Social Processing Deficits in Remitted Adolescent Depression

青少年抑郁症缓解后的社会处理缺陷

• 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
• 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

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.

重度抑郁障碍（MDD）是世界范围内致残的主要原因之一，其发病高峰出现在