Sandrine Bédard et Julien Cohen-Adad
Article de revue (2022)
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Abstract
Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in neurodegenerative diseases. However, the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models required manual intervention and used vertebral levels as a reference, which is an imprecise prediction of the spinal levels. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated factors to explain variability, and developed normalization strategies on a large cohort (N = 804). Following automatic spinal cord segmentation, vertebral labeling and PMJ labeling, the spinal cord CSA was computed on T1w MRI scans from the UK Biobank database. The CSA was computed using two methods. For the first method, the CSA was computed at the level of the C2–C3 intervertebral disc. For the second method, the CSA was computed at 64 mm caudally from the PMJ, this distance corresponding to the average distance between the PMJ and the C2–C3 disc across all participants. The effect of various demographic and anatomical factors was explored, and a stepwise regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. CSA measured at C2–C3 disc and using the PMJ differed significantly (paired t-test, p-value = 0.0002). The best normalization model included thalamus, brain volume, sex and the interaction between brain volume and sex. The coefficient of variation went down for PMJ CSA from 10.09 (without normalization) to 8.59%, a reduction of 14.85%. For CSA at C2–C3, it went down from 9.96 to 8.42%, a reduction of 15.13 %. This study introduces an end-to-end automatic pipeline to measure and normalize cord CSA from a neurological reference. This approach requires further validation to assess atrophy in longitudinal studies. The inter-subject variability of CSA can be partly accounted for by demographics and anatomical factors.
Mots clés
spinal cord; MRI; normalization; inter-subject variability; biomarker
Département: |
Département de génie électrique Institut de génie biomédical |
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Centre de recherche: | NeuroPoly - Laboratoire de Recherche en Neuroimagerie |
Organismes subventionnaires: | Canada Research Chair in Quantitative Magnetic Resonance Imaging, Canadian Institute of Health Research, Canada Foundation for Innovation, Fonds de Recherche du Québec—Santé, Natural Sciences and Engineering Research Council of Canada, Canada First Research Excellence Fund, Courtois NeuroMod project, Quebec BioImaging Network, Mila—Tech Transfer Funding Program, Spinal Research and Wings for Life (INSPIRED project) |
Numéro de subvention: | 950-230815, CIHR FDN- 143263, 32454 and 34824, 28826, RGPIN-2019-07244, 5886; 35450 |
URL de PolyPublie: | https://publications.polymtl.ca/53915/ |
Titre de la revue: | Frontiers in Neuroimaging (vol. 1) |
Maison d'édition: | Frontiers Media |
DOI: | 10.3389/fnimg.2022.1031253 |
URL officielle: | https://doi.org/10.3389/fnimg.2022.1031253 |
Date du dépôt: | 10 juil. 2023 16:30 |
Dernière modification: | 03 oct. 2024 11:47 |
Citer en APA 7: | Bédard, S., & Cohen-Adad, J. (2022). Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Frontiers in Neuroimaging, 1, 1031253 (15 pages). https://doi.org/10.3389/fnimg.2022.1031253 |
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