Amodeo Carlo, Fortel Igor, Ajilore Olusola, Zhan Liang, Leow Alex, Tulabandhula Theja
Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA.
Department of Psychiatry, University of Illinois Chicago, Chicago, IL, USA.
Med Image Comput Comput Assist Interv. 2022 Sep;13431:406-415. doi: 10.1007/978-3-031-16431-6_39. Epub 2022 Sep 15.
Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain. With the advent of connectomics, the primary graphs or networks of interest are structural connectome (derived from DTI tractography) and functional connectome (derived from resting-state fMRI). However, most published connectome studies have focused on either structural or functional connectome, yet complementary information between them, when available in the same dataset, can be jointly leveraged to improve our understanding of the brain. To this end, we propose a function-constrained structural graph variational autoencoder (FCS-GVAE) capable of incorporating information from both functional and structural connectome in an unsupervised fashion. This leads to a joint low-dimensional embedding that establishes a unified spatial coordinate system for comparing across different subjects. We evaluate our approach using the publicly available OASIS-3 Alzheimer's disease (AD) dataset and show that a variational formulation is necessary to optimally encode functional brain dynamics. Further, the proposed joint embedding approach can more accurately distinguish different patient sub-populations than approaches that do not use complementary connectome information.
图论分析已成为建模大脑功能和解剖连接性的标准工具。随着连接组学的出现，主要关注的图或网络是结构连接组（源自扩散张量成像纤维束追踪）和功能连接组（源自静息态功能磁共振成像）。然而，大多数已发表的连接组研究都只关注结构连接组或功能连接组，然而，当同一数据集中同时存在这两者时，它们之间的互补信息可以共同利用，以增进我们对大脑的理解。为此，我们提出了一种功能约束结构图变分自编码器（FCS-GVAE），它能够以无监督的方式整合功能连接组和结构连接组的信息。这会产生一个联合低维嵌入，从而建立一个统一的空间坐标系，以便在不同受试者之间进行比较。我们使用公开可用的OASIS-3阿尔茨海默病（AD）数据集评估了我们的方法，并表明变分公式对于最佳编码大脑功能动态是必要的。此外，与不使用互补连接组信息的方法相比，所提出的联合嵌入方法能够更准确地区分不同的患者亚群。
