Neural sensitivity to social evaluation and daily online and in-person social experience with peers: Predicting fluctuations in suicidality, self-harm, and depressive symptoms in adolescent girls

对社会评价以及与同龄人的日常在线和面对面社交体验的神经敏感性：预测青春期女孩自杀、自残和抑郁症状的波动

• 批准号: 10651699
• 负责人: Cecile D. Ladouceur
• 金额: $ 71.61万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651699
• 关键词: Acceleration; Address; Adolescent; Affect; Affective; Age; Amygdaloid structure; Anterior; Behavior; Brain; Buffers; Clinical assessments; Collection; Data; Detection; Diagnostic; Dorsal; Ecological momentary assessment; Emotional; Evaluation; Exclusion; Exposure to; Feeling suicidal; Female Adolescents; Frequencies; Functional Magnetic Resonance Imaging; Future; Goals; Health; Insula of Reil; Link; Machine Learning; Measures; Mental Depression; Mental Health; Modeling; Nucleus Accumbens; Outcome; Outcome Assessment; Participant; Perception; Personal Satisfaction; Persons; Play; Policies; Population; Predisposition; Prevention approach; Puberty; Published Comment; Reporting; Research; Rewards; Risk; Risk Factors; Risk Marker; Sampling; Self-Injurious Behavior; Social Environment; Social Interaction; Suicide; Suicide attempt; Suicide prevention; Symptoms; Techniques; Technology; Testing; Text; Text Messaging; Thinking; Time; Youth; cingulate cortex; contextual factors; data modeling; depressive symptoms; design; digital; experience; follow-up; girls; high risk; ideation; improved; multilevel analysis; neural; neuroimaging; non-suicidal self injury; peer; response; social; social media; suicidal; suicidal behavior; suicidal risk; suicide rate; theories; tv watching; video chat; virtual assessment

PROJECT SUMMARY/ABSTRACT Rates of suicide, self-harm, and depression are alarmingly high among adolescent girls, with a dramatic spike over the last decade, yet it is not clear which girls are at highest risk for these serious problems and when they might escalate. Some have argued that the dramatic rise in social media (SM) use may play a role, but more rigorous data are needed to evaluate the positive and negative effects of SM use in adolescent girls. Sensitivity to social evaluation, such as rejection or exclusion, is a potential cross-diagnostic risk factor for self-injurious thoughts and behaviors (SITB; including suicidal ideation and behavior and nonsuicidal self-injury) and de- pressive symptoms (DEPsx). This study examines how socially threatening and rewarding peer interactions— experienced online or in-person—contribute to real-time fluctuations in SITB, DEPsx, and well-being. The first aim is to use ecological momentary assessment (EMA) to identify how adolescent girls’ daily subjective experi- ence of online and in-person social threat and reward predict proximal within-person changes and longer-term between-person differences in SITB and DEPsx, along with wellbeing. The sample will include 177 adolescent girls (ages 13-17), with 75% of the sample at high-risk for suicide (e.g. active ideation within past 6 weeks, at- tempt in past year, or ≥ five episodes of nonsuicidal self-injury in past year), with at least half of high-risk par- ticipants also reporting elevated DEPsx. Girls will complete three 10-day bursts of EMA and will also provide access to passively obtained data on SM use and digital social interactions (i.e. text, video-chat). Multilevel modeling analyses will be used to identify predictors of near-term changes in SITB, DEPsx, and positive affect (PA), as well as long-term outcomes assessed at 6- and 12-month follow-up. A second aim is to identify which girls are at highest risk for DEPsx and/or SITB when exposed to near-term precipitants, focusing on differences in brain activation to social evaluation within an affective salience network (ASN) that includes the amygdala, anterior insula, dorsal and subgenual anterior cingulate cortex, and nucleus accumbens. Building on the differ- ential susceptibility to context theory, the study proposes that heightened ASN activation to social evaluation is a susceptibility factor that could amplify both the positive and negative effects of daily social experience on SITB and DEPsx. Girls will complete neuroimaging tasks that assess brain response to social evaluation from virtual peers. It is hypothesized that within-person increases and higher rates of peer social threat experiences (online and in-person) will be most strongly associated with SITB and DEPsx for girls with heightened ASN activation to social evaluation, and that within-person increases and higher rates of peer social reward experi- ences will also be more strongly associated with PA/wellbeing and more protective against SITB and DEPsx for girls with heightened ASN activation to social evaluation. Finally, machine learning will be used to determine which objective features of SM use track in real-time with subjective experience and emotional health, and to identify the strongest set of predictors of short-term and long-term emotional health.

项目总结/摘要 青少年少女的自杀率、自残率和抑郁率高得惊人， 在过去的十年里，但目前尚不清楚哪些女孩面临这些严重问题的最高风险，以及何时 可能会升级一些人认为，社交媒体（SM）使用的急剧增加可能发挥了作用，但更多的是 需要严格的数据来评估SM使用对少女的积极和消极影响。灵敏度 社会评价，如拒绝或排斥，是自我伤害的潜在交叉诊断风险因素 思想和行为（SITB;包括自杀意念和行为以及非自杀性自伤）， 抑郁症状（DEPsx）。这项研究探讨了社会威胁和奖励的同伴互动- 经验丰富的在线或亲自-有助于实时波动的SITB，DEPsx和福祉。第一 目的是使用生态瞬时评估（EMA），以确定如何青春期女孩的日常主观经验， 在线和面对面的社会威胁和奖励的作用预测了近端的人内变化和长期变化 人与人之间在SITB和DEPsx方面的差异，沿着幸福感。样本将包括177名青少年 女孩（13-17岁），75%的样本处于自杀高风险（例如，在过去6周内有积极的想法， 在过去一年中，或在过去一年中≥ 5次非自杀性自伤事件），至少有一半的高风险患者 也报告了DEPsx升高。女孩将完成三个10天的EMA爆发，也将提供 访问被动获得的SM使用和数字社交互动（即文本，视频聊天）数据。多级 模型分析将用于确定SITB、DEPsx和积极影响的近期变化的预测因子 (PA)以及在6个月和12个月随访时评估的长期结局。第二个目标是确定 当接触短期沉淀物时，女孩患DEPsx和/或SITB的风险最高，重点关注差异 在包括杏仁核在内的情感突显网络（Affective Salience Network，简称SANN）中， 前扣带皮层、背侧和膝下前扣带皮层和前扣带核。基于不同的- 根据语境理论的基本敏感性，该研究提出，对社会评价的高度激活是 一个易感因素，可以放大日常社会经验的积极和消极影响， SITB和DEPsx。女孩将完成神经成像任务，评估大脑对社会评价的反应， 虚拟对等体。据推测，在人的增加和较高的利率同侪社会威胁的经验， （在线和面对面）将与SITB和DEPsx最密切相关， 社会评价的激活，以及人内的增加和同伴社会奖励经验的更高比率， 此外，这些因素与PA/福祉的关联性更强，对SITB和DEPsx的保护性更强， 女孩对社会评价的敏感度更高。最后，机器学习将用于确定 SM的客观特征与主观体验和情绪健康实时跟踪， 确定短期和长期情绪健康的最强预测因素。