Identifying neural fingerprints of suicidality

识别自杀的神经指纹

• 批准号: 10554099
• 负责人: Michael Esterman
• 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: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10554099
• 关键词: Biological Markers; Brain; Brain imaging; Brain region; Center for Translational Science Activities; Cessation of life; Characteristics; Clinical; Clinical Data; Data; Data Collection; Data Set; Development; Diagnostic; Early identification; Effectiveness; Emotional; Evaluation; Evidence based intervention; Family; Feeling suicidal; Financial Hardship; Fingerprint; Friends; Functional Magnetic Resonance Imaging; Future; Goals; Impairment; Implicit Association Test; Individual; Intervention; Machine Learning; Mental Depression; Mental Health; Methodology; Methods; Modeling; Neurocognitive; Neuropsychology; Outcome; Pathology; Patient Self-Report; Pattern; Persons; Post-Traumatic Stress Disorders; Prevention; Productivity; Psychiatric Diagnosis; Psychiatry; Psychopathology; Public Health; Quality of life; Recording of previous events; Reproducibility; Risk; Sampling; Scanning; Self Disclosure; Self-Injurious Behavior; Social Functioning; Social isolation; Suicide; Suicide attempt; Suicide prevention; Techniques; Testing; Thinking; Trauma; Traumatic Brain Injury; Veterans; Work; behavior measurement; behavioral health; biomarker validation; clinical database; cognitive neuroscience; cohort; completed suicide; cost; current pandemic; daily functioning; design; disability; emotion regulation; functional outcomes; improved; individual patient; neural; neural model; neuroimaging; neuropsychiatry; novel; post 9/11; prevention practice; prospective; psychologic; recruit; stress disorder; suicidal; suicidal behavior; suicidal individual; suicidal morbidity; suicidal risk; targeted biomarker; tool

Death by suicide has been steadily increasing in the last 20 years, and the social isolation and financial stress associated with the current pandemic may unfortunately provide the perfect conditions for a dramatic increase in suicidality. This risk is elevated among veterans, particularly those with traumatic brain injury and psychiatric diagnoses, and the current public health crisis has alarming implications for mental health. Current suicide prevention practices are largely informed by the evaluation of suicidal thoughts and behaviors (STBs), and other clinical characteristics. One significant limitation in suicide risk and prevention is the exclusive reliance on self-report, which is severely limited in its effectiveness to predict future suicide attempts and deaths, and does not identify individuals who do not disclose thoughts or acts of self-harm. To test an alternative to current modes of prevention, we propose that complementary neuroimaging- based biomarkers of suicide risk can improve the identification of at-risk individuals. DESIGN AND METHODS. Our lab is a leader in the application of cognitive neuroscience tools toward precision psychiatry. We accomplish this by acquiring functional MRI, known to be consistently reproducible within an individual but subject to great variability across individuals, making it unique to the person, as well as their neuropsychiatric and neurocognitive profile. In this proposal, we will use these scans to parse out brain connections across large-scale networks including emotional and inhibitory control circuitry that are implicated in STBs. Then, by applying machine learning techniques, we will isolate the pattern of brain activity that identifies suicidal individuals. Further, we will validate these neural markers of STBs by collecting new fMRI data from veterans at risk for suicide while they perform the Suicide Implicit Association Test (S-IAT), an objective behavioral measure known to predict future suicide attempt. Finally, we will determine if these neural markers of STBs are also associated with impaired daily and social functioning, a contributor to STBs. This will be one of the first studies to leverage these methods towards the goal of identifying individuals at risk for suicide. The proposed study will accomplish these aims using both existing neuroimaging and clinical data from the Translational Research Center for TBI and Stress Disorders, as well as ongoing data collection in which 60 additional veterans will complete the S-IAT with concurrent fMRI. OBJECTIVES. Aim 1: Develop a neuroimaging-based model to detect individuals with current suicidal ideation and/or a history of suicide attempt(s). Hypothesis 1. Model will distinguish suicidal individuals from those who are not suicidal but who have comparable mental health conditions, based on functional connectivity between brain regions associated with emotional regulation and inhibitory control. Aim 2: Determine if the cross-sectional model from Aim 1 can predict which individuals will attempt suicide in the next 1-2 years. Hypothesis 2. Model will identify at least 50% of individuals who will attempt suicide from those with comparable mental health conditions who will not attempt suicide. Aim 3: Determine if the expression of the STB neural markers is associated with reduced functional outcomes. Hypothesis 3. Neural markers of STBs will be associated with reduced functional outcomes. Aim 4: Determine fMRI activation-based markers of STBs using the S-IAT. Hypothesis 4. Veterans with STBs will have higher associations between “me” and “death” alongside greater activation in brain regions associated with emotional regulation and inhibitory control. IMPACT. Successful identification of a neural signature of suicidality would remove reliance upon self- disclosure of suicidal thoughts and have dramatic clinical impact upon the precise identification and prevention of suicidality. Future studies will then focus on validation of these biomarkers in independent, prospective samples, as well as circuit-specific brain stimulation interventions targeting these biomarkers.

过去 20 年来，自杀死亡人数稳步增加，社会孤立和经济压力 不幸的是，与当前的大流行相关的可能为戏剧性的事件提供了完美的条件 退伍军人，尤其是患有脑外伤的退伍军人，自杀倾向增加。 和精神病学诊断，而当前的公共卫生危机对心理健康具有令人震惊的影响。 当前的自杀预防实践很大程度上是通过对自杀想法和自杀倾向的评估来了解的。 行为（STB）和其他临床特征是自杀风险和预防的一个重要限制。 完全依赖自我报告，其预测未来自杀的有效性受到严重限制 尝试和死亡，并且不识别未透露自残想法或行为的个人。 测试当前预防模式的替代方案，我们建议补充神经影像学- 基于自杀风险的生物标志物可以提高对高危个体的识别。 设计和方法我们的实验室是认知神经科学工具应用的领导者。 我们通过获取一致的功能性 MRI 来实现这一目标，该 MRI 已知具有可重复性。 在一个人内部，但个体之间存在很大的差异，这使得它对人来说也是独一无二的 作为他们的神经精神和神经认知概况，在这个提案中，我们将使用这些扫描来解析。 跨大规模网络的大脑连接，包括情绪和抑制控制电路 然后，通过应用机器学习技术，我们将分离出大脑的模式。 此外，我们将通过以下方式验证 STB 的这些神经标记。 在执行自杀隐式关联时，从有自杀风险的退伍军人那里收集新的功能磁共振成像数据 测试（S-IAT），一种已知可预测未来自杀企图的客观行为测量。 确定 STB 的这些神经标记是否也与日常和社会功能受损有关， 这将是利用这些方法实现以下目标的首批研究之一。 拟议的研究将利用现有的方法来识别有自杀风险的个体。 来自 TBI 和应激障碍转化研究中心的神经影像和临床数据 作为持续的数据收集，另外 60 名退伍军人将完成 S-IAT 和并发功能磁共振成像。 目标 1：开发基于神经影像的模型来检测当前有自杀倾向的个体 假设 1. 模型将区分自杀个体。 来自那些没有自杀倾向但具有类似心理健康状况的人，基于功能 与情绪调节和抑制控制相关的大脑区域之间的连接。 目标 2：确定目标 1 的横截面模型是否可以预测哪些人会尝试 假设 2 会识别出至少 50% 的人会在未来 1-2​​ 年内自杀。 那些具有类似心理健康状况但不会尝试自杀的人尝试自杀。 目标 3：确定 STB 神经标志物的表达是否与功能下降相关 假设 3：STB 的神经标志物与功能结果降低相关。 目标 4：使用 S-IAT 确定基于 fMRI 激活的 STB 标记。患有 STB 的退伍军人。 “我”和“死亡”之间会有更高的关联，同时相关大脑区域也会有更大的激活 具有情绪调节和抑制控制作用。 影响。成功识别自杀的神经特征将消除对自我的依赖。 自杀想法的披露，对精确识别和诊断具有巨大的临床影响 未来的研究将集中于独立验证这些生物标志物。 前瞻性样本以及针对这些生物标志物的特定电路脑刺激干预措施。