TopoScanPE: Topological Positional Encodings for Brain Network Transformers

Brain regions can have similar functional connectivity strengths while playing different roles within the connectome. To capture these differences, we introduce *TopoScanPE*, a positional encoding that represents each region of interest (ROI) through the topology of its local neighborhood. For every ROI we extract its local neighborhood from a sparse connectivity graph and apply TopoScan (Uddin et al., 2026), a sliding-window filtration over complementary geometric node functions that summarizes each slice by the topology of its clique complex. A shared encoder maps the resulting TopoScan (Uddin et al., 2026) sequences into a fixed-width code that is concatenated to the ROI token. Experiments on ABIDE and ADNI show that TopoScanPE competitive state-of-the-art brain graph transformers and graph neural networks.