Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is influenced by an intricate mix of risk factors including age, genetics, and environmental variables. In our study, we employed mouse models with human APOE alleles and nitric oxide synthase 2, along with environmental factors like diet, to simulate controlled genetic risk and immune response of AD. We utilized a Feature Attention Graph Neural Network (FAGNN), integrating brain structural connectomes, genetic traits, environmental factors, and behavioral data, to estimate brain age. Our method demonstrated improved accuracy in age prediction over other methods and highlighted age-associated brain connections. The most impactful connections included the cingulum, striatum, corpus callosum, and hippocampus. We further investigated these findings through fractional anisotropy in different age groups of mice which underlined the significance of white matter degradation in the aging process. Our research underscores the effectiveness of integrative graph neural networks in predicting brain age and delineating important neural connectivity in brain aging.
This paper presents a deep learning approach to automated segmentation of cardiac structures in 5D (3D + Time + Energy) Photon-Counting micro-CT (PCCT) imaging sets. We have acquired, reconstructed, and fully segmented a preclinical dataset of cardiac micro-PCCT scans in APOE mouse models. These APOE genotypes serve as models of varying degrees of risk of Alzheimer’s disease and cardiovascular disease. The dataset of user-guided segmentations served as the training data for a deep learning 3D UNet model capable of segmenting the four primary cardiac chambers, the aorta, pulmonary artery, inferior and superior vena cava, myocardium, and the pulmonary tree. Experimental results demonstrate the effectiveness of the proposed methodology in achieving reliable and efficient cardiac segmentation. We demonstrate the difference in performance when using single-energy PCCT images versus decomposed iodine maps as input. We achieved an average Dice score of 0.799 for the network trained on single-energy images and 0.756 for the network trained using iodine maps. User-guided segmentations took approximately 45 minutes/mouse while CNN segmentation took less than one second on a system with a single RTX 5000 GPU. This novel deep learning-based cardiac segmentation approach holds significant promise for advancing phenotypical analysis in mouse models of cardiovascular disease, offering a reliable and time-efficient solution for researchers working with photon-counting micro-CT imaging data.
Brain region segmentation and morphometry in mouse models of Alzheimer’s Disease (AD) risk allow us to understand how various factors affect the brain. Photon-Counting Detector (PCD) micro-CT can provide faster brain imaging than MRI and superior contrast and spatial resolution to Energy-Integrating Detector (EID) micro-CT. This paper demonstrates a PCD micro-CT based approach for mouse brain imaging, segmentation, and morphometry. We extracted and stained the brains of 26 mice from three genotypes (APOE22HN, APOE33HN, APOE44HN). We scanned these brains with PCD and EID micro-CT, performed hybrid (PCD and EID) iterative reconstruction, and used the Small Animal Multivariate Brain Analysis (SAMBA) tool to segment the brains via registration to our new PCD CT mouse brain atlas. We used the outputs of SAMBA to run region-based and voxel-based comparisons by genotype and sex. Together, PCD and EID scanning take approximately five hours and produce images with a voxel size of 22 μm, which is faster than prior MRI protocols that produce images with a voxel size above 40 μm. PCD images from hybrid iterative reconstruction have minimal artifacts and higher spatial resolution and contrast than EID images. Qualitative and quantitative analyses confirmed that our PCD atlas is similar to the prior MRI atlas and that it successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant difference in relative size in 26 brain regions. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus. This study successfully establishes a PCD micro-CT approach for mouse brain analysis that can be used for future AD research.
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