The neurobiological mechanisms of untreated pain and depression to relapse risk in substance dependence

未经治疗的疼痛和抑郁导致物质依赖复发风险的神经生物学机制

• 批准号: 10657480
• 负责人: Donna Murray
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657480
• 关键词: 21 year old; Adult; Alcohols; Algorithms; Behavior; Behavioral; Brain; Brain Diseases; Cause of Death; Clinical; Cognition; Communication; Computer Analysis; Computer Models; Computing Methodologies; Data; Disease; Exclusion; Functional Magnetic Resonance Imaging; Glutamates; Goals; Individual; Interdisciplinary Study; Knowledge; Machine Learning; Measures; Mental Depression; Mentors; Methods; Modeling; Multimodal Imaging; N-acetylaspartate; Neurobiology; Neurons; Outcome; Pain; Participant; Patient Self-Report; Patients; Pattern; Persons; Psychiatry; Questionnaires; Relapse; Reporting; Research; Research Personnel; Rest; Sampling; Scientist; Severities; Substance Addiction; Substance Use Disorder; Symptoms; Synaptic plasticity; Techniques; Testing; Training; United States; Veterans; Work; alcohol use disorder; behavior measurement; biosignature; career; chronic depression; chronic pain; clinical practice; comorbid depression; comorbidity; depressive symptoms; dual diagnosis; effective therapy; evidence base; individualized medicine; machine learning algorithm; machine learning method; magnetic resonance spectroscopic imaging; military veteran; multidimensional data; multimodal neuroimaging; neural; neural circuit; neural patterning; neurobiological mechanism; neurochemistry; neuroimaging; neuromechanism; pain symptom; precision medicine; predictive modeling; programs; recruit; relapse risk; symptomatology; treatment response; unsupervised learning

It is well known that alcohol use disorder (AUD) is highly co-morbid with both depression and chronic pain. Chronic pain and depression are particularly high and debilitating in the veteran population. The majority of research in AUD has been in samples of participants without co- occurring disorders since research typically excludes common co-morbidities from recruitment. Therefore, we would benefit enormously from studies where we include these co-morbidities, examine them, and build a neural signature (constellation of neuroimaging and clinical symptoms) of chronic pain and depression in AUD as a model to test with advanced machine learning algorithms. What is currently unknown is the neurobiological and neurochemical patterns that form a brain signature (the neural circuitry) of depression and of chronic pain within AUD. This study will use multi-modal neuroimaging data and behavioral symptomology measures to attain the overall objective of this proposal, which is to delineate the separate and overlapping contributions of co-morbid depression and chronic pain brain signatures on the neural signature of AUD. We will use advanced computational modeling algorithms (machine learning) of clinical and multi-modality neuroimaging data. Results from this proposal will provide a deeper understanding of AUD neurobiology and will identify a pattern of neural circuitry that signifies depression versus chronic pain in AUD neurobiology as the scientific basis for individualized precision medicine treatment approaches that target AUD co-morbidities of depression and chronic pain. My overarching career goal as an independent investigator is to build a multidisciplinary research program on neuroimaging of substance use disorders. I will achieve this by clarifying brain mechanisms contributing to co-occurring symptomology (depression, chronic pain) that often presents in substance use disorders and particularly high and debilitating in Veterans. A better understanding of to what extent behavior and co-morbid symptomatology relates to brain neurobiology could facilitate more accurate predictive modeling of individual treatment response and relapse using multi-modal imaging and advanced computational analyses methods. My short-term research goals for this career mentored proposal are to identify the separate and overlapping neural mechanisms of co-morbidities (depression and chronic pain) prevalent in alcohol use disorders, and relate these mechanisms to behavior and relapse risk in Veterans. These goals will be accomplished using state-of-the art multi-modal neuroimaging techniques (whole-brain magnetic resonance (MR) spectroscopic imaging, resting state functional MR imaging) which will be directly related to clinical/behavioral measures and self-report questionnaires of depression and pain, combined with the use of advanced computational analysis methods such as machine learning techniques of neuroimaging and clinical measures.

众所周知，酒精使用障碍(AUD)与抑郁症和 慢性疼痛。在退伍军人中，慢性疼痛和抑郁尤其严重，使人虚弱 人口。AUD的大部分研究都是在没有共同研究的情况下进行的。 由于研究通常将常见的并存疾病排除在招募范围之外，因此出现了精神障碍。 因此，我们将从包括这些并存疾病的研究中受益匪浅， 检查它们，并建立神经特征(神经成像和临床星座 症状)的慢性疼痛和抑郁在澳大利亚作为模型，用先进的机器进行测试 学习算法。目前尚不清楚的是神经生物学和神经化学 形成抑郁和慢性疼痛的大脑信号(神经回路)的模式 澳元。这项研究将使用多模式神经成像数据和行为症状学 为实现这项提案的总体目标而采取的措施，即划分独立的和 共病抑郁和慢性疼痛的大脑信号对 AUD的神经特征。我们将使用高级计算建模算法(机器 学习)临床和多模式神经成像数据。这项提案的结果将是 提供对AUD神经生物学的更深层次的理解，并将识别神经模式 在作为科学基础的AUD神经生物学中表示抑郁与慢性疼痛的回路 为个体化精确医学治疗方法，针对和UD并存疾病 抑郁和慢性疼痛。 作为一名独立调查员，我的首要职业目标是建立一个多学科的 物质使用障碍的神经影像研究计划。我将通过澄清以下内容来实现这一点 导致共同症状(抑郁、慢性疼痛)的大脑机制 通常表现为物质使用障碍，尤其是退伍军人中的高和虚弱。一个 更好地理解行为和共病症状与大脑的关系 神经生物学可以促进对个体治疗反应进行更准确的预测建模 并使用多模式成像和先进的计算分析方法进行复发。 我对这份职业指导提案的短期研究目标是找出独立的 以及共病(抑郁和慢性疼痛)普遍存在的重叠神经机制 在酒精使用障碍中，并将这些机制与退伍军人的行为和复发风险联系起来。 这些目标将使用最先进的多模式神经成像技术来实现 (全脑磁共振(MR)波谱成像，静息状态功能磁共振 成像)，这将与临床/行为测量和自我报告直接相关 抑郁和疼痛的问卷调查，结合高级计算的使用 分析方法有机器学习技术、神经影像和临床测量等。