Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid

Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an ever increasing rate. The need to store and exchange data in meaningful ways in support of data analysis, hypothesis testing and future collaborative use is pervasive. Because trans-disciplinary projects rely on effective use of data from many domains, there is a genuine interest in informatics community on how best to store and combine this data while maintaining a high level of data quality and documentation. The difficulties in sharing and combining raw data become amplified after post-processing and/or data analysis in which the new dataset of interest is a function of the original data and may have been collected by multiple collaborating sites. Simple meta-data, documenting which subject and version of data were used for a particular analysis, becomes complicated by the heterogeneity of the collecting sites yet is critically important to the interpretation and reuse of derived results. This manuscript will present a case study of using the XML-Based Clinical Experiment Data Exchange (XCEDE) schema and the Human Imaging Database (HID) in the Biomedical Informatics Research Network's (BIRN) distributed environment to document and exchange derived data. The discussion includes an overview of the data structures used in both the XML and the database representations, insight into the design considerations, and the extensibility of the design to support additional analysis streams

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data

<p>Abstract</p> <p>Background</p> <p>Neuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.</p> <p>Findings</p> <p>In this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.</p> <p>Conclusions</p> <p>The BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.</p

Advances in studying brain morphology: the benefits of open-access data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of agerelated differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Van Horn and Gazzaniga, 2013; Poldrack and Gorgolewski, 2014; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016; Gilmore et al., 2017), as well as in other fields (Choudhury et al., 2014; Ascoli et al., 2017; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research

Towards structured sharing of raw and derived neuroimaging data across existing resources

Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery

NEArBy : normalização lexical na pesquisa de imagens cerebrais com atlas

Mestrado em Engenharia de Computadores e TelemáticaBrain atlases have been used as spatial references to classify and tag either structural or functional topological information from brain images. Semantic information obtained from the existing image data is thus spatially mapped according the atlas descriptors. However the process of classifying and tagging brain images using an atlas is often tedious and mostly dependent on human observation and validation. At the same time, even when available, it is often difficult to use, particularly when using standard query and retrieve services in modern imaging repositories (e.g. DICOM based PACS). In this work we propose NEArBy, a cloud based solution that provides query and retrieve services based on brain atlas semantics that can be easily integrated in existing DICOM based imaging repositories. Using a web interface, NEArBy supports not only typical DICOM query retrieve searches but also query tokens matching the brain atlas dictionary. To automate the semantic tagging of the brain images we rely on external methods to identify relevant spatial features that are later labelled using standard brain atlas. Being DICOM a tag based standard, atlas related tags are then privately embedded into DICOM files as NEArBy JSON descriptors using lexicon as proposed in NeuroLex. These descriptors encode the mapping between feature type, spatial location in the atlas and the respective atlas tag. JSON encoded tags are also suitable for indexing by a medical imaging Q/R tool such as Dicoogle allowing queries based both on standard DICOM tags and specifically on atlas related tokens included by NEArBy middleware. NEArBy provides a new way to perform non- patient centric queries over neuro-imaging repositories using technical and atlas based topological information. During this dissertation, the NEArBy potential usage is illustrated over a set of functional magnetic resonance imaging (fMRI) datasets using the web user interface to formulate the queries with atlas related criteria and access the retrieved results.Brain atlases have been used as spatial references to classify and tag either structural or functional topological information from brain images. Semantic information obtained from the existing image data is thus spatially mapped according the atlas descriptors. However the process of classifying and tagging brain images using an atlas is often tedious and mostly dependent on human observation and validation. At the same time, even when available, it is often difficult to use, particularly when using standard query and retrieve services in modern imaging repositories (e.g. DICOM based PACS). In this work we propose NEArBy, a cloud based solution that provides query and retrieve services based on brain atlas semantics that can be easily integrated in existing DICOM based imaging repositories. Using a web interface, NEArBy supports not only typical DICOM query retrieve searches but also query tokens matching the brain atlas dictionary. To automate the semantic tagging of the brain images we rely on external methods to identify relevant spatial features that are later labelled using standard brain atlas. Being DICOM a tag based standard, atlas related tags are then privately embedded into DICOM files as NEArBy JSON descriptors using lexicon as proposed in NeuroLex. These descriptors encode the mapping between feature type, spatial location in the atlas and the respective atlas tag. JSON encoded tags are also suitable for indexing by a medical imaging Q/R tool such as Dicoogle allowing queries based both on standard DICOM tags and specifically on atlas related tokens included by NEArBy middleware. NEArBy provides a new way to perform non- patient centric queries over neuro-imaging repositories using technical and atlas based topological information. During this dissertation, the NEArBy potential usage is illustrated over a set of functional magnetic resonance imaging (fMRI) datasets using the web user interface to formulate the queries with atlas related criteria and access the retrieved results