Difference between revisions of "Collaboration:mBIRN"

From NAMIC Wiki
Jump to: navigation, search
 
(2 intermediate revisions by the same user not shown)
Line 2: Line 2:
  
 
==Grant#==
 
==Grant#==
*U24RR021760
+
*U24RR021382
 
==Key Personnel==
 
==Key Personnel==
*mBIRN: Arthur Toga, UCLA
+
*mBIRN: Bruce Rosen, MGH
 
*NA-MIC: Ron Kikinis, Steve Pieper
 
*NA-MIC: Ron Kikinis, Steve Pieper
 
==Grant Duration==  
 
==Grant Duration==  
05/01/2005-04/30/2010
+
09/30/2004-05/31/2010
  
 
==Grant Abstract==
 
==Grant Abstract==
DESCRIPTION (from NIH Reporter): This cj>BIRN renewal application describes a research and development effort that will result in a distributed adaptive database and multiscale, multimodality atlases of the mouse brain. Tools to incorporate and compare data from gene expression patterns to gross morphology from multiple laboratories at scales from nanometers to centimeters will be built. The goals are to create an infrastructure for relating previously disparate data collections into a single system capable of quantitative visualization and linkage with previously disconnected knowledge bases. cj>BIRN will integrate the activities of five laboratories - the Laboratory of Neuro Imaging (LONI) at UCLA, the Biological Imaging Center (BIC) at CalTech, the Center for In Vivo Microscopy (CIVM) at Duke University, the Mouse Brain Library (MBL) at UT and the National Center for Microscopy and Imaging Research (NCMIR) at UCSD. The cJsBIRN is structured as five cores titled: Imaging, Atlasing, Data Federation, Applications, and Administration. The Imaging Core will acquire data for the entire project, encompassing imaging modalities from the whole brain scale to the supramolecular. The Atlasing Core will enable the processing of imaging data, reconstruction and registration of it, using techniques to integrate the various data collected into multimodal digital atlases. The Data Federation Core will organize, manage, and archive all the data collected and develop mechanisms for interaction between databases. The Applications Core contains the neurodegenerative disease test beds, the research projects that drive the development of infrastructure. And finally, the Administration Core will manage communication between and within cores, to the BIRN Central Coordinating site (BIRN-CC), and to the scientific community at large. The infrastructure will be tested by focusing the neuroscience of this project around degenerative brain disease. We chose this for several reasons. First, there are degenerative diseases such as Alzheimer's disease (AD), multiple sclerosis (MS), and Parkinson's disease (PD) that result in characteristic morphological changes that can be detected in vivo and histologically. There are effects in gray and white matter that can be measured, catalogued, and visualized. Second, these diseases have mouse models that could greatly benefit from the integrative approach proposed here. Third, AD is the focus of MorphBIRN and a synergy afforded by comparisons between these two BIRN efforts will be amplified with a common disease test bed. The Experimental Autoimmune Encephalomyelitis (EAE) model of MS and the alpha-synuclein knock-out model of PD examined during the previous funding cycle demonstrated the feasibility and potential of this model to utilize the tools and infrastructure proposed here.
+
DESCRIPTION (from NIH Reporter): Technological advances in imaging have revolutionized the biomedical investigation of illness. The tremendous potential that this methodology brings to advancing diagnostic and prognostic capabilities and in treatment of illnesses has as yet remained largely an unfulfilled promise. This potential has been limited by a number of technological impediments that could be in large part overcome by the availability of a federated imaging database and the attendant infrastructure. Specifically, the ability to conduct clinical imaging studies across multiple sites, to analyze imaging data with the most powerful software regardless of development site, and to test new hypotheses on large collections of subjects with well characterized image and clinical data would have a demonstrable and positive impact on progress in this field. The Morphometry BIRN (mBIRN), established in October 2001, has made substantial progress in the development of this national infrastructure to develop a data and computational network based on a federated data acquisition and database across seven sites in the service of facilitating multi-site neuroanatomic analysis. Standardized structural MRI image acquisition protocols have been developed and implemented that demonstrably reduce initial sources of inter-site variance. Data structure, transmission, storage and querying aspects of the federated database have been implemented. In this continuation of the mBIRN efforts, we propose three broad areas of work: 1) continuing structural MRI acquisition optimization, calibration and validation to include T2 and DTI; 2) translation of site specific state-of-the-art image analysis, visualization and machine learning technologies to work in the federated, multi-site BIRN environment; and 3) extension of data management and database query capabilities to include additional imaging modalities, clinical disorders and individualized human genetic covariates. These broad areas of work will come together in through key collaborations that will ensure utilization promotion by facilitating data entry into the federated database and creation of database incentive functionality. Our participating sites include MGH (PI), BWH, UCI, Duke, UCLA, UCSD, John Hopkins, and newly added Washington University and MIT. We have made a concerted effort to bridge the gap that can exist between biomedical and computational sciences by recruiting to our group leaders in both of these domains. Our efforts will be coordinated with those of the entire BIRN consortium in order to insure that acquisition and database functionality, and application-based disorder queries are interoperable across sites and designed to advance the capabilities to further knowledge and understanding of health and disease.

Latest revision as of 14:49, 10 February 2010

Home < Collaboration:mBIRN

Back to NA-MIC External Collaborations

Grant#

  • U24RR021382

Key Personnel

  • mBIRN: Bruce Rosen, MGH
  • NA-MIC: Ron Kikinis, Steve Pieper

Grant Duration

09/30/2004-05/31/2010

Grant Abstract

DESCRIPTION (from NIH Reporter): Technological advances in imaging have revolutionized the biomedical investigation of illness. The tremendous potential that this methodology brings to advancing diagnostic and prognostic capabilities and in treatment of illnesses has as yet remained largely an unfulfilled promise. This potential has been limited by a number of technological impediments that could be in large part overcome by the availability of a federated imaging database and the attendant infrastructure. Specifically, the ability to conduct clinical imaging studies across multiple sites, to analyze imaging data with the most powerful software regardless of development site, and to test new hypotheses on large collections of subjects with well characterized image and clinical data would have a demonstrable and positive impact on progress in this field. The Morphometry BIRN (mBIRN), established in October 2001, has made substantial progress in the development of this national infrastructure to develop a data and computational network based on a federated data acquisition and database across seven sites in the service of facilitating multi-site neuroanatomic analysis. Standardized structural MRI image acquisition protocols have been developed and implemented that demonstrably reduce initial sources of inter-site variance. Data structure, transmission, storage and querying aspects of the federated database have been implemented. In this continuation of the mBIRN efforts, we propose three broad areas of work: 1) continuing structural MRI acquisition optimization, calibration and validation to include T2 and DTI; 2) translation of site specific state-of-the-art image analysis, visualization and machine learning technologies to work in the federated, multi-site BIRN environment; and 3) extension of data management and database query capabilities to include additional imaging modalities, clinical disorders and individualized human genetic covariates. These broad areas of work will come together in through key collaborations that will ensure utilization promotion by facilitating data entry into the federated database and creation of database incentive functionality. Our participating sites include MGH (PI), BWH, UCI, Duke, UCLA, UCSD, John Hopkins, and newly added Washington University and MIT. We have made a concerted effort to bridge the gap that can exist between biomedical and computational sciences by recruiting to our group leaders in both of these domains. Our efforts will be coordinated with those of the entire BIRN consortium in order to insure that acquisition and database functionality, and application-based disorder queries are interoperable across sites and designed to advance the capabilities to further knowledge and understanding of health and disease.