SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation

SchizConnect：大规模精神分裂症神经影像数据中介

• 批准号: 8638985
• 负责人: Jose Luis Ambite
• 金额: $ 47.75万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-19 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8638985
• 关键词: Alzheimer' s Disease; Architecture; Beds; Behavioral; Biomedical Informatics Research Network; Brain; Client; Clinical; Cognitive; Collaborations; Communities; Complex; Data; Data Analyses; Data Files; Data Set; Data Sources; Data Storage and Retrieval; Databases; Diagnosis; Diffusion; Directories; Disease; Generations; Genetic; Image; Image Analysis; Impaired cognition; Individual; Institution; Interdisciplinary Study; Internet; Investigation; Laboratories; Letters; Measures; Mediating; Mediation; Mediator of activation protein; Mental disorders; Methods; Mind; Mind-Body Method; Modeling; National Institute of Mental Health; Neurosciences Research; Online Systems; Process; Protocols documentation; Provider; Psychiatry; Published Directory; Publishing; Research; Research Personnel; Resources; Rest; Sampling; Schizophrenia; Site; Source; Structure; Symptoms; System; Terminology; Testing; Treatment outcome; Universities; Work; base; cohort; computer science; cost; data integration; data sharing; federated computing; improved; innovation; member; neuroimaging; novel; prevent; programs; prototype; public health relevance; repository; success; user-friendly; virtual; web site

DESCRIPTION (provided by applicant): The schizophrenia research community has invested substantial resources to develop methods to collect, manage and share neuroimaging data, along with other meta-data such as clinical, behavioral, cognitive and genetics data. The exploration and analysis of multi-site, multi-dimensional, multi-modal data has improved our understanding of the relationships among abnormalities of brain circuitry, brain function and genetic variability in schizophrenia, as demonstrated by efforts from multi-site consortiums such as the Functional Biomedical Informatics Research Network (FBIRN) and the Mind Research Network (MRN) Clinical Imaging Consortium (MCIC). However, to draw meaningful conclusions about these complex measures, data from large samples are needed, far more than what would be possible at any individual site. Presently, such large-scale data analysis and discovery in schizophrenia research using data from multiple sources would depend upon the effort of separately obtaining data from different places. Practical challenges related to data sharing due to cost of duplication of data, disparate database architecture and querying are preventing many research teams from being able to participate in state-of-the-art neuroscience research. We propose to create a SchizConnect Mediator as a data mediation and integration platform for establishing a true federation of schizophrenia neuroimaging-related databases to support hypothesis generation and testing, and to deliver a web portal, SchizConnect, to interact with the federated databases. In this approach, information from disparate, heterogeneous databases are queried and integrated in a uniform, semantically- consistent structured manner. To a user or a client program the system appears as a single (virtual) database with a uniform schema/model of the domain, but the data remains at the repositories and under the control of the data providers. We will first test and validate this approach on three large data sets and extend this federation to other data sets in the final year. By bringing information from disparate neuroimaging repositories into a common integrated system, we will provide resources for investigators to conduct unique investigations not addressable at any single site. By integrating existing data sources that are native to their own institutions and heterogeneous in the terminology and modeling constructs, we will enable more schizophrenia researchers to share their already-collected existing data with no changes to their existing data repositories, thus greatly promoting discovery related to the mechanisms underlying schizophrenia, a key NIMH strategic objective.

描述（由申请人提供）：精神分裂症研究界投入了大量资源来开发收集、管理和共享神经影像学数据沿着其他元数据（如临床、行为、认知和遗传学数据）的方法。多站点，多维，多模态数据的探索和分析提高了我们对精神分裂症患者脑回路异常，脑功能和遗传变异之间关系的理解，如功能生物医学信息学研究网络（FBIRN）和精神研究网络（MRN）临床成像联盟（MCIC）等多站点联盟的努力所证明。然而，要对这些复杂的措施得出有意义的结论，需要大样本的数据，远远超过任何单个站点的可能性。目前，在精神分裂症研究中，使用来自多个来源的数据进行如此大规模的数据分析和发现将依赖于从不同地方单独获取数据的努力。由于数据重复、不同的数据库架构和查询的成本，与数据共享相关的实际挑战正在阻止许多研究团队参与最先进的神经科学研究。我们建议创建一个SchizConnect中介作为数据中介和集成平台，建立一个真正的联邦精神分裂症神经影像学相关的数据库，以支持假设生成和测试，并提供一个门户网站，SchizConnect，与联邦数据库进行交互。在这种方法中，来自完全不同的异构数据库的信息以统一的、语义一致的结构化方式被查询和集成。对于用户或客户端程序，系统表现为具有域的统一模式/模型的单个（虚拟）数据库，但数据保留在存储库中并处于数据提供者的控制之下。我们将首先在三个大型数据集上测试和验证这种方法，并在最后一年将这种联合扩展到其他数据集。通过将信息从不同的 通过将神经影像学资料库整合到一个通用的集成系统中，我们将为研究人员提供资源，以进行无法在任何单一地点进行的独特研究。通过整合现有的数据源，这些数据源是他们自己的机构本地的，并且在术语和建模结构上是异构的，我们将使更多的精神分裂症研究人员能够分享他们已经收集的现有数据，而不改变他们现有的数据存储库，从而大大促进与精神分裂症相关机制的发现，这是NIMH的一个关键战略目标。