Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision
Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision
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DOI：
10.1109/isbi52829.2022.9761588
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Fan Yang;Guorong Wu;Won Hwa Kim
中科院分区：
文献类型：
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作者：
Fan Yang;Guorong Wu;Won Hwa Kim
正电子发射断层扫描（PET）成像的出现使我们能够量化体内淀粉样斑块的负担，这是阿尔茨海默病（AD）的标志之一。然而，侵入性暴露于辐射和高成像成本显着限制PET在表征病理负担的演变，这往往需要纵向PET图像序列的应用。在这方面，我们提出了一种概念验证解决方案，以基于非常有限数量的PET扫描来生成整个大脑中病理事件的完整轨迹。我们提出了一种新的变分自编码器模型，以学习神经退行性疾病过程的潜在群体水平表示，基于每个大脑区域和纵向诊断阶段的纵向β-淀粉样蛋白测量。由于病理负担的传播遵循大脑连接体的拓扑结构，我们进一步将神经网络转换为有监督的序列图VAE，在那里我们使用大脑网络来指导表示学习。实验表明，解纠缠表示可以捕获疾病相关的淀粉样蛋白的动态和预测的淀粉样蛋白沉积在未来的时间点的水平。
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.