Social Processing Deficits in Remitted Adolescent Depression

严重抑郁障碍(MDD)是世界范围内导致残疾的主要原因，其发病高峰在 青春期。虽然干预措施对许多青少年来说是中等有效的，但40%到70%的青少年将在5年内复发 好几年了。此外，MDD复发预示着学习困难、危险行为和自杀。因此，识别 MDD复发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，社交过程和动态(尤其是与同龄人的)尤其重要， 尽管目前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，一套涉及社会相关信息的接收和传递的机制 信息。社会交往对青少年来说尤其重要，因为他们不适应社会交往。 会对建立和维持关系产生负面影响，从而增加MDD复发的风险。 对于拟议的研究，我们将使用NIMH的研究领域标准(RDoC)镜头，并使用多个 比较青少年(14-17岁)的指标(行为、事件相关电位、眼球跟踪) 缓解抑郁(remMDD；N=200)的健康对照组(HC；N=100)在以下几个方面存在缺陷 社会交流，包括：(一)处理非语言社会信息；(二)处理 社会情感反馈，以及(Iii)数字交流。首先，目标1将测试remdd青少年 异常处理两种类型的非语言社交信息-面部表情(由Reduced索引 准确性和异常事件相关电位[即N170])和手势行为(通过眼球跟踪进行评估)。 其次，目标2将测试remdd青少年是否异常地处理社会情绪反馈(Being (被同龄同龄人接受与拒绝)，这是青少年MDD的公认触发因素。具体地说，瞄准 2将测试remdd青少年是否表现出降低的晚期正电位[LPP])，这是一种事件相关电位指标 情绪编码，在同伴评估任务中遵循来自虚假同伴的积极社会反馈。第三, 使用一款创新的智能手机应用程序，Aim 3将收集有关 青少年数字社交网络的结构(即，网络规模；交流频率)和 与他们的数字社交网络进行交流的情感(即，从他们的文本、社交 媒体帖子)；使我们能够测试reMDD青少年是否表现出异常的数字交流。最后的, 我们将对青少年进行为期一年的跟踪调查，以确定非语言信息、社会反馈、 数字交流可以预测抑郁症状的升级和MDD的复发。此外，还受到监督 机器学习将探索哪些社交沟通障碍(S)预测症状或MDD复发，以及 这些社交过程是否独立于(或与其他既定的)其他人交互预测结果 复发的预测因素(例如，有压力的生活事件)。总而言之，该项目有望识别社会 这是一个导致复发抑郁症的过程，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.