Atlas-free Brain Network Transformer

• URL: http://arxiv.org/abs/2510.03306v1
• Date: Tue, 30 Sep 2025 18:57:02 GMT
• Title: Atlas-free Brain Network Transformer
• Authors: Shuai Huang, Xuan Kan, James J. Lah, Deqiang Qiu,
• Abstract summary: We propose a novel atlas-free brain network transformer (atlas-free BNT)<n>Our approach computes ROI-to-voxel connectivity features in a standardized voxel-based feature space.<n>Our approach significantly improves the precision, robustness, and generalizability of brain network analyses.
• Score: 7.285780699359608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain networks. To address these challenges, we propose a novel atlas-free brain network transformer (atlas-free BNT) that leverages individualized brain parcellations derived directly from subject-specific resting-state fMRI data. Our approach computes ROI-to-voxel connectivity features in a standardized voxel-based feature space, which are subsequently processed using the BNT architecture to produce comparable subject-level embeddings. Experimental evaluations on sex classification and brain-connectome age prediction tasks demonstrate that our atlas-free BNT consistently outperforms state-of-the-art atlas-based methods, including elastic net, BrainGNN, Graphormer and the original BNT. Our atlas-free approach significantly improves the precision, robustness, and generalizability of brain network analyses. This advancement holds great potential to enhance neuroimaging biomarkers and clinical diagnostic tools for personalized precision medicine.

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