Transdiagnostic Reward System Dynamics and Social Disconnection in Suicide

项目总结/摘要 这个重新提交的项目评估了积极效价系统（PVS）在人际过程中的作用 在有自杀风险的跨诊断样本中的披露，寻求帮助和安全计划。人际 因素是当代自杀意念向行为转变的模式的核心， 资源是有效的自杀安全规划的关键。然而，促进非正式求助的干预措施 到目前为止，自杀的想法是无效的，几乎一半的人在自杀之前没有向任何人透露自杀的想法。 企图。自杀行为中社会接近或回避行为机制的基础研究 可以帮助确定更有效的自杀预防干预的新目标。在这里，我们专注于 调查奖励系统（即，评价、响应和/或学习）参与 危机前和危机期间的社会外展行为。在本提案中，我们将招募具有以下条件的参与者： 情感性或精神性障碍，按当前主动自杀意念分层。两者的有目的抽样 情感和精神病诊断使得能够包括具有类似的高背景自杀风险的组， 还具有不同的PVS和负价体系（NVS）分布。我们将使用测量突发纵向 该设计将基于实验室的任务与生态瞬时评估和被动传感相结合。 参与者将被纵向随访12个月。在实验室任务中，我们将管理我们的 验证的二元范式，同时评估PVS和NVS组件在模拟的社会 联系和披露上下文，并使面部情绪编码和自然语言处理， 演讲在目标1中，我们将管理基于实验室的任务，并专注于评估PVS对自杀的影响- 相关的社会关系，包括标准安全计划的社会要素（例如，危机中的联系人） 并帮助在每个人的社交网络中寻找机会。在目标2中，我们将使用移动的评估 模拟自杀意念的短期动态，以及意念是否被披露以及披露给谁。在目标3中， 将整合基于实验室和生态瞬时评估数据流，以建立和测试集成 12个月内自杀行为的预测模型，评估情感或 精神病综合征我们将评估社会关系动态是否介导了 PVS成分与自杀意念和行为。本研究的预期产品包括 综合数据集为翻译干预提供信息，以改善长期和急性自杀预防。 本研究的创新点包括基于实验室的二元社会关系任务、使用移动的 健康和计算方法来模拟寻求帮助的动态方面， 精神病和情感综合征。该提案通过探测跨域的连接与RDoC保持一致 (PVS，NVS和社会过程）在一个transdiagnosis样本，也是响应NIMH战略 计划（目标2.2，目标3.2）和NIMH数字心理健康特别关注通知。

PROJECT SUMMARY/ABSTRACT This resubmitted project evaluates the role of the positive valence system (PVS) in the interpersonal processes of disclosure, help-seeking, and safety planning in a transdiagnostic sample at risk for suicide. Interpersonal factors are central to contemporary models of the transition from suicide ideation to behavior, and social resources are key to effective suicide safety planning. Yet, interventions to promote informal help-seeking have been ineffective to date, and almost half of people do not disclose suicidal thoughts to anyone prior to attempt. Basic research on the mechanisms underlying social approach or avoidance behaviors in suicide could help identify new targets for more effective suicide prevention interventions. Here, we focus on investigating how the reward system (i.e., valuation, responsiveness and/or learning) is involved in engaging in social outreach behaviors before and during crises. In this proposal, we will recruit participants with either affective or psychotic disorders, stratified by current active suicidal ideation. Purposive sampling of both affective and psychotic diagnoses enables inclusion of a group with a similar high background risk of suicide, yet varying PVS and negative valence system (NVS) profiles. We will use a measurement burst longitudinal design which integrates lab-based tasks with bouts of ecological momentary assessment and passive sensing. Participants will be followed longitudinally for 12 months. Among lab-based tasks, we will administer our validated dyadic paradigm that simultaneously evaluates PVS and NVS components in a simulated social affiliation and disclosure context, and enables facial emotion coding and natural language processing of speech. In Aim 1, we will administer lab-based tasks and focus on evaluating the impact of PVS on suicide- related social affiliation, including social elements of the standard safety plan (e.g., people to contact in crisis) and help seeking opportunity within each individual’s social network. In Aim 2, we will use mobile assessments to model short-term dynamics of suicidal ideation and whether and to whom ideation is disclosed. In Aim 3, we will integrate lab-based and ecological momentary assessment data streams to build and test integrated predictive models of suicidal behavior over 12 months, evaluating stability of effects across affective or psychotic syndromes. We will evaluate whether social affiliation dynamics mediate the relationship between PVS components and suicidal ideation and behavior. The intended products of this research include an integrated dataset informative for translational interventions to improve long-term and acute suicide prevention. Innovative aspects of the proposed study include the lab-based dyadic social affiliation task, use of mobile health and computational methods to model dynamic aspects of help seeking, and the focal comparison of psychotic and affective syndromes. This proposal aligns with RDoC by probing connections across domains (PVS, NVS and Social Processes) in a transdiagnostic sample, and is also responsive to the NIMH Strategic Plan (Aim 2.2, Aim 3.2) and the NIMH Notice of Special Interest in Digital Mental Health.