Title: Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets Show Chinese title

Title: 通过自编码器对脑网络进行编码及其在分析大规模脑成像数据集中的应用

Abstract: There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connection strengths between ROIs. Due to the high-dimensionality and non-Euclidean nature of networks, it is challenging to depict their population distribution and relate them to human traits. Current approaches focus on summarizing the network using either pre-specified topological features or principal components analysis (PCA). In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits. We refer to our method as Graph AuTo-Encoding (GATE). We applied GATE to two large-scale brain imaging datasets, the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) for adults, to understand the structural brain connectome and its relationship with cognition. Numerical results demonstrate huge advantages of GATE over competitors in terms of prediction accuracy, statistical inference and computing efficiency. We found that structural connectomes have a stronger association with a wide range of human cognitive traits than was apparent using previous approaches. Show Chinese abstract

Abstract: 在研究从不同成像模态推断的人类大脑连接组并探索它们与人类特征（如认知）之间的关系方面，一直存在浓厚的兴趣。 大脑连接组通常表示为网络，其中节点对应于不同的感兴趣区域（ROIs），边对应于ROI之间的连接强度。 由于网络的高维性和非欧几里得性质，描述其群体分布并将它们与人类特征相关联具有挑战性。 当前的方法主要集中在使用预定义的拓扑特征或主成分分析（PCA）来总结网络。 在本文中，基于深度学习的最新进展，我们开发了一种非线性潜在因子模型，以表征大脑图的群体分布，并推断大脑结构连接组与人类特征之间的关系。 我们将我们的方法称为Graph AuTo-Encoding（GATE）。 我们将GATE应用于两个大规模脑成像数据集，即青少年大脑认知发展（ABCD）研究和成人人类连接组计划（HCP），以了解结构大脑连接组及其与认知的关系。 数值结果表明，在预测准确性、统计推断和计算效率方面，GATE比竞争对手有巨大优势。 我们发现，结构连接组与广泛的人类认知特征之间有更强的关联性，这比使用以前的方法所显示的更为明显。