SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation

描述(由申请人提供)：精神分裂症研究界已经投入大量资源来开发收集、管理和共享神经成像数据的方法，以及其他元数据，如临床、行为、认知和遗传学数据。对多站点、多维、多模式数据的探索和分析提高了我们对精神分裂症大脑电路异常、脑功能和遗传变异性之间关系的理解，多站点财团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)的努力证明了这一点。然而，要对这些复杂的措施得出有意义的结论，需要来自大样本的数据，远远超过任何单个地点的可能。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点单独获取数据的努力。由于数据复制的成本、不同的数据库架构和查询，与数据共享相关的实际挑战阻碍了许多研究团队能够参与最先进的神经科学研究。我们建议创建一个SchizConnect Mediator作为一个数据中介和集成平台，用于建立一个真正的精神分裂症神经影像相关数据库的联盟，以支持假设的生成和检验，并提供一个门户网站SchizConnect，以与联合数据库进行交互。在这种方法中，以统一的、语义一致的结构化方式查询和集成来自不同的、不同种类的数据库的信息。对于用户或客户端程序来说，系统表现为具有域的统一模式/模型的单个(虚拟)数据库，但数据保留在储存库中并受数据提供者的控制。我们将首先在三个大型数据集上测试和验证此方法，并在最后一年将此联合扩展到其他数据集。通过将来自不同国家的信息 将神经影像资料库整合到一个共同的整合系统中，我们将为调查人员提供资源，以便进行无法在任何单一地点寻址的独特调查。通过整合他们自己机构原生的、术语和建模结构不同的现有数据源，我们将使更多的精神分裂症研究人员能够共享他们已经收集的现有数据，而不需要改变他们现有的数据库，从而极大地促进与精神分裂症潜在机制相关的发现，这是NIMH的一个关键战略目标。

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.