通过函数约束结构图变异自动编码器统一嵌入结构和功能连接组

Carlo Amodeo, Igor Fortel, Olusola Ajilore, Liang Zhan, Alex Leow, Theja Tulabandhula
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引用次数: 0

摘要

图论分析已成为大脑功能和解剖连接建模的标准工具。随着连接组学的出现,人们关注的主要图或网络是结构连接组(来自 DTI tractography)和功能连接组(来自静息态 fMRI)。然而,大多数已发表的连接组学研究都集中在结构连接组或功能连接组上,但如果在同一数据集中可以获得两者之间的互补信息,则可以共同利用这些信息来提高我们对大脑的理解。为此,我们提出了一种功能约束结构图变异自动编码器(FCS-GVAE),它能以无监督的方式将功能和结构连接组的信息结合起来。这将产生一个联合低维嵌入,从而建立一个统一的空间坐标系,用于比较不同的研究对象。我们使用公开的 OASIS-3 阿尔茨海默病(AD)数据集对我们的方法进行了评估,结果表明,要想对大脑功能动态进行最佳编码,必须采用变异公式。此外,与不使用互补连接组信息的方法相比,我们提出的联合嵌入方法能更准确地区分不同的患者亚群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder.

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.

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