Orbitofrontal cortex volume and brain reward response in obesity.

Background/objectivesWhat drives overconsumption of food is poorly understood. Alterations in brain structure and function could contribute to increased food seeking. Recently, brain orbitofrontal cortex (OFC) volume has been implicated in dysregulated eating but little is known how brain structure relates to function.Subjects/methodsWe examined obese (n=18, age=28.7±8.3 years) and healthy control women (n=24, age=27.4±6.3 years) using a multimodal brain imaging approach. We applied magnetic resonance and diffusion tensor imaging to study brain gray and white matter volume as well as white matter (WM) integrity, and tested whether orbitofrontal cortex volume predicts brain reward circuitry activation in a taste reinforcement-learning paradigm that has been associated with dopamine function.ResultsObese individuals displayed lower gray and associated white matter volumes (P<0.05 family-wise error (FWE)- small volume corrected) compared with controls in the orbitofrontal cortex, striatum and insula. White matter integrity was reduced in obese individuals in fiber tracts including the external capsule, corona radiata, sagittal stratum, and the uncinate, inferior fronto-occipital, and inferior longitudinal fasciculi. Gray matter volume of the gyrus rectus at the medial edge of the orbitofrontal cortex predicted functional taste reward-learning response in frontal cortex, insula, basal ganglia, amygdala, hypothalamus and anterior cingulate cortex in control but not obese individuals.ConclusionsThis study indicates a strong association between medial orbitofrontal cortex volume and taste reinforcement-learning activation in the brain in control but not in obese women. Lower brain volumes in the orbitofrontal cortex and other brain regions associated with taste reward function as well as lower integrity of connecting pathways in obesity (OB) may support a more widespread disruption of reward pathways. The medial orbitofrontal cortex is an important structure in the termination of food intake and disturbances in this and related structures could contribute to overconsumption of food in obesity

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

Antrodia cinnamomea reduces obesity and modulates the gut microbiota in high-fat diet-fed mice.

BackgroundObesity is associated with gut microbiota dysbiosis, disrupted intestinal barrier and chronic inflammation. Given the high and increasing prevalence of obesity worldwide, anti-obesity treatments that are safe, effective and widely available would be beneficial. We examined whether the medicinal mushroom Antrodia cinnamomea may reduce obesity in mice fed with a high-fat diet (HFD).MethodsMale C57BL/6J mice were fed a HFD for 8 weeks to induce obesity and chronic inflammation. The mice were treated with a water extract of A. cinnamomea (WEAC), and body weight, fat accumulation, inflammation markers, insulin sensitivity and the gut microbiota were monitored.ResultsAfter 8 weeks, the mean body weight of HFD-fed mice was 39.8±1.2 g compared with 35.8±1.3 g for the HFD+1% WEAC group, corresponding to a reduction of 4 g or 10% of body weight (P<0.0001). WEAC supplementation reduced fat accumulation and serum triglycerides in a statistically significant manner in HFD-fed mice. WEAC also reversed the effects of HFD on inflammation markers (interleukin-1β, interleukin-6, tumor necrosis factor-α), insulin resistance and adipokine production (leptin and adiponectin). Notably, WEAC increased the expression of intestinal tight junctions (zonula occludens-1 and occludin) and antimicrobial proteins (Reg3g and lysozyme C) in the small intestine, leading to reduced blood endotoxemia. Finally, WEAC modulated the composition of the gut microbiota, reducing the Firmicutes/Bacteroidetes ratio and increasing the level of Akkermansia muciniphila and other bacterial species associated with anti-inflammatory properties.ConclusionsSupplementation with A. cinnamomea produces anti-obesogenic, anti-inflammatory and antidiabetic effects in HFD-fed mice by maintaining intestinal integrity and modulating the gut microbiota