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年里，自杀死亡一直在稳步上升，而在退伍军人中，这种风险更高，