Social Processing Deficits in Remitted Adolescent Depression

重度抑郁症（MDD）是世界范围内残疾的主要原因， 青春期虽然干预措施对许多青少年是中等有效的，但40%至70%的青少年将在5年内复发。 年此外，MDD复发预示着学业困难、危险行为和自杀。因此，识别 MDD复发的机制对于明确这一重大公共卫生问题的干预目标至关重要。 在青春期，社会过程和动态（特别是与同龄人）特别重要， 尽管还不清楚哪些社会过程对MDD复发最关键。本研究的重点是 社会沟通的作用，一套机制，涉及接收和提供社会相关的 信息.社会交往对青少年尤其重要，因为社会交往适应不良 可能会对关系的建立和维持产生负面影响，从而增加MDD复发的风险。 对于拟议的研究，我们将采用NIMH的研究领域标准（RDoC）透镜，并使用多个 比较青少年（14 - 17岁）的措施（行为、事件相关电位[ERP]、眼动追踪） 与健康对照组（HC; N = 100）相比，缓解型抑郁症患者（remMDD; N = 200）在以下几个方面的缺陷 社会沟通，包括：（i）处理非语言的社会信息，（ii）处理 社会情感反馈;（iii）数字通信。首先，目标1将测试remMDD青少年是否 异常处理两种类型的非语言社会信息-面部表情（如索引减少 准确性和异常ERP [即，N170]）和手势行为（通过眼动追踪评估）。 其次，目标2将测试remMDD青少年是否异常处理社会情绪反馈（ 同龄人接受与拒绝），这是青少年MDD的一个公认触发因素。具体来说，Aim 2将测试remMDD青少年是否表现出降低的晚期阳性电位[LPP]），ERP指数 情绪编码，在同伴评价任务期间，跟随来自人造同伴的积极社会反馈。第三、 Aim 3将使用一个创新的智能手机应用程序，收集有关数字通信的多个指标， 青少年数字社会网络的结构（即，网络的规模;通信的频率）和 与他们的数字社交网络的通信的情感（即，从他们的文本中编码情感，社交 媒体帖子）;允许我们测试remMDD青少年是否表现出异常的数字通信。最后， 我们将对青少年进行为期一年的跟踪调查，以确定他们对非语言信息，社会反馈， 和数字交流预测抑郁症状的升级和MDD复发。此外，监督 机器学习将探索哪些社交沟通缺陷可以预测症状或MDD复发， 这些社会过程是否独立于（或相互作用于）其他既定的 复发的预测因子（例如，紧张的生活事件）。总之，该项目有希望确定社会 这一过程有助于复发性抑郁症，最终将导致创新的治疗方法。

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.