Topological Cycle Graph Attention Network for Brain Functional Connectivity
This work addresses the challenge of distinguishing critical neural pathways from redundant connections in brain functional connectivity, with potential applications in understanding intelligence-related circuits, though it appears incremental in method development.
The study tackled the problem of identifying essential functional pathways in brain connectivity graphs by introducing CycGAT, which outperformed baseline models on fMRI data from 8,765 participants, identifying a functional backbone with significantly fewer cycles.
This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essential for signal transmissio--from non-essential, redundant connections that form cycles around this core structure. We first introduce a cycle incidence matrix that establishes an independent cycle basis within a graph, mapping its relationship with edges. We propose a cycle graph convolution that leverages a cycle adjacency matrix, derived from the cycle incidence matrix, to specifically filter edge signals in a domain of cycles. Additionally, we strengthen the representation power of the cycle graph convolution by adding an attention mechanism, which is further augmented by the introduction of edge positional encodings in cycles, to enhance the topological awareness of CycGAT. We demonstrate CycGAT's localization through simulation and its efficacy on an ABCD study's fMRI data (n=8765), comparing it with baseline models. CycGAT outperforms these models, identifying a functional backbone with significantly fewer cycles, crucial for understanding neural circuits related to general intelligence. Our code will be released once accepted.