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.

在过去的20年中，自杀死亡一直在稳步增加，这一风险在退伍军人中升高 特别是那些患有创伤性脑损伤和精神诊断的人。但是，在过去的50年中， 通常通过自我报告识别自杀风险最大的人的改善受到限制。 因此，我们提出了自杀思想的完整和客观的神经生物学标记 行为（STB）可以改善对风险最大的人的识别。初步的大脑标记 与STB有关的认知控制网络（CCN），边缘网络（LN）和 默认模式网络（DMN）。但是，STB的可靠和预测性脑标记仍然难以捉摸 是先前文献中的几个方法上的局限性。这项研究将解决这些局限性以及 使用两种不同的神经影像学方法研究STB的神经标志物：静止状态fMRI和大脑 自杀隐式关联任务（S-IAT）期间的活动。静止状态提供稳定可靠的 固有大脑连接性的测量，而S-AIT（称为D分数）测量的行为 参与者自我与死亡之间隐性关联的力量。 S-AIT上的D分数更好 对未来STB的预测因子比自我报告，但对与S-IAT相关的神经活动知之甚少。 设计和方法。该应用程序利用与翻译研究的密切合作 TBI和压力障碍中心（区域），具有全面的精神病学和神经影像学 超过800后9/11的数据库。该数据集为将STB组与 在抑郁症和PTSD等精神病诊断上匹配的对照组，仅由 缺乏STB（精神病控制； PC）。使用此现有数据集，静止状态fMRI将用于 识别与自杀未遂史（SA）和当前自杀思想（SI）有关的大脑标记。下一个， 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STB 技术。最后，另外100名退伍军人将使用同时的fMRI完成S-IAT，作为他们的一部分 参与区域。这将使我们能够调查发现与 自我与死亡之间的隐式关联（D分数）。 目标1：确定以前自杀尝试和当前自杀想法的神经特征（n = 800，约5％， 自杀企图的历史，约有10％的自杀想法）。假设1。我们将确定LN中的神经标记 CCN和DMN将STB与PC区分开。 AIM 2：确定AIM 1中确定的STB神经标记是否在1 - 2年后预测未来的STBS- UP评估（n = 400; 〜5％在未来1 - 2年内自杀，约有10％在以下情况下报告当前的SI 向上）。假设2：使用AIM 1中确定的SA和SI神经标记的模型1将预测哪些人 以可接受的诊断准确性（敏感性和特异性）进行后续评估报告STB。 目标3：获取有关自杀隐式关联任务（S-IAT）的初步fMRI数据以确定 测量与自杀关联有关的大脑激活的可行性（D分数）。假设3：我们将 发现与STB相关的认知过程的初步神经标记，该过程将部分重叠 STB的静止状态标记，还包括与自指加工相关的大脑区域。 培训目标。该CDA将提供1.）自杀的评估，预防和神经生物学的培训， 2.）高级统计和机器学习技术，3。）基于任务的fMRI和4.）准备提交 竞争性CDA-II。 影响。该项目将为调查这些神经标记的未来CDA-II提案提供基础 高风险人群中的STB，作为大脑刺激的目标，其长期目标的使用 神经标记以开发新疗法并改善自杀预防。