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In Vivo Detection of Perinatal Brain Metabolite Changes in a Rabbit Model of Intrauterine Growth Restriction (IUGR).

Simões RV, Muñoz-Moreno E, Carbajo RJ, González-Tendero A, Illa M, Sanz-Cortés M, Pineda-Lucena A, Gratacós E - PLoS ONE (2015)

Bottom Line: Lower birth weight was associated with (i) smaller brain sizes, (ii) slightly lower brain temperatures, and (iii) differential metabolite profile changes in specific regions of the brain parenchyma.Specifically, we found estimated lower levels of aspartate and N-acetylaspartate (NAA) in the cerebral cortex and hippocampus (suggesting neuronal impairment), and higher glycine levels in the striatum (possible marker of brain injury).Our results also suggest that the metabolic changes in cortical regions are more prevalent than those detected in hippocampus and striatum.

View Article: PubMed Central - PubMed

Affiliation: BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Fetal i+D Fetal Medicine Research Center, IDIBAPS, University of Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.

ABSTRACT

Background: Intrauterine growth restriction (IUGR) is a risk factor for abnormal neurodevelopment. We studied a rabbit model of IUGR by magnetic resonance imaging (MRI) and spectroscopy (MRS), to assess in vivo brain structural and metabolic consequences, and identify potential metabolic biomarkers for clinical translation.

Methods: IUGR was induced in 3 pregnant rabbits at gestational day 25, by 40-50% uteroplacental vessel ligation in one horn; the contralateral horn was used as control. Fetuses were delivered at day 30 and weighted. A total of 6 controls and 5 IUGR pups underwent T2-w MRI and localized proton MRS within the first 8 hours of life, at 7T. Changes in brain tissue volumes and respective contributions to each MRS voxel were estimated by semi-automated registration of MRI images with a digital atlas of the rabbit brain. MRS data were used for: (i) absolute metabolite quantifications, using linear fitting; (ii) local temperature estimations, based on the water chemical shift; and (iii) classification, using spectral pattern analysis.

Results: Lower birth weight was associated with (i) smaller brain sizes, (ii) slightly lower brain temperatures, and (iii) differential metabolite profile changes in specific regions of the brain parenchyma. Specifically, we found estimated lower levels of aspartate and N-acetylaspartate (NAA) in the cerebral cortex and hippocampus (suggesting neuronal impairment), and higher glycine levels in the striatum (possible marker of brain injury). Our results also suggest that the metabolic changes in cortical regions are more prevalent than those detected in hippocampus and striatum.

Conclusions: IUGR was associated with brain metabolic changes in vivo, which correlate well with the neurostructural changes and neurodevelopment problems described in IUGR. Metabolic parameters could constitute non invasive biomarkers for the diagnosis and abnormal neurodevelopment of perinatal origin.

No MeSH data available.


Related in: MedlinePlus

Classification of IUGR based on MRS pattern recognition analysis.(A) Average MRS vectors for Control (top) and IUGR (bottom), with overlaid standard deviation (grey lines), for each brain region—from left to right: cortex, hippocampus, striatum. Features objectively selected in cortex region highlighted in red (3.55, 3.15, 2.0 ppm: Sequential Forward Feature Selection method). (B) Latent space distribution of cortex spectral vectors based on LDA classifiers trained with different number of features (from left to right: 1, 2, and 3). (C) Evaluations of MRS classifiers. Three independent classifiers were generated for each combination of concatenated MRS vectors (cortex-hippocampus and cortex-striatum), according to the number of features. The features selected were the same for each vector concatenation and consistently from the cortex region only. Each classifier was evaluated by Bootstrapping (1000 repetitions).
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pone.0131310.g003: Classification of IUGR based on MRS pattern recognition analysis.(A) Average MRS vectors for Control (top) and IUGR (bottom), with overlaid standard deviation (grey lines), for each brain region—from left to right: cortex, hippocampus, striatum. Features objectively selected in cortex region highlighted in red (3.55, 3.15, 2.0 ppm: Sequential Forward Feature Selection method). (B) Latent space distribution of cortex spectral vectors based on LDA classifiers trained with different number of features (from left to right: 1, 2, and 3). (C) Evaluations of MRS classifiers. Three independent classifiers were generated for each combination of concatenated MRS vectors (cortex-hippocampus and cortex-striatum), according to the number of features. The features selected were the same for each vector concatenation and consistently from the cortex region only. Each classifier was evaluated by Bootstrapping (1000 repetitions).

Mentions: When analyzing our MRS data as spectral vectors, using pattern recognition analysis (Fig 3A), we noticed that all features objectively selected for classification corresponded to the cortex region. Thus, the same results were consistently obtained when using either concatenated spectral vectors from cortex and hippocampus or from cortex and striatum. Classifiers developed only with 1 feature (3.55 ppm, Fig 3B) reached 90% accuracy in discriminating IUGR and control MRS patterns, as evaluated by bootstrapping (Fig 3C). In this case, the feature selected (3.55 ppm) corresponds to the mixed myo-inositol and glycine in the cortex. Although the estimated concentrations for both metabolites were not significantly different in this brain region between the two groups, a slight elevation in glycine was noticed in the IUGR group (+9%). Full predictive accuracy of the training set was reached by increasing the number of features selected up to 3 (Fig 3B and 3C), in which case the additional features objectively selected were 3.15 ppm (possible contributions from choline compounds or phenylalanine) and 2.0 ppm (NAA region ‒ decreased in IUGR, according to model fitting quantification), all corresponding to cortical spectral vectors.


In Vivo Detection of Perinatal Brain Metabolite Changes in a Rabbit Model of Intrauterine Growth Restriction (IUGR).

Simões RV, Muñoz-Moreno E, Carbajo RJ, González-Tendero A, Illa M, Sanz-Cortés M, Pineda-Lucena A, Gratacós E - PLoS ONE (2015)

Classification of IUGR based on MRS pattern recognition analysis.(A) Average MRS vectors for Control (top) and IUGR (bottom), with overlaid standard deviation (grey lines), for each brain region—from left to right: cortex, hippocampus, striatum. Features objectively selected in cortex region highlighted in red (3.55, 3.15, 2.0 ppm: Sequential Forward Feature Selection method). (B) Latent space distribution of cortex spectral vectors based on LDA classifiers trained with different number of features (from left to right: 1, 2, and 3). (C) Evaluations of MRS classifiers. Three independent classifiers were generated for each combination of concatenated MRS vectors (cortex-hippocampus and cortex-striatum), according to the number of features. The features selected were the same for each vector concatenation and consistently from the cortex region only. Each classifier was evaluated by Bootstrapping (1000 repetitions).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4514800&req=5

pone.0131310.g003: Classification of IUGR based on MRS pattern recognition analysis.(A) Average MRS vectors for Control (top) and IUGR (bottom), with overlaid standard deviation (grey lines), for each brain region—from left to right: cortex, hippocampus, striatum. Features objectively selected in cortex region highlighted in red (3.55, 3.15, 2.0 ppm: Sequential Forward Feature Selection method). (B) Latent space distribution of cortex spectral vectors based on LDA classifiers trained with different number of features (from left to right: 1, 2, and 3). (C) Evaluations of MRS classifiers. Three independent classifiers were generated for each combination of concatenated MRS vectors (cortex-hippocampus and cortex-striatum), according to the number of features. The features selected were the same for each vector concatenation and consistently from the cortex region only. Each classifier was evaluated by Bootstrapping (1000 repetitions).
Mentions: When analyzing our MRS data as spectral vectors, using pattern recognition analysis (Fig 3A), we noticed that all features objectively selected for classification corresponded to the cortex region. Thus, the same results were consistently obtained when using either concatenated spectral vectors from cortex and hippocampus or from cortex and striatum. Classifiers developed only with 1 feature (3.55 ppm, Fig 3B) reached 90% accuracy in discriminating IUGR and control MRS patterns, as evaluated by bootstrapping (Fig 3C). In this case, the feature selected (3.55 ppm) corresponds to the mixed myo-inositol and glycine in the cortex. Although the estimated concentrations for both metabolites were not significantly different in this brain region between the two groups, a slight elevation in glycine was noticed in the IUGR group (+9%). Full predictive accuracy of the training set was reached by increasing the number of features selected up to 3 (Fig 3B and 3C), in which case the additional features objectively selected were 3.15 ppm (possible contributions from choline compounds or phenylalanine) and 2.0 ppm (NAA region ‒ decreased in IUGR, according to model fitting quantification), all corresponding to cortical spectral vectors.

Bottom Line: Lower birth weight was associated with (i) smaller brain sizes, (ii) slightly lower brain temperatures, and (iii) differential metabolite profile changes in specific regions of the brain parenchyma.Specifically, we found estimated lower levels of aspartate and N-acetylaspartate (NAA) in the cerebral cortex and hippocampus (suggesting neuronal impairment), and higher glycine levels in the striatum (possible marker of brain injury).Our results also suggest that the metabolic changes in cortical regions are more prevalent than those detected in hippocampus and striatum.

View Article: PubMed Central - PubMed

Affiliation: BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Fetal i+D Fetal Medicine Research Center, IDIBAPS, University of Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.

ABSTRACT

Background: Intrauterine growth restriction (IUGR) is a risk factor for abnormal neurodevelopment. We studied a rabbit model of IUGR by magnetic resonance imaging (MRI) and spectroscopy (MRS), to assess in vivo brain structural and metabolic consequences, and identify potential metabolic biomarkers for clinical translation.

Methods: IUGR was induced in 3 pregnant rabbits at gestational day 25, by 40-50% uteroplacental vessel ligation in one horn; the contralateral horn was used as control. Fetuses were delivered at day 30 and weighted. A total of 6 controls and 5 IUGR pups underwent T2-w MRI and localized proton MRS within the first 8 hours of life, at 7T. Changes in brain tissue volumes and respective contributions to each MRS voxel were estimated by semi-automated registration of MRI images with a digital atlas of the rabbit brain. MRS data were used for: (i) absolute metabolite quantifications, using linear fitting; (ii) local temperature estimations, based on the water chemical shift; and (iii) classification, using spectral pattern analysis.

Results: Lower birth weight was associated with (i) smaller brain sizes, (ii) slightly lower brain temperatures, and (iii) differential metabolite profile changes in specific regions of the brain parenchyma. Specifically, we found estimated lower levels of aspartate and N-acetylaspartate (NAA) in the cerebral cortex and hippocampus (suggesting neuronal impairment), and higher glycine levels in the striatum (possible marker of brain injury). Our results also suggest that the metabolic changes in cortical regions are more prevalent than those detected in hippocampus and striatum.

Conclusions: IUGR was associated with brain metabolic changes in vivo, which correlate well with the neurostructural changes and neurodevelopment problems described in IUGR. Metabolic parameters could constitute non invasive biomarkers for the diagnosis and abnormal neurodevelopment of perinatal origin.

No MeSH data available.


Related in: MedlinePlus