloane
- Harvard Medical School, Boston, MA, USA.,Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Rohit Bakshi
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
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13
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Mangeat G, Ouellette R, Wabartha M, De Leener B, Plattén M, Danylaité Karrenbauer V, Warntjes M, Stikov N, Mainero C, Cohen‐Adad J, Granberg T. Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis. J Neuroimaging 2020; 30:674-682. [DOI: 10.1111/jon.12725] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 04/28/2020] [Indexed: 02/07/2023] Open
Affiliation(s)
- Gabriel Mangeat
- NeuroPoly Lab, Institute of Biomedical Engineering Polytechnique Montreal Montreal Quebec Canada
| | - Russell Ouellette
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Department of Neuroradiology Karolinska University Hospital Stockholm Sweden
| | - Maxime Wabartha
- NeuroPoly Lab, Institute of Biomedical Engineering Polytechnique Montreal Montreal Quebec Canada
| | - Benjamin De Leener
- Department of Computer Sciences and Software Engineering Polytechnique Montreal Montreal Quebec Canada
| | - Michael Plattén
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Department of Neuroradiology Karolinska University Hospital Stockholm Sweden
- School of Engineering Sciences in Chemistry, Biochemistry and Health Royal Institute of Technology Stockholm Sweden
| | - Virginija Danylaité Karrenbauer
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Department of Neurology Karolinska University Hospital Stockholm Sweden
| | - Marcel Warntjes
- Center for Medical Imaging Science and Visualization CMIV Linköping Sweden
- SyntheticMR Linköping Sweden
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering Polytechnique Montreal Montreal Quebec Canada
- Montreal Heart Institute Montreal Quebec Canada
| | - Caterina Mainero
- Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging, MGH Charlestown MA
- Harvard Medical School Boston MA
| | - Julien Cohen‐Adad
- NeuroPoly Lab, Institute of Biomedical Engineering Polytechnique Montreal Montreal Quebec Canada
- and Functional Neuroimaging Unit, CRIUGM Université de Montréal Montreal Quebec Canada
| | - Tobias Granberg
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Department of Neuroradiology Karolinska University Hospital Stockholm Sweden
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Eden D, Gros C, Badji A, Dupont SM, De Leener B, Maranzano J, Zhuoquiong R, Liu Y, Granberg T, Ouellette R, Stawiarz L, Hillert J, Talbott 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 SA, Andrada Treaba C, Mainero C, Lefeuvre J, Reich DS, Nair G, Shepherd TM, Charlson E, Tachibana Y, Hori M, Kamiya K, Chougar L, Narayanan S, Cohen-Adad J. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord. Brain 2020; 142:633-646. [PMID: 30715195 DOI: 10.1093/brain/awy352] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 10/25/2018] [Accepted: 11/20/2018] [Indexed: 12/12/2022] Open
Abstract
Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.
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Affiliation(s)
- Dominique Eden
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Charley Gros
- 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
| | - 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
| | - Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Josefina Maranzano
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada.,Department of Anatomy, Université de Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Ren Zhuoquiong
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, P. R. China
| | - Yaou Liu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, P. R. China.,Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
| | - Tobias Granberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Massachusetts General Hospital, Boston, USA
| | - Russell Ouellette
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Massachusetts General Hospital, Boston, USA
| | - Leszek Stawiarz
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jason Talbott
- Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA
| | - Elise Bannier
- CHU Rennes, Radiology Department, Rennes, France.,Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN - ERL U 1228, Rennes, France
| | - Anne Kerbrat
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN - ERL U 1228, Rennes, France.,CHU Rennes, Neurology Department, Rennes, France
| | - Gilles Edan
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN - ERL U 1228, Rennes, France.,CHU Rennes, Neurology Department, Rennes, France
| | - Pierre Labauge
- MS Unit, Department of Neurology, University Hospital of Montpellier, Montpellier, France
| | - Virginie Callot
- Aix Marseille University, 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
| | - Henitsoa Rasoanandrianina
- Aix Marseille University, CNRS, CRMBM, Marseille, France.,APHM, CHU Timone, CEMEREM, Marseille, France
| | - Jean-Christophe Brisset
- Observatoire Français de la Sclérose en Plaques (OFSEP) ; Université de Lyon, Université Claude Bernard Lyon 1; Hospices Civils de Lyon; CREATIS-LRMN, UMR 5220 CNRS and 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, UK
| | - 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
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, 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
| | | | - Erik Charlson
- Department of Radiology, NYU Langone Medical Center, New York, USA
| | | | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kouhei Kamiya
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Lydia Chougar
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.,Hospital Cochin, Paris, France
| | - 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.,Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
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15
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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: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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16
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Martin AR, De Leener B, Cohen-Adad J, Kalsi-Ryan S, Cadotte DW, Wilson JR, Tetreault L, Nouri A, Crawley A, Mikulis DJ, Ginsberg H, Massicotte EM, Fehlings MG. Monitoring for myelopathic progression with multiparametric quantitative MRI. PLoS One 2018; 13:e0195733. [PMID: 29664964 PMCID: PMC5903654 DOI: 10.1371/journal.pone.0195733] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 03/28/2018] [Indexed: 12/04/2022] Open
Abstract
Background Patients with mild degenerative cervical myelopathy (DCM) are often managed non-operatively, and surgery is recommended if neurological progression occurs. However, detection of progression is often subjective. Quantitative MRI (qMRI) directly measures spinal cord (SC) tissue changes, detecting axonal injury, demyelination, and atrophy. This longitudinal study compared multiparametric qMRI with clinical measures of progression in non-operative DCM patients. Methods 26 DCM patients were followed. Clinical data included modified Japanese Orthopedic Association (mJOA) and additional assessments. 3T qMRI data included cross sectional area, diffusion fractional anisotropy, magnetization transfer ratio, and T2*-weighted white/grey matter signal ratio, extracted from the compressed SC and above/below. Progression was defined as 1) patients’ subjective impression, 2) 2-point mJOA decrease, 3) ≥3 clinical measures worsening ≥5%, 4) increased compression on MRI, or 5) ≥1 of 10 qMRI measures or composite score worsening (p < 0.004, corrected). Results Follow-up (13.5 ± 4.9 months) included mJOA in all 26 patients, MRI in 25, and clinical/qMRI in 22. 42.3% reported subjective worsening, compared with mJOA (11.5%), MRI (20%), comprehensive assessments (54.6%), and qMRI (68.2%). Relative to subjective worsening, qMRI showed 100% sensitivity and 53.3% specificity compared with comprehensive assessments (75%, 60%), mJOA (27.3%, 100%), and MRI (18.2%, 81.3%). A decision-making algorithm incorporating qMRI identified progression and recommended surgery for 11 subjects (42.3%). Conclusions Quantitative MRI shows high sensitivity to detect myelopathic progression. Our results suggest that neuroplasticity and behavioural adaptation may mask progressive SC tissue injury. qMRI appears to be a useful method to confirm subtle myelopathic progression in individual patients, representing an advance toward clinical translation of qMRI.
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Affiliation(s)
| | | | | | | | | | | | | | - Aria Nouri
- University of Toronto, Toronto, Ontario, Canada
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17
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Martin AR, De Leener B, Cohen-Adad J, Cadotte DW, Nouri A, Wilson JR, Tetreault L, Crawley AP, Mikulis DJ, Ginsberg H, Fehlings MG. Can microstructural MRI detect subclinical tissue injury in subjects with asymptomatic cervical spinal cord compression? A prospective cohort study. BMJ Open 2018; 8:e019809. [PMID: 29654015 PMCID: PMC5905727 DOI: 10.1136/bmjopen-2017-019809] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES Degenerative cervical myelopathy (DCM) involves extrinsic spinal cord compression causing tissue injury and neurological dysfunction. Asymptomatic spinal cord compression (ASCC) is more common, but its significance is poorly defined. This study investigates if: (1) ASCC can be automatically diagnosed using spinal cord shape analysis; (2) multiparametric quantitative MRI can detect similar spinal cord tissue injury as previously observed in DCM. DESIGN Prospective observational longitudinal cohort study. SETTING Single centre, tertiary care and research institution. PARTICIPANTS 40 neurologically intact subjects (19 female, 21 male) divided into groups with and without ASCC. INTERVENTIONS None. OUTCOME MEASURES Clinical assessments: modified Japanese Orthopaedic Association score and physical examination. 3T MRI assessments: automated morphometric analysis compared with consensus ratings of spinal cord compression, and measures of tissue injury: cross-sectional area, diffusion fractional anisotropy, magnetisation transfer ratio and T2*-weighted imaging white to grey matter signal intensity ratio (T2*WI WM/GM) extracted from rostral (C1-3), caudal (C6-7) and maximally compressed levels. RESULTS ASCC was present in 20/40 subjects. Diagnosis with automated shape analysis showed area under the curve >97%. Five MRI metrics showed differences suggestive of tissue injury in ASCC compared with uncompressed subjects (p<0.05), while a composite of all 10 measures (average of z scores) showed highly significant differences (p=0.002). At follow-up (median 21 months), two ASCC subjects developed DCM. CONCLUSIONS ASCC appears to be common and can be accurately and objectively diagnosed with automated morphometric analysis. Quantitative MRI appears to detect subclinical tissue injury in ASCC prior to the onset of neurological symptoms and signs. These findings require further validation, but offer the intriguing possibility of presymptomatic diagnosis and treatment of DCM and other spinal pathologies.
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Affiliation(s)
- Allan R Martin
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin De Leener
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Québec, Canada
| | - Julien Cohen-Adad
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Québec, Canada
| | - David W Cadotte
- Department of Neurosurgery, University of Calgary, Calgary, Alberta, Canada
| | - Aria Nouri
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jefferson R Wilson
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Lindsay Tetreault
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, University College Cork, Cork, Ireland
| | - Adrian P Crawley
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - David J Mikulis
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Howard Ginsberg
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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18
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Gros C, De Leener B, Dupont SM, Martin AR, Fehlings MG, Bakshi R, Tummala S, Auclair V, McLaren DG, Callot V, Cohen-Adad J, Sdika M. Automatic spinal cord localization, robust to MRI contrasts using global curve optimization. Med Image Anal 2017; 44:215-227. [PMID: 29288983 DOI: 10.1016/j.media.2017.12.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/29/2017] [Accepted: 12/02/2017] [Indexed: 12/14/2022]
Abstract
During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as "OptiC", from a large dataset involving 20 centers, 4 contrasts (T2-weighted n = 287, T1-weighted n = 120, T2∗-weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value < .01). Images containing brain regions were identified with a 99% precision, on which brain and spine regions were separated with a distance error of 9.37 mm compared to ground-truth. Validation results on a challenging dataset suggest that OptiC could reliably be used for subsequent quantitative analyses tasks, opening the door to more robust analysis on pathological cases.
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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
| | - Sara M Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - 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
| | - Rohit Bakshi
- Laboratory for Neuroimaging Research, Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Subhash Tummala
- Laboratory for Neuroimaging Research, Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | | | | | - Virginie Callot
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France; APHM, Hôpital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Michaël Sdika
- Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69100, Lyon, France.
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Prados F, Ashburner J, Blaiotta C, Brosch T, Carballido-Gamio J, Cardoso MJ, Conrad BN, Datta E, Dávid G, Leener BD, Dupont SM, Freund P, Wheeler-Kingshott CAMG, Grussu F, Henry R, Landman BA, Ljungberg E, Lyttle B, Ourselin S, Papinutto N, Saporito S, Schlaeger R, Smith SA, Summers P, Tam R, Yiannakas MC, Zhu A, Cohen-Adad J. Spinal cord grey matter segmentation challenge. Neuroimage 2017; 152:312-329. [PMID: 28286318 PMCID: PMC5440179 DOI: 10.1016/j.neuroimage.2017.03.010] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 01/27/2017] [Accepted: 03/06/2017] [Indexed: 11/26/2022] Open
Abstract
An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.
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Affiliation(s)
- Ferran Prados
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK.
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Claudia Blaiotta
- Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Tom Brosch
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
| | | | - Manuel Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Benjamin N Conrad
- Department of Electrical Engineering, Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Institute of Image Science at Vanderbilt University, Nashville, TN, USA
| | - Esha Datta
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gergely Dávid
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Switzerland
| | | | - Sara M Dupont
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Switzerland
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK; Brain MRI 3T Centre, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Italy
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK
| | - Roland Henry
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Bennett A Landman
- Department of Electrical Engineering, Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Institute of Image Science at Vanderbilt University, Nashville, TN, USA
| | - Emil Ljungberg
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada V6T 2B5
| | - Bailey Lyttle
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Nico Papinutto
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Regina Schlaeger
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Biomedical Engineering, Ophthalmology, Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Paul Summers
- Department of Radiology, European Institute of Oncology, University of Modena and Reggio Emilia, 41121, Modena, MO, Italy
| | - Roger Tam
- Department of Radiology, UBC MS/MRI Research Group, University of British Columbia, Vancouver, BC, Canada V6T 2B5
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK
| | - Alyssa Zhu
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Julien Cohen-Adad
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
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De Leener B, Mangeat G, Dupont S, Martin AR, Callot V, Stikov N, Fehlings MG, Cohen-Adad J. Topologically preserving straightening of spinal cord MRI. J Magn Reson Imaging 2017; 46:1209-1219. [PMID: 28130805 DOI: 10.1002/jmri.25622] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/18/2016] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To propose a robust and accurate method for straightening magnetic resonance (MR) images of the spinal cord, based on spinal cord segmentation, that preserves spinal cord topology and that works for any MRI contrast, in a context of spinal cord template-based analysis. MATERIALS AND METHODS The spinal cord curvature was computed using an iterative Non-Uniform Rational B-Spline (NURBS) approximation. Forward and inverse deformation fields for straightening were computed by solving analytically the straightening equations for each image voxel. Computational speed-up was accomplished by solving all voxel equation systems as one single system. Straightening accuracy (mean and maximum distance from straight line), computational time, and robustness to spinal cord length was evaluated using the proposed and the standard straightening method (label-based spline deformation) on 3T T2 - and T1 -weighted images from 57 healthy subjects and 33 patients with spinal cord compression due to degenerative cervical myelopathy (DCM). RESULTS The proposed algorithm was more accurate, more robust, and faster than the standard method (mean distance = 0.80 vs. 0.83 mm, maximum distance = 1.49 vs. 1.78 mm, time = 71 vs. 174 sec for the healthy population and mean distance = 0.65 vs. 0.68 mm, maximum distance = 1.28 vs. 1.55 mm, time = 32 vs. 60 sec for the DCM population). CONCLUSION A novel image straightening method that enables template-based analysis of quantitative spinal cord MRI data is introduced. This algorithm works for any MRI contrast and was validated on healthy and patient populations. The presented method is implemented in the Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1209-1219.
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Affiliation(s)
- Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Gabriel Mangeat
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Sara Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Allan R Martin
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Virginie Callot
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.,AP-HM, Hopital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, 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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De Leener B, Lévy S, Dupont SM, Fonov VS, Stikov N, Louis Collins D, Callot V, Cohen-Adad J. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage 2016; 145:24-43. [PMID: 27720818 DOI: 10.1016/j.neuroimage.2016.10.009] [Citation(s) in RCA: 324] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Revised: 10/03/2016] [Accepted: 10/04/2016] [Indexed: 11/17/2022] Open
Abstract
For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures.
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Affiliation(s)
- Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Simon Lévy
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Sara M Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Vladimir S Fonov
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - D Louis Collins
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Virginie Callot
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France; AP-HM, Hopital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - 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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22
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Dupont SM, De Leener B, Taso M, Le Troter A, Nadeau S, Stikov N, Callot V, Cohen-Adad J. Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter. Neuroimage 2016; 150:358-372. [PMID: 27663988 DOI: 10.1016/j.neuroimage.2016.09.026] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/23/2016] [Accepted: 09/12/2016] [Indexed: 10/21/2022] Open
Abstract
The spinal cord white and gray matter can be affected by various pathologies such as multiple sclerosis, amyotrophic lateral sclerosis or trauma. Being able to precisely segment the white and gray matter could help with MR image analysis and hence be useful in further understanding these pathologies, and helping with diagnosis/prognosis and drug development. Up to date, white/gray matter segmentation has mostly been done manually, which is time consuming, induces a bias related to the rater and prevents large-scale multi-center studies. Recently, few methods have been proposed to automatically segment the spinal cord white and gray matter. However, no single method exists that combines the following criteria: (i) fully automatic, (ii) works on various MRI contrasts, (iii) robust towards pathology and (iv) freely available and open source. In this study we propose a multi-atlas based method for the segmentation of the spinal cord white and gray matter that addresses the previous limitations. Moreover, to study the spinal cord morphology, atlas-based approaches are increasingly used. These approaches rely on the registration of a spinal cord template to an MR image, however the registration usually doesn't take into account the spinal cord internal structure and thus lacks accuracy. In this study, we propose a new template registration framework that integrates the white and gray matter segmentation to account for the specific gray matter shape of each individual subject. Validation of segmentation was performed in 24 healthy subjects using T2*-weighted images, in 8 healthy subjects using diffusion weighted images (exhibiting inverted white-to-gray matter contrast compared to T2*-weighted), and in 5 patients with spinal cord injury. The template registration was validated in 24 subjects using T2*-weighted data. Results of automatic segmentation on T2*-weighted images was in close correspondence with the manual segmentation (Dice coefficient in the white/gray matter of 0.91/0.71 respectively). Similarly, good results were obtained in data with inverted contrast (diffusion-weighted image) and in patients. When compared to the classical template registration framework, the proposed framework that accounts for gray matter shape significantly improved the quality of the registration (comparing Dice coefficient in gray matter: p=9.5×10-6). While further validation is needed to show the benefits of the new registration framework in large cohorts and in a variety of patients, this study provides a fully-integrated tool for quantitative assessment of white/gray matter morphometry and template-based analysis. All the proposed methods are implemented in the Spinal Cord Toolbox (SCT), an open-source software for processing spinal cord multi-parametric MRI data.
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Affiliation(s)
- Sara M Dupont
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada
| | | | - Manuel Taso
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France; AP-HM, Hopital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Arnaud Le Troter
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France; AP-HM, Hopital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Sylvie Nadeau
- Pathokinesiology Laboratory, Centre for Interdisciplinary Research in Rehabilitation, Institut de réadaptation Gingras-Lindsay-de-Montréal- CIUSSS Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada; School of Rehabilitation, Université de Montréal, Montreal, QC, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Virginie Callot
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France; AP-HM, Hopital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Julien Cohen-Adad
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
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Martin AR, De Leener B, Cohen-Adad J, Aleksanderek I, Cadotte DW, Kalsi-Ryan S, Tetreault L, Crawley A, Ginsberg HJ, Fehlings MG. 163 Microstructural MRI Quantifies Tract-Specific Injury and Correlates With Global Disability and Focal Neurological Deficits in Degenerative Cervical Myelopathy. Neurosurgery 2016. [DOI: 10.1227/01.neu.0000489732.76982.7f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Aleem I, Xu Y, Rampersaud YR, Pahuta M, St-Pierre GH, Crawford E, Zarrabian M, St-Pierre GH, Yang M, Scheer J, St-Pierre GH, Lou E, Malleck S, Soroceanu A, Soroceanu A, White B, Holtz KA, Fallah N, Noonan V, Finkelstein J, Rivers C, Tee J, Paquet J, Rutges J, Martin AR, Martin AR, Jack A, Malakoutian M, Kwon B, St-Pierre GH, Nater A, Versteeg A, Pahuta M, Fenton E, Nagoshi N, Tetreault L, Witiw C, Santaguida C, Aziz M, Khashan M, Tomkins-Lane C, Miyanji F, Johnson M, Tee J, Roffey DM, Evaniew N, Nouri A, Tetreault L, Arnold P, Tetreault L, Fehlings M, Wilson J, Smith JS, Charest-Morin R, Charest-Morin R, Marion T, Marion T, Kato S, Miyanji F, Enright A, Daly E, Fehlings M, Dakson A, Dakson A, Leck E, Khashan M, Abraham E, Manson N, Pahuta M, Duncan J, Ahmed A, Eck J, Rhee J, Currier B, Nassr A, Yen D, Johnson A, Bidos A, Schultz S, Fanti C, Young B, Drew B, Puskas D, Henry D, Frombach A, Mitera G, Coyle D, Werier J, Wai E, Hurlbert J, Ravinsky R, Bidos A, Rampersaud YR, Bidos A, Fanti C, Young B, Drew B, Puskas D, Rampersaud R, Yang M, Hurlbert J, Thomas K, St-Pierre GH, Duplessis S, Ailon T, Smith J, Shaffrey C, Klineberg E, Schwab F, Ames C, Yang M, Hurlbert J, Thomas K, Nataraj A, Zheng R, Hill D, Moreau M, Hedden D, Southon S, Johnson M, Goytan M, Passmore S, McIntosh G, Smith J, Lafage V, Klineberg E, Ailon T, Ames C, Shaffrey C, Gupta M, Kebaish K, Scubbia D, Hart R, Hostin R, Schwab F, Kelly M, Smith J, Scheer J, Lafage V, Protopsaltis T, Lafage R, Hostin R, Kebaish K, Gupta M, Hart R, Schwab F, Ames C, Dea N, Street J, Dvorak M, Lipson R, Noonan VK, Kwon BK, Mills PB, Noonan V, Shum J, Rivers C, Street J, Park SE, Chan E, Plashkes T, Dvorak M, Fallah N, Bedi M, Chan E, Rivers C, Street J, Plashkes T, Dvorak M, Noonan V, Fallah N, Ho C, Tsai E, Rivers C, Truchon C, Linassi AG, O’Connell C, Townson A, Ahn H, Drew B, Dvorak M, Fehlings MG, Schwartz C, Noreau L, Warner F, Noonan V, Fallah N, Fisher C, O’Connell C, Tsai E, Ahn H, Attabib N, Christie S, Drew B, Finkelstein J, Fourney D, Paquet J, Parent S, Kuerban D, Dvorak M, Paquet J, Noonan V, Kwon B, Tsai E, Christie S, Rivers C, Kuerban D, Ahn H, Attabib N, Bailey C, Drew B, Fehlings M, Finkelstein J, Fourney D, Hurlbert RJ, Parent S, Fisher C, Dvorak M, Noonan V, Kwon B, Tsai E, Christie S, Rivers C, Ahn H, Attabib N, Bailey C, Drew B, Fehlings M, Finkelstein J, Fourney D, Hurlbert RJ, Parent S, Kuerban D, Dvorak M, Kwon B, Dvorak M, Aleksanderek I, Cohen-Adad J, Cadotte DW, Kalsi-Ryan S, De Leener B, Wang J, Crawley A, Mikulis DJ, Ginsberg H, Fehlings MG, Aleksanderek I, Cohen-Adad J, Tarmohamed Z, Tetreault L, Smith N, Cadotte DW, Crawley A, Ginsberg H, Mikulis DJ, Fehlings MG, Nataraj A, Fouad K, Street J, Wilke HJ, Stavness I, Dvorak M, Fels S, Oxland T, Streijger F, Fallah N, Noonan V, Paquette S, Boyd M, Ailon T, Street J, Fisher C, Dvorak M, Hurlbert J, Fehlings M, Tetreault L, Kopjar B, Arnold P, Dekutoski M, Finkelstein J, Fisher C, France J, Gokaslan Z, Massicotte E, Rhines L, Rose P, Sahgal A, Schuster J, Vaccaro A, Dea N, Boriani S, Varga PP, Luzzati A, Fehlings M, Bilsky M, Rhines L, Reynolds J, Dekutoski M, Gokaslan Z, Germscheid N, Fisher C, van Walraven C, Coyle D, Werier J, Wai E, Mercier P, Bains I, Jacobs WB, Tetreault L, Nakashima H, Nouri A, Fehlings M, Kopjar B, Wilson J, Arnold P, Fehlings M, Tetreault L, Kopjar B, Massicotte E, Fehlings M, Fehlings M, Kopjar B, Arnold P, Defino H, Kale S, Yoon ST, Barbagallo G, Bartels R, Zhou Q, Vaccaro A, Johnson M, Passmore S, Goytan M, Glazebrook C, Golan J, McIntosh G, Barker J, Weber M, Hu R, Norden J, Sinha A, Smuck M, Desai S, Samdani AF, Shah SA, Asghar J, Yaszay B, Shufflebarger HL, Betz RR, Newton P, Passmore S, McCammon J, Goytan M, McIntosh G, Fisher C, Alfasi A, Hashem EL, Papineau GD, Kingwell SP, Wai EK, Belley-Côté EP, Fallah N, Noonan VK, Rivers CS, Dvorak MF, Tetreault L, Dalzell K, Zamorano JJ, Fehlings M, Shamji M, Rhee J, Wilson J, Andersson I, Dembek A, Pagarigan K, Dettori J, Fehlings M, Kopjar B, Tetreault L, Nakashima H, Fehlings M, Kopjar B, Arnold P, Kotter M, Fehlings M, Wilson J, Arnold P, Shaffrey C, Shamji M, Mroz T, Skelly A, Chapman J, Tetreault L, Aarabi B, Casha S, Jaglal S, Voth J, Yee A, Fehlings M, Klineberg E, Shaffrey CI, Lafage V, Schwab FJ, Protopsaltis T, Scheer JK, Ailon T, Ramachandran S, Daniels A, Mundis G, Gupta M, Deviren V, Ames CP, Street J, Stobart L, Ryerson CJ, Flexman A, Street J, Flexman A, Rivers C, Kuerban D, Cheng C, Noonan V, Dvorak M, Fisher C, Kwon B, Street J, Ailon T, Boyd M, Dvorak M, Fisher C, Kwon B, Paquette S, Street J, Lewis S, Reilly C, Shah SA, Clements DH, Samdani AF, Desai S, Lonner BS, Shufflebarger HL, Betz RR, Newton P, Johnson M, Passmore S, Goytan M, Manson N, Bigney E, Wagg K, Abraham E, Nater A, Tetreault L, Kopjar B, Arnold P, Dekutoski M, Finkelstein J, Fisher C, France J, Gokaslan Z, Massicotte E, Rhines L, Rose P, Sahgal A, Schuster J, Vaccaro A, Leck E, Christie S, Leck E, Christie S, Dakson A, Christie S, Weber M, McIntosh G, Barker J, Golan J, Wagg K, Armstrong M, Bigney E, Daly E, Manson N, Bigney E, Wagg K, Daly E, Abraham E, Perruccio A, Badley E, Rampersaud R. 2016 Canadian Spine Society Abstracts. Can J Surg 2016; 59:S39-63. [PMID: 27240290 DOI: 10.1503/cjs.006916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
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Cameron Craddock R, S Margulies D, Bellec P, Nolan Nichols B, Alcauter S, A Barrios F, Burnod Y, J Cannistraci C, Cohen-Adad J, De Leener B, Dery S, Downar J, Dunlop K, R Franco A, Seligman Froehlich C, J Gerber A, S Ghosh S, J Grabowski T, Hill S, Sólon Heinsfeld A, Matthew Hutchison R, Kundu P, R Laird A, Liew SL, J Lurie D, G McLaren D, Meneguzzi F, Mennes M, Mesmoudi S, O'Connor D, H Pasaye E, Peltier S, Poline JB, Prasad G, Fraga Pereira R, Quirion PO, Rokem A, S Saad Z, Shi Y, C Strother S, Toro R, Q Uddin L, D Van Horn J, W Van Meter J, C Welsh R, Xu T. Brainhack: a collaborative workshop for the open neuroscience community. Gigascience 2016; 5:16. [PMID: 27042293 PMCID: PMC4818387 DOI: 10.1186/s13742-016-0121-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 03/15/2016] [Indexed: 11/10/2022] Open
Abstract
Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.
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Affiliation(s)
- R Cameron Craddock
- The Neuro Bureau, Leipzig, 04317 Germany ; Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, 10962 USA ; Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
| | - Daniel S Margulies
- The Neuro Bureau, Leipzig, 04317 Germany ; Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103 Germany
| | - Pierre Bellec
- The Neuro Bureau, Leipzig, 04317 Germany ; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Québec H3W 1W5, Canada ; Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada
| | - B Nolan Nichols
- The Neuro Bureau, Leipzig, 04317 Germany ; Center for Health Sciences, SRI International, Menlo Park, California, 94025 USA ; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, 94305 USA
| | - Sarael Alcauter
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Fernando A Barrios
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Yves Burnod
- Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Université Paris 06, Paris, 75005 France ; Institut des Systèmes Complexes de Paris-Île-de-France, Paris, 75013 France
| | - Christopher J Cannistraci
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Julien Cohen-Adad
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada ; Institute of Biomedical Engineering, Ecole Polytechnique de Montréal, Montréal, Québec H3T 1J4, Canada
| | - Benjamin De Leener
- Institute of Biomedical Engineering, Ecole Polytechnique de Montréal, Montréal, Québec H3T 1J4, Canada
| | - Sebastien Dery
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Jonathan Downar
- MRI-Guided rTMS Clinic, University Health Network, Toronto, Ontario M5T 2S8, Canada ; Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario M5T 2S8, Canada ; Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Katharine Dunlop
- MRI-Guided rTMS Clinic, University Health Network, Toronto, Ontario M5T 2S8, Canada ; Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Alexandre R Franco
- The Neuro Bureau, Leipzig, 04317 Germany ; Faculdade de Engenharia, PUCRS, Porto Alegre, 90619 Brazil ; Instituto do Cérebro do Rio Grande do Sul, PUCRS, Porto Alegre, 90610 Brazil ; Faculdade de Medicina, PUCRS, Porto Alegre, 90619 Brazil
| | - Caroline Seligman Froehlich
- The Neuro Bureau, Leipzig, 04317 Germany ; Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, 10962 USA
| | - Andrew J Gerber
- New York State Psychiatric Institute, New York, New York, 10032 USA ; Division of Child and Adolescent Psychiatry, Department of Psychiatry, Columbia University, New York, New York, 10032 USA
| | - Satrajit S Ghosh
- The Neuro Bureau, Leipzig, 04317 Germany ; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA ; Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts, 02115 USA
| | - Thomas J Grabowski
- Department of Radiology, University of Washington, Seattle, Washington, 98105 USA ; Department of Neurology, University of Washington, Seattle, Washington, 98105 USA
| | - Sean Hill
- International Neuroinformatics Coordinating Facility, Stockholm, 171 77 Sweden ; Karolinska Institutet, Stockholm, 171 77 Sweden
| | | | - R Matthew Hutchison
- The Neuro Bureau, Leipzig, 04317 Germany ; Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138 USA
| | - Prantik Kundu
- The Neuro Bureau, Leipzig, 04317 Germany ; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, Florida, 33199 USA
| | - Sook-Lei Liew
- The Neuro Bureau, Leipzig, 04317 Germany ; Chan Division of Occupational Science and Occupational Therapy, Division of Physical Therapy and Biokinesiology, Department of Neurology, University of Southern California, Los Angeles, California, 90033 USA ; USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, Canada, 90033 USA
| | - Daniel J Lurie
- Department of Psychology,, University of California, Berkeley, California, 94720 USA
| | - Donald G McLaren
- The Neuro Bureau, Leipzig, 04317 Germany ; Biospective, Inc., Montréal,, Québec H4P 1K6, Canada ; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, 02114, USA
| | | | - Maarten Mennes
- The Neuro Bureau, Leipzig, 04317 Germany ; Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, 6525 EN The Netherlands
| | - Salma Mesmoudi
- Institut des Systèmes Complexes de Paris-Île-de-France, Paris, 75013 France ; Sorbonne Universités, Paris-1 Université, Equipement d'Excellence MATRICE, Paris, 75005, France
| | - David O'Connor
- Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
| | - Erick H Pasaye
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Scott Peltier
- Functional MRI Laboratory, University of Michigan, Ann Arbor, Michigan, 48109 USA
| | - Jean-Baptiste Poline
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, 94720 USA ; Henry H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, California, 94709 USA
| | - Gautam Prasad
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, 90033 USA
| | | | - Pierre-Olivier Quirion
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada
| | - Ariel Rokem
- The University of Washington eScience Institute, Seattle, Washington, 98195 USA
| | - Ziad S Saad
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland, 20892 USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, 90033 USA
| | - Stephen C Strother
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada ; Rotman Research Institute, Baycrest Hospital, Toronto, Ontario M6A 2E1, Canada ; Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Roberto Toro
- The Neuro Bureau, Leipzig, 04317 Germany ; Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, 75015 France ; Unité Mixte de Recherche 3571, Genes, Synapses and Cognition, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, 75015 France
| | - Lucina Q Uddin
- The Neuro Bureau, Leipzig, 04317 Germany ; Department of Psychology, University of Miami, Coral Gables, Florida, 33124 USA ; Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida, 33136 USA
| | - John D Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, Canada, 90033 USA
| | - John W Van Meter
- Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington,, 20007 DC USA
| | - Robert C Welsh
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, 48109 USA ; Department of Radiology,, University of Michigan, Ann Arbor, Michigan, 48109 USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
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De Leener B, Taso M, Cohen-Adad J, Callot V. Segmentation of the human spinal cord. MAGMA 2016; 29:125-53. [PMID: 26724926 DOI: 10.1007/s10334-015-0507-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 11/03/2015] [Accepted: 11/03/2015] [Indexed: 12/14/2022]
Abstract
Segmenting the spinal cord contour is a necessary step for quantifying spinal cord atrophy in various diseases. Delineating gray matter (GM) and white matter (WM) is also useful for quantifying GM atrophy or for extracting multiparametric MRI metrics into specific WM tracts. Spinal cord segmentation in clinical research is not as developed as brain segmentation, however with the substantial improvement of MR sequences adapted to spinal cord MR investigations, the field of spinal cord MR segmentation has advanced greatly within the last decade. Segmentation techniques with variable accuracy and degree of complexity have been developed and reported in the literature. In this paper, we review some of the existing methods for cord and WM/GM segmentation, including intensity-based, surface-based, and image-based methods. We also provide recommendations for validating spinal cord segmentation techniques, as it is important to understand the intrinsic characteristics of the methods and to evaluate their performance and limitations. Lastly, we illustrate some applications in the healthy and pathological spinal cord. One conclusion of this review is that robust and automatic segmentation is clinically relevant, as it would allow for longitudinal and group studies free from user bias as well as reproducible multicentric studies in large populations, thereby helping to further our understanding of the spinal cord pathophysiology and to develop new criteria for early detection of subclinical evolution for prognosis prediction and for patient management. Another conclusion is that at the present time, no single method adequately segments the cord and its substructure in all the cases encountered (abnormal intensities, loss of contrast, deformation of the cord, etc.). A combination of different approaches is thus advised for future developments, along with the introduction of probabilistic shape models. Maturation of standardized frameworks, multiplatform availability, inclusion in large suite and data sharing would also ultimately benefit to the community.
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Affiliation(s)
- Benjamin De Leener
- Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Manuel Taso
- Aix Marseille Université, IFSTTAR, LBA UMR_T 24, Marseille, France.,Aix Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.,APHM, Hôpital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Julien Cohen-Adad
- Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Virginie Callot
- Aix Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France. .,APHM, Hôpital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France.
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27
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Yiannakas MC, Mustafa AM, De Leener B, Kearney H, Tur C, Altmann DR, De Angelis F, Plantone D, Ciccarelli O, Miller DH, Cohen-Adad J, Gandini Wheeler-Kingshott CAM. Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis. Neuroimage Clin 2015; 10:71-7. [PMID: 26793433 PMCID: PMC4678307 DOI: 10.1016/j.nicl.2015.11.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Revised: 10/14/2015] [Accepted: 11/02/2015] [Indexed: 12/14/2022]
Abstract
Spinal cord (SC) atrophy, i.e. a reduction in the SC cross-sectional area (CSA) over time, can be measured by means of image segmentation using magnetic resonance imaging (MRI). However, segmentation methods have been limited by factors relating to reproducibility or sensitivity to change. The purpose of this study was to evaluate a fully automated SC segmentation method (PropSeg), and compare this to a semi-automated active surface (AS) method, in healthy controls (HC) and people with multiple sclerosis (MS). MRI data from 120 people were retrospectively analysed; 26 HC, 21 with clinically isolated syndrome, 26 relapsing remitting MS, 26 primary and 21 secondary progressive MS. MRI data from 40 people returning after one year were also analysed. CSA measurements were obtained within the cervical SC. Reproducibility of the measurements was assessed using the intraclass correlation coefficient (ICC). A comparison between mean CSA changes obtained with the two methods over time was performed using multivariate structural equation regression models. Associations between CSA measures and clinical scores were investigated using linear regression models. Compared to the AS method, the reproducibility of CSA measurements obtained with PropSeg was high, both in patients and in HC, with ICC > 0.98 in all cases. There was no significant difference between PropSeg and AS in terms of detecting change over time. Furthermore, PropSeg provided measures that correlated with physical disability, similar to the AS method. PropSeg is a time-efficient and reliable segmentation method, which requires no manual intervention, and may facilitate large multi-centre neuroprotective trials in progressive MS. PropSeg is a fully automated segmentation method to measure cord atrophy in MS. PropSeg offers comparable results to a widely used semi-automated method. PropSeg is available for immediate clinical utility.
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Affiliation(s)
- Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Ahmed M Mustafa
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Benjamin De Leener
- Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Hugh Kearney
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Daniel R Altmann
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Floriana De Angelis
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Domenico Plantone
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - David H Miller
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Julien Cohen-Adad
- Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK; Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy
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28
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De Leener B, Cohen-Adad J, Kadoury S. Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling. IEEE Trans Med Imaging 2015; 34:1705-1718. [PMID: 26011879 DOI: 10.1109/tmi.2015.2437192] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g., T1-, T2- and T2(∗) -weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter- and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 ± 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts.
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