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

Jiménez-Xarrié, E and Davila, M and Candiota, AP and Delgado-Mederos, R and Ortega-Martorell, S and Julià-Sapé, M and Arús, C and Martí-Fàbregas, J (2017) Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke. BMC Neuroscience, 18 (13). ISSN 1471-2202

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Abstract

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).

Item Type: Article
Uncontrolled Keywords: 1109 Neurosciences, 1702 Cognitive Science, 0601 Biochemistry And Cell Biology
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Applied Mathematics
Publisher: BioMed Central LTD
Related URLs:
Date Deposited: 24 Jan 2017 10:13
Last Modified: 08 Sep 2017 08:00
DOI or Identification number: 10.1186/s12868-016-0328-x
URI: http://researchonline.ljmu.ac.uk/id/eprint/5350

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