We will develop and apply novel geometric algorithms to brain MRIs obtained from the well-characterized Arizona APOE cohort of presymptomatic individuals and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary results have shown statistically significant differences in several brain regions between APOE e4 carriers and non-carriers who were either healthy controls or patients with mild cognitive impairment (MCI) in the ADNI. In the current project we will continue developing novel MRI analysis methods using diffusion geometry and hyperbolic conformal geometry. Specifically, we will develop brain grey matter morphology signatures to measure grey matter morphometry changes and will also extend our surface fluid registration method to hyperbolic Poincar disk model to match lateral ventricular surfaces. The proposed system will focus on two brain cortical and subcortical structures: grey matter and lateral ventricle and compute their complete geometric features to measure all AD related atrophy. This may provide an effective way to pinpoint subregional areas of differential vulnerability associated with known neurodegenerative risk and protective factors for late-onset AD. To investigate the reliability and practicality of our method, we seek to 1) validate developed algorithms with established models in an independent cohort of Arizona APOE carriers, 2) explore APOE genetic influence in MCI patients and healthy subjects in the ADNI dataset and younger presymptomatic individuals of Arizona APOE cohort, and 3) explore additional regions of anatomical interest so as to develop MRI biomarkers for the identification of preclinical stage Alzheimer's disease that in turn will facilitate the therapeutic goals of prevention and earlier intervention to delay dementia onset and retard dementia progression.

Public Health Relevance

This project proposes a novel surface-based brain morphometry system for Alzheimer's disease (AD) research. It will be applied to detect preclinical biomarkers for AD in presymptomatic individuals at three different levels of risk (defined by APOE genotype) by analyzing magnetic resonance imaging (MRI) brain scans.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AG043760-02
Application #
8696981
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Hsiao, John
Project Start
2013-07-15
Project End
2015-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Arizona State University-Tempe Campus
Department
Biomedical Engineering
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Tempe
State
AZ
Country
United States
Zip Code
85287
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Zhang, Jie; Fan, Yonghui; Li, Qingyang et al. (2017) EMPOWERING CORTICAL THICKNESS MEASURES IN CLINICAL DIAGNOSIS OF ALZHEIMER'S DISEASE WITH SPHERICAL SPARSE CODING. Proc IEEE Int Symp Biomed Imaging 2017:446-450
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Zhang, Jie; Stonnington, Cynthia; Li, Qingyang et al. (2016) APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proc IEEE Int Symp Biomed Imaging 2016:646-650
Zhang, Wen; Shi, Jie; Stonnington, Cynthia et al. (2016) MORPHOMETRIC ANALYSIS OF HIPPOCAMPUS AND LATERAL VENTRICLE REVEALS REGIONAL DIFFERENCE BETWEEN COGNITIVELY STABLE AND DECLINING PERSONS. Proc IEEE Int Symp Biomed Imaging 2016:14-18

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