SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation

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的方法，以及其他元数据，如临床、行为、认知和遗传学数据。功能生物医学信息学研究网络（FBIRN）和心智研究网络（MRN）临床成像联盟（MCIC）等多位点联盟的努力证明，对多位点、多维度、多模态数据的探索和分析提高了我们对精神分裂症脑回路异常、脑功能和遗传变异之间关系的理解。然而，要对这些复杂的测量方法得出有意义的结论，需要来自大样本的数据，远远超过在任何一个单独的地点所能获得的数据。目前，在精神分裂症研究中使用多来源数据进行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作为数据中介和集成平台，以建立一个真正的精神分裂症神经成像相关数据库联盟，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进行查询和集成。对于用户或客户端程序来说，系统表现为具有统一的域模式/模型的单个（虚拟）数据库，但是数据仍然保存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。通过从不同的地方获取信息

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.