Neural, hormonal and behavioral mechanisms of long-term weight maintenance

肥胖是一个主要的世界性健康问题，由于其高患病率和严重的医疗后果。治疗肥胖症的关键问题是在饮食诱导的体重减轻后体重频繁反弹，甚至可能导致体重水平高于基线水平。只有通过长期观察性研究才能认识到这种影响。这种体重循环过程的神经机制及其与能量稳态的激素和行为参数的关系在很大程度上是未知的。拟议的项目旨在同时评估神经，激素和行为参数与体重在最初的肥胖受试者在延长间隔2至3年后，他们参加了饮食干预研究。将使用功能性磁共振成像结合磁共振波谱、血液样本、行为方案和多变量分析技术来测量神经活动。该项目有两个主要目标。首先，它旨在描述基于神经，激素和行为参数的体重维持机制。其次，它的目的是从这些措施中获得合适的纵向预测长期的身体保养。关于大脑活动，我们假设体重的变化将通过参与调节非同质性食物摄入的奖励相关脑区的活动变化来反映（例如，纹状体、眶额皮质和小脑）。我们假设这些区域的活动也可以预测未来的体重变化。此外，我们提出了这样的假设，即参与食物相关自我控制的区域（例如，背外侧前额叶皮层）将与给定的体重相关，并包含未来体重变化的预测信息。为了实现这些目标，将应用高度敏感的多变量模式识别分析技术。这些方法还可以识别因素之间的微妙关系，这些关系只能通过考虑数据的协变结构来获得（大多数传统研究中应用的单变量分析方法忽略了这一点）。由于多模式数据采集和最先进的分析技术相结合，该项目有望大大提高我们对持续肥胖控制长期机制的理解。

Obesity is a major worldwide health problem due to its high prevalence and severe medical consequences. The key problem in the treatment of obesity is the frequent weight regain following a dietary induced weight loss that can even lead to weight levels above the level of baseline. Such effects will be recognized only with long-term observational studies. The neural mechanisms underlying such weight cycling processes and their relation to hormonal and behavioural parameters of energy homeostasis are largely unknown. The proposed project aims to simultaneously assess neural, hormonal, and behavioural parameters related to body weight in initially obese subjects during an extended interval of 2 to 3 years after they participated in the dietary intervention study. Neural activity will be measured using functional magnetic resonance imaging in combination with magnetic resonance spectroscopy, blood samples, behavioural protocols, and multivariate analysis techniques. The project has two major goals. First, it aims to characterize the mechanisms of weight maintenance based on neural, hormonal, and behavioural parameters. Second, it aims to derive suitable longitudinal predictors for long-term body maintenance from these measures. Regarding brain activity, we hypothesize that body weight changes will be reflected by changes in activity of rewardrelated brain areas involved in the regulation of non-homoeostatic food-intake (e.g., the striatum, the orbitofrontal cortex, and the insula). We assume that activity in these areas will also be predictive of future weight changes. Moreover, we put forward the hypothesis that activity in areas involved in food-related self-control (e.g., the dorsolateral prefrontal cortex) will be related to a given body weight and contain predictive information for future weight changes. To address these goals, highly sensitive multivariate pattern recognition analysis techniques will be applied. These methods can also identify subtle relations between factors that are only accessible by taking into account the co-variation structure of the data (which is ignored by univariate analysis methods applied in most traditional studies). Due to the combination of multimodal data acquisition and state-of-the-art analysis techniques, the project promises to substantially advance our understanding of long-term mechanisms of sustained obesity control.