在实验性中风中使用磁共振波谱分类软件进行脑代谢模式分析。

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

作者信息

Jiménez-Xarrié Elena, Davila Myriam, Candiota Ana Paula, Delgado-Mederos Raquel, Ortega-Martorell Sandra, Julià-Sapé Margarida, Arús Carles, Martí-Fàbregas Joan

机构信息

Stroke Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Sant Antoni Maria Claret 167, 08025, Barcelona, Spain.

Departament de Bioquímica i Biologia Molecular, Unitat de Biociències, Edifici C, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain.

出版信息

BMC Neurosci. 2017 Jan 13;18(1):13. doi: 10.1186/s12868-016-0328-x.

DOI:10.1186/s12868-016-0328-x

PMID:28086802

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5237280/

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).

摘要

背景

磁共振波谱（MRS）可在体内提供有关脑实质代谢模式的非侵入性信息。SpectraClassifier软件通过确定可用于客观分类波谱的光谱特征（代谢物）来执行MRS模式识别。我们的目的是使用SpectraClassifier在局灶性缺血性中风大鼠模型中开发梗死演变分类器和脑区分类器。

结果

使用SpectraClassifier（http://gabrmn.uab.es/?q=sc）分析了在回波时间为12毫秒时用7特斯拉磁体从非梗死脑实质、脑室下区和梗死脑实质获得的总共164个单体素质子波谱。这些波谱对应于Sprague-Dawley大鼠（健康大鼠，n = 7）以及中风后第1天（急性期，n = 6只大鼠）和中风后7±1天（亚急性期，n = 14）的中风大鼠。在梗死演变分类器中，乳酸+流动脂质（1.33 ppm）、总肌酸（3.05 ppm）和流动脂质（0.85 ppm）贡献的光谱特征可区分非梗死脑实质（敏感性100%，特异性100%）、梗死急性期（敏感性100%，特异性95%）和梗死亚急性期（敏感性78%，特异性100%）。在脑区分类器中，肌醇（3.62 ppm）和总肌酸（3.04/3.05 ppm）贡献的光谱特征可区分梗死脑实质（敏感性100%，特异性98%）、非梗死脑实质（敏感性84%，特异性84%）和脑室下区（敏感性76%，特异性93%）。