Multi-level statistical classification of substance use disorder

摘要 这项申请代表了我们对发展创新和跨学科研究的持续承诺 物质使用障碍（SUD）分类计划。这项研究是通过 结合联合收割机临床症状和诊断、影像学 标记和基因型。该团队有一个PI，具有计算科学和开发方面的专业知识， 实施创新的统计算法来理解多维数据;具有广泛 在系统，成像和成瘾神经科学方面的经验;以及一位在遗传学方面具有专业知识的co-I。 SUD。我们之前的R 01项目采用了来自多个遗传学基因的约12，000个个体的样本， 酒精和药物依赖研究，以根据临床症状生成SUD亚型。因为 临床表现是生物学途径的远端终点，确定的遗传效应通常是 弱且不一致，因此即使在大样本中也难以检测。由NIMH倡导， 研究领域标准（RDoC）研究，精神疾病的病因，包括SUD，可以是 富有成果地以维度神经特征为特征。因此，该项目扩展了我们正在进行的工作， 在SUD的分类中成像神经特征。具体而言，我们将利用英国的大型数据库 生物库项目，提供遗传和多模态磁共振成像（MRI）数据。 在我们与美国人类连接组项目合作的基础上，我们的目标是在目前的项目中整合 临床、影像学和基因型数据，以研究SUD诊断标签的神经生物学底物，以及 以获得针对基因发现优化的SUD亚型。从方法论上讲，我们取代了传统的统计方法， 一种通过强调模式的方法对先验假设进行验证和偏见的分析 大数据的发现。我们的具体目标是：（一）：确定代表鲁棒性的神经影像学特征 成瘾的标志物和区分SUD亚型，可以通过多模态证据证实;（II） 在图卷积神经网络的基础上，采用一种新的大脑连接模型来识别神经网络， 这些标记物精确地表征了结构变化和功能电路的差异， SUD;以及（III）推导出一种创新的机器学习模型，以识别高度遗传的神经生物学 SUD的亚型，有助于研究成瘾的遗传基础。我们将专注于酒精， 尼古丁使用障碍，以证明概念和方法的方法。我们认为，通过 提供了一个富有成效的概念和方法平台，以整合成像和遗传数据， 了解SUD的病因，这项研究高度响应RFA“利用大数据科学 阐明成瘾和SUD的神经机制。”为此开发的机器学习工具 项目将提供一个创新和可靠的基础，以加强对 多维数据，并满足心理健康研究中的诊断和预测挑战。

ABSTRACT This application represents our ongoing commitment to developing an innovative and interdisciplinary research program on the classification of substance use disorders (SUDs). This research is achieved through quantitative analysis of multidimensional data that combine clinical symptoms and diagnoses, imaging markers, and genotypes. The team has a PI with expertise in computational science and the development and implementation of innovative statistical algorithms to understand multidimensional data; a PI with extensive experience in systems, imaging and addiction neuroscience; and a co-I who has expertise in the genetics of SUDs. Our previous R01 project employed a sample of ~12,000 individuals aggregated from multiple genetic studies of alcohol and drug dependence to generate SUD subtypes based on clinical symptoms. Because clinical manifestations are distal endpoints in the biological pathway, the genetic effects identified are often weak and inconsistent, and consequently difficult to detect even in large samples. As championed by the NIMH Research Domain Criteria (RDoC) research, the etiologies of psychiatric disorders, including SUDs, can be fruitfully characterized by dimensional neural features. This project thus extends our ongoing work to include imaging neural features in the classification of SUDs. Specifically, we will utilize a large database from the UK Biobank Project that provides both genetic and multi-modality magnetic resonance imaging (MRI) data. Building on our work with the US Human Connectome Project, we aim in the current project to integrate clinical, imaging, and genotype data to investigate the neurobiological substrates of SUD diagnostic labels, and to derive SUD subtypes that are optimized for gene finding. Methodologically, we replace the classic statistical analysis that is confirmatory and biased to an a priori hypothesis by an approach that emphasizes pattern discoveries from big data. Our specific aims are to: (I): identify neuroimaging features that represent robust markers of addiction and differentiate SUD subtypes that can be confirmed by multi-modality evidence; (II) employ a novel brain connectivity model, on the basis of graph convolutional neural networks, to identify neural markers that precisely characterize the differences in structural changes and functional circuits related to SUDs; and (III) derive an innovative machine learning model to identify highly heritable neurobiological subtypes of SUDs that facilitate investigation of the genetic basis of addiction. We will focus on alcohol and nicotine use disorders to demonstrate the conceptual and methodological approaches. We believe that, by providing a productive conceptual and methodological platform to integrate imaging and genetic data to understand the etiologies of SUDs, this research is highly responsive to the RFA “Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and SUD.” The machine learning tools developed for this project will provide an innovative and reliable foundation to enhance the aggregation and analysis of multidimensional data, and to meet the diagnostic and predictive challenges in mental health research.