This proposal will contribute a new collaboration for implementing rigorous spatiotemporal medical image analysis in a large scale computing environment. The system will dramatically enhance the neuroimaging community's quantitative understanding of normal and pathological aging and correlated variables. Medical images capture the changes that occur in an individual over time and samples the range of anatomical and functional differences visible in a population lifespan. Our goal is to associate these differences with causes, for example, innate population variability, injury, pathology, or the effects of genotype on phenotype. The recently proposed Diffeomorphometry (DM) system quantifies and relates these variables to an optimal spatiotemporal coordinate system. This novel technique allows the atlas to evolve in time along with the population to statistically capture effects of age, disease or other factors. This common, evolving map space gives a wealth of prior knowledge, allowing one to build probabilities describing ranges and types of variation in shape and function. These aggregate population attributes may then be studied and visualized, used in research, as well as teaching and diagnosis. Our DM method is designed with the axioms of symmetry (the algorithms must be symmetric) and specificity (the analysis should be optimal in the study space) in mind and with the ability to automatically generate database-specific atlases. The rigorous and symmetric definition of change given by DM captures differences in neuroanatomy with superlative accuracy, reproducibility and high level of detail. Consequently, a DM study maximizes the information extracted from a neuroimaging cohort, especially when correlated with, for instance, genetic or behavioral variables. Furthermore, DM satisfies pressing research needs in neuroimaging: DM derives optimal atlases from arbitrarily sized databases and gives large deformation optimization of anatomical correspondence, through landmark and statistical guidance. The resources in the UCLA Center for Computational Biology (CCB) will allow these methods to be applied on the large datasets they were designed for and at an unprecedented resolution and scale. The proposed work has three distinctive aims: collaboration, methodology and clinical evaluation/application: Instantiate a collaboration between UPenn and UCLA, where data and algorithms are shared and disseminated via the Center for Computational Biology; Develop Diffeomorphometry into a cutting-edge, large-scale, publicly available computational tool; Evaluate and refine the developed methodology, as well as compare with CCB brain mapping tools, on neuroimaging studies of structure-function associations under neurodegenerative conditions ? ? ?

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-E (50))
Program Officer
Cohen, Zohara
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pennsylvania
Schools of Medicine
United States
Zip Code
Kandel, Benjamin M; Wang, Danny J J; Gee, James C et al. (2015) Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis. Methods 73:43-53
Lawson, Gwendolyn M; Duda, Jeffrey T; Avants, Brian B et al. (2013) Associations between children's socioeconomic status and prefrontal cortical thickness. Dev Sci 16:641-52
Zheng, Yuanjie; Lin, Stephen; Kang, Sing Bing et al. (2013) Single-image vignetting correction from gradient distribution symmetries. IEEE Trans Pattern Anal Mach Intell 35:1480-94
Hopkins, William D; Avants, Brian B (2013) Regional and hemispheric variation in cortical thickness in chimpanzees (Pan troglodytes). J Neurosci 33:5241-8
Okamura-Oho, Yuko; Shimokawa, Kazuro; Takemoto, Satoko et al. (2012) Transcriptome tomography for brain analysis in the web-accessible anatomical space. PLoS One 7:e45373
Avants, Brian B; Tustison, Nicholas J; Song, Gang et al. (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033-44
Zhang, Hui; Awate, Suyash P; Das, Sandhitsu R et al. (2010) A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. Med Image Anal 14:666-73
Avants, Brian B; Yushkevich, Paul; Pluta, John et al. (2010) The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49:2457-66
Petersen, R C; Aisen, P S; Beckett, L A et al. (2010) Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74:201-9
Awate, Suyash P; Yushkevich, Paul A; Song, Zhuang et al. (2010) Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development. Neuroimage 53:450-9

Showing the most recent 10 out of 25 publications