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

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

Elena Jiménez-Xarrié  1 , Myriam Davila  2   3 , Ana Paula Candiota  2   3   4 , Raquel Delgado-Mederos  1 , Sandra Ortega-Martorell  3   5 , Margarida Julià-Sapé  2   3   4 , Carles Arús  6   7   8 , Joan Martí-Fàbregas  1
Affiliations

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

Elena Jiménez-Xarrié et al. BMC Neurosci. .

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).

Keywords: Animal model; Magnetic resonance spectroscopy; Metabolomics; Pattern recognition; Stroke.

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Figures

Fig. 1
Fig. 1
Results from the Infarct Evolution Classifier. a Analysis of the Balanced Error Rate (BER) of the independent test set, the correctly classified cases (CCC) and the plot of the three ROC curves of the training set. The best performance was achieved using three features (red arrow). b Image of the voxel position and the mean spectrum ± SD (in gray shading) of the training set for each class with the approximate position of the features selected by the SFFS method indicated by red arrows (see also Table 1). c 2D Fisher’s LDA latent space representing the classification in the training set and the independent test set using three features
Fig. 2
Fig. 2
Results from the Brain Regions Classifier. a Analysis of the Balanced Error Rate (BER) of the independent test set and the correctly classified cases (CCC) and the plot of the three ROC curves of the training set. The best performance was achieved using three features (red arrow). b Image of the voxel position and the mean spectrum ± SD (in gray shading) of the training group for each class with the position of the features selected by the SFFS method indicated approximately by red arrows (see also Table 3). c 2D Fisher’s LDA latent space representing the classification in the training set and the independent test set using three features
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References

    1. Oz G, Alger JR, Barker PB, Bartha R, Bizzi A, Boesch C, et al. Clinical proton MR spectroscopy in central nervous system disorders. Radiology. 2014;270(3):658–679. doi: 10.1148/radiol.13130531. - DOI - PMC - PubMed
    1. Zhang AH, Sun H, Qiu S, Wang XJ. NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis. Magn Reson Chem. 2013;51(9):549–556. doi: 10.1002/mrc.3985. - DOI - PubMed
    1. Pfeuffer J, Tkac I, Provencher SW, Gruetter R. Toward an in vivo neurochemical profile: quantification of 18 metabolites in short-echo-time (1)H NMR spectra of the rat brain. J Magn Reson. 1999;141(1):104–120. doi: 10.1006/jmre.1999.1895. - DOI - PubMed
    1. Stefan D, Di Cesare F, Andrasescu A, Popa E, Lazariev A, Vescovo E, et al. Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package. Meas Sci Technol. 2009;20:10. doi: 10.1088/0957-0233/20/10/104035. - DOI
    1. Reynolds G, Wilson M, Peet A, Arvanitis TN. An algorithm for the automated quantitation of metabolites in in vitro NMR signals. Magn Reson Med. 2006;56(6):1211–1219. doi: 10.1002/mrm.21081. - DOI - PubMed

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