Individual Differences in Resting State Connectivity and Self-Regulation Failure

描述（由申请人提供）：尽管人类具有令人印象深刻的自我调节能力，但失败是常见的，人们有时在各种情况下难以控制自己的行为。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理解个体差异的神经基础，人们在多大程度上容易受到自我调节失败的影响。研究人员在30年的社会心理学研究的基础上，提出了一个自我调节失败的最新模型。具体而言，该模型根据当前关于执行控制和奖励敏感性的大脑机制的神经科学文献，考察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额叶区域自上而下的控制，而不是皮层下涉及奖励和情绪的区域，自下而上的皮层下活动导致自我调节失败。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大脑区域的统计一致性模式，以一种允许评估网络完整性的方式（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，该网络在休息时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，额顶叶网络完整性的个体差异与自我调节的长期成功或失败有关。本研究的目标自我调节行为是节食，因为它适合功能成像研究，并且可以在行为实验室实验中进行操纵。为了验证该项目的具体目标，提出了三项研究，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。检查静息状态下额顶叶网络和大脑奖励活动的连通性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): 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.