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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
7K01EB013633-03
Application #
8758885
Study Section
Special Emphasis Panel (ZEB1-OSR-D (M1))
Program Officer
Erim, Zeynep
Project Start
2012-04-01
Project End
2017-03-31
Budget Start
2013-07-16
Budget End
2014-03-31
Support Year
3
Fiscal Year
2013
Total Cost
$166,644
Indirect Cost
$12,344
Name
University of Southern California
Department
Neurology
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Leng, Yuan; Shi, Yonggang; Yu, Qiaowen et al. (2016) Phenotypic and Genetic Correlations Between the Lobar Segments of the Inferior Fronto-occipital Fasciculus and Attention. Sci Rep 6:33015
Li, Junning; Shi, Yonggang; Toga, Arthur W (2016) Transformation Invariant Control of Voxel-Wise False Discovery Rate. IEEE Trans Med Imaging 35:2243-2257
Li, Junning; Gahm, Jin Kyu; Shi, Yonggang et al. (2016) Topological false discovery rates for brain mapping based on signal height. Neuroimage :
Li, Junning; Shi, Yonggang; Toga, Arthur W (2016) Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain. IEEE Signal Process Mag 33:36-51
Kammen, Alexandra; Law, Meng; Tjan, Bosco S et al. (2016) Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis. Neuroimage 125:767-79
Ge, Xinting; Shi, Yonggang; Li, Junning et al. (2015) Development of the human fetal hippocampal formation during early second trimester. Neuroimage 119:33-43
Cunningham, Samantha I; Shi, Yonggang; Weiland, James D et al. (2015) Feasibility of Structural and Functional MRI Acquisition with Unpowered Implants in Argus II Retinal Prosthesis Patients: A Case Study. Transl Vis Sci Technol 4:6
Aydogan, Dogu Baran; Shi, Yonggang (2015) Track Filtering via Iterative Correction of TDI Topology. Med Image Comput Comput Assist Interv 9349:20-27
Tran, Giang; Shi, Yonggang (2015) Fiber Orientation and Compartment Parameter Estimation From Multi-Shell Diffusion Imaging. IEEE Trans Med Imaging 34:2320-32
Li, Junning; Shi, Yonggang; Toga, Arthur W (2015) Controlling False Discovery Rate in Signal Space for Transformation-Invariant Thresholding of Statistical Maps. Inf Process Med Imaging 24:125-36

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