Intramural Research Program, Neuroimaging Research Branch, National Institute of Drug Abuse, National Institutes of Health, Baltimore, Maryland.
Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychology, University of New Mexico, Albuquerque, New Mexico.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Feb;3(2):141-149. doi: 10.1016/j.bpsc.2017.07.003. Epub 2017 Aug 1.
Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop specialized, efficacious treatments. We investigated whether functional network connectivity (FNC) measures were predictive of substance abuse treatment completion using machine learning pattern classification of functional magnetic resonance imaging data.
Treatment-seeking stimulant- or heroin-dependent incarcerated participants (n = 139; 89 women) volunteered for a 12-week substance abuse treatment program. Participants performed a response inhibition Go/NoGo functional magnetic resonance imaging task prior to onset of the substance abuse treatment. We tested whether FNC related to the anterior cingulate cortex would be predictive of those who would or would not complete a 12-week substance abuse treatment program.
Machine learning pattern classification models using FNC between networks incorporating the anterior cingulate cortex, striatum, and insula predicted which individuals would (sensitivity: 81.31%) or would not (specificity: 78.13%) complete substance abuse treatment. FNC analyses predicted treatment completion above and beyond other clinical assessment measures, including age, sex, IQ, years of substance use, psychopathy, anxiety and depressive symptomatology, and motivation for change.
Aberrant neural network connections predicted substance abuse treatment outcomes, which could illuminate new targets for developing interventions designed to reduce or eliminate substance use while facilitating long-term outcomes. This work represents the first application of machine-learning models of FNC analyses of functional magnetic resonance imaging data to predict which substance abusers would or would not complete treatment.
成功治疗非法药物使用已变得至关重要，但仍难以实现。设计专门的治疗干预措施可以提高积极的结果，但有必要确定不良长期结果的风险因素，以制定专门的、有效的治疗方法。我们研究了功能网络连通性（FNC）测量是否可以通过机器学习对功能磁共振成像数据的模式分类来预测物质滥用治疗的完成情况。
寻求治疗的兴奋剂或海洛因依赖的被监禁参与者（n=139；89 名女性）自愿参加为期 12 周的物质滥用治疗计划。参与者在开始物质滥用治疗之前进行了反应抑制 Go/NoGo 功能磁共振成像任务。我们测试了与前扣带皮层相关的 FNC 是否可以预测那些能够或不能够完成 12 周物质滥用治疗计划的人。
使用包含前扣带皮层、纹状体和岛叶的网络之间的 FNC 的机器学习模式分类模型预测了哪些个体将（敏感性：81.31%）或不会（特异性：78.13%）完成物质滥用治疗。FNC 分析预测治疗完成情况优于其他临床评估措施，包括年龄、性别、智商、物质使用年限、精神病、焦虑和抑郁症状以及改变的动机。
异常的神经网络连接预测了物质滥用治疗结果，这可以为开发旨在减少或消除物质使用同时促进长期结果的干预措施提供新的目标。这项工作代表了机器学习模型对功能磁共振成像数据的 FNC 分析的首次应用，以预测哪些物质滥用者将完成或不完成治疗。
