# Topological Cycle Graph Attention Network for Brain Functional Connectivity Source: [Topological Cycle Graph Attention Network for Brain Functional Connectivity]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:42:28.011Z Authors: Jinghan Huang, Nanguang Chen, Anqi Qiu Published: 2024-03-28T05:07:41.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [General intelligence classification on ABCD fMRI](): General intelligence classification; ABCD fMRI; Accuracy: 0.682. ## Original sources and implementation [Original paper]() [PDF]()