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: 10816286
• 负责人: Nicholas Hubbard
• 金额: $ 26.1万
• 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 2024-03-07
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10816286
• 关键词: Adopted; Adult; Affective; Anxiety; Biological; Biological Factors; Brain; Brain imaging; Communities; Data; Data Set; Development; Diagnostic; Disease; Drug Addiction; Drug usage; Ensure; Evaluation; Event; Family history of; Foundations; Funding; Future; Goals; Human; Image; Individual; Intake; Interpersonal Violence; Intervention; Interview; Job loss; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Mental Depression; Methamphetamine; Methamphetamine dependence; Modeling; Neurocognitive; Neuropsychology; Occupational; Outcome; Outcome Measure; Overdose; Participant; Pattern; Performance; Prediction of Response to Therapy; Prevention; Probability; Psychiatric Diagnosis; Psychological Factors; Psychoses; Recording of previous events; Reporting; Research; Rewards; Risk; Rural drug addiction; Sampling; Social Network; Social support; Symptoms; System; Techniques; Time; Training; Validation; addiction; biopsychosocial; connectome; data-driven model; improved; indexing; insight; longitudinal analysis; machine learning model; methamphetamine use; multimodality; neural; neuroimaging; open data; outcome prediction; overdose death; peer networks; predictive modeling; psychologic; psychosocial; psychostimulant; social; social factors; standardize measure; substance use; success; violence exposure

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.

尽管有据报道在2000年代初期使用甲基苯丙胺的使用下降，但与精神刺激性相关的过量 在美国，甲基苯丙胺主要涉及的死亡人数比1999 - 2017年增加了约1,800％。 目前，只有在灾难性之后发现最需要干预的IDM才能发现 事件发生了（例如，服用过量，逮捕，失业）。这是客观手段迫切需要的 在发生这些后果之前识别IDM处于这种破坏性后果的情况下。 先前的研究表明，生物学，心理和 社会因素和关键结果（例如，依赖于物质使用模式） 甲基苯丙胺（IDM）。但是，没有研究利用生物学的综合力量， 心理和社会措施，以实现IDM的预测结果。结合神经影像的最近成功 （生物学）和心理社会措施采用先进的机器学习技术来预测治疗或 药物使用和其他疾病的诊断结果为实现类似的先例树立了先例 在IDM中的成功。该项目试图收集有史以来的第一个神经影像学（磁共振成像[MRI]， 心理（例如焦虑，抑郁和精神病症状）和社交（例如，人际暴力 历史记录，同行网络药物使用）IDM的数据集（AIM 1）。 MRI数据将使用最先进的 人类Connectome项目采用的序列和所有数据将公开可用，允许 对于全球科学社区的机会，可以从这些独特的数据中获得有限的见解。在这里，数据 将用于机器学习模型，以预测IDM中的两个关键结果：使用物质 在6个月的时间内（AIM 2）的模式和占用功能。机器学习模型 作为该项目的一部分开发将导致未来的资金提案，专注于扩展纵向 分析（例如3 - 5年）和收购额外的参与者进行模型评估。长期目标 这项研究是预测模型的开发，量化了IDM改善或 令人担忧的成瘾和与成瘾有关的后果。这样，该项目将为基础提供 对于客观，量身定制的生物心理社会模型用于预测和事件预防更大的伤害 到IDM。