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

尽管有报告称，2000年初甲基苯丙胺的使用有所下降，但与精神兴奋剂有关的过量使用 在美国，主要涉及甲基苯丙胺的死亡人数从1999年到2017年增加了约1，800%。 目前，确定最需要干预的IDM只有在发生灾难性事件后才能发现。 事件已经发生（例如，吸毒过量、被捕、失业）。因此，迫切需要客观手段， 在这些后果可能发生之前，确定IDM处于这种破坏性后果的风险之中。 先前的研究表明，生物学、心理学和生物学的独立测量之间存在相关性。 社会因素和关键结果（例如，物质使用模式）， 甲基苯丙胺（IDM）。然而，还没有研究利用生物， 心理和社会措施来预测IDM的结果。最近的成功结合神经成像 （生物）和心理社会措施与先进的机器学习技术，以预测治疗或 物质使用和其他疾病的诊断结果为实现类似的 IDM的成功该项目旨在收集有史以来第一个神经成像（磁共振成像[MRI]， 心理的（例如，焦虑、抑郁和精神病症状），和社交（例如，人际暴力 历史，同伴网络药物使用）数据集来自IDM（目标1）。将使用最新技术水平采集MRI数据 从人类连接组项目中采用的序列，所有数据都将公开提供， 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 将被用于机器学习模型中，以预测IDM中的两个关键结果：物质使用 在6个月的时间内，对模式和职业功能进行评估（目标2）。机器学习模型 作为该项目的一部分，将导致未来的资金建议，重点是扩大纵向 分析（例如，3-5年），并获得额外的参与者进行模型评估。的长期目标 这项研究是预测模型的发展量化IDM的概率改善或 成瘾和成瘾相关后果的恶化。这样，这个项目将提供基础 为预测和最终预防更大的危害而量身定制的客观的生物心理社会模型 到IDM。

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.