Examination of resting state functional connectivity as a marker of acute suicide risk

检查静息状态功能连接作为急性自杀风险的标志

• 批准号: 9780783
• 负责人: RICARDO CACEDA
• 金额: --
• 依托单位: NORTHPORT VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9780783
• 关键词: Acute; Address; Adult; Age; Alcohol or Other Drugs use; Algorithms; Anhedonia; Award; Behavior; Behavioral; Biological; Blood; Brain; C-reactive protein; Childhood; Classification; Clinic; Clinical; Cognitive; Computerized Medical Record; Data; Depressed mood; Feeling suicidal; Functional Magnetic Resonance Imaging; Goals; Hospitalization; Hour; Housing; Human; Impulsivity; Individual; Insula of Reil; Intervention; Interview; Machine Learning; Measures; Medial; Medical; Mental Depression; Operative Surgical Procedures; Pain; Pain Threshold; Pathway interactions; Patients; Pattern; Post-Traumatic Stress Disorders; Prefrontal Cortex; Process; Proxy; Race; Recording of previous events; Regression Analysis; Reproducibility; Research; Rest; Risk Assessment; Risk Factors; Sampling; Scanning; Severities; Social Functioning; Specificity; Stress; Suicide; Suicide attempt; Suicide prevention; Symptoms; Technology; Testing; Training; Translations; Trauma; Traumatic Brain Injury; United States; University Hospitals; Veterans; Work; acute stress; base; cingulate cortex; cohort; combat; depressed patient; emotion dysregulation; emotion regulation; executive function; experience; high risk; human old age (65+); improved; independent component analysis; neural patterning; pain sensitivity; perceived stress; pleasure; protective factors; protein expression; psychologic; recruit; response; sex; suicidal; suicidal behavior; suicidal risk; suicide attempter

This Merit Award resubmission in response to the RFA CX-18-023 addresses one of the top VA priorities, suicide prevention. Recognizing those at the highest risk of suicidal behavior with an imminent need for acute medical intervention remains a fallible subjective decision based on known risk and protective factors. Unfortunately, the contribution of each of these risk factors is small. Thus, there is an urgent need to develop adequate algorithms to predict imminent suicide risk. The overall objective of this application is to test the value of intrinsic brain activity as a marker of acute suicidal behavior and examine potential clinical correlates. Our central hypothesis is that a neural pattern classifier based on resting state functional connectivity will identify acute risk for suicidal behavior, by discriminating recent suicide attempters from current suicidal ideators, in a reproducible and specific fashion. This application is the progression of our pilot work that used machine learning to show that neural pattern classification of resting state-fMRI data allowed a specific differentiation of recent suicidal attempters (within three days of the attempt) from patients currently endorsing suicidal ideation with 79% accuracy. We plan to test our central hypothesis by using resting state functional connectivity to discriminate depressed Veterans who recently attempted suicide (n=80) from depressed Veterans with suicidal ideation (n=80), and non-suicidal stress controls (n=40). We will build on our previous work, replicating the same strategy that resulted in a trained classifier in a larger independent and more heterogeneous sample, and test whether the addition of demographic, clinical, cognitive and biological variables associated with suicide may improve the classifier accuracy (AIM 1). We will examine the temporal specificity of our classifier testing its ability to discriminate: a) clinically stable suicide attempters: attempters rescanned 5-8 days later when symptom severity had subsided, from suicidal ideators, and b) depressed patients with and without lifetime history of suicide attempts. We will also scan a stress-control cohort of age-, sex-matched non-suicidal controls hospitalized in medical-surgical units and attempt to distinguish them from suicidal ideators (AIM 2). Exploratory AIM1 will be a step towards translation, we will examine resting state functional connectivity obtained in 1.5T and 3T scanners. In exploratory AIM2 we will attempt to identify a responsible mechanism by using regression analysis between the most discriminating connectivity pathways between recent attempters and ideators and suicide attempt intent and lethality. We aim to test the reproducibility and specificity of a neural pattern classifier to discriminate recent suicide attempters from current suicidal ideators as a proxy measure of acute suicide risk. This neural pattern classifier, directly based on the function of the ultimate agent of human behavior, has the potential to significantly inform on suicide risk assessment, using an already widely available technology.

该功能奖励以响应RFA CX-18-023的重新提交，这是VA的最高优先事项之一， 预防自杀。认识到自杀行为风险最高的人，迫切需要急性 基于已知风险和保护因素，医疗干预仍然是一项违规的主观决定。 不幸的是，这些风险因素中的每一个的贡献都很小。因此，迫切需要发展 足够的算法来预测迫在眉睫的自杀风险。该应用程序的总体目的是测试 内在的大脑活性作为急性自杀行为的标志并检查潜在的临床相关性的值。 我们的中心假设是，基于静止状态功能连接的神经模式分类器将 通过区分最近的自杀式意见来确定自杀行为的急性风险 构思，以可再现和特定的方式。此应用是我们使用的飞行员工作的进步 机器学习以表明静止状态-FMRI数据的神经模式分类允许特定 近期自杀式预言（在尝试后三天内）与目前认可的患者的分化 自杀构想的精度为79％。我们计划通过使用静止状态功能来检验中心假设 与最近尝试自杀（n = 80）的歧视抑郁症退伍军人的连通性 具有自杀意念的退伍军人（n = 80）和非杀伤应力对照（n = 40）。我们将基于我们以前的 工作，复制相同的策略，该策略在更大的独立和更多的较大独立 异质样本，并测试增加人口统计学，临床，认知和生物学 与自杀相关的变量可以提高分类器准确性（AIM 1）。我们将检查时间 分类器测试其区分能力的特异性：a）临床上稳定的自杀式意见：Attempters 5-8天后，当症状严重程度从自杀式构想者那里消失时，恢复了5-8天，b）沮丧 有和没有终生自杀企图的患者。我们还将扫描年龄的应力控制队列 - 在医疗外科单位住院的性匹配的非婚姻对照，并试图将其与众不同 自杀构想者（AIM 2）。探索性AIM1将是翻译的一步，我们将检查静止状态 功能连通性在1.5T和3T扫描仪中获得。在探索性AIM2中，我们将尝试确定 通过使用最具区别的连通性途径之间的回归分析，负责任的机制 在最近的意图和自杀企业的意图和致命性之间。我们旨在测试 神经模式分类器的可重复性和特异性，以区分最近的自杀案件与 当前的自杀构想者是急性自杀风险的替代量度。这个神经模式分类器，直接基于 关于人类行为的最终代理的功能，有可能大大告知自杀风险 评估，使用已经广泛可用的技术。