Publications

Abstract

Magnetic resonance spectroscopy (MRS) is the primary method that can measure the levels of metabolites in the brain in vivo. To achieve its potential in clinical usage, the reliability of the measurement requires further articulation. Although there are many studies that investigate the reliability of gamma-aminobutyric acid (GABA), comparatively few studies have investigated the reliability of other brain metabolites, such as glutamate (Glu), N-acetyl-aspartate (NAA), creatine (Cr), phosphocreatine (PCr), or myo-inositol (mI), which all play a significant role in brain development and functions. In addition, previous studies which predominately used only two measurements (two data points) failed to provide the details of the time effect (e.g., time-of-day) on MRS measurement within subjects. Therefore, in this study, MRS data located in the anterior cingulate cortex (ACC) were repeatedly recorded across 1 year leading to at least 25 sessions for each subject with the aim of exploring the variability of other metabolites by using the index coefficient of variability (CV); the smaller the CV, the more reliable the measurements. We found that the metabolites of NAA, tNAA, and tCr showed the smallest CVs (between 1.43% and 4.90%), and the metabolites of Glu, Glx, mI, and tCho showed modest CVs (between 4.26% and 7.89%). Furthermore, we found that the concentration reference of the ratio to water results in smaller CVs compared to the ratio to tCr. In addition, we did not find any time-of-day effect on the MRS measurements. Collectively, the results of this study indicate that the MRS measurement is reasonably reliable in quantifying the levels of metabolites.

Abstract

The neural cognitive mechanism in processing static facial expressions (FEs) has been well documented, whereas the one underlying perceiving dynamic faces remains unclear. In this study, Fourier transformation and time–frequency analysis of Electroencephalography (EEG) data were carried out to detect the brain activation underlying dynamic or static FEs while twenty-one participants were viewing dynamic or static faces flicking at 10 Hz. In particular, steady-state visual evoked potentials (SSVEPs) were quantified through spectral power analysis of EEG recordings. Besides, Granger causality (GC) analysis (GCA) was also performed to capture the causal cortical network dynamics during dynamic or static FEs of emotion. It was discovered that the dynamic (from neural to happy (N2H) or vice versa (H2N)) FEs elicited larger SSVEPs than the static ones. Additionally, GCA demonstrated that the H2N case, in which happy FEs were being gradually changed into neutral ones, exhibited larger GC measure during the late processing stage than that from the early stage. Consequently, enhanced SSVEPs and effective brain connectivity for dynamic FEs illustrated that participants might need consume more attentional resources to process the dynamic faces, particularly for the change from happy to neutral faces. The new neural index might facilitate us to better understand the cognitive processing of dynamic and static FEs.