The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer?s disease (AD). While there is growing evidence that APOE affects early life brain development, an exact quantification of the extent of this effect is unknown. We propose to develop a complete morphometric analysis of cortical grey matter (cGM) from neonatal MRI, and to use these tools quantify the difference in brain between term and pre- term APOE4 carriers and non-carriers to assess whether the deficit due to APOE4 is significantly higher in preterm as compared to term neonates. Our laboratories combine expertise in shape analysis of brain structures using MRI, and we have designed numerous tools for this purpose, particularly in pediatrics. In the parent version of this supplement, we are using some of these tools for the comparison of subcortical structures in preterm vs. term neonates, and to study associated long-term outcomes. In fall 2019, we also obtained a supplement for this R01 titled: ?Effects of genetic and gestational risk factors for late onset Alzheimer's disease on neonatal brain morphology?, for which we are calling back preterm and term subjects 0-18 years old who obtained an MRI at neonatal ages. We are determining ApoE status on these subjects through saliva collection, and then using this information in conjunction with their neonatal scans to study how hippocampal morphology is affected by ApoE and prematurity statuses. However, the cortex is also known to be affected in both prematurity and Alzheimer?s disease. Hence, here we will build on our prior supplement and use this dataset to determine cortical morphology in these populations. First, we will extend our methods in two ways: (1) Current online segmentation methods require 20-40 hours of subsequent expert manual editing. We will build a new geodesic transform based infant brain cortical segmentation. Our preliminary work has shown great success in improving manual correction times. (2) Additionally, we will use our multivariate tensor-based morphometry (mTBM) algorithm that measures local cortical area changes, combined with a new morphometric Gaussian process (M-GP) latent cortical thickness landmark selection method to powerfully determine cortical differences between preterm and term-born neonates based on their ApoE status. Our descriptions of normal development may be used as normative comparison values for neurodevelopmental pathologies, which may lead to earlier detection and optimized treatment. The software developed here will be made available to the research community. Secondly, these tools will be used to obtain a complete picture of the differences in cortical growth between APOE4 carriers and non-carriers. In addition to being a risk factor for AD, APOE4 carriers tend to get worse outcomes in conditions such as traumatic brain injury, chemotherapy induced brain damage, hypoalphalipoproteinemia and cardiovascular disease, among others. When children are affected, this may have important implications for long-term brain development. Understanding of the neuroanatomical correlates of APOE status may empower future interventions in AD and in these disorders.
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer?s disease (AD), while there is growing evidence that APOE affects early life brain development, an exact quantification of the extent, degree or trajectory of this effect is unknown. We propose to develop a complete morphometric analysis tool of cortical grey matter (cGM) to quantify the difference in brain between term and pre-term APOE- 4 carriers and non-carriers to assess whether the deficit due to APOE-4 is significantly higher in preterm infants as compared to term infants. Understanding of the neuroanatomical correlates of APOE status may empower future interventions in AD.
|Fan, Yonghui; Wang, Gang; Lepore, Natasha et al. (2018) A Tetrahedron-based Heat Flux Signature for Cortical Thickness Morphometry Analysis. Med Image Comput Comput Assist Interv 11072:420-428|
|Zhang, Wen; Shu, Kai; Wang, Suhang et al. (2018) Multimodal Fusion of Brain Networks with Longitudinal Couplings. Med Image Comput Comput Assist Interv 11072:3-11|
|Wu, Jianfeng; Zhang, Jie; Shi, Jie et al. (2018) HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS. Proc IEEE Int Symp Biomed Imaging 2018:1555-1559|
|Zhang, Jie; Tu, Yanshuai; Li, Qingyang et al. (2018) MULTI-TASK SPARSE SCREENING FOR PREDICTING FUTURE CLINICAL SCORES USING LONGITUDINAL CORTICAL THICKNESS MEASURES. Proc IEEE Int Symp Biomed Imaging 2018:1406-1410|
|Tu, Yanshuai; Wen, Chengfeng; Zhang, Wen et al. (2018) Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease. Conf Proc IEEE Eng Med Biol Soc 2018:427-4631|