Functional Anatomic Studies of Self-Affect: A Multimodal Approach

项目摘要 自我是如何被体验到的，是健康情绪功能的核心，也是许多精神障碍的核心 心理功能正常。这种相互竞争的更新使用结构、功能和静息状态的神经成像， 再加上被动式智能手机传感技术和生态瞬时评估，以检查 自我的情感成分。理解导致情感方面变化的因素 由环境应激源产生的自我有可能提供对 精神障碍的发展，并帮助确定哪些人可能最需要及早 干预或治疗。前两个获奖期(R01 MH059282)的研究结果显示 大脑的几个关键区域参与处理与自我有关的信息。此外，我们发现， 这些区域和其他已知参与脑内活动的脑区之间的结构和功能连接 情绪过程与自我影响的衡量标准有关。这项研究的首要目标是 检查大脑连通性和活动如何与主观痛苦和相关的改变相关 功能障碍。拟议工作的一个令人兴奋的方面是，我们将利用这所大学 在大学四年的时间里跟踪调查一大批参与者，以评估自我的变化 情感是由相关的大脑网络预测的，以及这些网络如何随着时间的推移而变化。任务 在扫描过程中将执行自我影响评估。鉴于约30%的参与者是 可能会发展成显著的主观痛苦，一个目标是检查是否有生物标志物 预测这些结果。额外的扫描研究将引起人际关系的困扰，以检查 影响任务绩效的暂时性因素。该项目将使用最近开发的应用程序 评估大脑回路中静息状态连通性及其与自我影响和健康的关系的网络分析- 相关结果。这项研究的指导性假设是，这些完整性的个体差异 网络可以预测个体对压力的易感性的差异，以及它们与自我影响的关系。具体的 本研究的目的是：(1)。用扩散张量刻画引起自我影响的神经网络 成像、静息状态功能连接和任务相关功能成像。此外，多变量 将使用模式分析和表示相似度分析来将参与者分类为高或低 自我影响(如自尊、抑郁、焦虑)；研究自我影响是如何随着时间的推移发生变化的 通过相关大脑网络内的变化来反映，并通过基线网络连接来预测； 和(3)。研究诱发的人际困扰如何影响自我影响和相关的功能连通性 跨网络。了解导致自我影响变化的因素 环境应激源有可能为心理发展提供重要的见解 并帮助确定哪些人可能最需要早期干预或治疗。

Project Summary How the self is experienced is central to healthy emotional functioning as well as many disturbances in psychological functioning. This competing renewal uses structural, functional, and resting-state neuroimaging, coupled with passive smartphone sensing technology and ecological momentary assessments, to examine the affective components of self. Understanding the factors that contribute to changes in the affective aspects of self that result from environmental stressors has the potential to provide important insights into the development of mental disorders and help identify individuals who might be in greatest need of early intervention or treatment. Research findings during the prior two award periods (R01 MH059282) revealed several key brain regions involved in processing information related to self. Moreover, we discovered that structural and functional connectivity between these regions and other brain regions known to be involved in emotional processes are associated with measures of self-affect. The overarching goal of this research is to examine how brain connectivity and activity is related to change in subjective distress and associated functional impairment. An exciting aspect of the proposed work is that we will take advantage of the university setting to follow a large cohort of participants over their four years of college to assess how changes in self- affect are predicted by relevant brain networks as well as how those networks change over time. Tasks assessing self-affect will be performed during scanning. Given that approximately 30% of participants are likely to develop a significant subjective distress, one goal is to examine whether there are biomarkers that predict these outcomes. Additional scanning studies will induce interpersonal distress to examine the temporary inductions of affect on task performance. This project will use recently developed applications of network analysis to assess resting state connectivity in brain circuitry and its relation to self-affect and health- relevant outcomes. The guiding hypothesis of this research is that individual differences in the integrity of these networks can predict individual differences in vulnerability to stress and their relation to self-affect. The specific aims of the study are: (1). Characterize neural networks that give rise to self-affect using diffusion tensor imaging, resting state functional connectivity, and task-related functional imaging. In addition, multivariate pattern analysis and representation similarity analysis will be used to classify participants as having high or low self-affect (e.g., self-esteem, depression, anxiety); (2). Examine how changes in self-affect that occur over time are reflected by changes within relevant brain networks and are predicted by baseline network connectivity; and (3). Examine how induced interpersonal distress impacts self-affect and related functional connectivity across networks. Understanding the factors that contribute to changes in self-affect that result from environmental stressors has the potential to provide important insights into the development of mental disorders and help identify individuals who might be in greatest need of early intervention or treatment.