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

项目总结/摘要 研究表明，患有情绪障碍或有患情绪障碍风险的人在情绪方面存在异常。 他们日常生活中情绪的时间方面。与重度抑郁症（MDD）特别相关的是 情绪惰性，这表明情绪系统是僵化的，不能灵活地应对变化 环境需求。实验室研究已经证实了对阳性和阴性反应的减弱 在MDD的刺激，导致情绪情境不敏感理论。尽管情感动力学 对于抑郁症，很少有研究评估了神经机制，这些神经机制是改变情绪状态的基础。 生态有效的方式。静息状态的功能磁共振成像研究表明， 额叶-岛叶和皮质中线脑区之间的差异对日常生活中的情感和MDD都有影响 严重性，但对于像休息这样的非结构化任务，很难评估这些模式如何反映情绪 经验这个项目通过3个具体的目标来解决这些局限性， 刺激：（1）确定与情绪体验动态相关的随时间变化的大脑模式， 对电影的反应;（2）评估与反应中的情绪动力学相关的随时间变化的大脑模式 实验设计的新的音乐刺激;（3）与时变大脑中的个体差异 在a）日常生活中的情绪动力和B）与以下方面相关的情感特征中具有个体差异的模式： 萧条该假说认为，额叶-岛叶、皮质下和皮质- 中线区域将反映由电影和音乐引起的情绪变化， 将预测日常生活中积极和消极情绪动态的变化（在数周的过程中）， 抑郁症的严重程度这些结果将详细说明神经机制之间的联系 情绪惰性和抑郁，为情绪的几种情绪理论之一提供神经行为支持 紊乱PI的长期目标是成为R1大学的NIH资助的教师， 一项研究计划，专注于模拟涉及复杂的社会情感行为的大脑系统， 利用结果来测试表征、评估风险和治疗情绪障碍的新工具。的 培训目标是（1）获得应用动态计算模型所需的实践知识， BOLD信号对自然刺激的反应;（2）学习临床医生如何概念化和测量情感 情绪障碍的症状;（3）进一步的经验采样数据分析技能，以模拟情感动力学 预测大脑模式的个体差异;（4）提高学术/专业技能， 过渡到独立。职业发展将发生在哥伦比亚大学，一个充满活力的研究 环境与优秀的资源，以支持建议，包括相关的课程/研讨会，国家- 最先进的MRI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。

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.