fMRI prediction of the severity of alcohol use disorder

功能磁共振成像预测酒精使用障碍的严重程度

• 批准号: 10573154
• 负责人: Jeremy Myslowski
• 金额: $ 4.17万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2024-02-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10573154
• 关键词: Alcohol consumption; Alcohols; Algorithms; Attention; Behavioral; Behavioral Model; Biological Assay; Brain; Brain Mapping; Brain imaging; Cessation of life; Clinical; Cognitive; Collaborations; Computer Models; Control Groups; Correlation Studies; Cues; Data; Development; Environment; Feedback; Fostering; Foundations; Functional Magnetic Resonance Imaging; Goals; Grant; Impairment; Individual; Learning; Link; Literature; Machine Learning; Maps; Measurable; Measures; Medicine; Mentorship; Methodology; Modeling; Monitor; Morbidity - disease rate; Neurosciences; Occupations; Participant; Performance; Persons; Positioning Attribute; Public Health; Publishing; Real-Time Systems; Relapse; Research; Rest; Role; Sample Size; Sampling; Severities; Short-Term Memory; Substance Use Disorder; Techniques; Testing; Time; Training; Treatment Efficacy; United States; Writing; addiction; alcohol abuse therapy; alcohol use disorder; clinically relevant; craving; design; drinking; examination questions; experimental study; head-to-head comparison; imaging study; improved; insight; large datasets; machine learning model; machine learning prediction; mortality; neural; neural model; neuroeconomics; neurofeedback; neuroimaging; novel; oral communication; predictive modeling; skills; task analysis; tool; treatment planning; undergraduate student

PROJECT SUMMARY Alcohol use disorder (AUD) remains a leading cause of morbidity and mortality in the United States, and most people who attempt to quit drinking relapse within six months. Since treatment efficacy seems in part to be a function of alcohol use severity, existing treatments could benefit from better brain measures that explicitly characterize the severity of AUD. Unfortunately, most imaging studies compare individuals with AUD to healthy controls – or if they do assess severity, only do so in one cognitive domain and in one sample. Therefore, we propose two avenues to investigate AUD severity in fMRI data. Both lines of research are influenced by the observation that the default mode network (DMN) is altered in AUD and in addiction generally. Although the DMN is typically more prominent in resting-state fMRI, analysis of task-based fMRI has suggested a plausible functional role for DMN activity in addiction. High DMN activity seems to attribute high value to commodities like alcohol, driven by preoccupation during the unsatiated and craving states. Based on these observations, Aim 1 predicts AUD severity from both task-based fMRI maps and resting-state-derived DMN maps. We will examine the relative level at which each task or DMN map encodes AUD severity, as assayed by machine- learning predictions of AUDIT score. To accomplish this Aim, we will use multiple machine-learning techniques with a secondary goal of performing a head-to-head comparison of different algorithms. Aim 2 directly tests the ability to modulate DMN activity as a function of AUD severity using real-time fMRI. The real-time system created by the sponsor of this application has been used to demonstrate that healthy participants can successfully learn to gain volitional control over their own DMN activity, and that this ability appears to be impaired in psychiatric conditions. In this Aim, individuals with AUD will see their DMN activity and attempt to increase and decrease its level prompted by neurofeedback. We will assess whether this ability is a function of AUD severity by correlating the DMN activity with the experimentally-controlled “increase” and “decrease” cues, and then comparing this ability to AUDIT score. This experiment constitutes an experimental medicine approach to AUD because it may reveal a novel target of severity-informed treatments for AUD. This project is driven by a unique and comprehensive training plan designed to integrate expertise in real-time neuroimaging, behavioral training in AUD, neuroeconomic and computational modeling, and advanced statistical methodology. It emphasizes the development of technical and programming skills, written and oral communication skills, grant writing, and undergraduate mentorship. Further, this proposal is supported by a sponsor (Dr. LaConte) and cosponsor (Dr. Bickel) whose labs actively collaborate to study neural and behavioral models of addiction. Therefore, the project will be conducted in an ideal environment to study the severity of AUD and its effects on the brain.

项目总结 酒精使用障碍(AUD)仍然是美国发病率和死亡率的主要原因，而且大多数 试图戒酒的人会在六个月内复发。由于治疗效果似乎在一定程度上是 酒精使用严重程度的功能，现有的治疗方法可以受益于更好的大脑措施，明确地 描述AUD的严重程度。不幸的是，大多数成像研究将患有AUD的人与健康的人进行比较 对照--或者如果他们确实评估了严重性，那么只在一个认知领域和一个样本中这样做。因此，我们 提出两种方法来调查功能性磁共振成像数据中的AUD严重程度。这两个研究方向都受到 观察到默认模式网络(DMN)在AUD和成瘾中普遍改变。尽管DMN 通常在静息状态的功能磁共振成像中更为突出，对基于任务的功能磁共振成像的分析表明， DMN活性在成瘾中的功能作用。DMN的高活跃度似乎将高价值归因于 酒精，在不满足和渴望的状态下被全神贯注所驱使。基于这些观察，目标1 根据基于任务的fMRI图和基于静息状态的DMN图预测AUD的严重程度。我们会 检查每项任务或DMN映射编码AUD严重性的相对水平，如机器- 学习对审计分数的预测。为了实现这一目标，我们将使用多种机器学习技术 第二个目标是对不同的算法进行逐一比较。AIM 2直接测试 使用实时功能磁共振成像作为AUD严重程度的函数来调节DMN活动的能力。实时系统 由此应用程序的赞助商创建的已用于演示健康的参与者可以 成功地学会了对自己DMN活动的意志控制，这种能力似乎是 精神状态受损的。在这个目标中，AUD患者将看到他们的DMN活动，并尝试 通过神经反馈提高和降低其水平。我们将评估这种能力是否是 通过将DMN活性与实验控制的“增加”和“减少”线索相关联， 然后将这一能力与审计得分进行比较。这项实验构成了一种实验医学方法 AUD，因为它可能揭示AUD严重程度知情治疗的新靶点。这个项目是由一个 独特而全面的培训计划，旨在整合实时神经成像、行为 在澳元、神经经济学和计算建模以及高级统计方法方面的培训。它 强调技术和编程技能、书面和口头沟通技能的发展，赠款 写作和本科生指导。此外，这项提案还得到了赞助商(拉孔特博士)和 共同发起人(比克尔博士)，他的实验室积极合作研究成瘾的神经和行为模型。 因此，该项目将在一个理想的环境中进行，以研究澳门氏症的严重性及其对 大脑。