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Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

BACKGROUND: Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classif...

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Autores principales: Jiménez-Xarrié, Elena, Davila, Myriam, Candiota, Ana Paula, Delgado-Mederos, Raquel, Ortega-Martorell, Sandra, Julià-Sapé, Margarida, Arús, Carles, Martí-Fàbregas, Joan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237280/
https://www.ncbi.nlm.nih.gov/pubmed/28086802
http://dx.doi.org/10.1186/s12868-016-0328-x
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author Jiménez-Xarrié, Elena
Davila, Myriam
Candiota, Ana Paula
Delgado-Mederos, Raquel
Ortega-Martorell, Sandra
Julià-Sapé, Margarida
Arús, Carles
Martí-Fàbregas, Joan
author_facet Jiménez-Xarrié, Elena
Davila, Myriam
Candiota, Ana Paula
Delgado-Mederos, Raquel
Ortega-Martorell, Sandra
Julià-Sapé, Margarida
Arús, Carles
Martí-Fàbregas, Joan
author_sort Jiménez-Xarrié, Elena
collection PubMed
description BACKGROUND: Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier. RESULTS: A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier (http://gabrmn.uab.es/?q=sc). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity). CONCLUSION: SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12868-016-0328-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-52372802017-01-18 Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke Jiménez-Xarrié, Elena Davila, Myriam Candiota, Ana Paula Delgado-Mederos, Raquel Ortega-Martorell, Sandra Julià-Sapé, Margarida Arús, Carles Martí-Fàbregas, Joan BMC Neurosci Research Article BACKGROUND: Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier. RESULTS: A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier (http://gabrmn.uab.es/?q=sc). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity). CONCLUSION: SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12868-016-0328-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-13 /pmc/articles/PMC5237280/ /pubmed/28086802 http://dx.doi.org/10.1186/s12868-016-0328-x Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Jiménez-Xarrié, Elena
Davila, Myriam
Candiota, Ana Paula
Delgado-Mederos, Raquel
Ortega-Martorell, Sandra
Julià-Sapé, Margarida
Arús, Carles
Martí-Fàbregas, Joan
Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
title Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
title_full Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
title_fullStr Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
title_full_unstemmed Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
title_short Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
title_sort brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237280/
https://www.ncbi.nlm.nih.gov/pubmed/28086802
http://dx.doi.org/10.1186/s12868-016-0328-x
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