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 物质使用障碍分类方案(SUDS)。这项研究是通过 结合临床症状和诊断、成像的多维数据的定量分析 标记和基因分型。该团队拥有一名在计算科学和开发方面具有专业知识的PI 实施创新的统计算法以理解多维数据；具有广泛的 在系统、成像和成瘾神经科学方面的经验；以及在遗传学方面拥有专业知识的合作伙伴 肥皂水。我们之前的R01项目使用了从多个基因聚合而来的约12,000个个体的样本 酒精和药物依赖以临床症状为基础产生sud亚型的研究。因为 临床表现是生物途径的远端终点，通常被识别为遗传效应。 薄弱且不一致，因此即使在大样本中也很难检测到。作为NIMH的拥护者 研究领域标准(RDoC)研究，包括肥胖症在内的精神障碍的病因可以是 以维度神经特征为特征的富有成效的。因此，该项目将我们正在进行的工作扩展到包括 神经影像特征在肥厚性脊柱炎分类中的应用。具体地说，我们将利用来自英国的大型数据库 生物库项目，提供遗传和多模式磁共振成像(MRI)数据。 在我们与美国人类连接体项目合作的基础上，我们在当前项目中的目标是整合 临床、影像和基因分型数据，以研究SUD诊断标记的神经生物学底物，以及 以推导出针对基因发现而优化的SUD亚型。在方法论上，我们取代了经典的统计学 通过强调模式的方法进行验证性的、偏向先验假设的分析 来自大数据的发现。我们的具体目标是：(I)：确定代表健壮的神经成像特征 成瘾的标志和可由多模式证据证实的SUD亚型的区分； 采用一种新的基于图形卷积神经网络的大脑连通性模型来识别神经网络 精确表征与之相关的结构变化和功能回路的差异的标记 以及(Iii)推导出一种创新的机器学习模型，以识别高度可遗传的神经生物学 肥皂水的亚型，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上的方法。我们认为，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SODS的病因，这项研究对RFA“利用大数据科学”做出了高度响应 目的：阐明成瘾和躯体功能障碍的神经机制。为此开发的机器学习工具 该项目将提供一个创新和可靠的基础，以加强对 多维数据，以及应对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.