Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications

Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls.In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).     

Gene Expression Profile Provides Novel Insights of Fasting-Refeeding Response in Zebrafish Skeletal Muscle

Recently, fasting has been spotlighted from a healthcare perspective. However, the de-tailed biological mechanisms and significance by which the effects of fasting confer health benefits are not yet clear. Due to certain advantages of the zebrafish as a vertebrate model, it is widely utilized in biological studies. However, the biological responses to nutrient metabolism within zebrafish skeletal muscles have not yet been amply reported. Therefore, we aimed to reveal a gene expression profile in zebrafish skeletal muscles in response to fasting-refeeding. Accordingly, mRNA-sequencing and bioinformatics analysis were performed to examine comprehensive gene expression changes in skeletal muscle tissues during fasting-refeeding. Our results produced a novel set of nutrition-related genes under a fasting-refeeding protocol. Moreover, we found that five genes were dramatically upregulated in each fasting (for 24 h) and refeeding (after 3 h), exhibiting a rapid response to the provided conditional changes. The assessment of the gene length revealed that the gene set whose expression was elevated only after 3 h of refeeding had a shorter length, suggesting that nutrition-related gene function is associated with gene length. Taken together, our results from the bioinformatics analyses provide new insights into biological mechanisms induced by fasting-refeeding conditions within zebrafish skeletal muscle.     

The antiobesity action of (S)-(+)-1-(4-chlorophenylthiomethyl)-N-methylethylamine fumarate (AO-124)

The antiobesity effects of (S)-(+)-1-(4-chlorophenylthiomethyl)-N-methylethylamine fumarate (AO-124) were examined in rats and dogs. AO-124 suppressed food intake dose dependently in normal, Zucker fatty and VMH-obese rats, and beagle dogs. Its anorectic activity was not altered by pretreatment with methysergide, a serotonin receptor blocker. AO-124 also reduced the hyperphagia induced by 2-deoxy-D-glucose but not that induced by insulin, noradrenaline or muscimol, suggesting that the anoretic mechanism of AO-124 may be implicated in a glucostatic regulatory system of feeding. In addition, AO-124 decreased insulin secretion in response to an oral, but not an intravenous, glucose load. Such a suppression in insulin secretion may be explained by slow absorption of glucose from the intestine: AO-124 delayed the gastric emptying time of glucose and inhibited the active transport of glucose as observed in the everted small intestine. Two week administration of AO-124 to Zucker fatty rats resulted in a significant reduction of plasma insulin levels, body weight gain, and body lipid without exerting any changes in body protein. These findings indicate that AO-124 may be useful as an antibesity agent on the basis of its unique mechanisms of action.     

Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study

Background and HypothesisMachine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs).Study DesignTotal 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat.Study ResultsThe accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%).ConclusionWe built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.