The overall goal of this project is the integration of advanced image-processing, data-analysis, and data management techniques into a brain-image database (BRAID), for the support of image-based clinical trials. The integration of these components has greatly aided our collaborators' management and analysis of image-based clinical trials (IBCTs) for the elucidation of structure-function associations for the human brain. In the previous cycle, we added segmentation capabilities, implemented Bayesian methods for lesion-deficit analysis, and began construction of a functional white-matter atlas based on lesion-deficit data. These methods, in conjunction with BRAID's visualization and other statistical tools, have resulted in contributions to the peer-reviewed clinical and engineering literature. However, our experience also demonstrates the need for extensions to BRAID. First, our segmentation algorithm is limited by its foundation on a statistical signal-intensity model of T 1-weighted spoiled gradient-recalled echo images. Second, given improvements in our registration techniques, we now have high-quality morphological data from our collaborators' IBCTs, but lack sophisticated methods for morphology-function analysis. Third, given the brain's plasticity, construction of an advanced functional atlas requires access to acute lesion-deficit data; our current collaborators are collecting subacute or chronic lesion-deficit data. Finally, we could facilitate the interchange of data, software, and analytic results with our collaborators and other colleagues by implementing BRAID based on open-source software. Towards these ends, we propose four specific aims to further extend BRAID's functionality: extension of our segmentation algorithm to incorporate more complex spatial and signal-intensity information; development of Bayesian methods for morphological analysis, to complement our Bayesian methods for lesion-deficit analysis; extension of BRAID to accommodate acute-stroke data, including magnetic-resonance (MR) perfusion and diffusion sequences; and reimplementation of BRAID using open-source components. We will test these image analysis, segmentation, and acutestroke extensions to BRAID using data from three IBCTs.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG013743-10
Application #
6929350
Study Section
Special Emphasis Panel (ZRG1-SSS-E (95))
Program Officer
Wagster, Molly V
Project Start
1995-09-30
Project End
2008-07-31
Budget Start
2005-08-15
Budget End
2006-07-31
Support Year
10
Fiscal Year
2005
Total Cost
$429,822
Indirect Cost
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Chen, Rong; Krejza, Jaroslaw; Arkuszewski, Michal et al. (2017) Brain morphometric analysis predicts decline of intelligence quotient in children with sickle cell disease: A preliminary study. Adv Med Sci 62:151-157
Chen, Rong; Herskovits, Edward H (2015) Examining the multifactorial nature of a cognitive process using Bayesian brain-behavior modeling. Comput Med Imaging Graph 41:117-25
Chen, Rong; Herskovits, Edward H (2012) Graphical model based multivariate analysis (GAMMA): an open-source, cross-platform neuroimaging data analysis software package. Neuroinformatics 10:119-27
Jiao, Yun; Chen, Rong; Ke, Xiaoyan et al. (2012) Single nucleotide polymorphisms predict symptom severity of autism spectrum disorder. J Autism Dev Disord 42:971-83
Chen, Rong; Resnick, Susan M; Davatzikos, Christos et al. (2012) Dynamic Bayesian network modeling for longitudinal brain morphometry. Neuroimage 59:2330-8
Chen, Rong; Jiao, Yun; Herskovits, Edward H (2011) Structural MRI in autism spectrum disorder. Pediatr Res 69:63R-8R
Jiao, Y; Chen, R; Ke, X et al. (2011) Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging. Adv Med Sci 56:334-42
Chen, Rong; Herskovits, Edward H (2010) Machine-learning techniques for building a diagnostic model for very mild dementia. Neuroimage 52:234-44
Chen, Rong; Herskovits, Edward H (2010) Voxel-based Bayesian lesion-symptom mapping. Neuroimage 49:597-602
Jiao, Yun; Chen, Rong; Ke, Xiaoyan et al. (2010) Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 50:589-99

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