Connectomes-related to Active Methamphetamine-dependence Project (CAMP)

与主动甲基苯丙胺依赖项目 (CAMP) 相关的连接组

• 批准号: 10377951
• 负责人: Nicholas Hubbard
• 金额: $ 22.92万
• 依托单位: UNIVERSITY OF NEBRASKA LINCOLN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-05 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10377951
• 关键词: Adopted; Anxiety; Biological; Biological Factors; Communities; Data; Data Set; Development; Diagnostic; Disease; Drug usage; Evaluation; Event; Foundations; Funding; Future; Goals; Human; Individual; Interpersonal Violence; Intervention; Job loss; Machine Learning; Magnetic Resonance Imaging; Measures; Mental Depression; Methamphetamine; Methamphetamine dependence; Modeling; Occupational; Outcome; Overdose; Participant; Pattern; Prevention; Probability; Psychological Factors; Psychoses; Recording of previous events; Reporting; Research; Risk; Rural drug addiction; Symptoms; Techniques; Time; addiction; biopsychosocial; connectome; improved; insight; longitudinal analysis; machine learning model; machine learning prediction; methamphetamine use; neuroimaging; outcome prediction; overdose death; peer networks; predictive modeling; psychologic; psychosocial; psychostimulant; social; social factors; substance use; success

Despite reports of declining methamphetamine use in the early 2000’s, psychostimulant-related overdose deaths in the US, of which methamphetamine is primarily involved, increased ~1,800% from 1999-2017. Currently, identifying IDM who are in the greatest need for intervention is only discovered after catastrophic events have occurred (e.g., overdose, arrest, job loss). Thus, there is an urgent necessity for objective means of identifying IDM at-risk for such devastating consequences, before these consequences can occur. Previous studies have shown correlations between independent measures of biological, psychological, and social factors and critical outcomes (e.g., substance use patterns) in individuals dependent upon methamphetamine (IDM). However, no research has leveraged the combined power of biological, psychological, and social measures to the predict outcomes in IDM. Recent success combining neuroimaging (biological) and psychosocial measures with advanced machine-learning techniques to predict treatment or diagnostic outcomes in substance use and other disorders establish a precedent for achieving similar success in IDM. This project seeks to collect the first ever neuroimaging (magnetic resonance imaging [MRI], psychological (e.g., anxiety, depression, and psychosis symptoms), and social (e.g., interpersonal violence histories, peer network drug use) dataset from IDM (Aim 1). MRI data will be collected using state-of-the art sequences adopted from the Human Connectome Project and all data will made openly available allowing for the global scientific community opportunities to gain limitless insights from these unique data. Here, data will be used in machine-learning models for the prediction of two critical outcomes in IDM: substance use patterns and occupational functioning over a 6-month time period (Aim 2). Machine-learning models developed as part of this project will result in future funding proposals focusing on extending longitudinal analyses (e.g., 3-5 years) and acquiring additional participants for model evaluation. The long-term goal of this research is the development of predictive models quantifying an IDM’s probability of improving or worsening addiction and addiction-related consequences. In this way, this project will provide the foundation for objective, biopsychosocial models tailored toward the prediction and eventual prevention of greater harms to IDM.

尽管2000年初有报道称甲基苯丙胺的使用量在下降，但在美国，与精神刺激剂相关的过量死亡人数(主要涉及甲基苯丙胺)比1999年至2017年增加了约1800%。目前，只有在发生灾难性事件(如服药过量、被捕、失业)后才能发现最需要干预的IDM。因此，迫切需要在这些后果发生之前，以客观手段确定IDM面临这种毁灭性后果的风险。以前的研究表明，在依赖甲基苯丙胺(IDM)的个人中，生物、心理和社会因素的独立衡量标准与关键结果(例如，物质使用模式)之间存在相关性。然而，还没有研究利用生物、心理和社会措施的综合力量来预测IDM的结果。最近成功地将神经成像(生物)和心理社会措施与先进的机器学习技术相结合，以预测药物使用和其他疾病的治疗或诊断结果，为在IDM方面取得类似成功奠定了先例。该项目寻求从IDM(目标1)收集有史以来第一个神经成像(磁共振成像[MRI])、心理(例如，焦虑、抑郁和精神病症状)和社会(例如，人际暴力史、同伴网络吸毒)数据集。核磁共振数据将使用人类连接组项目采用的最先进的序列来收集，所有数据都将公开提供，使全球科学界有机会从这些独特的数据中获得无限的见解。在这里，数据将用于机器学习模型，以预测IDM的两个关键结果：物质使用模式和6个月期间的职业功能(目标2)。作为该项目的一部分开发的机器学习模型将产生未来的供资建议，重点放在扩展纵向分析(例如3-5年)和获得更多的参与者以进行模型评价。这项研究的长期目标是开发预测模型，量化IDM改善或恶化成瘾和成瘾相关后果的可能性。这样，该项目将为预测和最终预防IDM的更大危害而量身定做的客观生物-心理-社会模型奠定基础。