Dynamic Network Analysis of Social Cognition and Suicide in Psychosis

精神病社会认知与自杀的动态网络分析

基本信息

批准号：
10677212
负责人：
Emma Parrish
金额：
$ 4.03万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-28 至 2025-04-27
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10677212
关键词：
Affect Age Behavior Bipolar Disorder Cellular Phone Clinical Trials Cognitive Complex Computational Technique Computing Methodologies Cross-Sectional Studies Data Data Science Ecological momentary assessment Emotions Exclusion Face Feedback Feeling Feeling suicidal Frequencies Funding Mechanisms Future General Population Goals Individual Intervention Link Major Depressive Disorder Measures Mediating Methods Modeling Motivation National Institute of Mental Health National Research Service Awards Participant Pathway Analysis Perception Persons Population Psychoses Psychotic Disorders Research Research Personnel Role Sampling Schizoaffective Disorders Schizophrenia Series Social Behavior Social Perception Suicide Suicide attempt Suicide prevention Symptoms Time Training Withdrawal Work biobehavior cognitive ability cognitive testing density digital health effective intervention follow-up ideation novel parent project preventive intervention protective factors psychologic psychotic psychotic symptoms response secondary analysis severe mental illness social social cognition social determinants social influence social relationships suicidal suicidal behavior suicidal risk theories tool training opportunity

项目摘要

PROJECT SUMMARY/ABSTRACT People with serious mental illness (SMI; i.e., schizophrenia, schizoaffective disorder, bipolar disorder, and major depressive disorder with psychotic features) are at an increased risk of suicidal ideation and behavior as compared to the general population. Despite this, there is still relatively little understanding of the mechanisms that contribute to suicidal ideation in SMI, and people with SMI are excluded in almost 80% of biobehavioral clinical trials. Recent research indicates that aberrant social cognitive emotion processing, in particular over- attribution of threat, is common in psychosis and is associated with suicide ideation and behavior. Our prior work indicates that perceived burdensomeness and thwarted belongingness can be measured validly with ecological momentary assessment (EMA) in SMI and relate to suicidal ideation in this population. However, the relationships social cognitive abilities and biases, which can be objectively measured, have not been explored in a dynamic fashion. Social cognitive interventions are effective in psychosis but to date have not been evaluated as a component of suicide prevention. This project will evaluate real-time dynamic and longitudinal linkages between social cognitive abilities and biases measured by a validated mobile social cognitive testing tool, in a large sample of people SMI stratified by suicide ideation. Furthermore, the candidate will apply a novel computational technique, network analysis adapted for EMA data, to understand time-varying factors affecting these modifiable constructs related to suicide in psychosis. Network analyses augment traditional within-person analyses with EMA data to evaluate complex time interdependencies among variables, including density and time-lagged relationships. This project will utilize existing EMA, mobile cognitive, and longitudinal in-lab data collected from an NIMH supported study that includes 304 people with SMI. This proposed F31 project aims to understand the individual time-varying relationships between over-attribution of threat, social variables, perceived burdensomeness, and thwarted belongingness using traditional mixed-effects models, and utilize network analyses to evaluate the interdependencies among these constructs and symptoms between people with and without suicidal ideation. Then, this project aims to examine how these baseline associations and networks predict ideation trajectories and suicidal behavior at 12-month follow-up. In conjunction, the training opportunities afforded by the F31 funding mechanism will facilitate the applicant’s long-term goal of becoming an independent investigator in the psychological and social cognitive contributors to suicide in psychotic disorders, capable of applying sophisticated computational approaches to inform new translational interventions including those delivered in real time through digital health. This proposal is consistent with NIMH strategy aim 2.2.A. by evaluating novel objective mechanisms that could lead to novel suicide intervention approaches for SMI.

项目概要/摘要患有严重精神疾病（SMI；即精神分裂症、分裂情感性障碍、双相情感障碍和具有精神病特征的重度抑郁症）自杀意念和行为的风险增加尽管如此，与一般人群相比，人们对其机制的了解仍然相对较少。导致 SMI 自杀意念的因素，并且 SMI 患者被排除在近 80% 的生物行为之外临床试验表明，异常的社会认知情绪处理，特别是过度的情绪处理。威胁归因在精神病中很常见，并且与我们先前的自杀意念和行为有关。工作表明，感知到的负担和受挫的归属感可以通过以下方法有效地衡量： SMI 中的生态瞬时评估（EMA）与该人群的自杀意念相关。可以客观测量的社会认知关系尚未得到探索以动态的方式进行社会认知干预对精神病有效，但迄今为止尚未见效。作为自杀预防的一部分进行评估该项目将评估实时动态和纵向。通过经过验证的移动社交认知测试测量社交认知能力和偏见之间的联系工具，在大量样本中按自杀意念进行 SMI 分层此外，候选人将应用一种方法。新颖的计算技术，适用于 EMA 数据的网络分析，以了解时变因素影响这些与精神病自杀相关的可修改结构网络分析增强了传统。使用 EMA 数据进行内部分析，以评估变量之间复杂的时间相互依赖性，包括该项目将利用现有的 EMA、移动认知和纵向关系。实验室内数据收集自 NIMH 支持的一项研究，该研究包括 304 名 SMI 患者。该项目旨在了解威胁的过度归因、社会因素之间的个体随时间变化的关系使用传统的混合效应模型来分析变量、感知的负担和受挫的归属感，并利用网络分析来评估这些结构和症状之间的相互依赖性然后，该项目旨在研究这些基线如何在有自杀意念的人和没有自杀意念的人之间进行。关联并预测 12 个月随访时的意念轨迹和自杀行为。同时，F31资助机制提供的培训机会将有利于申请人成为心理和社会认知贡献者的独立研究者的长期目标精神病自杀，能够应用复杂的计算方法来告知新的转化干预措施，包括通过数字健康实时提供的干预措施。通过评估可能导致新的目标机制，与 NIMH 战略目标 2.2.A 保持一致。 SMI 的自杀干预方法。