Atlas-free Brain Network Transformer
This work addresses reliability and interpretability issues in brain network analysis for neuroimaging and clinical diagnostics, representing a novel method for a known bottleneck rather than a foundational shift.
The paper tackled the limitations of atlas-based brain network analysis by proposing an atlas-free brain network transformer that uses individualized parcellations from resting-state fMRI data, resulting in consistent outperformance of state-of-the-art atlas-based methods in sex classification and brain-connectome age prediction tasks.
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.