Functional Anatomic Studies of Self-Affect: A Multimodal Approach

自我影响的功能解剖学研究：多模式方法

• 批准号: 9352869
• 负责人: JAMES V HAXBY
• 金额: $ 68.63万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-11-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9352869
• 关键词: Affect; Affective; Anatomy; Anxiety; Anxiety Disorders; Award; Behavioral; Biological Markers; Biological Neural Networks; Body Image; Brain; Brain region; Cellular Phone; Cognition; Collection; Coupled; Coupling; Data; Depressive disorder; Development; Diffusion Magnetic Resonance Imaging; Distress; Early Intervention; Early treatment; Ecological momentary assessment; Emotional; Emotions; Feeling suicidal; Functional Imaging; Functional Magnetic Resonance Imaging; Funding; Goals; Health; Image; Impairment; Individual; Individual Differences; Longitudinal Studies; Measures; Mental Depression; Mental Health; Mental disorders; Methods; National Institute of Mental Health; Outcome; Participant; Pathway Analysis; Pattern; Process; Publishing; Reproducibility; Research; Research Personnel; Rest; Scanning; Strategic Planning; Stress; Task Performances; Technology; Time; Universities; Work; brain circuitry; clinical biomarkers; cohort; college; environmental stressor; experience; functional disability; information processing; innovation; insight; mental development; mental state; multimodality; negative mood; neuroimaging; neuromechanism; outcome prediction; predictive marker; pressure; psychologic; relating to nervous system; response; self esteem; stressor; university student; young adult

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.

项目摘要 如何体验自我对于健康的情感功能以及许多干扰都至关重要。 心理功能。这种竞争性的更新使用结构、功能和静息态神经成像， 结合被动智能手机传感技术和生态瞬时评估， 自我的情感成分了解导致情感方面变化的因素， 环境压力源导致的自我有可能提供重要的见解， 精神障碍的发展，并帮助确定谁可能是最需要早期 干预或治疗。前两个授予期（R 01 MH 059282）的研究结果显示， 几个关键的大脑区域参与处理与自我相关的信息。此外，我们发现， 这些区域和其他已知参与的大脑区域之间的结构和功能连接 情绪过程与自我情感的测量有关。这项研究的首要目标是 研究大脑的连通性和活动如何与主观痛苦的变化以及相关的 功能障碍一个令人兴奋的方面，拟议的工作是，我们将利用大学 在大学四年的时间里，跟踪一大批参与者，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 将在扫描期间进行自我影响评估。鉴于约30%的参与者 可能会产生显著的主观痛苦，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困扰，以检查 临时诱导影响任务绩效。该项目将使用最近开发的 网络分析，以评估大脑回路中的静息状态连接及其与自我影响和健康的关系， 相关成果。本研究的指导假设是，这些完整性的个体差异 网络可以预测个体对压力的脆弱性差异及其与自我影响的关系。具体 本研究的目的是：（1）.使用扩散张量表征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 模式分析和表示相似性分析将用于将参与者分类为具有高或低 自我影响（例如，自尊、抑郁、焦虑）;（2）.检查自我影响的变化如何随着时间的推移而发生 由相关大脑网络内的变化反映，并由基线网络连接预测; （3）.研究诱发的人际困扰如何影响自我情感和相关的功能连接 跨网络。了解导致自我影响变化的因素， 环境压力源有可能为心理发展提供重要的见解 这有助于确定哪些人可能最需要早期干预或治疗。