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: 9891504
• 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/9891504
• 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; 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; Pattern; Population; 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; base; 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; multidimensional data; multimodality; neural circuit; neural patterning; neurobiological mechanism; neurochemistry; neuroimaging; neuromechanism; pain symptom; precision medicine; predictive modeling; programs; recruit; relapse risk; relating to nervous system; 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）与抑郁症和抑郁症高度共病， 慢性疼痛慢性疼痛和抑郁症在退伍军人中尤其严重， 人口AUD的大多数研究都是在没有共同参与者的样本中进行的， 由于研究通常将常见的合并症排除在招募范围之外，因此可能会发生疾病。 因此，我们将从包括这些合并症的研究中受益匪浅， 检查他们，并建立一个神经签名（神经成像和临床的星座）， 症状）的慢性疼痛和抑郁症的AUD作为一个模型，以测试与先进的机器 学习算法目前尚不清楚的是， 形成抑郁症和慢性疼痛的大脑信号（神经回路）的模式， 澳元。本研究将使用多模态神经影像学数据和行为神经病学 为实现本建议的总体目标，即划分独立的和 共病抑郁症和慢性疼痛脑签名对脑功能的重叠贡献 AUD的神经信号我们将使用先进的计算建模算法（机器 学习）的临床和多模态神经成像数据。该提案的结果将 提供对AUD神经生物学的更深入理解，并将识别神经生物学的模式。 在AUD神经生物学中表示抑郁与慢性疼痛的电路作为科学基础 针对AUD合并症的个体化精准医学治疗方法 抑郁症和慢性疼痛。 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 药物使用障碍的神经成像研究计划。我将通过澄清 大脑机制有助于共同发生的神经病学（抑郁症，慢性疼痛）， 经常出现在物质使用障碍中，特别是在退伍军人中。一 更好地理解行为和共病神经病学在多大程度上与大脑相关 神经生物学有助于更准确地预测个体治疗反应 以及使用多模态成像和先进的计算分析方法来检测复发。 我对这个职业指导建议的短期研究目标是确定单独的 以及共病（抑郁症和慢性疼痛）的重叠神经机制普遍存在 在酒精使用障碍，并将这些机制与退伍军人的行为和复发风险。 这些目标将使用最先进的多模态神经成像技术来实现 （全脑磁共振（MR）光谱成像，静息状态功能MR 成像），这将直接关系到临床/行为措施和自我报告 抑郁症和疼痛的问卷调查，结合使用先进的计算 分析方法，如神经成像和临床测量的机器学习技术。