Multi-Atlas Disentangled Connectivity Learning for Brain Disorder Representations
A team of researchers has introduced Multi-Atlas Disentangled Connectivity LEarning (MADCLE), a framework for multi-branch representation learning that encodes functional connectivity (FC) matrices from various brain atlases simultaneously. While resting-state fMRI-derived FC is frequently utilized to examine changes in brain networks associated with disorders, outcomes can differ based on the selected atlas. MADCLE tackles the issue of cross-atlas variability by developing atlas-specific representations related to diseases, avoiding reliance on a unified latent variable. Further information about this method can be found in the arXiv preprint 2605.07026.
Key facts
- MADCLE is a multi-branch representation learning framework
- It jointly encodes FC matrices from different brain atlases
- FC construction depends on brain atlas choice
- Different parcellations emphasize distinct organizational features
- Existing multi-atlas approaches fuse features at shallow level
- Single-atlas disentanglement methods do not address cross-atlas heterogeneity
- MADCLE learns atlas-wise disease-related representations
- The preprint is on arXiv with ID 2605.07026
Entities
Institutions
- arXiv