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Biomedical Informatics Research Network

Michael Marron, Ph.D., Director Division of Biomedical Technology National Center for Research Resources National Institutes of Health Department of Health and Human Services Bethesda, Maryland, USA. Biomedical Informatics Research Network. Biomedical Informatics Research Network

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Biomedical Informatics Research Network

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  1. Michael Marron, Ph.D., DirectorDivision of Biomedical TechnologyNational Center for Research ResourcesNational Institutes of HealthDepartment of Health and Human ServicesBethesda, Maryland, USA Biomedical Informatics Research Network

  2. Biomedical Informatics Research Network Testbed for biomedical knowledge infrastructure

  3. BIRN Biomedical Informatics Research Network A testbed for a biomedical knowledge infrastructure Federated database of neuroimaging data Fusion of diverse data sources (location; level of aggregation) Grid access to computational resources Datamining software Scalable and extensible Driven by research needs-pull, not technology-push

  4. BIRNSites

  5. BIRN Information Integration What is the cerebellar distribution of rat proteins with more than 70% homology with human NCS-1? Integrated View Integrated View Definition Mediator Wrapper Wrapper Wrapper Wrapper Web protein localization morphometry neurotransmission CaBP, Expasy

  6. BIRN Grid Infrastructure • The BIRN Portal is built on many “layers” • Layers are modular, allowing for extension of any layer without great disruption to the entire system • Every Layer has its own complexity and administration that was previously passed on to the end-user • Portal centralizes all adminis-trative details of each layer into a single username and pass phrase

  7. BIRNGrid Infrastructure BIRN Portal BIRN Toolkit Collaboration Applications Queries/Results Data Management Viewing/Visualization GridPort Toolkit Custom APIs Security API Visualization APIs Mediator API Grid Middleware SRB Mediator Globus Database Database MCAT Data Storage Computation Distributed Resources

  8. BIRNKnowledge IntegrationSupports queries across heterogeneous data sources Mediator Services API Query Deductive Engine Mediator Layer Model Reasoner Query Formulation Source Model Lifting Optimizer Query Processing Source Registration Wrapper Layer Query interface Result interface SRB Files RDB OODB HTML XML

  9. MouseBIRN Studying animal models of disease across dimensional scales to test hypothesis with human neurological disorders • Experimental Allergic Encephalomyelitis (EAE) mouse models (both chemically induced and transgenic) exhibit episodic weakness and demyelination characteristic of multiple sclerosis • Dopamine Transporter (DAT) knockout mouse for studies of schizophrenia, attention-deficit hyperactivity disorder (ADHD), Tourette’s disorder and substance abuse • Using an alpha-synuclein mouse to model the symptoms/pathology of Parkinson’s Disease Duke, UCLA, UC San Diego, Cal Tech

  10. MouseBIRN Integrating brain data across scales and disciplines Reconstructed spiny dendrite Coronal sections of mouse brains UCSD-NCMIR Duke - CIVM UCSD-NCMIR & Duke - CIVN

  11. MorphologyBIRN Examining neuroanatomical correlates of neuropsychiatric illnesses, including Unipolar Depression, mild Alzheimer’s Disease and mild cognitive impairment Combining data from multiple acquisition sites Increase statistical power for study of rare populations Harvard (MGH and BWH), Duke, UCLA, UC–San Diego, Johns Hopkins

  12. MorphologyBIRN Harvard-MGH Surface based coordinate system Harvard-BWH Model based three dimensional medical image segmentation UCLA-LONI Dynamics of gray matter loss rates, mapped in a schizophrenia population Harvard-BWH 3D-Slicer - An Integrated visualization system for surgical planning and guidance using image fusion and interventional imaging

  13. FunctionalBIRN • Developing a common fMRI protocol to study regional brain dysfunction related to the progression and treatment of schizophrenia • Investigating techniques to insure interoperability of existing tools for multi-modal analysis • Correlating functional data with anatomical data acquired from the Morphology test-bed to study if there are neuroanatomical correlates with cognitive dysfunction across disorders • UCLA, UC San Diego, UC Irvine, Harvard (MGH and BWH), Stanford, Minnesota, Iowa, New Mexico, Duke/U. North Carolina

  14. FunctionalBIRN Harvard-MGH neocortical network mediates semantic processing of sentences in healthy individuals Patients with schizophrenia show abnormal modulation of temporal and frontal regions during semantic processing fMRI response at 3T and 1.5T with identical software and hardware platforms Stanford – Lucas Center

  15. BIRNCoordinating Center • Deploy a network infrastructure capable of quickly moving large amounts of data between BIRN sites across the country • Create federated databases pertaining to the BIRN scientific test-beds • Develop software to find, compare, and analyze complex neurological imaging data • Ensure regulatory compliance (HIPAA) without inhibiting collaboration • UC San Diego

  16. Wired and Wireless Ultimately, access from anywhere to BIRN data everywhere BIRN Network Operations Center Standardized Site Rack 24x7 Operations Monitoring Internet 2 BIRN NOC Monitoring UCSD/SDSC Network NCRR Cisco 4006 GB Switches • Network Switch • BIRN Statistics • BIRN Network Probe • Network Attached Storage • Grid POP/SRB, Globus • General purpose ( eg., encryption) • UPS for Rack DL380 - Network Stats BIRN Network Management GigE Net Probe N2400 NAS 1 - 10 TB Gigabit Network Probes DL380 Grid POP DL380 NOC Servers and Testers/Analyzers APC UPS BIRN Site Rack

  17. BIRNCC Services • Network Monitoring • Statistics & Measurement • 7 x 24 Help Desk • Problem Tracking • High Level Project Management • Portal Services & Tools Integration • Training • Data Integration • Visualization Tools • Documentation – web site, best practices, lessons learned, checklists

  18. Challenges Large data sets • ISCAR - information storage, curation, archiving, and retrieval • Bandwidth issues • Quality control of data only analyzed by machine • Visual interfaces for complex data sets Fostering collaboration • Reward structures in government and academic institutions • Intellectual property issues • Data integrity • Resistance to collaborate with competitors Data sharing issues (ownership, privacy, authorship)

  19. More Challenges Federated DB /Knowledge Engineering • Dictionaries & Ontologies • Integrating different data types • Accessing legacy systems • Distributed query computations High-level management of projects • Overcome technical & institutional barriers • Degree of centralization needed • Role of standards • Project subdivision and optimization

  20. BIRN Future • Addition of new sites in a rapid, well-defined, affordable fashion • Grid-based National Clinical Research Network • Cross-species integration of data • Characterization of treatment at multiple levels • Expand model to other areas of biomedical research

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