Identifying neural signatures of current and future suicidal thoughts and behaviors

识别当前和未来自杀想法和行为的神经特征

• 批准号: 10478372
• 负责人: Audreyana Jagger-Rickels
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478372
• 关键词: Address; Base of the Brain; Behavior; Brain; Brain region; Center for Translational Science Activities; Cessation of life; Cognitive; Collaborations; Control Groups; Data; Data Set; Databases; Diagnostic; Emotional; Ensure; Feeling suicidal; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Health; Individual; Intervention; Knowledge; Laboratories; Lead; Life; Link; Literature; Machine Learning; Matched Group; Measures; Mental Depression; Mental Health; Methodology; Methods; Modeling; Neurobiology; Outcome; Participant; Patient Self-Report; Performance; Post-Traumatic Stress Disorders; Preparation; Prevention; Prevention strategy; Psychiatric Diagnosis; Psychopathology; Quality of life; Recording of previous events; Reporting; Research; Rest; Risk; Risk Factors; Risk Marker; Sensitivity and Specificity; Suicide; Suicide attempt; Suicide prevention; Techniques; Testing; Thinking; Training; Trauma; Traumatic Brain Injury; Veterans; Work; base; behavior prediction; catalyst; cognitive control; cognitive process; completed suicide; diagnostic accuracy; emotion regulation; follow up assessment; follow-up; high risk population; implicit bias; improved; neural correlate; neuroimaging; post 9/11; predictive marker; relating to nervous system; self reported behavior; statistical and machine learning; stress disorder; suicidal behavior; suicidal morbidity; suicidal risk; suicide rate

Death by suicide has been steadily increasing in the last 20 years, and this risk is elevated among veterans, particularly those with traumatic brain injury and psychiatric diagnoses. However, in the last 50 years, improvements in identifying those at greatest risk for suicide, typically via self-report, have been limited. Therefore, we propose that complementary and objective neurobiological markers of suicidal thoughts and behaviors (STBs) can improve the identification of those at greatest risk. Preliminary brain markers related to STBs have been identified in the cognitive control network (CCN), limbic network (LN), and the default mode network (DMN). However, reliable and predictive brain markers of STBs remain elusive as there are several methodological limitations in the previous literature. This study will address these limitations and investigate neural markers of STBs using two different neuroimaging methods: resting-state fMRI and brain activity during the suicide Implicit Association Task (s-IAT). Resting-state provides a stable and reliable measure of intrinsic brain connectivity, whereas the behavior on the s-IAT (known as the d-score) measures the strength of a participant’s implicit association between self and death. The d-score on the s-IAT is a better predictor of future STBs than self-report, but little is known about neural activity related to the s-IAT. DESIGN AND METHODS. This application utilizes a close collaboration with the Translational Research Center for TBI and Stress Disorders (TRACTS), which has a comprehensive psychiatric and neuroimaging database of over 800 post-9/11. This dataset provides the unique opportunity to compare STB groups with control groups matched on psychiatric diagnoses, like depression and PTSD, that are differentiable only by the absence of STBs (psychiatric controls; PCs). Using this existing dataset, resting-state fMRI will be used to identify brain markers related to both a history of suicide attempt (SA) and current suicidal ideation (SI). Next, we will determine if these brain markers predict future STBs using state-of-the-art machine learning techniques. Lastly, an additional 100 veterans will complete the s-IAT with concurrent fMRI as part of their participation in TRACTS. This will allow us to investigate the feasibility of detecting neural makers related to implicit associations between self and death (d-score). Aim 1: Identify neural signatures of previous suicide attempt and current suicidal ideation (n = 800, ~5% with history of suicide attempt, ~10% with suicidal ideation). Hypothesis 1. We will identify neural markers in the LN, CCN, and DMN, that differentiate those with STBs from PCs. Aim 2: Determine if the STB neural markers identified in Aim 1 predict future STBs 1-2 years later at a follow- up assessment (n=400; ~5% attempt suicide within the next 1-2 years and ~10% reporting current SI at follow- up). Hypothesis 2: Models using the SA and SI neural markers identified in Aim 1 will predict which individuals report STBs at a follow-up assessment with acceptable diagnostic accuracy (sensitivity and specificity). Aim 3: Acquire preliminary fMRI data on the suicide Implicit Association Task (s-IAT) to determine the feasibility of measuring brain activation related to self-death associations (d-score). Hypothesis 3: We will discover preliminary neural markers of this STB-related cognitive process, which will partially overlap with resting-state markers of STBs, and also include brain regions associated with self-referential processing. Training Aims. This CDA will provide training in 1.) The assessment, prevention, and neurobiology of suicide, 2.) Advanced statistical and machine learning techniques, 3.) Task-based fMRI, and 4.) Preparation to submit a competitive CDA-II. IMPACT. This project will provide a foundation for a future CDA-II proposal investigating these neural markers of STBs in high-risk populations and as targets for brain stimulation with the long-term goal of using these neural markers to develop new treatments and improve suicide prevention.

在过去的20年里，自杀死亡率一直在稳步上升，退伍军人的自杀风险更高， 特别是那些有创伤性脑损伤和精神病诊断的人。然而，在过去的50年里， 在确定自杀风险最大的人方面，特别是通过自我报告，进展有限。 因此，我们建议，补充和客观的神经生物学标记的自杀想法， 和行为（STBs）可以提高对最高风险人群的识别。初步大脑标记 在认知控制网络（CCN）、边缘网络（LN）和大脑皮层中， 默认模式网络（DMN）。然而，可靠的和预测性的STBs大脑标记物仍然难以捉摸， 在以前的文献中有一些方法上的局限性。本研究将解决这些局限性， 使用两种不同的神经成像方法研究STBs的神经标志物：静息态fMRI和脑 自杀内隐联想任务（S-IAT）。静止状态提供稳定可靠的 衡量内在的大脑连接，而行为上的s-IAT（称为d-得分）措施 参与者对自我和死亡的内隐联系的强度。s-IAT的d分比 预测未来的STBs比自我报告，但鲜为人知的是有关的s-IAT的神经活动。 设计和方法。该应用程序利用与翻译研究的密切合作 创伤性脑损伤和应激障碍中心（TRACTS），该中心拥有全面的精神病学和神经影像学 超过800个911后的数据库该数据集提供了将STB组与 对照组在精神病诊断上相匹配，如抑郁症和创伤后应激障碍，这些诊断只能通过 无STBs（精神对照; PC）。使用现有的数据集，静息状态功能磁共振成像将用于 确定与自杀企图（SA）和当前自杀意念（SI）历史相关的脑标记物。接下来， 我们将使用最先进的机器学习来确定这些大脑标记是否能预测未来的STB 技术.最后，另外100名退伍军人将完成s-IAT，同时进行fMRI，作为他们的一部分。 参与TRACTS。这将使我们能够研究检测与以下相关的神经标记的可行性： 自我与死亡之间的内隐关联（d-评分）。 目的1：识别既往自杀企图和当前自杀意念的神经特征（n = 800，约5%， 有自杀企图史，约10%有自杀意念）。假设1.我们将在LN中识别神经标记， CCN和DMN，将机顶盒与PC区分开来。 目标2：确定目标1中确定的STB神经标志物是否预测1-2年后的随访中的未来STB。 向上评估（n=400;约5%在未来1-2年内尝试自杀，约10%在随访时报告当前SI） up）。假设2：使用目标1中确定的SA和SI神经标志物的模型将预测哪些个体 在随访评估时报告STB，诊断准确性（灵敏度和特异性）可接受。 目的3：获得自杀内隐联想任务（s-IAT）的初步fMRI数据，以确定 测量与自我死亡相关的大脑激活的可行性（d-评分）。假设3：我们会 发现这种与STB相关的认知过程的初步神经标志物，这将与 STBs的静息状态标记，还包括与自我参照处理相关的大脑区域。 培训目标。本CDA将提供1.）自杀的评估、预防和神经生物学， 2.）的情况。先进的统计和机器学习技术，3。基于任务的fMRI，和4.）准备提交 一个有竞争力的CDA-II 冲击该项目将为未来的CDA-II提案调查这些神经标记物提供基础 高危人群中的STBs，并作为脑刺激的目标，长期目标是使用这些 神经标记来开发新的治疗方法和改善自杀预防。