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Cook SR, Vasamreddy K, Combes A, Vandekar S, Visagie M, Houston D, Wald L, Kumar A, McGrath M, McKnight CD, Bagnato F, Smith SA, O’Grady KP. Biological variation in cervical spinal cord MRI morphometry in healthy individuals and people with multiple sclerosis. J Neuroimaging 2024; 34:466-474. [PMID: 38858847 PMCID: PMC11236499 DOI: 10.1111/jon.13219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND AND PURPOSE Conclusions from prior literature regarding the impact of sex, age, and height on spinal cord (SC) MRI morphometrics are conflicting, while the effect of body weight on SC morphometrics has been found to be nonsignificant. The purpose of this case-control study is to assess the associations between cervical SC MRI morphometric parameters and age, sex, height, and weight to establish their potential role as confounding variables in a clinical study of people with multiple sclerosis (MS) compared to a cohort of healthy volunteers. METHODS Sixty-nine healthy volunteers and 31 people with MS underwent cervical SC MRI at 3 Tesla field strength. Images were centered at the C3/C4 intervertebral disc and processed using Spinal Cord Toolbox v.4.0.2. Mixed-effects linear regression models were used to evaluate the effects of biological variables and disease status on morphometric parameters. RESULTS Sex, age, and height had significant effects on cord and gray matter (GM) cross-sectional area (CSA) as well as the GM:cord CSA ratio. There were no significant effects of body weight on morphometric parameters. The effect of MS disease duration on cord CSA in the C4 level was significant when controlling for all other variables. CONCLUSIONS Studies of disease-related changes in SC morphometry should control for sex, age, and height to account for physiological variation.
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Affiliation(s)
- Sarah R. Cook
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Yale University, New Haven, CT, United States
| | - Kritin Vasamreddy
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anna Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mereze Visagie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Delaney Houston
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lily Wald
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ashwin Kumar
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Megan McGrath
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Colin D. McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Neurology, VA Hospital, TN Valley Healthcare Center, Nashville, TN, United States
| | - Seth A. Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Kristin P. O’Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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Trolle C, Goldberg E, Linnman C. Spinal cord atrophy after spinal cord injury - A systematic review and meta-analysis. Neuroimage Clin 2023; 38:103372. [PMID: 36931004 PMCID: PMC10026037 DOI: 10.1016/j.nicl.2023.103372] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/12/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
Cervical spinal cord atrophy occurs after spinal cord injury. The atrophy and how level of injury affects atrophy differs between studies. A systematic review and metaanalysis were done after systematic searches of PubMed, CINAHL, APA PsycInfo and Web of Science. English language original studies analyzing MRI cervical spinal cord cross-sectional area in adults with spinal cord injury were included. Atrophy and correlation between injury level and atrophy were estimated with random-effects models, standardized mean differences, and 95% confidence intervals. 24 studies were identified. 13/24 studies had low risk of bias. Cord atrophy meta-analysis of 18 articles corresponded to a standardized mean difference of -1.48 (95% CI -1.78 to -1.19) with moderate to large interstudy heterogeneity. Logarithmic time since injury influenced heterogeneity. Longitudinal atrophy was best described by a logarithmic model, indicating that rate of spinal atrophy decreases over time. Meta-correlation of eight studies indicated more severe atrophy in more rostral injuries (0.41, 95% CI 0.20-0.59). Larger and preferably longitudinal studies, data sharing, and standardized protocols are warranted.
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Affiliation(s)
- Carl Trolle
- Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA; Department of Medical Sciences, Rehabilitation Medicine, Uppsala University, Uppsala, Sweden.
| | - Estee Goldberg
- Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | - Clas Linnman
- Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA.
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Bédard S, Cohen-Adad J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. FRONTIERS IN NEUROIMAGING 2022; 1:1031253. [PMID: 37555172 PMCID: PMC10406309 DOI: 10.3389/fnimg.2022.1031253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/04/2022] [Indexed: 08/10/2023]
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.
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Affiliation(s)
- Sandrine Bédard
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), University of Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
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