This R03 application is for one year of funding to develop and release SPHARM-MAT, a 3D shape modeling and analysis toolkit for neuroanatomical studies. Shape analysis is becoming of increasing interest to the neuroimaging community because of its potential to provide important information beyond simple volume measurements. SPHARM-MAT is a suite of tools that are designed to effectively characterize and normalize morphometric features of 3D brain structures using spherical harmonic functions (SPHARM). It establishes a foundation on which statistical shape analysis can be performed to discover morphometric changes in neuroanatomical structures related to specific brain disorders. The development of SPHARM-MAT is a synergistic effort in relation to existing tools. It provides an alternative software platform as well as an opportunity for tool comparison and cross-validation. More importantly, it is a powerful toolkit with several new features that add value. It has a broader applicability due to the implementation of several new algorithms that overcome the limitations of the traditional SPHARM method. It is user-friendly and interoperable, offering a graphical interface, modular design structure, and well-documented user manual and source code. The objectives of this research include (1) implementing an improved method for individual SPHARM modeling, (2) implementing an improved method for group analysis using SPHARM, and (3) packaging this functionality together with other necessary components in SPHARM-MAT and releasing the toolkit. SPHARM-MAT will be developed by packaging the existing prototype implementation of SPHARM processing components from previous studies as well as by implementing additional necessary components including a graphical user interface, a visualization module, a user manual, a project wiki site, and documentation of source code. SPHARM-MAT will be released at NITRC (www.nitrc.org) using GNU General Public License for wide dissemination. The dissemination of this new toolkit will enable investigators working on many brain disorders to more effectively test neuroanatomical hypotheses and therefore this project will benefit public health outcomes.
Shape analysis is becoming of increasing interest to the neuroimaging community because of its potential to provide important information beyond simple volume measurements and to understand morphometric changes in neuroanatomical structures related to specific brain disorders. The purpose of this project is to develop and release SPHARM-MAT, a 3D shape modeling and analysis toolkit for neuroanatomical studies. SPHARM-MAT is a synergistic effort in relation to existing tools, and is a powerful toolkit with several new features that add value, including ease of use, broad applicability, good interoperability, and wide dissemination.
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|Nho, Kwangsik; Shen, Li; Kim, Sungeun et al. (2011) The effect of reference panels and software tools on genotype imputation. AMIA Annu Symp Proc 2011:1013-8|
|Wan, Jing; Kim, Sungeun; Inlow, Mark et al. (2011) Hippocampal surface mapping of genetic risk factors in AD via sparse learning models. Med Image Comput Comput Assist Interv 14:376-83|
|Wan, Jing; Shen, Li; Fang, Shiaofen et al. (2010) A Framework for 3D Analysis of Facial Morphology in Fetal Alcohol Syndrome. Lect Notes Comput Sci 6326:118-127|
|Saykin, Andrew J; Shen, Li; Foroud, Tatiana M et al. (2010) Alzheimer's Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement 6:265-73|
|Shen, Li; Saykin, Andrew J; Kim, Sungeun et al. (2010) Comparison of manual and automated determination of hippocampal volumes in MCI and early AD. Brain Imaging Behav 4:86-95|
|Risacher, Shannon L; Shen, Li; West, John D et al. (2010) Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging 31:1401-18|
|Shen, Li; Qi, Yuan; Kim, Sungeun et al. (2010) Sparse bayesian learning for identifying imaging biomarkers in AD prediction. Med Image Comput Comput Assist Interv 13:611-8|
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