Abstract
The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies’ implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.
Highlights
Public repositories of functional and structural imaging data are becoming more prevalent in the neuroimaging research community, with, e.g., the human connectome project (Marcus et al, 2011), the biomedical informatics research network (BIRN; Keator et al, 2008, 2009), XNAT Central (Marcus et al, 2007), the Alzheimer’s disease neuroimaging initiative (ADNI; Jack et al, 2008), the mind clinical imaging consortium (MCIC; Bockholt et al, 2010), and the neuroimaging informatics tools and resources clearinghouse (NITRC; Buccigrossi et al, 2008) all making imaging data available in a variety of formats with varying levels of detail
These data are highly valuable for discovery, including identifying regions and structural circuits associated with mild cognitive impairment, Alzheimer’s disease, and genetic risk for various cognitive dysfunctions (Kim et al, 2009, 2010; Potkin et al, 2009; Petersen et al, 2010; Petrella et al, 2011)
The ability of an institution to facilitate data sharing across departments and methodologies is key to understanding complex diseases, as reflected by the clinical translational science center (CTSC) initiatives supported by National Institutes of Health (NIH) within a number of universities1
Summary
Public repositories of functional and structural imaging data are becoming more prevalent in the neuroimaging research community, with, e.g., the human connectome project (Marcus et al, 2011), the biomedical informatics research network (BIRN; Keator et al, 2008, 2009), XNAT Central (Marcus et al, 2007), the Alzheimer’s disease neuroimaging initiative (ADNI; Jack et al, 2008), the mind clinical imaging consortium (MCIC; Bockholt et al, 2010), and the neuroimaging informatics tools and resources clearinghouse (NITRC; Buccigrossi et al, 2008) all making imaging data available in a variety of formats with varying levels of detail. Researchers often modify the standard assessments or protocols to adapt to the specific needs of their study, or collect fundamentally new data, and this lack of uniformity imposes yet another challenge when storing metadata for future retrieval. When datasets include multiple types of observational data, such as multi-modal imaging and neuropsychological assessments (NAs) of varied types of subjects across multiple studies, providing a query interface with high IEU and producing combined data output that is easy to consume approaches the edges of implementation challenges. Powerful query interface When repository data includes several types of observational data, such as multi-modal imaging and NAs of varied types of subjects across multiple studies, providing a query interface with high IEU and producing combined data output that is easy to consume broaches the edges of implementation challenges. COINS is the backbone for both the internal studies at the institution and active multi-site collaborations with remote institutions
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