SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation—translating across data sources—so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information are being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.
The schizophrenia research community has invested substantial resources on collecting, managing and sharing large neuroimaging datasets. As part of this effort, our group has collected high resolution magnetic resonance (MR) datasets from individuals with schizophrenia, their non-psychotic siblings, healthy controls and their siblings. This effort has resulted in a growing resource, the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), an NIH-funded data sharing project to stimulate new research. This resource resides on XNAT Central, and it contains neuroimaging (MR scans, landmarks and surface maps for deep subcortical structures, and FreeSurfer cortical parcellation and measurement data), cognitive (cognitive domain scores for crystallized intelligence, working memory, episodic memory, and executive function), clinical (demographic, sibling relationship, SAPS and SANS psychopathology), and genetic (20 polymorphisms) data, collected from more than 450 subjects, most with 2-year longitudinal follow-up. A neuroimaging mapping, analysis and visualization software tool, CAWorks, is also part of this resource. Moreover, in making our existing neuroimaging data along with the associated meta-data and computational tools publically accessible, we have established a web-based information retrieval portal that allows the user to efficiently search the collection. This research-ready dataset meaningfully combines neuroimaging data with other relevant information, and it can be used to help facilitate advancing neuroimaging research. It is our hope that this effort will help to overcome some of the commonly recognized technical barriers in advancing neuroimaging research such as lack of local organization and standard descriptions.
A Medical Image Resource Center (MIRC)-compliant teaching file system was created that can be integrated into a picture archiving and communication system (PACS) environment. This system models the three-step process necessary for efficient teaching file creation in a PACS environment: (a) identifying and transferring a case quickly and easily during primary interpretation, (b) editing and authoring the case outside of primary interpretation time, and (c) publishing the case locally and via MIRC standard-based mechanisms. Images from interesting cases are e-mailed to the teaching file system from either the PACS workstation or the radiologist's personal computer. Notes and clinical information may be included in the e-mail text to prompt the recollection of case details. Images are automatically extracted from the e-mail and sent to an image repository, and text fields are captured in a database. The World Wide Web-based authoring component provides tools for final authoring of cases and for the manipulation of existing cases. Authors designate access levels for each case, which is then made available locally and, potentially, to the entire MIRC-compliant community. Although this application has not yet been implemented as a departmental solution, it promises to improve and streamline medical education and promote better patient care.
The Northwestern University Neuroimaging Data Archive (NUNDA), an XNAT-powered data archiving system, aims to facilitate secure data storage; centralized data management; automated, standardized data processing; and simple, intuitive data sharing. NUNDA is a federated data archive, wherein individual project owners regulate access to their data. NUNDA supports multiple methods of data import, enabling data collection in a central repository. Data in NUNDA are available by project to any authorized user, allowing coordinated data management and review across sites. With NUNDA pipelines, users capitalize on existing procedures or standardize custom routines for consistent, automated data processing. NUNDA can be integrated with other research databases to simplify data exploration and discovery. And data on NUNDA can be confidently shared for secure collaboration.
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