Multi-level statistical classification of substance use disorder

摘要 该应用程序代表了我们对开发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 结合临床症状与诊断、影像学的多维数据定量分析 标记和基因型。该团队拥有一位具有计算科学和开发专业知识的 PI 实施创新的统计算法来理解多维数据；具有广泛的 PI 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拥有专业知识的同事 SUD。我们之前的 R01 项目采用了约 12,000 名个体的样本，这些样本来自多个基因组 对酒精和药物依赖的研究，根据临床症状生成 SUD 亚型。因为 临床表现是生物途径的远端终点，所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，包括 SUD 在内的精神疾病的病因学可以是 维度神经特征卓有成效。因此，该项目扩展了我们正在进行的工作，包括 SUD 分类中的成像神经特征。具体来说，我们将利用来自英国的大型数据库 提供遗传和多模态磁共振成像 (MRI) 数据的生物银行项目。 在我们与美国人类连接组项目合作的基础上，我们的目标是在当前项目中整合 临床、影像和基因型数据，以研究 SUD 诊断标签的神经生物学底物，以及 派生针对基因发现进行优化的 SUD 亚型。在方法论上，我们取代了经典的统计方法 通过强调模式的方法对先验假设进行验证和偏向的分析 大数据的发现。我们的具体目标是：(I)：识别代表稳健的神经影像特征 成瘾标志物并区分 SUD 亚型，可以通过多模态证据确认； （二） 采用基于图卷积神经网络的新型大脑连接模型来识别神经网络 精确表征相关结构变化和功能电路差异的标记 SUD； (III) 推导出创新的机器学习模型来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。我们将重点关注酒精和 尼古丁使用障碍来展示概念和方法方法。我们相信，通过 提供一个富有成效的概念和方法平台，将成像和遗传数据整合到 了解 SUD 的病因，这项研究对 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 项目将为加强数据的汇总和分析提供创新和可靠的基础 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.