Understanding the emotional dynamics of everyday life: modeling brain state changes and their implications for mental health

项目摘要/摘要 研究表明，有情绪障碍或有患情绪障碍风险的人在 他们在日常生活中的情绪的时间层面。与重度抑郁障碍(MDD)特别相关的是 情绪惰性，表明情绪系统僵化，不能灵活应对变化 环境要求。实验室研究证实，对积极和消极的反应都会减弱 MDD中的刺激，导致了情绪语境不敏感理论。尽管情感动力学与 对于MDD，很少有研究评估在一种情况下改变情绪状态的神经机制 生态上有效的方式。静息状态功能磁共振研究表明，脑内和脑内的时间变化模式 额叶-岛叶和皮质中线脑区之间的关系对日常生活和MDD都有影响 严重性，但对于像休息这样的非结构化任务，很难评估这些模式是如何反映情绪的 经历。这个项目通过3个具体目标来解决这些限制，这些目标使用自然主义的、情绪化的 刺激：(1)确定与情绪体验动态相关的时变大脑模式 对电影的反应；(2)评估与反应中情绪动态有关的时变大脑模式 一种新的实验设计的音乐刺激；(3)将时变大脑中的个体差异联系起来 在a)日常生活中的情绪动态和b)与以下方面有关的情感特征方面的个体差异模式 抑郁症。假说是额叶-岛叶、皮质下和皮质--的时变激活模式 中线区域将反映电影和音乐引起的情绪变化以及这些模式的变化 将预测日常生活中积极和消极情绪动态的变化(在几周内)和 抑郁症的严重程度。这样的结果将具体说明两者之间联系的神经机制 情绪惰性和抑郁，为情绪的几种情绪理论之一提供了神经行为支持 精神错乱。PI的长期目标是成为美国国立卫生研究院资助的一所R1大学的教员，拥有 研究计划侧重于对参与复杂的社会情绪行为的大脑系统进行建模 利用结果来测试新的工具，以表征、评估风险和治疗情绪障碍。这个 培训目标是(1)获得将动态计算模型应用于 对自然刺激反应的大胆信号；(2)了解临床医生如何概念化和测量情感 情绪障碍的症状；(3)经验抽样数据分析的进一步技能，以模拟情绪动力学 并预测大脑模式的个体差异；(4)提高学术/专业技能，以成功 向独立过渡。职业发展将在哥伦比亚大学进行，这是一个充满活力的研究 有充足资源支持该提案的环境，包括相关课程/研讨会，国家-- 最先进的核磁共振设备，以及计算神经科学、生物统计学和精神病学方面的专家教师。

PROJECT SUMMARY / ABSTRACT Research has shown that individuals with, or at risk for developing, mood disorders have abnormalities in temporal aspects of their emotions in everyday life. Particularly relevant for major depressive disorder (MDD) is emotional inertia, which indicates an emotional system that is rigid and cannot flexibly respond to changing environmental demands. Lab studies have confirmed attenuated responses to both positive and negative stimuli in MDD, leading to the Emotion Context Insensitivity theory. Despite the relevance of affect dynamics for MDD, few studies have assessed the neural mechanisms that underly changing emotional states in an ecologically-valid manner. Resting-state fMRI studies have shown that time-varying patterns within and between fronto-insular and cortical midline brain regions have implications for both affect in daily life and MDD severity, but with an unstructured task like rest, it is difficult to assess how these patterns reflect emotional experiences. This project addresses these limitations through 3 specific aims that use naturalistic, emotional stimuli: (1) Determine time-varying brain patterns associated with dynamics of emotional experience in response to films; (2) Assess the time-varying brain patterns associated with emotions dynamics in response to a novel experimentally-designed musical stimuli; (3) Relate individual differences in time-varying brain patterns with individual differences in a) emotional dynamics in everyday life and b) affective traits related to depression. The hypothesis is that time-varying activation patterns in fronto-insular, subcortical, and cortical- midline regions will reflect changing emotions induced by movies and music and that variation in these patterns will predict variation in positive and negative emotion dynamics in daily life (over the course of weeks) and depression severity. Such results will specify the neural mechanisms underlying the association between emotional inertia and depression, lending neurobehavioral support for one of several emotion theories of mood disorders. The PI’s long-term goal is to become an NIH-funded faculty member of an R1 university, with a research program focused on modelling brain systems involved in complex, socioemotional behaviors and leveraging results to test new tools for characterizing, assessing risk for, and treating mood disorders. The training objectives are to (1) acquire practical knowledge needed to apply dynamic computational models to BOLD signal in response to naturalistic stimuli; (2) learn how clinicians conceptualize and measure affective symptoms of mood disorders; (3) further skills in experience sampling data analysis to model affect dynamics and predict individual differences in brain patterns; (4) enhance academic/professional skills to successfully transition to independence. Career development will take place at Columbia University, a vibrant research environment with outstanding resources to support the proposal, including relevant courses/ seminars, state- of-the-art MRI facilities, and expert faculty in Computational Neuroscience, Biostatistics, and Psychiatry.