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The emergence of Positron Emission Tomography (PET) imaging allows us to quantify the burden of amyloid plaques in-vivo, which is one of the hallmarks of Alzheimer’s disease (AD). However, the invasive exposure to radiation and high imaging cost significantly restrict the application of PET in characterizing the evolution of pathology burden which often requires longitudinal PET image sequences. In this regard, we propose a proof-of-concept solution to generate the complete trajectory of pathological events throughout the brain based on very limited number of PET scans. We present a novel variational autoencoder model to learn a latent population-level representation of neurodegeneration process based on the longitudinal β-amyloid measurements at each brain region and longitudinal diagnostic stages. As the propagation of pathological burdens follow the topology of brain connectome, we further cast our neural network into a supervised sequential graph VAE, where we use the brain network to guide the representation learning. Experiments show that the disentangled representation can capture disease-related dynamics of amyloid and forecast the level of amyloid depositions at future time points.
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Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer’s Disease Characterizations from ADNI Study
Given a population longitudinal neuroimaging measurements defined on a brain network, exploiting temporal dependencies within the sequence of data and corresponding latent variables defined on the graph (i.e., network encoding relationships between regions of interest (ROI)) can highly benefit characterizing the brain. Here, it is important to distinguish time-variant (e.g., longitudinal measures) and time-invariant (e.g., gender) components to analyze them individually. For this, we propose an innovative and ground-breaking Disentangled Sequential Graph Autoencoder which leverages the Sequential Variational Autoencoder (SVAE), graph convolution and semi-supervising framework together to learn a latent space composed of time-variant and time-invariant latent variables to characterize disentangled representation of the measurements over the entire ROIs. Incorporating target information in the decoder with a supervised loss let us achieve more effective representation learning towards improved classification. We validate our proposed method on the longitudinal cortical thickness data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Our method outperforms baselines with traditional techniques demonstrating benefits for effective longitudinal data representation for predicting labels and longitudinal data generation.
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- Award ID(s):
- 1948510
- PAR ID:
- 10349983
- Date Published:
- Journal Name:
- Medical Image Computing and Computer Assisted Intervention (MICCAI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-87196-3_34
