M-Ê Paquin1,2, M M El Mendili3,4, C Gros2, S M Dupont2, J Cohen-Adad5,2, P-F Pradat3,6. 1. From the Faculté de Médecine (M.-Ê.P.). 2. NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal (M.-Ê.P., C.G., S.M.D., J.C.-A.), Montreal, Quebec, Canada. 3. Sorbonne Universités (M.M.E.M., P.-F.P.) UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Paris, France. 4. Department of Neurology (M.M.E.M.), Icahn School of Medicine, Mount Sinai, New York, New York. 5. Functional Neuroimaging Unit, CRIUGM (J.C.-A.), Université de Montréal, Montreal, Quebec, Canada jcohen@polymtl.ca. 6. Département des Maladies du Système Nerveux (P.-F.P.), Centre Référent Maladie Rare SLA, Hôpital de la Pitié-Salpêtrière, Paris, France.
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 cross-sectional 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.
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 cross-sectional 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.
Authors: C Tsagkas; A Horvath; A Altermatt; S Pezold; M Weigel; T Haas; M Amann; L Kappos; T Sprenger; O Bieri; P Cattin; K Parmar Journal: AJNR Am J Neuroradiol Date: 2019-08-22 Impact factor: 3.825
Authors: Julien Cohen-Adad; Eva Alonso-Ortiz; Mihael Abramovic; Carina Arneitz; Nicole Atcheson; Laura Barlow; Robert L Barry; Markus Barth; Marco Battiston; Christian Büchel; Matthew Budde; Virginie Callot; Anna J E Combes; Benjamin De Leener; Maxime Descoteaux; Paulo Loureiro de Sousa; Marek Dostál; Julien Doyon; Adam Dvorak; Falk Eippert; Karla R Epperson; Kevin S Epperson; Patrick Freund; Jürgen Finsterbusch; Alexandru Foias; Michela Fratini; Issei Fukunaga; Claudia A M Gandini Wheeler-Kingshott; Giancarlo Germani; Guillaume Gilbert; Federico Giove; Charley Gros; Francesco Grussu; Akifumi Hagiwara; Pierre-Gilles Henry; Tomáš Horák; Masaaki Hori; James Joers; Kouhei Kamiya; Haleh Karbasforoushan; Miloš Keřkovský; Ali Khatibi; Joo-Won Kim; Nawal Kinany; Hagen Kitzler; Shannon Kolind; Yazhuo Kong; Petr Kudlička; Paul Kuntke; Nyoman D Kurniawan; Slawomir Kusmia; René Labounek; Maria Marcella Laganà; Cornelia Laule; Christine S Law; Christophe Lenglet; Tobias Leutritz; Yaou Liu; Sara Llufriu; Sean Mackey; Eloy Martinez-Heras; Loan Mattera; Igor Nestrasil; Kristin P O'Grady; Nico Papinutto; Daniel Papp; Deborah Pareto; Todd B Parrish; Anna Pichiecchio; Ferran Prados; Àlex Rovira; Marc J Ruitenberg; Rebecca S Samson; Giovanni Savini; Maryam Seif; Alan C Seifert; Alex K Smith; Seth A Smith; Zachary A Smith; Elisabeth Solana; Yuichi Suzuki; George Tackley; Alexandra Tinnermann; Jan Valošek; Dimitri Van De Ville; Marios C Yiannakas; Kenneth A Weber; Nikolaus Weiskopf; Richard G Wise; Patrik O Wyss; Junqian Xu Journal: Nat Protoc Date: 2021-08-16 Impact factor: 17.021
Authors: Kenneth A Weber; Yufen Chen; Monica Paliwal; Christine S Law; Benjamin S Hopkins; Sean Mackey; Yasin Dhaher; Todd B Parrish; Zachary A Smith Journal: Neuroimage Date: 2020-05-06 Impact factor: 6.556
Authors: Nicholas T Olney; Antje Bischof; Howard Rosen; Eduardo Caverzasi; William A Stern; Catherine Lomen-Hoerth; Bruce L Miller; Roland G Henry; Nico Papinutto Journal: PLoS One Date: 2018-11-29 Impact factor: 3.240
Authors: K R Servelhere; R F Casseb; F D de Lima; T J R Rezende; L P Ramalho; M C França Journal: AJNR Am J Neuroradiol Date: 2021-01-21 Impact factor: 3.825