Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1477-1480. doi: 10.1109/EMBC48229.2022.9871803.
Mental disorders such as schizophrenia have been challenging to characterize due in part to their heterogeneous presentation in individuals. Most studies have focused on identifying groups differences and have typically ignored the heterogeneous patterns within groups. Here we propose a novel approach based on a variational autoencoder (VAE) to interpolate static functional network connectivity (sFNC) across individuals, with group-specific patterns between schizophrenia patients and controls captured simultaneously. We then visualize the original sFNC in a 2D grid according to the samples in the VAE latent space. We observe a high correspondence between the generated and the original sFNC. The proposed framework facilitates data visualization and can potentially be applied to predict the stage that a subject falls within a disorder continuum as well as characterize individual heterogeneity within and between groups.
精神障碍，如精神分裂症，由于其在个体中的表现存在异质性，因此一直难以进行特征描述。大多数研究都集中在识别组间差异上，而通常忽略了组内的异质模式。在这里，我们提出了一种基于变分自编码器（VAE）的新方法，该方法可以在个体之间插值静态功能网络连接（sFNC），同时捕捉精神分裂症患者和对照组之间的组特异性模式。然后，我们根据 VAE 潜在空间中的样本，将原始 sFNC 以 2D 网格的形式可视化。我们观察到生成的 sFNC 和原始 sFNC 之间具有高度的一致性。所提出的框架有助于数据可视化，并可潜在地应用于预测个体在疾病连续统中所处的阶段，以及描述组内和组间的个体异质性。
