Social Processing Deficits in Remitted Adolescent Depression

青少年抑郁症缓解后的社会处理缺陷

基本信息

批准号：
10064641
负责人：
RANDY PATRICK AUERBACH
金额：
$ 72.03万
依托单位：
NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-01 至 2024-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10064641
关键词：
Adolescence Adolescent Adopted Attention Behavior Cellular Phone Code Communication Data Depressed mood Emotional Evaluation Event Event-Related Potentials Exhibits Face Face Processing Facial Expression Feedback Frequencies Gestures Hand Individual Individual Differences Intervention Laboratories Life Maintenance Major Depressive Disorder Measures Mental Depression National Institute of Mental Health Process Process Measure Public Health Recurrence Relapse Research Research Domain Criteria Risk Risk Behaviors Role Social Network Social Processes Structure Suicide Symptoms Testing Text Messaging Time Youth aged child depression depressive symptoms digital disability follow-up indexing information processing innovation lens negative affect neurophysiology outcome prediction peer recurrent depression relapse prediction relapse risk showing emotion smartphone Application social social communication social media stem supervised learning visual tracking

项目摘要

Major depressive disorder (MDD) is a leading cause of disability worldwide and has a peak onset during adolescence. While interventions are moderately effective for many adolescents, 40 to 70% will relapse within 5 years. Further, MDD relapse predicts academic difficulties, risky behaviors, and suicide. Thus, identifying mechanisms of MDD relapse is critical to clarify intervention targets for this significant public health problem. During adolescence, social processes and dynamics (especially with peers) are particularly significant, although it is unclear which social processes are most critical to MDD relapse. The present study focuses on the role of social communication, a set of mechanisms involving the receiving and delivery of socially relevant information. Social communication is especially significant to adolescents, as maladaptive social communication can negatively impact the establishment and maintenance of relationships, thus increasing risk for MDD relapse. For the proposed study, we will employ an NIMH’s Research Domain Criteria (RDoC) lens and use multiple measures (behavior, event-related potentials [ERPs], eye tracking) to compare adolescents (aged 14-17 years) with remitted depression (remMDD; N=200) to healthy controls (HC; N=100) on deficits in several aspects of social communication, including: (i) processing of nonverbal social information, (ii) processing of socioemotional feedback, and (iii) digital communication. First, Aim 1 will test whether remMDD adolescents abnormally process two types of nonverbal social information—facial expressions (as indexed by reduced accuracy and abnormal ERPs [i.e., the N170]) and hand gesturing behaviors (assessed via eye-tracking). Second, Aim 2 will test whether remMDD adolescents abnormally process socioemotional feedback (being accepted versus rejected by same-aged peers), a well-established trigger of adolescent MDD. Specifically, Aim 2 will test whether remMDD adolescents exhibit a reduced Late Positive Potential [LPP]), an ERP indexing emotional encoding, following positive social feedback from faux peers during a peer evaluation task. Third, using an innovative smartphone app, Aim 3 will collect multiple indicators of digital communication regarding the structure of adolescents’ digital social network (i.e., size of the network; frequency of communication) and sentiment of the communication with their digital social network (i.e., coding sentiment from their texts, social media posts); allowing us to test whether remMDD adolescents exhibit abnormal digital communication. Last, we will follow adolescents for 1-year to determine whether processing of nonverbal information, social feedback, and digital communication predict the escalation of depression symptoms and MDD relapse. Further, supervised machine learning will explore which social communication deficit(s) predict symptoms or MDD relapse, and whether these social processes predict outcomes independent of (or interaction with) other established predictors of relapse (e.g., stressful life events). In summary, the project has the promise to identify social process that contribute to recurrent depression, which, ultimately, will lead to innovative treatment approaches.

重度抑郁症（MDD）是全球残疾的主要原因，在青少年。虽然干预措施对许多青少年有效地有效，但40％至70％将在5次内中继年。此外，MDD继电器预测学术困难，冒险行为和自杀。那，识别 MDD继电器的机制对于阐明这一重大公共卫生问题的干预目标至关重要。在青少年，社会过程和动态（尤其是与同龄人）特别重要的过程中，尽管目前尚不清楚哪些社会过程对MDD继电器最重要。本研究的重点是社会交流的作用，一组机制涉及接收和交付社会相关的信息。作为适应不良的社会交流，社会交流对青少年尤其重要可能会对建立和维持关系产生负面影响，从而增加MDD继电器的风险。对于拟议的研究，我们将采用NIMH的研究领域标准（RDOC）镜头并使用多个措施（行为，事件相关电位[ERP]，眼动追踪）比较青少年（14-17岁）拒绝抑郁（REMMDD; n = 200）对健康对照组（HC; n = 100）的定义社交交流，包括：（i）处理非语言社会信息，（ii）处理社会情感反馈和（iii）数字通信。首先，AIM 1将测试REMMDD青少年是否绝对过程两种类型的非语言社会信息 - 种族表表达式（如减少所索引精度和异常ERP [即N170]）和手势行为（通过眼睛轨道评估）。第二，AIM 2将测试REMMDD青少年是否绝对过程社会情感反馈（at）被同龄同龄人所接受的与拒绝的人），这是青少年MDD的扳机。具体来说，目标 2将测试REMMDD青少年是否暴露了ERP索引的晚期正势[LPP]）在同伴评估任务中，在人造同伴的积极社会反馈下进行情感编码。第三， AIM 3使用创新的智能手机应用程序，将收集有关数字通信的多个指标青少年数字社交网络的结构（即网络的大小；通信频率）和与他们的数字社交网络的沟通的情感（即，他们的文本，社会的编码情绪媒体帖子）；允许我们测试REMMDD青少年是否暴露了异常数字通信。最后的，我们将跟随青少年1年，以确定是否处理非语言信息，社会反馈，数字通信预测抑郁症状和MDD继电器的升级。此外，有监督机器学习将探讨哪些社会沟通不足（S）预测符号或MDD继电器，以及这些社会过程是否预测结果独立于（或与）其他已建立的结果救济的预测因素（例如，压力大的生活事件）。总而言之，该项目有望确定社会导致复发性抑郁症的过程，最终将导致创新的治疗方法。