Neuroimaging plays an increasingly important role in the early diagnosis of Alzheimer's disease (AD). The availability of data from large scale, multi-site studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Dominantly Inherited Alzheimer's Network (DIAN), provide unprecedented opportunities of improving our understanding of this complicated disease. On the other hand, these large scale, high dimensional imaging data of ever growing size call for the urgent needs of developing and validating robust and automated mapping tools. To become in independent investigator of brain imaging research in AD, the candidate proposes in this K01 application to receive training in multimodal image analysis, clinical diagnosis of AD, MR imaging techniques, and biostatistics. These training activities will greatly augment the candidate's background in neuroimage analysis and establish a solid foundation for his long term goal of being a leading researcher in computer-aided early diagnosis of AD. In the research plan, the candidate will develop and validate a suite of novel tools for the mapping of neuroanatomy during the development and progression of AD using intrinsic geometry of the anatomical structure. In contrast to conventional approaches that align brains in a canonical Euclidean space such as the Talairach atlas, the candidate models the anatomy intrinsically with the eigenfunctions of the Laplace-Beltrami (LB) operator and their Reeb graphs. This spectral approach is invariant to natural pose variations, robust to geometric deformations due to pathology and disease progression, and leads to novel methods for surface reconstruction, modeling, and mapping.
The specific aims are: 1. Validate and continue to develop an intrinsic framework for the mapping of sub-cortical structures based on the LB eigenfunctions. 2. Develop and validate an automated system for cortical surface extraction, major sulci identification, and mapping. 3. Develop and validate novel algorithms for multimodal fusion with cortical mapping. The new algorithms will be validated with cognitive measures using data from ADNI and DIAN, and compared with existing methods in terms of the discrimination power in the early diagnosis of AD. The software tools developed in this project will be distributed publicly.
The intrinsic modeling tools developed in this project will significantly improve the robustness in large scale mappings of neuroanatomy such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Dominantly Inherited Alzheimer's Network (DIAN). The widespread access to these tools will ultimately lead to an enhanced ability to diagnose AD early in clinical practice.
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