From Networks to the Real World: Integrating Neural and Autonomic Processes of Loss

从网络到现实世界：整合损失的神经和自主过程

• 批准号: 10248574
• 负责人: Jonathan P Stange
• 金额: $ 17.9万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-13 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248574
• 关键词: Address; Adult; Affect; Affective; Age; Area; Attenuated; Award; Behavioral; Chicago; Cognitive; Data; Data Collection; Depressed mood; Development; Dimensions; Disease; Early Diagnosis; Effectiveness; Emotional; Emotions; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Grant; Illinois; Impaired cognition; Individual; Individual Differences; Informal Social Control; Intervention; K-Series Research Career Programs; Laboratories; Lead; Learning; Life; Link; Literature; Longevity; Major Depressive Disorder; Mental Depression; Mental disorders; Mentors; Methodology; Methods; Modality; Mood Disorders; Nature; Network-based; Participant; Phenotype; Physiological; Preparation; Prevention; Process; Psychophysiology; Recording of previous events; Regulation; Relapse; Research; Research Domain Criteria; Research Technics; Resources; Rest; Risk; Risk Factors; Risk Marker; Robin bird; Role; Sinus Arrhythmia; Statistical Methods; Statistical Models; Supervision; System; Testing; Time; Training; Universities; Work; affective neuroscience; base; behavior measurement; behavioral phenotyping; career; cognitive control; cognitive neuroscience; depressive symptoms; design; economic cost; experience; improved; indexing; innovation; multimodal data; multimodality; negative affect; neural circuit; neural network; novel; prospective; relating to nervous system; respiratory; response; skills; societal costs; support network; tool development; young adult

PROJECT SUMMARY/ABSTRACT Major depressive disorder (MDD) is a prevalent and debilitating disorder that is characterized by high levels of negative affect (NA). One mechanism that may serve as a phenotype for depression risk is impaired cognitive control of NA following loss, which is also linked to atypical parasympathetic responses to loss. The Candidate will extend his background in cognitive and affective risk factors for MDD to examine the neural networks supporting cognitive control and affect regulation integrating an RDoC loss construct across multiple modalities. This will develop his mechanistic understanding and expertise in interactions between cognitive and affective systems that underlie dysfunctional self-regulation in depression. The Candidate will learn to: 1) evaluate task-based activity and interactions between intrinsic connectivity networks supporting cognitive control and emotion processing (Training Aim 1 (TA1)); 2) integrate multi-modal, multi-level data and learn the advanced statistical modeling necessary to dimensionally link fMRI to parasympathetic and affective responses (TA2). In addition, in the latter years of the award, the candidate will learn to use the methodology of EMA and ambulatory parasympathetic assessment to link neural networks that support the cognitive control of emotion to lab and real-world affective/physiological regulation (TA3). In line with these training aims, the Candidate's short-term career goals are to understand the neural networks underlying the cognitive control of emotion, and to test the ecological validity of lab-based assessments of neural and parasympathetic responses to loss for affect regulation. This Career Development Award will allow the Candidate to advance the cognitive neuroscience of depression with the long-term career goal of identifying cognitive and affective phenotypic risk markers for the development and progression of mood disorders. The University of Illinois at Chicago (UIC) is the ideal setting for the candidate's research, with ongoing development of local resources such as an independent research-dedicated 3T scanner and one of only 22 nationwide National Network of Depression Centers. Mentor Scott Langenecker is an expert in the cognitive and affective neuroscience of mood disorders across the adult lifespan, a leader in RDoC research techniques, and has an established expertise in mechanistic approaches for studying depression. The Specific Research Aims afford an excellent opportunity for the Candidate to learn and demonstrate expertise in the necessary skills to propel him to independence. Contextually-appropriate task-based activation in networks supporting cognitive control and emotion processing (Specific Aim 1) will be evaluated among thirty-five young adults (ages 18-27) with a history of MDD who are currently remitted (rMDD) and thirty-five matched healthy controls (HCs). Parasympathetic activity and affect regulation will be assessed during a laboratory-based loss paradigm and linked dimensionally to task-based network activation (Specific Aim 2). Pilot data also are collected later in the award for a seven-day prospective period via ambulatory assessment with EMA. Specific Aim 1 addresses TA1 by affording the Candidate the opportunity to assess disrupted network functioning in rMDD. Specific Aim 2 supports TA2 by allowing the Candidate to learn novel procedural and statistical methods to assess dimensional relationships to link disrupted network functioning to parasympathetic and affective responses. Along with methodology integration consultant Erika Forbes and statistical consultant Donald Hedeker, the mentoring team will provide the Candidate guidance and supervision on the integration of multi-modal data and preparation toward the development of an independent laboratory. TA3 is met through the Exploratory Aims in the latter part of the award, via the application of ambulatory parasympathetic assessment and EMA (with Co- Mentor Robin Mermelstein and Consultant Tim Trull) to link real-world affect regulation with affective responses to loss in the lab, and with neural networks that support the cognitive control of emotion. This will eventually enable the Candidate to move his research out of the lab and into real world contexts. The data collection provided by this study will provide critical preliminary data for planned R01 submissions examining cognitive-affective mechanisms underlying affect regulation and that influence risk for the onset and progression of depression. Executing the complimentary and integrated training and research aims will promote the long-term career goals of the Candidate and establish his independent expertise in identifying neural, psychophysiological, and behavioral targets that influence the course of mood disorders.

项目总结/摘要 重度抑郁症（MDD）是一种流行的和使人衰弱的疾病，其特征在于高水平的 负面影响（NA）。一种可能作为抑郁症风险表型的机制是认知功能受损 控制NA后的损失，这也与非典型的副交感神经反应的损失。候选 我将扩展他在认知和情感风险因素方面的背景，以研究神经网络 支持认知控制和情感调节，整合多个RDoC损失结构 方式。这将发展他的机械理解和专业知识之间的相互作用的认知和 情感系统是抑郁症自我调节功能失调的基础。候选人将学习：1） 评估基于任务的活动和支持认知控制的内在连接网络之间的交互 和情绪处理（训练目标1（TA 1））; 2）整合多模态，多层次的数据，并学习 先进的统计建模必要的维度链接功能磁共振成像副交感神经和情感反应 （TA2）。此外，在该奖项的后几年，候选人将学习使用EMA的方法， 非卧床副交感神经评估，以连接支持情绪认知控制的神经网络 实验室和现实世界的情感/生理调节（TA 3）。根据这些培训目标，候选人 短期职业目标是了解情绪认知控制背后的神经网络， 为了测试基于实验室的神经和副交感神经对损失的反应评估的生态有效性， 影响监管。这个职业发展奖将允许候选人提高认知 抑郁症的神经科学与识别认知和情感表型风险的长期职业目标 情绪障碍的发展和进展的标志物。伊利诺伊大学芝加哥分校（UIC） 候选人研究的理想环境，当地资源的持续发展，如 独立研究专用的3 T扫描仪和全国仅有的22个全国抑郁症网络之一 中心.导师Scott Langenecker是情绪障碍的认知和情感神经科学专家 在整个成年人的生命周期中，RDoC研究技术的领导者，并在以下方面拥有成熟的专业知识： 研究抑郁症的机械方法。具体的研究目标提供了一个极好的机会 让候选人学习和展示必要技能方面的专业知识，以推动其独立。 支持认知控制和情绪的网络中基于情境的任务激活 将在35名有以下病史的年轻人（18-27岁）中评估加工（具体目标1） 目前缓解的MDD患者（rMDD）和35名匹配的健康对照（HC）。副交感 活动和情感调节将在基于实验室的损失范式中进行评估， 基于任务的网络激活（具体目标2）。试验数据也将在颁奖后期收集 通过EMA的动态评估进行为期7天的前瞻性研究。具体目标1通过以下方式解决TA 1： 为候选人提供评估rMDD中中断的网络功能的机会。具体目标2 通过允许候选人学习新的程序和统计方法来支持TA 2， 三维关系，以链接中断网络功能的副交感神经和情感反应。 沿着的还有方法整合顾问埃里卡福布斯和统计顾问唐纳德·赫德克， 指导团队将为候选人提供多模态数据整合方面的指导和监督， 为建立独立的实验室做准备。TA 3通过以下探索性目标得到满足： 该奖项的后半部分，通过应用动态副交感神经评估和EMA（与Co， 导师Robin Mermelstein和顾问Tim特吕尔尔）将真实世界的情感调节与情感调节联系起来。 在实验室中对损失的反应，以及支持情绪认知控制的神经网络。这将 最终使候选人能够将他的研究从实验室转移到真实的世界环境中。数据 本研究提供的数据集将为计划的R 01提交材料提供关键的初步数据， 认知-情感机制是情感调节的基础，影响发病风险， 抑郁症的进展。执行补充和综合培训和研究目标将 促进候选人的长期职业目标，并建立他的独立专业知识， 影响情绪障碍进程的神经、心理生理和行为目标。