Individual Differences in Resting State Connectivity and Self-Regulation Failure

描述（由申请人提供）：虽然人类具有令人印象深刻的自我调节能力，但失败是常见的，人们有时很难在各种情况下控制自己的行为。这种失败与许多与死亡和疾病有关的可预防的健康问题有关，包括肥胖、营养不良、运动不足、酗酒和成瘾以及危险的性活动。这项研究的首要目标是更好地了解个体差异的神经基础，即人们对自我调节失败的敏感程度。这项拟议中的研究考察了研究人员最近开发的一种自我调节失败模型，该模型建立在三十年的社会心理学研究基础上。具体来说，该模型根据当前有关执行控制和奖励敏感性的大脑机制的神经科学文献，检查了自我调节失败的情境和背景因素。该模型表明，成功的自我调节依赖于从额叶区域到皮层下区域的自上而下的控制，而皮层下区域的自下而上的活动则会导致自我调节失败。该项目使用最近开发的网络分析应用程序来评估静息状态连接（rs-fcMRI）及其与自我调节成功和失败的关系。基于网络的rs-fcMRI允许检查大脑网络的功能耦合，在整个发育过程中出现的跨大脑区域的统计一致性模式，其方式允许评估网络的完整性（即，网络中节点之间的连接强度）。当受试者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网络中的连贯活动。其中之一是额顶叶网络初步研究表明，在休息时，这个网络的活动可以预测体重和有氧能力（在单独的研究中）。这项研究的指导假设是，这种额顶叶网络的完整性的个体差异与自我调节的长期成功或失败有关。在这项研究中，目标自我调节行为是节食，因为它是服从功能成像研究，它可以在行为实验室实验中操纵。提出了三项研究来测试该项目的具体目标，其中包括评估rs-fcMRI和大脑奖励活动，以预测（1）饮食挑战后食物消耗的实验室评估中的饮食行为，（2）自我调节耗尽后的功能性大脑活动，以及（3）饮食成功的长期结果。研究额顶叶网络和大脑奖励活动中的静息状态连接将为自我调节成功和失败的个体差异提供新的见解。 公共卫生相关性：许多与死亡和疾病相关的可预防的健康问题都涉及自我调节失败。本研究测试了一个模型，该模型整合了社会心理学和认知神经科学的研究，以表征自我调节及其失败的神经机制。该模型进行了测试，使用网络分析的静息状态功能连接和功能磁共振成像的奖励敏感性，以预测自我调节的成功和失败。最终，这可能会为许多健康相关行为的行为干预和心理治疗提供信息，例如肥胖，危险的性行为和成瘾。

DESCRIPTION (provided by applicant): Although humans have an impressive capacity for self-regulation, failures are common and people sometimes have difficulty controlling their behavior across a wide variety of circumstances. Such failures are implicated in many preventable health problems associated with death and disease, including obesity, poor nutrition, inadequate exercise, alcoholism and addiction, and risky sexual activity. The overarching goal of this research is to better understand the neural basis of individual differences in the extent to which people are susceptible to self-regulatory failure. The proposed research examines a recent model of self-regulatory failure developed by the investigators that that builds on three decades of social psychological research. Specifically, the model examines the situational and contextual factors under which self-regulation fails in light of the current neuroscience literature on brain mechanisms underlying executive control and reward sensitivity. This model indicates that successful self-regulation is dependent on top-down control from frontal regions over subcortical regions involved in reward and emotion and that botom-up subcortical activity contributes to self-regulation failure. This project uses recently developed applications of network analysis to assess resting state connectivity (rs-fcMRI) and its relation to self-regulatory success and failure. Network-based rs-fcMRI allows for the examination of functional coupling of brain networks, patterns of statistical coherence across brain regions that arise throughout development, in a manner that permits assessment of a network's integrity (i.e., strength of connections between nodes in the network). When subjects are not performing an explicit task, coherent activity within several separable and reproducible brain networks can be identified. One of these is the fronto-parietal network-preliminary research shows that activity in this network at rest predicts body weight and aerobic capacity (in separate studies). The guiding hypothesis of this research is that individual differences in the integrity of this fronto-parietal network are associated with long-term success or failure in self-regulation. The target self-regulatory behavior in this research is dieting because it is amenable to functional imaging research and it can be manipulated in behavioral laboratory experiments. Three studies are proposed to test the specific aims of this project, which include assessing rs-fcMRI and brain reward activity to predict (1) eating behavior in laboratory assessments of food consumption following dietary challenges, (2) functional brain activity following self-regulatory depletion, and (3) long-term outcomes in dietary success. Examining resting state connectivity in the fronto-parietal network and brain reward activity will provide novel insights into individual differences in self-regulatory success and failure. PUBLIC HEALTH RELEVANCE: Self-regulation failures are implicated in many preventable health problems associated with death and disease. This research tests a model that integrates research in social psychology and cognitive neuroscience to characterize the neural mechanisms underlying self-regulation and its failure. The model is tested using network analyses of resting state functional connectivity and functional magnetic resonance imaging of reward sensitivity to predict self-regulatory success and failure. Ultimately, this may inform behavioral interventions and psychological treatments for many health-relevant behaviors, such as obesity, risky sexual behavior, and addiction.