# IC-GNN

IC-GNN regroups the same atlas-defined brain regions into discrete, multiscale functional communities for each person, then uses those communities to guide edge refinement and region-specific weights in a graph neural network. Group-level atlases had smoothed over individual differences in community boundaries, while voxel-level individualized parcellations demand long scans and complicate cross-subject comparison. Across more than 30,000 participants in ABIDE, ADNI and the UK Biobank, IC-GNN reached the highest mean AUROC and accuracy among 15 baselines, with AUROC of 75.41% on ABIDE, 86.46% on ADNI and 60.62% on the UK Biobank.

IC-GNN regroups the same atlas-defined brain regions into discrete, multiscale functional communities for each person, then uses those communities to guide edge refinement and region-specific weights in a graph neural network. Group-level atlases had smoothed over individual differences in community boundaries, while voxel-level individualized parcellations demand long scans and complicate cross-subject comparison. Across more than 30,000 participants in ABIDE, ADNI and the UK Biobank, IC-GNN reached the highest mean AUROC and accuracy among 15 baselines, with AUROC of 75.41% on ABIDE, 86.46% on ADNI and 60.62% on the UK Biobank.