Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

Metabolic Dynamics in the Prefrontal Cortex during a Working Memory Task in Young Adult Smokers

INTRODUCTION

Cigarette smoking is known to modulate brain metabolism and brain function. How the dynamics of these metabolic alterations influence the active performance of higher order cognitive tasks in smokers, compared to non-smokers, is still unclear. The present exploratory study sought to examine the impact of smoking on the "complete" metabolic profile while the participants performed a working memory (N-back) task.

METHODS

The study sample consisted of 40 young male healthy participants (smokers [n = 20] and non-smokers [n = 20]). Functional magnetic resonance spectroscopy data were acquired using a 3 T whole-body MR system. Data analysis was performed using Java-based Magnetic Resonance User Interface software, and metabolite ratios with respect to creatine (Cr) were calculated.

RESULTS

On a behavioural level, smokers showed worse performance (measured by d') than non-smokers. However, we observed significant differences in the metabolite concentrations in smokers compared to non-smokers, which also changed over the course of the N-back task. A significant effect of the group was observed with smokers showing lower glutamate/Cr (Glx/Cr) and choline/Cr (Cho/Cr) ratios than non-smokers. Further, N-acetyl aspartate (NAA/Cr) and Cho/Cr ratios were significantly different during the rest and the task conditions. In addition, our results demonstrated the metabolite interactions (NAA and Cho, Glx and myo-inositol [mI], and Cho and mI).

CONCLUSION

Further studies are necessary to shed more light on the association between smoking behaviours and metabolic alterations. However, our preliminary findings would assist in this future research to have a complete understanding of the metabolite interactions not only in smoking but also in addiction research.

Quantitative metabolic characteristics in the peritumoral region of gliomas at 7T

BACKGROUND

The determination of tumor peripheral is of great significance in clinical diagnosis and treatment.

OBJECTIVE

In this study, we aim to obtain the metabolic condition in tumor peripheral of gliomas in vivo at 7T.

METHODS

C6 glioma cells were implanted into the right basal ganglia of Sprague-Dawley (SD) rats under stereotactic guided to create the glioma models. The models were sequentially undergone MRI and MRS examination on an 7T MR scanner designed for animals 7 days after the operation. Neuro metabolites were investigated from the center of the tumor, solid part of the tumor, peritumoral region, and contralateral white matter, and be quantified using the LCmodel software. Glial fibrillary acidic protein (GFAP) immunohistochemistry and conventional hematoxylin and eosin (HE) staining were performed after the imaging protocol.

RESULTS

Our results found that the inositol (Ins) and taurine (Tau) significantly defected in tumor peripheral compared to both tumor solid and normal tissues (P< 0.05). In contrast, the glutamate and glutamine (Glx) escalated and peaked at the tumor peripheral (P< 0.05).

CONCLUSIONS

This study revealed that Ins, Tau, and Glx have the potential to provide specific biomarkers for the location of tumor peripheral of glioma.

Reliability of Glutamate Quantification in Human Nucleus Accumbens Using Proton Magnetic Resonance Spectroscopy at a 70-cm Wide-Bore Clinical 3T MRI System

The human nucleus accumbens is a challenging region to study using proton magnetic resonance spectroscopy (¹H-MRS) on a 70-cm wide-bore clinical 3T MRI system. The aim of this study was to investigate the reliability for quantitative measurement of glutamate concentration in the nucleus accumbens using a 70-cm wide-bore clinical 3T MRI. ¹H-MRS of the nucleus accumbens was acquired using the Point-Resolved Spectroscopic Sequence (PRESS) with echo time of 40 ms from 10 healthy volunteers (5 female; age range: 18–30 years) on two separate visits (a baseline, and 1-month time point). The Java-based Magnetic Resonance User Interface (jMRUI) software package was used to quantitatively measure the absolute metabolite concentrations. The test-retest reliability and reproducibility were assessed using intraclass correlations coefficients (ICC), and coefficients of variation (CV). Glutamate concentrations were similar across visits (P = 0.832). Reproducibility measures for all metabolites were good with CV ranging from 7.8 to 14.0%. The ICC values of all metabolites for the intra-class measures were excellent (ICC > 0.8), except that the reliability for Glx (glutamate + glutamine) was good (ICC = 0.768). Pearson correlations for all metabolites were all highly significant (r = 0.636–0.788, P < 0.05). In conclusion, the short-echo-time PRESS can reliably obtain high quality glutamate spectrum from a ~3.4 cm³ voxel of the nucleus accumbens using a 70-cm wide-bore clinical 3T MRI.

Brain tumor classification using AFM in combination with data mining techniques

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.