Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
Recent advancements in neuroimaging have opened new avenues for understanding complex brain disorders through functional connectivity (FC) derived from resting-state fMRI. However, the construction of FC is heavily influenced by the choice of brain atlas, which can lead to inconsistent and heterogeneous representations of brain networks. A new study presents a solution to this problem by introducing a multi-branch representation learning framework known as Multi-Atlas Disentangled Connectivity Learning (MADCLE).
The Challenge of Functional Connectivity Construction
Functional connectivity is essential for characterizing large-scale brain network alterations associated with neurological and psychiatric disorders. Despite its significance, the methodology behind FC construction can introduce variability depending on the brain atlas used for analysis. Different parcellations can emphasize unique organizational features of the brain, which can complicate the interpretation of results and hinder the identification of disease markers.
Limitations of Existing Approaches
- Multi-atlas approaches often fuse atlas-derived features or predictions at a shallow level, which may not fully capture the complexity of brain disorders.
- Single-atlas disentanglement methods typically fail to address the cross-atlas heterogeneity, leading to potential misrepresentation of disease-related features.
Introducing MADCLE
MADCLE aims to mitigate these issues by concurrently encoding FC matrices derived from various brain atlases. This innovative framework does not rely on a single shared latent variable across different parcellations. Instead, MADCLE learns disease-related representations that are consistent across atlases through a process known as distributional alignment.
Methodology and Framework Components
- Atlas-wise disease-related representations: MADCLE creates specific representations for each brain atlas while enforcing consistency across them.
- Covariate-related and atlas-dependent modeling: The framework employs covariate similarity supervision and atlas-specific reconstruction to capture relevant factors while minimizing the influence of non-disease and parcellation-dependent information.
- Decorrelation constraints: These constraints are implemented to further refine the disease-related embeddings, ensuring that they are not biased by extraneous factors.
Experimental Validation
The effectiveness of MADCLE was evaluated through experiments conducted on two prominent datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the ADHD-200 dataset. The results indicate that MADCLE not only achieves competitive performance compared to existing single-atlas baselines but also outperforms multi-atlas GNN/Transformer models and recent multi-atlas consistency frameworks.
Conclusion
The findings from this study underscore the potential of structured disentanglement in enhancing functional connectivity-based disorder identification. By addressing the challenges posed by heterogeneous parcellation schemes, MADCLE offers a promising avenue for more accurate and consistent representations of brain disorders, ultimately contributing to improved diagnostic and therapeutic strategies in neurology and psychiatry.
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