Estimating the connectional brain template based on multi-view networks with bi-channel graph neural network

Multi-view networks constructed from multiple brain functional or structural measures (e.g., fractional anisotropy, fiber number, and fiber length, etc.) describe brain connectivity from different views. The connectional brain template (CBT) of multi-view networks from a given population provides a standard graph template incorporating complementary information for analyzing intergroup differences. However, preserving the complex topology of multi-view networks and building the CBT end-to-end remains challenging. Hence, we proposed a bi-channel convolution framework, including a local connection channel (LCC) with edge-conditioned convolution layers and a global network channel (GNC) with graph convolution network, to estimate the CBT in an end-to-end manner. The LCC captured the local connection topology across individual networks, and the GNC learned the global network topology through the high-order population network built by the similarity between the node strength distributions of individual networks for each view. Additionally, we improved topological similarity constraint in the loss function by minimizing the KL divergence of the node strength and degree distributions between the CBT and multi-view networks. Compared with four existing methods, our CBT exhibited optimal centrality, topological structure retention ability and differentiation between the younger and older age groups. Using the harmonic bases of the CBT, we found that the total harmonic energy of cortical thickness presented significant differences among different age groups. Our method provided a standard common template for examining the structural connection changes in the spatial domain and the anatomical features alterations in the harmonic domain, thus distinguishing the different population groups.