Neural Predictors of Self-Regulation Failure and Success for Appetitive Behavior

描述（由申请人提供）：自我控制能力受损是成瘾的一个定义特征。事实上，自我调节失败与当代社会面临的许多最令人烦恼的问题有关，从药物滥用和成瘾，到吸烟和癌症，到暴饮暴食和肥胖，再到冲动的性行为和艾滋病毒/艾滋病。人们可能倾向于从事不想要的行为的一个原因是因为对与这些行为相关的线索的高度敏感性。先前奖励期（R 01 DA 022582）的研究结果表明，偶然暴露于药物线索会激活大脑奖励区域（即，神经核[NAcc]以及参与运动规划的那些）用于随后的消费。我们还发现，对食物和性暗示做出反应的NAcc升高分别与暴饮暴食和性活动有关，并为与成瘾和食欲行为相关的共同神经机制提供了证据。这项研究的总体目标是确定自我调节失败和成功的神经预测使用的研究人员开发的模型，集成了什么是已知的从三十年的社会心理学研究到情境和背景因素下，自我调节失败与神经科学文献的大脑机制执行控制和奖励敏感性。该模型认为，成功的自我调节依赖于皮层脑网络对涉及奖励和情绪的皮层下区域的自上而下的控制。该项目使用最近开发的网络分析应用程序来评估大脑回路中的静息状态连接（rs-fcMRI）及其与健康相关结果的关系。作为刺激驱动的fMRI激活研究的补充，基于网络的rs-fcMRI允许以允许评估网络完整性的方式检查脑回路的功能耦合。当个体没有执行明确的任务时，可以识别出几个可分离和可重复的大脑回路，并已被证明可以预测大脑成熟度，体重和有氧能力。本研究的指导假设是，这些网络的完整性的个体差异可以预测成功或失败的自我调节导致显着不同的结果时，自我调节受到挑战的日常诱惑，自我调节的力量耗尽，负面情绪，或轻微的放纵。本研究中的目标自我调节行为是吸烟和节食，因为它们适合于功能成像研究，并且可以在行为实验室实验中操纵。建议进行研究以测试该项目的具体目标，其中包括使用rs-fcMRI和大脑奖励活动来预测（1）戒烟，饮食成功和日常冲动抵抗的长期结果，（2）自我调节耗尽后的功能性大脑活动，以及（3）测试自我调节训练是否可以加强静息态连接，从而增强长期的自我调节行为。总的来说，这些研究将为自我调节成功和失败的个体差异提供新的见解。

DESCRIPTION (provided by applicant): Impaired self-control is a defining feature of addiction. Indeed, self-regulation failures are implicated in many of the most vexing problems facing contemporary society, from drug abuse and addiction, to smoking and cancer, to overeating and obesity, to impulsive sexual behavior and HIV/AIDS. One reason that people may be prone to engaging in unwanted behaviors is because of heightened sensitivity to cues related to those behaviors. Researches findings from the prior award period (R01 DA022582) demonstrate that incidental exposure to drug cues activate brain reward regions (i.e., the nucleus accumbens [NAcc] as well as those involved in motor planning) for subsequent consumption. We also found that heightened NAcc responsively to food and sexual cues is associated with indulgence in overeating and sexual activity, respectively, and provides evidence for a common neural mechanism associated with addictive and appetitive behaviors. The overarching goal of this research is to identify neural predictors of self-regulatory failure and success using a model developed by the investigators that integrates what is known from three decades of social psychological research into the situational and contextual factors under which self-regulation fails with the neuroscience literature on brain mechanisms underlying executive control and reward sensitivity. This model argues that successful self- regulation is dependent on top-down control from cortical brain networks over subcortical regions involved in reward and emotion. This project uses recently developed applications of network analysis to assess resting state connectivity (rs-fcMRI) in brain circuitry and its relation to health-relevant outcomes. Complementary to stimulus-driven fMRI activation studies, network-based rs-fcMRI allows for the examination of functional coupling of brain circuits in a manner that permits assessment of a network's integrity. When individuals are not performing an explicit task, several separable and reproducible brain circuits can be identified and have been demonstrated to predict, for example, brain maturity, body weight and aerobic capacity. The guiding hypothesis of this research is that individual differences in the integrity of these networks can predict success or failure in self-regulation leading to markedly different outcomes when self-regulation is challenged by daily temptations, self-regulatory strength depletion, negative moods, or minor indulgences. The target self- regulatory behaviors in this research are smoking and dieting because they are amenable to functional imaging research and can be manipulated in behavioral laboratory experiments. Studies are proposed to test the specific aims of this project, which include using rs-fcMRI and brain reward activity to predict (1) long-term outcomes in smoking cessation, dietary success, and daily resistance to impulses, (2) functional brain activity following self-regulatory depletion, and (3) to test whether self-regulatory training can strengthen resting-state connectivity and in so doing enhance long-term self-regulatory behavior. Collectively, these studies will provide novel insights into individual differences in sef-regulatory success and failure.