Spinal cord gray matter atrophy in amyotrophic lateral sclerosis

M. E. Paquin; M. M.El Mendili; C. Gros; S. M. Dupont; J. Cohen-Adad; P. F. Pradat

doi:10.3174/ajnr.A5427

Spinal cord gray matter atrophy in amyotrophic lateral sclerosis

M. E. Paquin
, M. M.El Mendili
, C. Gros
, S. M. Dupont
, J. Cohen-Adad
, P. F. Pradat

School of Biomedical Sciences

Research output: Contribution to journal › Article › peer-review

46 Citations (Scopus)

Abstract

Background and purpose: There is an emerging need for biomarkers to better categorize clinical phenotypes and predict progression in amyotrophic lateral sclerosis. This study aimed to quantify cervical spinal gray matter atrophy in amyotrophic lateral sclerosis and investigate its association with clinical disability at baseline and after 1 year. MATERIALS AND METHODS: Twenty-nine patients with amyotrophic lateral sclerosis and 22 healthy controls were scanned with 3T MR imaging. Standard functional scale was recorded at the time of MR imaging and after 1 year. MR imaging data were processed automatically to measure the spinal cord, gray matter, and white matter cross-sectional areas. A statistical analysis assessed the difference in crosssectional areas between patients with amyotrophic lateral sclerosis and controls, correlations between spinal cord and gray matter atrophy to clinical disability at baseline and at 1 year, and prediction of clinical disability at 1 year. RESULTS: Gray matter atrophy was more sensitive to discriminate patients with amyotrophic lateral sclerosis from controls (P = .004) compared with spinal cord atrophy (P = .02). Gray matter and spinal cord cross-sectional areas showed good correlations with clinical scores at baseline (R = 0.56 for gray matter and R =0.55 for spinal cord; P < .01). Prediction at 1 year with clinical scores (R² = 0.54) was improved when including a combination of gray matter and white matter cross-sectional areas (R² =0.74). CONCLUSIONS: Although improvements over spinal cord cross-sectional areas were modest, this study suggests the potential use of gray matter cross-sectional areas as an MR imaging structural biomarker to monitor the evolution of amyotrophic lateral sclerosis.

Original language	English
Pages (from-to)	184-192
Number of pages	9
Journal	American Journal of Neuroradiology
Volume	39
Issue number	1
DOIs	https://doi.org/10.3174/ajnr.A5427
Publication status	Published (in print/issue) - 1 Jan 2018

Funding

Received March 30, 2017; accepted after revision August 17. From the Facultéde Médecine (M.-Ê.P.) and Functional Neuroimaging Unit, CRIUGM (J.C.-A.), Universitéde Montréal, Montreal, Quebec, Canada; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal (M.-Ê.P., C.G., S.M.D., J.C.-A.), Montreal, Quebec, Canada; Sorbonne Universités (M.M.E.M., P.-F.P.) UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, Paris, France; Department of Neurology (M.M.E.M.), Icahn School of Medicine, Mount Sinai, New York, New York; and Département des Maladies du Système Nerveux (P.-F.P.), Centre Référent Maladie Rare SLA, Hôpital de la Pitié-Salpêtrière, Paris, France. This work was supported by the Canada Research Chair in Quantitative Magnetic Resonance Imaging (J.C.-A.), the Canadian Institute of Health Research (CIHR FDN-143263), the Canada Foundation for Innovation (32454), the Fonds de Recherche du Québec - Santé(28826), the Fonds de Recherche du Québec – Nature et Technologies (2015-PR-182754), the Natural Sciences and Engineering Research Council of Canada (435897-2013), the Quebec BioImaging Network and the Association Fran-¸caise contre les Myopathies, the Institut pour la Recherche sur la Moelle épinière et l’Encéphale, and the “Investissements d’avenir” (ANR-10-IAIHU-06). We would like to acknowledge all members of the NeuroPoly Lab, especially Benjamin De Leener and Gabriel Mangeat for the useful discussions, and Dominique Eden for the English review. This study was supported by the Association Franc¸aise contre les Myopathies-Téléthon (AFM-Téléthon) and the Institut pour la Recherche sur la Moelle épinière et l’Encéphale (IRME). The research leading to these results has also received funding from the program “Investissements d’Avenir” ANR-10-IAIHU-06. We thank the neurologists of the Paris ALS center who participated in the recruitment of the patients in the study: Drs. Nadine Le For-estier, Vincent Meininger, Gaelle Bruneteau; Franc¸ois Salachas, Maria del Mar Amador, and Rabab Debs. We thank Sophie Blan-cho (IRME) for her help in the management and coordination of the study. We deeply thank the subjects who participated in this research. Disclosures: Marie-Êve Paquin—RELATED: Grant: Canada Research Chair in Quantitative Magnetic Resonance Imaging (JCA), Canadian Institute of Health Research (CIHR FDN-143263), Canada Foundation for Innovation (32454), Fonds de Recherche du Québec – Santé (28826), Fonds de Recherche du Québec – Nature et Technologies (2015-PR-182754), Natural Sciences and Engineering Research Council of Canada (435897-2013), Quebec BioImaging Network*. Mohamed M. El Mendili—RELATED: Grant: Association Fran¸caise contre les Myopathies-Téléthon (AFM-Téléthon), Insti-tut pour la Recherche sur la Moelle épinière et l’Encéphale (IRME), program “Inves-tissements d’Avenir” ANR-10-IAIHU-06, Comments: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript*. Charley Gros—RELATED: Grant: Canada Research Chair in Quantitative Magnetic Resonance Imaging (JCA), Canadian Institute of Health Research (CIHR FDN-143263), Canada Foundation for Innovation (32454), Fonds de Recherche du Québec – Santé(28826), Fonds de Recherche du Québec – Nature et Technologies (2015-PR-182754), Natural Sciences and Engineering Research Council of Canada (435897-2013), Quebec BioImaging Network*. Sara Dupont—RELATED: Grant: Canada Research Chair in Quantitative Magnetic Resonance Imaging (JCA), Canadian Institute of Health Research, (CIHR FDN-143263), Canada Foundation for Innovation (32454), Fonds de Recherche du Québec – Santé(28826), Fonds de Recherche du Québec – Nature et Technologies (2015-PR-182754), Natural Sciences and Engineering Research Council of Canada (435897-2013), Quebec BioImaging Network*. Julien Cohen-Adad—RELATED: Grant: Canadian Institutes of Health Research*. *Money paid to the institution.

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abstract = "Background and purpose: There is an emerging need for biomarkers to better categorize clinical phenotypes and predict progression in amyotrophic lateral sclerosis. This study aimed to quantify cervical spinal gray matter atrophy in amyotrophic lateral sclerosis and investigate its association with clinical disability at baseline and after 1 year. MATERIALS AND METHODS: Twenty-nine patients with amyotrophic lateral sclerosis and 22 healthy controls were scanned with 3T MR imaging. Standard functional scale was recorded at the time of MR imaging and after 1 year. MR imaging data were processed automatically to measure the spinal cord, gray matter, and white matter cross-sectional areas. A statistical analysis assessed the difference in crosssectional areas between patients with amyotrophic lateral sclerosis and controls, correlations between spinal cord and gray matter atrophy to clinical disability at baseline and at 1 year, and prediction of clinical disability at 1 year. RESULTS: Gray matter atrophy was more sensitive to discriminate patients with amyotrophic lateral sclerosis from controls (P = .004) compared with spinal cord atrophy (P = .02). Gray matter and spinal cord cross-sectional areas showed good correlations with clinical scores at baseline (R = 0.56 for gray matter and R =0.55 for spinal cord; P < .01). Prediction at 1 year with clinical scores (R2 = 0.54) was improved when including a combination of gray matter and white matter cross-sectional areas (R2 =0.74). CONCLUSIONS: Although improvements over spinal cord cross-sectional areas were modest, this study suggests the potential use of gray matter cross-sectional areas as an MR imaging structural biomarker to monitor the evolution of amyotrophic lateral sclerosis.",

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AU - Gros, C.

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AU - Cohen-Adad, J.

AU - Pradat, P. F.

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N2 - Background and purpose: There is an emerging need for biomarkers to better categorize clinical phenotypes and predict progression in amyotrophic lateral sclerosis. This study aimed to quantify cervical spinal gray matter atrophy in amyotrophic lateral sclerosis and investigate its association with clinical disability at baseline and after 1 year. MATERIALS AND METHODS: Twenty-nine patients with amyotrophic lateral sclerosis and 22 healthy controls were scanned with 3T MR imaging. Standard functional scale was recorded at the time of MR imaging and after 1 year. MR imaging data were processed automatically to measure the spinal cord, gray matter, and white matter cross-sectional areas. A statistical analysis assessed the difference in crosssectional areas between patients with amyotrophic lateral sclerosis and controls, correlations between spinal cord and gray matter atrophy to clinical disability at baseline and at 1 year, and prediction of clinical disability at 1 year. RESULTS: Gray matter atrophy was more sensitive to discriminate patients with amyotrophic lateral sclerosis from controls (P = .004) compared with spinal cord atrophy (P = .02). Gray matter and spinal cord cross-sectional areas showed good correlations with clinical scores at baseline (R = 0.56 for gray matter and R =0.55 for spinal cord; P < .01). Prediction at 1 year with clinical scores (R2 = 0.54) was improved when including a combination of gray matter and white matter cross-sectional areas (R2 =0.74). CONCLUSIONS: Although improvements over spinal cord cross-sectional areas were modest, this study suggests the potential use of gray matter cross-sectional areas as an MR imaging structural biomarker to monitor the evolution of amyotrophic lateral sclerosis.

AB - Background and purpose: There is an emerging need for biomarkers to better categorize clinical phenotypes and predict progression in amyotrophic lateral sclerosis. This study aimed to quantify cervical spinal gray matter atrophy in amyotrophic lateral sclerosis and investigate its association with clinical disability at baseline and after 1 year. MATERIALS AND METHODS: Twenty-nine patients with amyotrophic lateral sclerosis and 22 healthy controls were scanned with 3T MR imaging. Standard functional scale was recorded at the time of MR imaging and after 1 year. MR imaging data were processed automatically to measure the spinal cord, gray matter, and white matter cross-sectional areas. A statistical analysis assessed the difference in crosssectional areas between patients with amyotrophic lateral sclerosis and controls, correlations between spinal cord and gray matter atrophy to clinical disability at baseline and at 1 year, and prediction of clinical disability at 1 year. RESULTS: Gray matter atrophy was more sensitive to discriminate patients with amyotrophic lateral sclerosis from controls (P = .004) compared with spinal cord atrophy (P = .02). Gray matter and spinal cord cross-sectional areas showed good correlations with clinical scores at baseline (R = 0.56 for gray matter and R =0.55 for spinal cord; P < .01). Prediction at 1 year with clinical scores (R2 = 0.54) was improved when including a combination of gray matter and white matter cross-sectional areas (R2 =0.74). CONCLUSIONS: Although improvements over spinal cord cross-sectional areas were modest, this study suggests the potential use of gray matter cross-sectional areas as an MR imaging structural biomarker to monitor the evolution of amyotrophic lateral sclerosis.

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Spinal cord gray matter atrophy in amyotrophic lateral sclerosis

Abstract

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