Deep gray matter volume loss drives disability worsening in multiple sclerosis
Objective Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS. Methods We analyzed 3,604 brain high‐resolution T1‐weighted magnetic resonance im...
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| Veröffentlicht in: | Annals of neurology Jg. 83; H. 2; S. 210 - 222 |
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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United States
Wiley Subscription Services, Inc
01.02.2018
John Wiley and Sons Inc |
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| ISSN: | 0364-5134, 1531-8249, 1531-8249 |
| Online-Zugang: | Volltext |
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| Abstract | Objective
Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.
Methods
We analyzed 3,604 brain high‐resolution T1‐weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing‐remitting [RRMS], 128 secondary‐progressive [SPMS], and 125 primary‐progressive [PPMS]), over an average follow‐up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow‐up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time‐to‐EDSS progression.
Results
SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time‐to‐EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow‐up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (–1.45%), PPMS (–1.66%), and RRMS (–1.34%) than CIS (–0.88%) and HCs (–0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (–1.21%) was significantly faster than RRMS (–0.76%), CIS (–0.75%), and HCs (–0.51%). Similarly, the rate of parietal GM atrophy in SPMS (–1.24‐%) was faster than CIS (–0.63%) and HCs (–0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).
Interpretation
This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210–222 |
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| AbstractList | ObjectiveGray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.MethodsWe analyzed 3,604 brain high‐resolution T1‐weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing‐remitting [RRMS], 128 secondary‐progressive [SPMS], and 125 primary‐progressive [PPMS]), over an average follow‐up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow‐up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time‐to‐EDSS progression.ResultsSPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time‐to‐EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow‐up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (–1.45%), PPMS (–1.66%), and RRMS (–1.34%) than CIS (–0.88%) and HCs (–0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (–1.21%) was significantly faster than RRMS (–0.76%), CIS (–0.75%), and HCs (–0.51%). Similarly, the rate of parietal GM atrophy in SPMS (–1.24‐%) was faster than CIS (–0.63%) and HCs (–0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).InterpretationThis large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210–222 Objective Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS. Methods We analyzed 3,604 brain high‐resolution T1‐weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing‐remitting [RRMS], 128 secondary‐progressive [SPMS], and 125 primary‐progressive [PPMS]), over an average follow‐up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow‐up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time‐to‐EDSS progression. Results SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time‐to‐EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow‐up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (–1.45%), PPMS (–1.66%), and RRMS (–1.34%) than CIS (–0.88%) and HCs (–0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (–1.21%) was significantly faster than RRMS (–0.76%), CIS (–0.75%), and HCs (–0.51%). Similarly, the rate of parietal GM atrophy in SPMS (–1.24‐%) was faster than CIS (–0.63%) and HCs (–0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001). Interpretation This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210–222 Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS. We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression. SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001). This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222. Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.OBJECTIVEGray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression.METHODSWe analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression.SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).RESULTSSPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222.INTERPRETATIONThis large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222. |
| Author | Ciccarelli, Olga Rovira, Alex Sastre‐Garriga, Jaume Battaglini, Marco Ourselin, Sebastien van de Pavert, Steven H. Ruggieri, Serena Filippi, Massimo Leurs, Cyra E. Enzinger, Christian Rocca, Maria A. Chard, Declan Altmann, Daniel R. Stromillo, M. Laura Cawley, Niamh Barkhof, Frederik Thompson, Alan J. Wheeler‐Kingshott, Claudia A.M. Gandini Alexander, Daniel C. Gasperini, Claudio Pirpamer, Lukas Eshaghi, Arman Killestein, Joep Tur, Carmen Cardoso, M. Jorge Vrenken, Hugo De Stefano, Nicola De Angelis, Floriana Prados, Ferran Brownlee, Wallace J. |
| AuthorAffiliation | 10 MR Unit and Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona Barcelona Spain 8 Department of Neurology and Psychiatry University of Rome Sapienza Rome Italy 15 Division of Neuroradiology, Vascular & Interventional Radiology, Department of Radiology Medical University of Graz Graz Austria 6 Department of Medicine, Surgery and Neuroscience University of Siena Siena Italy 7 Department of Neurosciences S Camillo Forlanini Hospital Rome Italy 4 National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC) London United Kingdom 5 Medical Statistics Department London School of Hygiene & Tropical Medicine London United Kingdom 12 Department of Radiology and Nuclear Medicine VU University Medical Centre Amsterdam The Netherlands 13 Department of Neurology, MS Center Amsterdam VU University Medical Center Amsterdam The Netherlands 9 Neuroimaging Research Unit, Institute |
| AuthorAffiliation_xml | – name: 11 Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona Barcelona Spain – name: 5 Medical Statistics Department London School of Hygiene & Tropical Medicine London United Kingdom – name: 13 Department of Neurology, MS Center Amsterdam VU University Medical Center Amsterdam The Netherlands – name: 16 Department of Brain and Behavioral Sciences University of Pavia Pavia Italy – name: 10 MR Unit and Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona Barcelona Spain – name: 15 Division of Neuroradiology, Vascular & Interventional Radiology, Department of Radiology Medical University of Graz Graz Austria – name: 4 National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC) London United Kingdom – name: 12 Department of Radiology and Nuclear Medicine VU University Medical Centre Amsterdam The Netherlands – name: 6 Department of Medicine, Surgery and Neuroscience University of Siena Siena Italy – name: 2 Centre for Medical Image Computing (CMIC), Department of Computer Science University College London London United Kingdom – name: 14 Department of Neurology Medical University of Graz Graz Austria – name: 17 Brain MRI 3T Mondino Research Center C. Mondino National Neurological Institute Pavia Italy – name: 9 Neuroimaging Research Unit, Institute of Experimental Neurology Division of Neuroscience, San Raffaele Scientific Institute, Vita‐Salute San Raffaele University Milan Italy – name: 1 Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology Faculty of Brain Sciences University College London – name: 7 Department of Neurosciences S Camillo Forlanini Hospital Rome Italy – name: 8 Department of Neurology and Psychiatry University of Rome Sapienza Rome Italy – name: 3 Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering University College London London United Kingdom |
| Author_xml | – sequence: 1 givenname: Arman orcidid: 0000-0002-6652-3512 surname: Eshaghi fullname: Eshaghi, Arman email: arman.eshaghi.14@ucl.ac.uk organization: University College London – sequence: 2 givenname: Ferran surname: Prados fullname: Prados, Ferran organization: University College London Hospitals (UCLH) Biomedical Research Centre (BRC) – sequence: 3 givenname: Wallace J. surname: Brownlee fullname: Brownlee, Wallace J. organization: Faculty of Brain Sciences – sequence: 4 givenname: Daniel R. surname: Altmann fullname: Altmann, Daniel R. organization: London School of Hygiene & Tropical Medicine – sequence: 5 givenname: Carmen surname: Tur fullname: Tur, Carmen organization: Faculty of Brain Sciences – sequence: 6 givenname: M. Jorge surname: Cardoso fullname: Cardoso, M. Jorge organization: University College London – sequence: 7 givenname: Floriana surname: De Angelis fullname: De Angelis, Floriana organization: Faculty of Brain Sciences – sequence: 8 givenname: Steven H. surname: van de Pavert fullname: van de Pavert, Steven H. organization: Faculty of Brain Sciences – sequence: 9 givenname: Niamh surname: Cawley fullname: Cawley, Niamh organization: Faculty of Brain Sciences – sequence: 10 givenname: Nicola orcidid: 0000-0003-0272-4347 surname: De Stefano fullname: De Stefano, Nicola organization: University of Siena – sequence: 11 givenname: M. Laura surname: Stromillo fullname: Stromillo, M. Laura organization: University of Siena – sequence: 12 givenname: Marco orcidid: 0000-0002-9188-4408 surname: Battaglini fullname: Battaglini, Marco organization: University of Siena – sequence: 13 givenname: Serena surname: Ruggieri fullname: Ruggieri, Serena organization: University of Rome Sapienza – sequence: 14 givenname: Claudio surname: Gasperini fullname: Gasperini, Claudio organization: S Camillo Forlanini Hospital – sequence: 15 givenname: Massimo surname: Filippi fullname: Filippi, Massimo organization: Division of Neuroscience, San Raffaele Scientific Institute, Vita‐Salute San Raffaele University – sequence: 16 givenname: Maria A. surname: Rocca fullname: Rocca, Maria A. organization: Division of Neuroscience, San Raffaele Scientific Institute, Vita‐Salute San Raffaele University – sequence: 17 givenname: Alex surname: Rovira fullname: Rovira, Alex organization: Universitat Autònoma de Barcelona – sequence: 18 givenname: Jaume surname: Sastre‐Garriga fullname: Sastre‐Garriga, Jaume organization: Universitat Autònoma de Barcelona – sequence: 19 givenname: Hugo surname: Vrenken fullname: Vrenken, Hugo organization: VU University Medical Centre – sequence: 20 givenname: Cyra E. surname: Leurs fullname: Leurs, Cyra E. organization: VU University Medical Center – sequence: 21 givenname: Joep surname: Killestein fullname: Killestein, Joep organization: VU University Medical Center – sequence: 22 givenname: Lukas surname: Pirpamer fullname: Pirpamer, Lukas organization: Medical University of Graz – sequence: 23 givenname: Christian surname: Enzinger fullname: Enzinger, Christian organization: Medical University of Graz – sequence: 24 givenname: Sebastien surname: Ourselin fullname: Ourselin, Sebastien organization: University College London Hospitals (UCLH) Biomedical Research Centre (BRC) – sequence: 25 givenname: Claudia A.M. Gandini surname: Wheeler‐Kingshott fullname: Wheeler‐Kingshott, Claudia A.M. Gandini organization: C. Mondino National Neurological Institute – sequence: 26 givenname: Declan surname: Chard fullname: Chard, Declan organization: University College London Hospitals (UCLH) Biomedical Research Centre (BRC) – sequence: 27 givenname: Alan J. surname: Thompson fullname: Thompson, Alan J. organization: Faculty of Brain Sciences – sequence: 28 givenname: Daniel C. orcidid: 0000-0003-2439-350X surname: Alexander fullname: Alexander, Daniel C. organization: University College London – sequence: 29 givenname: Frederik surname: Barkhof fullname: Barkhof, Frederik organization: VU University Medical Centre – sequence: 30 givenname: Olga surname: Ciccarelli fullname: Ciccarelli, Olga organization: University College London Hospitals (UCLH) Biomedical Research Centre (BRC) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29331092$$D View this record in MEDLINE/PubMed |
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| Snippet | Objective
Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy... Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is... ObjectiveGray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy... |
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| SubjectTerms | Accumulation Adult Atrophy Atrophy - pathology Brain Brain - diagnostic imaging Brain - pathology Brain stem Cerebellum Confidence intervals Correlation analysis Cortex Disability Evaluation Disease Progression Female Gray Matter - diagnostic imaging Gray Matter - pathology Humans Image Interpretation, Computer-Assisted Longitudinal Studies Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Middle Aged Multiple sclerosis Multiple Sclerosis - diagnostic imaging Multiple Sclerosis - pathology Neuroimaging Neuroimaging - methods Patients Regional analysis Regional development Retrospective Studies Standard deviation Substantia alba Substantia grisea Therapeutic applications |
| Title | Deep gray matter volume loss drives disability worsening in multiple sclerosis |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fana.25145 https://www.ncbi.nlm.nih.gov/pubmed/29331092 https://www.proquest.com/docview/2006620054 https://www.proquest.com/docview/1989579584 https://pubmed.ncbi.nlm.nih.gov/PMC5838522 |
| Volume | 83 |
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