The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a “common” brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.

神经影像技术和网络理论的最新发展使我们能够可视化和表征体内的全脑功能连接。广泛用于基于人群的神经影像学研究中的常规结构图像图谱的重要性已得到充分验证。类似地，“通用”大脑连接图（也称为连接组图谱）) 跨个体可以开辟一条新途径来解释与疾病相关的大脑认知和行为。然而，将经典图像图集构建方法应用于连接组数据的主要障碍是此类方法中的规则数据结构（例如网格）破坏了源自不规则数据域（在图的设置）。为了解决这个障碍，我们首先通过扩散映射技术将大脑网络嵌入到欧几里得空间中的一组图形信号中。此外，我们将连接组图谱构建问题转化为一种新颖的基于学习的图推理模型。它可以通过迭代以下过程来构建：（1）将所有单独的大脑网络对齐到由潜在公共网络的图谱库跨越的公共空间，(2) 学习与所有对齐的大脑网络一致的公共网络的图拉普拉斯算子。与非基于学习的对应方法相比，我们已经评估了我们用于连接组图谱构建的新方法。基于使用来自患有神经退行性疾病和神经小儿疾病的人群的网络连接数据的实验，我们的方法已经证明了对现有方法的统计学意义的改进。