Collaboration:mBIRN - Revision history

Tkapur at 14:49, 10 February 2010

2010-02-10T14:49:46Z

Tkapur at 14:48, 10 February 2010

2010-02-10T14:48:45Z

Tkapur at 14:47, 10 February 2010

2010-02-10T14:47:48Z

Tkapur at 02:18, 8 February 2010

2010-02-08T02:18:06Z

Tkapur at 02:11, 8 February 2010

2010-02-08T02:11:49Z

Tkapur: Created page with 'Back to NA-MIC External Collaborations ==Grant#== U24RR025736 ==Key Personnel== fBIRN: Steve Potkin, UC Irvine *NA-MIC: Ron Kikinis, Steve P…'

2010-02-08T02:09:16Z

Created page with 'Back to NA-MIC External Collaborations ==Grant#== *U24RR025736 ==Key Personnel== *fBIRN: Steve Potkin, UC Irvine *NA-MIC: Ron Kikinis, Steve P…'

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Back to [[NA-MIC_External_Collaborations|NA-MIC External Collaborations]]

==Grant#==
*U24RR025736
==Key Personnel==
*fBIRN: Steve Potkin, UC Irvine
*NA-MIC: Ron Kikinis, Steve Pieper
==Grant Duration==
02/08/2006-11/30/2010

==Grant Abstract==
DESCRIPTION (from NIH Reporter): The overarching goal of the Function BIRN (FBIRN) is to develop technology and methods to conduct multi- site functional imaging studies, and to produce a knowledge base that would not otherwise be available through single-site imaging studies. The technology includes the development and refinement of multi-site, functional imaging protocols using robust cognitive tasks; the development and refinement of algorithms to reduce inter-site variability; the use of federated, distributed databases and storage; flexible and robust image processing software integrated with these databases and storage infrastructure; tools for data querying across the distributed databases to extract subsets of data from multiple sources; and the development of both classical statistical and datamining methods to reveal the patterns of imaging', clinical, and behavioral data which differentiate important population clusters. To meet this goal, the FBIRN will develop the capacity to conduct a multi-center functional imaging study in a focused group of subjects with a neuropsychiatric disorder, patients with schizophrenia. The lessons learned, the statistical methods developed, and the informatics structure constructed will be generalizable and ' applicable to a wide variety of clinical investigations. The ability to integrate subsets of clinical, functional imaging, and behavioral datasets from disparate sites to form novel datasets will be assessed in a phased series of developments and optimizations. The overall goal of this proposal is to develop tools to make multi-site functional MRI studies a common research practice. Completion of this goal enables researchers to investigate the pathophysiology of complex diseases more thoroughly, through the increased power of large-scale, collaborative neuroimaging studies. When completed, researchers in different fields and physical locations will be able to draw on a common set of tools and database systems not possible at a single site.

@@ Line 2: / Line 2: @@
 ==Grant#==
-*U24RR021760
+*U24RR021382
 ==Key Personnel==
 *mBIRN: Bruce Rosen, MGH

@@ Line 4: / Line 4: @@
 *U24RR021760
 ==Key Personnel==
-*mBIRN: Arthur Toga, UCLA
+*mBIRN: Bruce Rosen, MGH
 *NA-MIC: Ron Kikinis, Steve Pieper
 ==Grant Duration==
-/01/2005-04/30/2010
+/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.

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	==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.