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Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis

INTRODUCTION: The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different...

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Autores principales: La, Parker L., Bell, Tiffany K., Craig, William, Doan, Quynh, Beauchamp, Miriam H., Zemek, Roger, Yeates, Keith Owen, Harris, Ashley D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157208/
https://www.ncbi.nlm.nih.gov/pubmed/37151330
http://dx.doi.org/10.3389/fpsyg.2023.1130188
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author La, Parker L.
Bell, Tiffany K.
Craig, William
Doan, Quynh
Beauchamp, Miriam H.
Zemek, Roger
Yeates, Keith Owen
Harris, Ashley D.
author_facet La, Parker L.
Bell, Tiffany K.
Craig, William
Doan, Quynh
Beauchamp, Miriam H.
Zemek, Roger
Yeates, Keith Owen
Harris, Ashley D.
author_sort La, Parker L.
collection PubMed
description INTRODUCTION: The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data. METHODS: The data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates. RESULTS: Models 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences. CONCLUSION: Using data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects.
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spelling pubmed-101572082023-05-05 Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis La, Parker L. Bell, Tiffany K. Craig, William Doan, Quynh Beauchamp, Miriam H. Zemek, Roger Yeates, Keith Owen Harris, Ashley D. Front Psychol Psychology INTRODUCTION: The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data. METHODS: The data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates. RESULTS: Models 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences. CONCLUSION: Using data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects. Frontiers Media S.A. 2023-04-20 /pmc/articles/PMC10157208/ /pubmed/37151330 http://dx.doi.org/10.3389/fpsyg.2023.1130188 Text en Copyright © 2023 La, Bell, Craig, Doan, Beauchamp, Zemek, Yeates and Harris. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
La, Parker L.
Bell, Tiffany K.
Craig, William
Doan, Quynh
Beauchamp, Miriam H.
Zemek, Roger
Yeates, Keith Owen
Harris, Ashley D.
Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
title Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
title_full Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
title_fullStr Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
title_full_unstemmed Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
title_short Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
title_sort comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157208/
https://www.ncbi.nlm.nih.gov/pubmed/37151330
http://dx.doi.org/10.3389/fpsyg.2023.1130188
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