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 #
1R21AG043760-01A1
Application #
8584203
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (80))
Program Officer
Hsiao, John
Project Start
2013-07-15
Project End
2015-06-30
Budget Start
2013-07-15
Budget End
2014-06-30
Support Year
1
Fiscal Year
2013
Total Cost
$192,457
Indirect Cost
$50,455
Name
Arizona State University-Tempe Campus
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
943360412
City
Tempe
State
AZ
Country
United States
Zip Code
85287
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
Shi, Jie; Zhang, Wen; Tang, Miao et al. (2017) Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis. Med Image Anal 35:517-529
Mi, Liang; Zhang, Wen; Zhang, Junwei et al. (2017) An Optimal Transportation based Univariate Neuroimaging Index. Proc IEEE Int Conf Comput Vis 2017:182-191
Shi, Rui; Zeng, Wei; Su, Zhengyu et al. (2017) Hyperbolic Harmonic Mapping for Surface Registration. IEEE Trans Pattern Anal Mach Intell 39:965-980
Zhang, Wen; Shi, Jie; Yu, Jun et al. (2017) Enhancing Diffusion MRI Measures By Integrating Grey and White Matter Morphometry With Hyperbolic Wasserstein Distance. Proc IEEE Int Symp Biomed Imaging 2017:520-524
Wang, Gang; Wang, Yalin; Alzheimer's Disease Neuroimaging Initiative (2017) Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures. Neuroimage 147:360-380
Zhang, Jie; Shi, Jie; Stonnington, Cynthia et al. (2016) Hyperbolic Space Sparse Coding with Its Application on Prediction of Alzheimer's Disease in Mild Cognitive Impairment. Med Image Comput Comput Assist Interv 9900:326-334
Joshi, Shantanu H; Espinoza, Randall T; Pirnia, Tara et al. (2016) Structural Plasticity of the Hippocampus and Amygdala Induced by Electroconvulsive Therapy in Major Depression. Biol Psychiatry 79:282-92
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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