Organization of neurochemical interactions in young and older brains as revealed with a network approach: Evidence from proton magnetic resonance spectroscopy (1H-MRS).

Aging is associated with alterations in the brain including structural and metabolic changes. Previous research has focused on neurometabolite level differences associated to age in a variety of brain regions, but the relationship among metabolites across the brain has been much less studied. Investigating these relationships can reveal underlying neurometabolic processes, their interdependency, and their progress throughout the lifespan. Using 1H-MRS, we investigated the relationship among metabolite concentrations of N-acetylaspartate (NAA), creatine (Cr), choline (Cho), myo-Inositol (mIns) and glutamate-glutamine complex (Glx) in seven voxel locations, i.e., bilateral sensorimotor cortex, bilateral striatum, pre-supplementary motor area, right inferior frontal gyrus and occipital cortex. These measurements were performed on 59 human participants divided in two age groups: young adults (YA: 23.2 ± 4.3; 18-34 years) and older adults (OA: 67.5 ± 3.9; 61-74 years). Our results showed age-related differences in NAA, Cho, and mIns across brain regions, suggesting the presence of neurodegeneration and altered gliosis. Moreover, associative patterns among NAA, Cho and Cr were observed across the selected brain regions, which differed between young and older adults. Whereas most of metabolite concentrations were inhomogeneous across different brain regions, Cho levels were shown to be strongly related across brain regions in both age groups. Finally, we found metabolic associations between homologous brain regions (SM1 and striatum) in the OA group, with NAA showing a significant correlation between bilateral sensorimotor cortices (SM1) and mIns levels being correlated between the bilateral striata. We posit that a network perspective provides important insights regarding the potential interactions among neurochemicals underlying metabolic processes at a local and global level and their relationship with aging.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas.

BACKGROUND: We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas. METHODS: In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients. RESULTS: Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%. CONCLUSIONS: Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.