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[2510.03306] Atlas-free Brain Network Transformer
Summary
The paper presents an atlas-free brain network transformer (BNT) that improves brain network analysis by utilizing individualized brain parcellations from resting-state fMRI data, outperforming traditional atlas-based methods.
Why It Matters
This research addresses significant limitations in current brain network analysis methods that rely on fixed atlases, which can lead to inaccuracies. By introducing an atlas-free approach, the study enhances the reliability and interpretability of neuroimaging data, potentially advancing personalized medicine and neuroimaging biomarkers.
Key Takeaways
- The atlas-free BNT leverages individualized brain parcellations for improved analysis.
- It outperforms existing atlas-based methods in precision and robustness.
- The approach enhances neuroimaging biomarkers for personalized medicine.
- Reproducible code is available, promoting transparency and further research.
- This advancement could lead to better clinical diagnostic tools.
Quantitative Biology > Neurons and Cognition arXiv:2510.03306 (q-bio) [Submitted on 30 Sep 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:Atlas-free Brain Network Transformer Authors:Shuai Huang, Xuan Kan, James J. Lah, Deqiang Qiu View a PDF of the paper titled Atlas-free Brain Network Transformer, by Shuai Huang and 3 other authors View PDF HTML (experimental) 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. O...