Brain connectivity and genetics as predictors of opioid abuse treatment outcomes

阿片类药物使用障碍(OUD)是美国的一个主要问题，目前已达到流行水平。 不幸的是，OUD在退伍军人中尤其普遍，因为退伍军人需要疼痛是很常见的 阿片类药物的治疗和在医学上的大量使用是导致OUD问题的主要原因之一 一直在增长。 治疗OUD有很好的选择：丁丙诺啡和美沙酮都可以用于 维持性疗法，只要患者继续接受治疗，就不太可能真正滥用 阿片类药物。这一点非常重要，因为OUD中死亡的一个主要原因是芬太尼引起的死亡 如果服药过量，接受维持治疗的患者很可能会避免这种命运。然而，这是非常常见的。 患者停止治疗。 知识上的一个重要差距是我们无法预测哪些患者 更有可能从治疗中下降。由于资源有限，这样的预测将引起极大的兴趣 可以得到最优分配。此外，对OUD和OUD背后的大脑回路的理解 治疗结果对于下一波治疗的合理设计是必要的。 接近了。 然而，在精神病学的进步中，解决科学问题的大数据方法越来越常见 (像往常一样，精神病学可能是医学中最复杂的领域)落后。我们已经证明了 使用机器学习方法对人脑图像进行分析，我们可以将精神病患者 患者根据既往自杀未遂和较高的自杀意念。我们建议使用类似的(尽管 改进的)预测丁丙诺啡治疗退伍军人的方法。 我们建议使用不同的MRI模式(结构、白质、静息状态功能 连接性)和限制性基因分型(阿片受体和阿片受体的两个单核苷酸多态 已知与OUD风险相关的α5烟碱型乙酰胆碱受体亚单位 用于预测OUD治疗结果的学习算法。核磁共振和遗传学将在之前收集 在治疗开始后10天内再次进行治疗和核磁共振检查(较小的组在6个月时进行成像 也)，退伍军人将被跟踪研究结果。 如果成功，这项提议将提供机械性数据(大脑电路和功能，包括 遗传部分)关于OUD和OUD治疗结果，以及对OUD的公正方法 治疗预测。

Opioid use disorder (OUD) is a major problem in America, currently reaching epidemic levels. Unfortunately, OUD is especially prevalent among Veterans, as it is common that Veterans need pain treatment and the liberal use of opioids in medicine is one of the major reasons why the OUD problem keeps growing. There are good treatment options for OUD: Both buprenorphine and methadone can be used in maintenance therapies in which, as long as the patient stays in treatment, they will not likely truly abuse opioids. This is extremely important as one major reason for death in OUD is death by fentanyl overdose, and a patient in maintenance therapies will likely avoid that fate. However, it is very common that patients discontinue treatment. An important gap in knowledge arises from the fact that we have no means to predict which patients are more likely to drop from treatment. Such prediction would be of great interest as limited resources could be optimally allocated. In addition, an understanding of the brain circuitry behind both OUD and OUD treatment outcomes is necessary for the rational design of the next wave of therapeutic approaches. Big data approaches to scientific questions are increasingly common, however in psychiatry advances are (as usual, psychiatry likely being the most complex field in medicine) lagging. We have shown that using a machine learning approach to human brain imaging analysis, we can classify psychiatric patients according to past suicide attempt and high suicide ideation. We propose to use a similar (albeit improved) approach to the prediction of buprenorphine treatment in Veterans with OUD. We propose to use different MRI modalities (structure, white matter, resting state functional connectivity) and limited genotyping (two single nucleotide polymorphisms in the µ opioid receptor and the α 5 nicotinic acetylcholine receptor subunit known to be associated with OUD risk) in machine learning algorithms to predict OUD treatment outcomes. MRI and genetics will be collected before treatment and MRI again within 10 days of treatment initiation (with a smaller group imaged at 6 months also), and Veterans will be followed up to study outcomes. If successful, this proposal would provide both mechanistic data (brain circuitry and function, including a genetic component) about OUD and OUD treatment outcome, and an unbiased approach to OUD treatment prediction.