Enabling collaborative research using the Biomedical Informatics Research Network (BIRN)

Helmer, Karl G.; Ambite, José Luis; Ames, Joseph; Ananthakrishnan, R.; Burns, Gully A. P. C.; Chervenak, Ann; Foster, Ian; Liming, Lee; Keator, David B.; Macciardi, Fabìo; Madduri, Ravi; Navarro, John-Paul; Potkin, Steven G.; Rosen, Bruce R.; Ruffins, Seth; Schuler, Robert; Turner, Jessica A.; Toga, Arthur W.; Williams, C.S.; Kesselman, Carl

doi:10.1136/amiajnl-2010-000032

Cited by 54 publications

(44 citation statements)

References 26 publications

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“…Human Imaging Database (HID): HID (Keator et al, 2009) is a Web application developed by the Biomedical Informatics Research Network (BIRN) (Helmer et al, 2011). It supports medical imaging using DICOM and entry of clinical measures using a clinical assessment layout manager (CALM).…”

Section: Electronic Capturing Managing and Sharing Of Neurocognimentioning

confidence: 99%

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

Nichols

Pohl

2015

Neuropsychol Rev

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Accelerating insight into the relation between brain and behavior entails conducting small and large-scale research endeavors that lead to reproducible results. Consensus is emerging between funding agencies, publishers, and the research community that data sharing is a fundamental requirement to ensure all such endeavors foster data reuse and fuel reproducible discoveries. Funding agency and publisher mandates to share data are bolstered by a growing number of data sharing efforts that demonstrate how information technologies can enable meaningful data reuse. Neuroinformatics evaluates scientific needs and develops solutions to facilitate the use of data across the cognitive and neurosciences. For example, electronic data capture and management tools designed to facilitate human neurocognitive research can decrease the setup time of studies, improve quality control, and streamline the process of harmonizing, curating, and sharing data across data repositories. In this article we outline the advantages and disadvantages of adopting software applications that support these features by reviewing the tools available and then presenting two contrasting neuroimaging study scenarios in the context of conducting a cross-sectional and a multisite longitudinal study.

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Section: Electronic Capturing Managing and Sharing Of Neurocognimentioning

confidence: 99%

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

Nichols

Pohl

2015

Neuropsychol Rev

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“…Neste contexto, a área da Saúde apresenta inúmeras tarefas e aplicações que podem ser realizadas por meio de sistemas inteligentes, desde a utilização da tecnologia para auxiliar no gerenciamento e processamento de dados clínicos em simples planilhas informatizadas, até a criação de um modelo robusto de gerenciamento desses dados [1][2][3][4][5][6][7].…”

Section: Introductionunclassified

Biomedical Data Management and Processing -A New Framework

Ribeiro

Albuquerque

Fernandes

et al. 2015

IEEE Latin Am. Trans.

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Abstract-The integration of information from biomedical devices is of fundamental importance for effective medical diagnosis. In this sense, the present work aimed to develop a new framework able to manage and process biomedical data in real time. The major advantage of the proposed solution is the ability to add new medical devices and integrate their results with the existing ones. The devices tested include brainwave sensors, a baropodometric treadmill and a biomedical kit composed of a patient position sensor (accelerometer), glucometer, body temperature, blood pressure, pulse and oxygen in blood, airflow, galvanic skin response and electrocardiogram sensors. From the tests conducted, it can be concluded that the proposed framework is robust, accurate and fast, and can manage and process large volumes of data in real time. Customizable graphs can be built from the electroencephalogramsignals acquired during patient gait, which can be analyzed based on barographic image registration. Finally, it can be concluded that the framework is quite promising to be used to assist medical diagnosis and improve and accelerate the treatment of patients.

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“…In the neuroimaging front, the schizophrenia research community has witnessed consortium efforts such as the Functional Biomedical Informatics Research Network (FBIRN) (Friedman et al, 2008; Keator et al, 2008; Helmer et al, 2011), the Mind Clinical Imaging Consortium (MCIC) (Bockholt et al, 2010), and the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) (Tamminga et al, 2013), which have warehoused or federated increasingly large magnetic resonance imaging (MRI) datasets. These data sharing efforts have led to consistent findings of brain network dysfunctions within the dorsolateral prefrontal cortex (Potkin et al, 2009) and the rest of the cerebral cortex (Potkin and Ford, 2009) during memory-related tasks (Kim et al, 2009) and at rest (Allen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Northwestern University Schizophrenia Data and Software Tool (NUSDAST)

Wang

Kogan

Cobia

et al. 2013

Front. Neuroinform.

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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.

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Enabling collaborative research using the Biomedical Informatics Research Network (BIRN)

Abstract: BIRN's mission is to provide capabilities and services related to data sharing to the biomedical research community. It does this by forming partnerships and solving specific, user-driven problems whose solutions are then available for use by other groups.

Cited by 54 publications

References 26 publications

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

Biomedical Data Management and Processing -A New Framework

Northwestern University Schizophrenia Data and Software Tool (NUSDAST)

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