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.
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.
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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|
|Lao, Yi; Wang, Yalin; Shi, Jie et al. (2016) Thalamic alterations in preterm neonates and their relation to ventral striatum disturbances revealed by a combined shape and pose analysis. Brain Struct Funct 221:487-506|
|Li, Bolun; Shi, Jie; Gutman, Boris A et al. (2016) Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 11:e0152901|
|Su, Zhengyu; Zeng, Wei; Wang, Yalin et al. (2015) Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis. Inf Process Med Imaging 24:411-23|
|Zhan, Liang; Liu, Yashu; Wang, Yalin et al. (2015) Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci 9:257|
|Wang, Gang; Zhang, Xiaofeng; Su, Qingtang et al. (2015) A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel. Med Image Anal 22:1-20|
|Kong, Dehan; Giovanello, Kelly S; Wang, Yalin et al. (2015) Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. J Alzheimers Dis 46:695-702|
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