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: 10707037
• 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/10707037
• 关键词: Address; Adult; Behavior; Brain; Brain region; Center for Translational Science Activities; Cessation of life; Cognitive; Collaborations; Control Groups; Data; Data Set; Databases; Diagnostic; Early-life trauma; Emotional; Ensure; Feeling suicidal; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Health; Individual; Intervention; Knowledge; Laboratories; Link; Literature; Machine Learning; Matched Group; Measures; Mental Depression; Mental Health; Meta-Analysis; 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; Research Priority; Rest; Risk; Risk Factors; Risk Marker; Sensitivity and Specificity; Suicide; Suicide attempt; Suicide prevention; Techniques; Testing; Thinking; Training; Trauma; Traumatic Brain Injury; Veterans; Work; behavior prediction; biomarker identification; brain based; catalyst; cognitive control; cognitive process; completed suicide; design; diagnostic accuracy; emotion regulation; follow up assessment; follow-up; high risk population; implicit bias; improved; neural; neural correlate; neuroimaging; post 9/11; predictive marker; self reported behavior; statistical and machine learning; stress disorder; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; suicide model; 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.

过去 20 年来，自杀死亡人数一直在稳步增加，退伍军人的自杀风险也更高。 特别是那些患有创伤性脑损伤和精神病诊断的人 然而，在过去 50 年里， 通常通过自我报告来识别自杀风险最大的人的进展有限。 因此，我们建议自杀想法的补充和客观神经生物学标记 和行为（STB）可以提高对高危人群的初步识别。 与 STB 相关的认知控制网络 (CCN)、边缘网络 (LN) 和 然而，可靠的、可预测的 STB 大脑标记仍然难以捉摸。 先前文献中存在一些方法学局限性，本研究将解决这些局限性。 使用两种不同的神经影像方法研究 STB 的神经标志物：静息态功能磁共振成像和大脑 自杀期间的活动内隐联想任务（s-IAT）提供了稳定和可靠的静息状态。 衡量内在大脑连接性，而 s-IAT（称为 d 分数）的行为衡量 参与者在自我和死亡之间的隐性关联的强度 s-IAT 的 d 分数更好。 与自我报告相比，它更能预测未来的 STB，但人们对与 s-IAT 相关的神经活动知之甚少。 设计和方法该应用程序利用与转化研究的密切合作。 TBI 和应激障碍中心 (TRACTS)，拥有全面的精神科和神经影像学 该数据集提供了将 STB 组与 9/11 后的 800 多个组进行比较的独特机会。 对照组与精神科诊断相匹配，如抑郁症和创伤后应激障碍（PTSD），这些诊断只能通过 缺乏 STB（精神病学对照；PC）。 识别与自杀企图历史 (SA) 和当前自杀意念 (SI) 相关的大脑标记。 我们将使用最先进的机器学习来确定这些大脑标记是否可以预测未来的机顶盒 最后，另外 100 名退伍军人将完成 s-IAT 并同时进行 fMRI，作为他们的一部分。 参与 TRACTS 将使我们能够研究检测与相关的神经制造商的可行性。 自我与死亡之间的隐含关联（d 分数）。 目标 1：识别先前自杀企图和当前自杀意念的神经特征（n = 800，~5% 自杀企图史，约 10% 有自杀意念）假设 1。我们将识别 LN 中的神经标记， CCN 和 DMN，将 STB 与 PC 区分开来。 目标 2：确定目标 1 中确定的 STB 神经标记是否可以预测 1-2 年后的未来 STB 评估（n=400；约 5% 在未来 1-2​​ 年内尝试自杀，约 10% 在后续报告中报告当前 SI 假设 2：使用目标 1 中确定的 SA 和 SI 神经标记的模型将预测哪些个体。 在后续评估中以可接受的诊断准确性（敏感性和特异性）报告 STB。 目标 3：获取自杀内隐关联任务 (s-IAT) 的初步 fMRI 数据，以确定 测量与自我死亡关联相关的大脑激活的可行性（d-分数）：我们会。 发现这种与 STB 相关的认知过程的初步神经标记，这些标记将与 STB 的静息态标记，还包括与自我参照处理相关的大脑区域。 培训目标。本 CDA 将提供以下方面的培训： 1.) 自杀的评估、预防和神经生物学， 2.) 先进的统计和机器学习技术，3.) 基于任务的功能磁共振成像，以及 4.) 准备提交 有竞争力的 CDA-II。 影响。该项目将为未来研究这些神经标记的 CDA-II 提案奠定基础。 STB 在高危人群中的应用，并作为脑刺激的目标，长期目标是使用这些 神经标记物以开发新的治疗方法并改善自杀预防。