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

基本信息

批准号：
9975226
负责人：
JAMES V HAXBY
金额：
$ 58.78万
依托单位：
DARTMOUTH COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2000
资助国家：
美国
起止时间：
2000-11-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9975226
关键词：
Affect Affective Anatomy Anxiety Anxiety Disorders Award Behavioral Biological Markers 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 Structure Task Performances Time Universities Work brain circuitry clinical biomarkers cohort college emotional functioning environmental stressor experience functional disability information processing innovation insight mental development mental state multimodality negative mood neural network neuroimaging neuromechanism outcome prediction predictive marker pressure psychologic relating to nervous system response self esteem sensor technology stressor university student young adult

项目摘要

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.

项目摘要自我的经历是健康的情感功能以及许多干扰的核心心理功能。这种竞争的更新使用结构，功能和静止状态神经影像学，再加上被动智能手机传感技术和生态瞬时评估，以检查自我的情感组成部分。了解导致变化情感方面的因素由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可能最需要早期需要的个人干预或治疗。前两个奖项期间的研究结果（R01 MH059282）揭示了处理与自我相关的信息涉及的几个关键大脑区域。而且，我们发现这些区域与已知参与的其他大脑区域之间的结构和功能连通性情绪过程与自我影响的度量有关。这项研究的总体目标是检查大脑的连通性和活动与主观困扰和相关性的变化如何相关功能障碍。拟议工作的一个令人兴奋的方面是我们将利用大学的优势设定在四年的大学期间跟随大量参与者，以评估自我的变化情感由相关的大脑网络以及这些网络随时间变化而预测。任务评估自我影响将在扫描过程中进行。鉴于大约30％的参与者是可能会产生重大的主观困扰，一个目标是检查是否有生物标志物预测这些结果。其他扫描研究将引起人际关系困扰，以检查暂时对任务绩效的影响。该项目将使用最近开发的应用程序网络分析以评估脑电路中的静止状态连通性及其与自我影响和健康的关系 - 相关结果。这项研究的指导假设是这些完整性的个体差异网络可以预测压力脆弱性及其与自我影响的关系的个体差异。具体该研究的目的是：（1）。表征神经网络，通过扩散张量引起自我影响成像，静止状态功能连接和与任务相关的功能成像。另外，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高或低自我影响（例如自尊，抑郁，焦虑）；（2）。检查如何随着时间的推移发生的自我影响的变化相关大脑网络中的变化反映了，并由基线网络连接预测；（3）。检查诱导的人际遇险如何影响自我影响和相关的功能连接性跨网络。了解导致自我影响改变的因素环境压力源有潜力为精神发展提供重要的见解疾病并帮助确定可能最需要早期干预或治疗的个人。