Single-voxel proton magnetic resonance imaging (1H-MRS) and proton MR spectroscopic imaging (1H-MRSI) were used to compare brain metabolite levels in semi-acute mild traumatic brain injury (mTBI) patients (n = 10) and matched healthy controls (n = 9). The 1H-MRS voxel was positioned in the splenium, a region known to be susceptible to axonal injury in TBI, and a single 1H-MRSI slice was positioned above the lateral ventricles. To increase sensitivity to the glutamate (Glu) and the combined glutamate-glutamine (Glx) signal, an inter-pulse echo time shown to emphasize the major Glu signals was used along with an analysis method that reduces partial volume errors by using water as a concentration standard. Our preliminary findings indicate significantly lower levels of gray matter Glx and higher levels of white matter creatine-phosphocreatine (Cr) in mTBI subjects relative to healthy controls. Furthermore, Cr levels were predictive of executive function and emotional distress in the combined groups. These results suggest that perturbations in Cr, a critical component of the brain’s energy metabolism, and Glu, the brain’s major neurotransmitter, may occur following mTBI. Moreover, the different pattern of results for gray and white matter suggests tissue-specific metabolic responses to mTBI.

To investigate the biochemical correlates of normal personality we utilized proton magnetic resonance spectroscopy (1H-MRS). Our sample consisted of 60 subjects ranging in age from 18 to 32 (27 females). Personality was assessed with the NEO Five-Factor Inventory (NEO-FFI). We measured brain biochemistry within the precuneus, the cingulate cortex, and underlying white matter. We hypothesized that brain biochemistry within these regions would predict individual differences across major domains of personality functioning. Biochemical models were fit for all personality domains including Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Our findings involved differing concentrations of Choline (Cho), Creatine (Cre), and N-acetylaspartate (NAA) in regions both within (i.e., posterior cingulate cortex) and white matter underlying (i.e., precuneus) the Default Mode Network (DMN). These results add to an emerging literature regarding personality neuroscience, and implicate biochemical integrity within the default mode network as constraining major personality domains within normal human subjects.

Purpose Accumulation of mitochondrial DNA (mtDNA) deletions and the resultant impaired oxidative phosphorylation may play a pathogenic role in the mediation of age-related sarcopenia. Methods Twenty four participants of the New Mexico Aging Process Study were classified as normal lean (n=15) or sarcopenic (n=9) based on body composition determined by Dual Energy X-ray Absorptiometry. Complex I and IV activities were measured in the skeletal muscle samples obtained from gastrocnemius muscle. A two-stage nested PCR strategy was used to identify the mtDNA deletions in the entire mitochondrial genome in the skeletal muscle samples. Results While complex I activity was not significantly different (5.5 ± 0.9 vs. 4.6 ± 0.7 mU/mg protein, p>0.05), complex IV activity was higher in sarcopenic subjects (1.4 ± 0.3 vs. 1.0 ± 0.1 mU/mg protein, p<0.05). mtDNA deletions were mostly located in the region of complex I and spanned from NADH dehydrogenase (ND)1 to ND6. Deletions in the 8577–10407 bp and 10233–11249 bp regions were associated with a significant decrease in complex I activity (p<0.05 and p=0.02 respectively). Total cumulative deletion, defined as the sum of individual length of deletions in a subject, was comparable in subjects with and without sarcopenia (1760 ± 726 vs. 1782 ± 888 bp, p>0.05). The magnitude of mtDNA deletion, however, correlated positively with lean body mass (r=0.43, p<0.05). Conclusion Thus, mtDNA deletions are common in elderly subjects and are negatively related to complex I activity. The positive association between mtDNA deletions and lean body mass needs to be confirmed by studies in a larger study population.

This study investigates the potential of independent component analysis (ICA) to provide a data-driven approach for group level analysis of magnetic resonance (MR) spectra. ICA collectively analyzes data to identify maximally independent components, each of which captures covarying resonances, including those from different metabolic sources. A comparative evaluation of the ICA approach with the more established LCModel method in analyzing two different noise-free, artifact-free, simulated data sets of known compositions is presented. The results from such ideal simulations demonstrate the ability of data-driven ICA to decompose data and accurately extract components resembling modeled basis spectra from both data sets, whereas the LCModel results suffer when the underlying model deviates from assumptions, thus highlighting the sensitivity of model-based approaches to modeling inaccuracies. Analyses with simulated data show that independent component weights are good estimates of concentrations, even of metabolites with low intensity singlet peaks, such as scyllo-inositol. ICA is also applied to single voxel spectra from 193 subjects, without correcting for baseline variations, line-width broadening or noise. The results provide evidence that, despite the presence of confounding artifacts, ICA can be used to analyze in vivo spectra and extract resonances of interest. ICA is a promising technique for decomposing MR spectral data into components resembling metabolite resonances, and therefore has the potential to provide a data-driven alternative to the use of metabolite concentrations derived from curve-fitting individual spectra in making group comparisons.