This paper presents a comprehensive study focused on disentangling
hippocampal shape variations from diffusion tensor imaging (DTI) datasets
within the context of neurological disorders. Leveraging a Graph Variational
Autoencoder (VAE) enhanced with Supervised Contrastive Learning, our approach
aims to improve interpretability by disentangling two distinct latent variables
corresponding to age and the presence of diseases. In our ablation study, we
investigate a range of VAE architectures and contrastive loss functions,
showcasing the enhanced disentanglement capabilities of our approach. This
evaluation uses synthetic 3D torus mesh data and real 3D hippocampal mesh
datasets derived from the DTI hippocampal dataset. Our supervised
disentanglement model outperforms several state-of-the-art (SOTA) methods like
attribute and guided VAEs in terms of disentanglement scores. Our model
distinguishes between age groups and disease status in patients with Multiple
Sclerosis (MS) using the hippocampus data. Our Graph VAE with Supervised
Contrastive Learning shows the volume changes of the hippocampus of MS
populations at different ages, and the result is consistent with the current
neuroimaging literature. This research provides valuable insights into the
relationship between neurological disorder and hippocampal shape changes in
different age groups of MS populations using a Graph VAE with Supervised
Contrastive loss.

本研究通过使用图变分自编码器和有监督对比学习的方法，对神经系统疾病背景下弥漫张量成像数据集中的海马形态变异进行了细致研究，以提高其可解释性和分离出与年龄和疾病存在相关的两个独立潜变量。通过对合成的 3D 环面网格数据和真实的 DTI 海马数据集进行评估，我们的有监督分离模型在离散度分数方面优于其他先进方法，对多发性硬化症（MS）患者的年龄组和疾病状态进行了区分，并显示了不同年龄下 MS 人群海马体积变化的结果，与当前神经成像学文献一致。这项研究对于了解不同年龄组 MS 人群的神经系统疾病与海马形态变化之间的关系提供了有价值的洞察。