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Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ((1)H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendation...

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Autores principales: Gudmundson, Aaron T., Koo, Annie, Virovka, Anna, Amirault, Alyssa L., Soo, Madelene, Cho, Jocelyn H., Oeltzschner, Georg, Edden, Richard A.E., Stark, Craig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187197/
https://www.ncbi.nlm.nih.gov/pubmed/37205343
http://dx.doi.org/10.1101/2023.02.10.528046
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author Gudmundson, Aaron T.
Koo, Annie
Virovka, Anna
Amirault, Alyssa L.
Soo, Madelene
Cho, Jocelyn H.
Oeltzschner, Georg
Edden, Richard A.E.
Stark, Craig
author_facet Gudmundson, Aaron T.
Koo, Annie
Virovka, Anna
Amirault, Alyssa L.
Soo, Madelene
Cho, Jocelyn H.
Oeltzschner, Georg
Edden, Richard A.E.
Stark, Craig
author_sort Gudmundson, Aaron T.
collection PubMed
description Proton ((1)H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we’ve identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T(2) relaxation times are established based upon a meta-analyses of healthy and diseased brains.
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spelling pubmed-101871972023-05-17 Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease Gudmundson, Aaron T. Koo, Annie Virovka, Anna Amirault, Alyssa L. Soo, Madelene Cho, Jocelyn H. Oeltzschner, Georg Edden, Richard A.E. Stark, Craig bioRxiv Article Proton ((1)H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we’ve identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T(2) relaxation times are established based upon a meta-analyses of healthy and diseased brains. Cold Spring Harbor Laboratory 2023-06-15 /pmc/articles/PMC10187197/ /pubmed/37205343 http://dx.doi.org/10.1101/2023.02.10.528046 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Gudmundson, Aaron T.
Koo, Annie
Virovka, Anna
Amirault, Alyssa L.
Soo, Madelene
Cho, Jocelyn H.
Oeltzschner, Georg
Edden, Richard A.E.
Stark, Craig
Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease
title Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease
title_full Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease
title_fullStr Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease
title_full_unstemmed Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease
title_short Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease
title_sort meta-analysis and open-source database for in vivo brain magnetic resonance spectroscopy in health and disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187197/
https://www.ncbi.nlm.nih.gov/pubmed/37205343
http://dx.doi.org/10.1101/2023.02.10.528046
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