Additional file 4. Test set data used for the Brain Regions Classifier. Contains data from 56 processed spectral data vectors corresponding to non-infarcted parenchyma (n=16), SVZ (n=28) and infarcted parenchyma (n=12)

Arús i Caraltó, Carles

Candiota Silveira, Ana Paula

Delgado Mederos, Raquel

Dávila Huerta, Myriam

Jiménez Xarrié, Elena

Julià Sapé, Ma. Margarita

Martí-Fàbregas, Joan

Ortega-Martorell, Sandra

Universitat Autònoma de Barcelona

BMC Neuroscience

PubMed

Elena Jiménez-Xarrié

Myriam Davila

Ana Paula Candiota

Raquel Delgado-Mederos

Sandra Ortega-Martorell

Margarida Julià-Sapé

Carles Arús

Joan Martí-Fàbregas

Springer - Publisher Connector

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

Elena Jiménez-Xarrié (721201)

Myriam Davila (3638011)

Ana Candiota (3638008)

Raquel Delgado-Mederos (721198)

Sandra Ortega-Martorell (126305)

Margarida Julià-Sapé (126317)

Carles Arús (126319)

Joan Martí-Fàbregas (721197)

FigShare

MOESM4 of Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Unprocessed raw data for each spectrum is included as a supplementary material (including water unsuppressed files)Ajuts: EU FEDER funds Redes Temáticas de Investigación Cooperativa Sanitaria RETICS-INVICTUS-RD12/014/000

Diposit Digital de Documents de la UAB

Ajuts: EU FEDER funds Redes Temáticas de Investigación Cooperativa Sanitaria RETICS-INVICTUS-RD12/014/0002Magnetic 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. 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 (). 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). SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content). The online version of this article (doi:10.1186/s12868-016-0328-x) contains supplementary material, which is available to authorized users

Crossref

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)

Jiménez-Xarrié, E

Davila, M

Candiota, AP

Delgado-Mederos, R

Ortega-Martorell, S

Julià-Sapé, M

Arús, C

Martí-Fàbregas, J

LJMU Research Online (Liverpool John Moores University)

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

MOESM4 of Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

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Springer - Publisher Connector

Diposit Digital de Documents de la UAB

Diposit Digital de Documents de la UAB

Crossref

LJMU Research Online (Liverpool John Moores University)