The Neuroimage Analysis Center (NAC), a national resource center, is developing algorithms and image analysis software tools for improving our understanding of brain diseases and enabling innovative treatments. The focus of this competitive renewal is the creation of a new kind of atlases. These atlases will integrate information from multiple image modalities, multiple subjects, and multiple time points together with canonical knowledge of function, structure and connectivity for normal and disease populations. The proposal is organized around five cores. The White Matter Architecture from Diffusion Tensor Images Core will focus on algorithms to elucidate and quantify white matter in individuals and in groups. The fMRI Informatics Core will extract information on brain function by further developing statistical technologies for the analysis of fMRI in populations. New lines of research will focus on the analysis of multi-modal functional data, and on the relationship between structure and function. The Time-Series Analysis Core will research dynamic models of the morphological correlates of disease over time. The Clinical Computational Anatomy Core will focus on the integration of symbolic descriptions of neural systems with a new high-resolution structural brain atlas. This will serve as a bridge between the declarative information in neuroscientific and disease-related databases and the morphology of subjects and populations. The Engineering Core will continue to develop and support the software environment that enables interaction between the cores and makes their scientific advances usable by biomedical scientists. The BWH team of investigators is augmented by subcontractors from MIT, GE, Isomics, and Georgia Tech, each of which provides expertise needed in our atlas creation efforts. A key deliverable of the NAC as a Resource Center is the integration of our atlas and related technology into 3D Slicer for unrestricted use, and we will continue to offer support and training at local and national venues as part of our outreach activities. Our close collaboration with clinician-scientists continues to be critical to the implementation of these tools for the purpose of understanding the brain and treating brain disorders, an important mission of NIH.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR013218-12
Application #
7676819
Study Section
Special Emphasis Panel (ZRG1-SBIB-L (40))
Program Officer
Yang, Liming
Project Start
1998-09-30
Project End
2013-05-31
Budget Start
2009-06-01
Budget End
2010-05-31
Support Year
12
Fiscal Year
2009
Total Cost
$1,993,709
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Gallardo, Guillermo; Wells 3rd, William; Deriche, Rachid et al. (2018) Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach. Neuroimage 170:307-320
Saito, Yukiko; Kubicki, Marek; Koerte, Inga et al. (2018) Impaired white matter connectivity between regions containing mirror neurons, and relationship to negative symptoms and social cognition, in patients with first-episode schizophrenia. Brain Imaging Behav 12:229-237
Ratner, Vadim; Gao, Yi; Lee, Hedok et al. (2017) Cerebrospinal and interstitial fluid transport via the glymphatic pathway modeled by optimal mass transport. Neuroimage 152:530-537
Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve et al. (2017) Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging 27:5-15
Chen, Yongxin; Georgiou, Tryphon T; Ning, Lipeng et al. (2017) Matricial Wasserstein-1 Distance. IEEE Control Syst Lett 1:14-19
Niethammer, Marc; Pohl, Kilian M; Janoos, Firdaus et al. (2017) ACTIVE MEAN FIELDS FOR PROBABILISTIC IMAGE SEGMENTATION: CONNECTIONS WITH CHAN-VESE AND RUDIN-OSHER-FATEMI MODELS. SIAM J Imaging Sci 10:1069-1103
Chen, Yongxin; Cruz, Filemon Dela; Sandhu, Romeil et al. (2017) Pediatric Sarcoma Data Forms a Unique Cluster Measured via the Earth Mover's Distance. Sci Rep 7:7035
Schabdach, Jenna; Wells 3rd, William M; Cho, Michael et al. (2017) A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies. Inf Process Med Imaging 10265:170-183
Wachinger, Christian; Brennan, Matthew; Sharp, Greg C et al. (2017) Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means. IEEE Trans Biomed Eng 64:1492-1502
Chen, Yongxin; Georgiou, Tryphon; Pavon, Michele et al. (2017) Robust transport over networks. IEEE Trans Automat Contr 62:4675-4682

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