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: 10417010
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10417010
• 关键词: Acute; Address; Adult; Age; 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; substance use; 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.

根据 RFA CX-18-023 重新提交的优异奖解决了 VA 的首要优先事项之一， 预防自杀。认识到那些自杀行为风险最高且迫切需要紧急救治的人 基于已知的风险和保护因素，医疗干预仍然是一个容易出错的主观决定。 不幸的是，这些风险因素的贡献都很小。因此，迫切需要开发 足够的算法来预测即将发生的自杀风险。该应用程序的总体目标是测试 内在大脑活动作为急性自杀行为标志的价值，并检查潜在的临床相关性。 我们的中心假设是，基于静息状态功能连接的神经模式分类器将 通过区分最近的自杀企图者和当前的自杀者来识别自杀行为的急性风险 创意者，以可复制和特定的方式。该应用程序是我们使用的试点工作的进展 机器学习表明静息态功能磁共振成像数据的神经模式分类允许特定的 区分最近的自杀企图者（企图自杀后三天内）与目前支持的患者 自杀意念的准确率是 79%。我们计划使用静息态泛函来检验我们的中心假设 连接性以区分最近试图自杀的抑郁退伍军人 (n=80) 和抑郁症 有自杀意念的退伍军人 (n=80) 和非自杀压力控制者 (n=40)。我们将在之前的基础上 工作，复制相同的策略，该策略导致训练有素的分类器在更大的独立和更多 异质样本，并测试是否添加人口统计学、临床、认知和生物学 与自杀相关的变量可能会提高分类器的准确性（AIM 1）。我们将检查时间 我们的分类器的特异性测试其区分能力：a）临床稳定的自杀企图者：企图者 5-8 天后，当有自杀念头的症状严重程度减轻时重新扫描，b) 抑郁 有或没有终生自杀未遂史的患者。我们还将扫描压力控制队列的年龄， 性别匹配的非自杀对照住院于医疗外科单位，并试图将其与 有自杀念头（AIM 2）。探索性 AIM1 将是迈向翻译的一步，我们将检查静息状态 在 1.5T 和 3T 扫描仪中获得的功能连接。在探索性 AIM2 中，我们将尝试确定 通过使用最具区别性的连接路径之间的回归分析来建立负责任的机制 最近的自杀企图者和想法者以及自杀企图意图和致命性之间的关系。我们的目的是测试 神经模式分类器的可重复性和特异性，用于区分最近的自杀企图者 当前的自杀念头作为急性自杀风险的替代指标。这个神经模式分类器，直接基于 关于人类行为最终代理人的功能，有可能显着提示自杀风险 使用已经广泛使用的技术进行评估。