Informed Data-Driven Fusion of Behavior, Brain Function, and Genes

行为、大脑功能和基因的数据驱动融合

• 批准号: 7663033
• 负责人: VINCE D CALHOUN
• 金额: $ 51.97万
• 依托单位: THE MIND RESEARCH NETWORK
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-08 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7663033
• 关键词: Affect; Alcohol dependence; Alcoholic beverage heavy drinker; Algorithms; Alzheimer' s Disease; Arts; Attention; Behavior; Behavioral; Behavioral Genetics; Behavioral Model; Biological Assay; Biological Markers; Biological Markers; Brain; Brain-Derived Neurotrophic Factor; Clinical; Cognition; Cognitive; Communities; Complex; Complex Mixtures; Computer software; DNA Methylation; DRD2 gene; Data; Data Set; Databases; Development; Diagnosis; Disease; Dopamine; Drug usage; EP300 gene; Educational process of instructing; Electroencephalography; Environmental Risk Factor; Epigenetic Process; Family; Fiber Optics; Frequencies; Functional Magnetic Resonance Imaging; Funding; Gene Expression; Generations; Genes; Genetic; Genetic Load; Genetic Models; Genome; Genomics; Genotype; Goals; Grant; Guidelines; Hand; Haplotypes; Housing; Hybrids; Hyperactive behavior; Image; Imagery; Individual; Institutes; International; Internet; Joints; Knowledge; Measures; Methods; Methylation; Mind; Modality; Modeling; Morbidity - disease rate; Nicotine; Paper; Patients; Pattern; Performance; Pharmaceutical Preparations; Prevention; Progress Reports; Property; Publications; Publishing; Reading; Research; Research Personnel; Resolution; Resources; Sample Size; Schizophrenia; Series; Short-Term Memory; Simulate; Single Nucleotide Polymorphism; Smoking; Sorting - Cell Movement; System; Technology; Testing; Time; Validation; Variant; Work; addiction; alcohol craving; alcohol response; alcohol use disorder; aldehyde dehydrogenases; base; density; disability; drinking; flexibility; gene function; genetic profiling; genetic resource; improved; independent component analysis; infancy; instrument; method development; neurogenetics; neurotransmission; psychopathic personality; public health relevance; response; simulation; spatiotemporal; tool; user friendly software

DESCRIPTION (provided by investigator): Even though multiple imaging and genetic modalities can easily be collected on the same set of individuals, methods for effectively combining these different types of information are still in their infancy. All of these modalities typically involve thousands of data points per subject, and thus simple correlative approaches are of very limited use for uncovering hidden patterns and associations in these data and can easily be computationally overwhelming. This problem is only growing as the technologies improve (e.g. currently we can derive information on over 1 million single nucleotide polymorphisms (SNPs) and with the recent advent of epigenetic assays even more genetic information is available). We propose to develop a class of multivariate methods to enable research on healthy versus diseased brain by identifying associations among these different high dimensional data types. Our development of methods for the effective fusion of behavioral, fMRI, EEG, and genetic array data involves a two-level approach. In the first level, we start from a framework that makes strong assumptions about the associations and the underlying generative model across data types, and then extend this framework to allow for more flexible types of associations and underlying assumptions. In the second level, we consider ways to incorporate reliable prior information into a particular fusion framework and develop methods that improve upon those developed in the first level by effectively using prior information or meaningful constraints. Thus we provide a set of effectively "informed" data-driven tools for the task. Complementary data-driven approaches we will develop include methods based upon canonical correlation analysis (CCA) which utilizes second-order statistical information. We will also continue to develop methods based upon independent component analysis (ICA) utilizing higher-order statistical information. Joint ICA (jICA) is an approach which assumes a common linear relationship among modalities. Though jICA has proven quite useful, we will also be investigating a number of ways to relax the assumptions of jICA for increased flexibility as well as the incorporation of prior information. For example, we can relax the assumption of common profiles while still emphasizing interrelationship among a subset of components using parallel ICA. We can emphasize group differences using constrained coefficient ICA. We will also investigate the utility of nonlinear ICA (in this case relaxing the assumption of linear interrelationships between modalities) as well as approaches which do not assume stationarity. The methods we develop will provide a nice framework for allowing investigators to ask more realistic questions about high dimensional data and will provide a much needed set of tools to the community. We will also focus on integrating data spanning from genetic to behavior and focus upon two important applications where integrating such data is important, schizophrenia and addiction. This will help us to further generalize the algorithms developed. We will work with data collected from two studies, one on 720 schizophrenia patients and controls, and another with 310 heavy drinkers. With access to these highly unique, large data sets, combined with our work on the development of computational approaches for fusing high dimensional data, applied to the conceptual models we have developed for disease, we are poised to fill an important gap in the field and produce new tools which have applicability to a wide variety of diseases. PUBLIC HEALTH RELEVANCE: In this proposal we will develop a family of data driven approaches to effectively integrate fMRI, ERP, genetic, and behavioral data and enable the incorporation of available information. We will develop, validate, and apply our methods to schizophrenia and addiction, both of which are extremely complex, mixtures of genetic and environmental factors, and affect a large number of individuals (and which share co-morbidity with one another). Our methods will be implemented in a user-friendly software toolbox with anonymized data provided. 37

描述（由研究者提供）：即使可以在同一个人集上很容易收集多个成像和遗传方式，但有效结合这些不同类型信息的方法仍处于起步阶段。 所有这些模式通常涉及每个受试者的数千个数据点，因此，简单的相关方法对于发现这些数据中的隐藏模式和关联的使用非常有限，并且可以很容易地在计算上被压倒性。 这个问题只会随着技术的改进而越来越长（例如，目前我们可以获取超过100万个单核苷酸多态性（SNP）的信息，并且随着最近的表观遗传测定法的出现，甚至提供了更多遗传信息）。 我们建议开发一类多元方法，以通过确定这些不同高维数据类型之间的关联来研究健康与患病大脑的研究。 我们开发有效融合行为，fMRI，脑电图和遗传阵列数据的方法涉及两级方法。 在第一级中，我们从一个框架开始，该框架对关联和跨数据类型的基本生成模型做出了强有力的假设，然后扩展此框架以允许更灵活的关联类型和基础假设。 在第二层中，我们考虑将可靠的先验信息纳入特定的融合框架中，并通过有效使用先前的信息或有意义的约束来开发对第一级开发的信息进行改进的方法。 因此，我们为任务提供了一组有效的“知情”数据驱动工具。 我们将开发的互补数据驱动方法包括基于使用二阶统计信息的规范相关分析（CCA）的方法。 我们还将继续利用高阶统计信息的独立组件分析（ICA）开发方法。 联合ICA（JICA）是一种假定模式之间的线性关系的方法。 尽管JICA已被证明非常有用，但我们还将研究多种方法来放松JICA的假设以提高灵活性以及合并先验信息。 例如，我们可以放松使用平行ICA的组件部分之间的相互关系的同时，放宽普通概况的假设。 我们可以使用约束系数ICA强调组差异。 我们还将研究非线性ICA的实用性（在这种情况下，放宽了模态之间的线性相互关系的假设）以及不假定平稳性的方法。 我们开发的方法将为调查人员提出有关高维数据的更现实的问题提供一个不错的框架，并将为社区提供一组急需的工具。 我们还将集中于整合跨越遗传到行为的数据，并专注于两个重要的应用程序，在这些应用程序中，将这种数据集成至关重要，精神分裂症和成瘾。 这将有助于我们进一步推广开发的算法。 我们将与两项研究收集的数据合作，其中一项对720名精神分裂症患者和对照组，另一项与310名重型饮酒者一起工作。 通过访问这些高度独特的大型数据集，加上我们在开发融合高维数据的计算方法方面的工作，应用于我们为疾病开发的概念模型，我们准备填补该领域的重要空白，并产生具有多种疾病的新工具。 公共卫生相关性：在此提案中，我们将开发一种以数据驱动的方法为家族，以有效整合fMRI，ERP，遗传和行为数据，并能够合并可用信息。 我们将开发，验证和应用我们的方法对精神分裂症和成瘾，这两者都是非常复杂的，遗传和环境因素的混合物，并影响了许多个体（并且它们相互共享）。 我们的方法将在用户友好的软件工具箱中实现，并提供匿名数据。 37