Identifying neural fingerprints of suicidality

识别自杀的神经指纹

• 批准号: 10358809
• 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/10358809
• 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; Family; Feeling suicidal; Financial Hardship; Fingerprint; Foundations; Friends; Functional Magnetic Resonance Imaging; Future; Goals; Impairment; Implicit Association Test; Individual; Intervention; Lead; Machine Learning; Mental Depression; Mental Health; Methodology; Methods; Modeling; Neurocognitive; Neuropsychology; Outcome; Patient Self-Report; Pattern; Persons; Post-Traumatic Stress Disorders; Prevention; Psyche structure; 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; base; behavior measurement; behavioral health; biomarker validation; clinical database; cognitive neuroscience; cohort; completed suicide; cost; daily functioning; disability; emotion regulation; evidence base; functional outcomes; improved; individual patient; neural model; neuroimaging; neuropsychiatry; novel; pandemic disease; post 9/11; prospective; psychologic; recruit; relating to nervous system; 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.

在过去的20年里，自杀死亡率一直在稳步上升，社会孤立和经济压力 不幸的是，与当前流行病相关的疾病可能为戏剧性的 自杀倾向增加。这种风险在退伍军人中升高，特别是那些患有创伤性脑损伤的人 和精神病诊断，目前的公共卫生危机对心理健康的影响令人担忧。 目前的自杀预防措施主要是通过评估自杀想法和 行为（STBs）和其他临床特征。自杀风险和预防的一个重要限制 是完全依赖自我报告，这在预测未来自杀的有效性方面受到严重限制 企图和死亡，并没有确定个人谁不透露的想法或行为的自我伤害。到 测试替代目前的预防模式，我们建议补充神经成像- 基于生物标志物的自杀风险可以提高识别风险个体。 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， 精准精神病学我们通过获取功能性MRI来实现这一点，已知其具有一致的可重复性 在个体内部，但在个体之间存在很大的差异，使其对人来说也是独特的， 作为他们的神经精神和神经认知特征。在本提案中，我们将使用这些扫描来解析出 大规模网络中的大脑连接，包括情绪和抑制控制电路， 与STBs有关。然后，通过应用机器学习技术，我们将分离出大脑的模式， 识别自杀者的活动。此外，我们将通过以下方式验证STB的这些神经标志物： 在对有自杀风险的退伍军人进行自杀内隐联想时， S-IAT是一种客观的行为测量方法，可以预测未来的自杀企图。最后我们将 确定这些STB的神经标志物是否也与日常和社会功能受损有关， STB的贡献者。这将是利用这些方法实现以下目标的首批研究之一： 识别有自杀风险的人。拟议的研究将利用现有的 来自TBI和应激障碍转化研究中心的神经影像学和临床数据，以及 作为正在进行的数据收集，其中60名额外的退伍军人将完成S-IAT与同步fMRI。 目标.目的1：开发一种基于神经成像的模型来检测当前有自杀倾向的个体 意念和/或自杀企图史。假设1.模型将区分自杀者 从那些没有自杀倾向，但有类似的心理健康状况，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中的横截面模型是否可以预测哪些人会尝试 未来一两年内自杀。假设2.模型将确定至少50%的个人， 试图自杀的人与精神健康状况相当但不会试图自杀的人。 目的3：确定STB神经标志物的表达是否与功能降低相关。 结果。假设3. STB的神经标志物将与功能结局降低相关。 目的4：使用S-IAT确定STBs的基于fMRI激活的标记物。假设4.患有性病的退伍军人 在“我”和“死亡”之间有更高的联系，同时在相关的大脑区域有更大的激活， 情绪调节和抑制控制。 冲击成功识别自杀倾向的神经信号将消除对自我的依赖， 披露自杀想法，并对精确识别和 预防自杀。未来的研究将集中在这些生物标志物的独立验证， 前瞻性样本，以及针对这些生物标志物的特定回路脑刺激干预。