The EVENet framework could be extended to incorporate additional imaging modalities, such as T1-weighted MRI and functional MRI (fMRI), by integrating these modalities into the existing architecture to enhance the model's ability to capture both structural and functional aspects of the brain.
Multi-Modal Input Integration: The architecture could be modified to accept multi-modal inputs, where T1-weighted images provide high-resolution anatomical context, while fMRI data offers insights into brain activity patterns. This could involve creating separate subnetworks for each modality, similar to the existing subnetworks for diffusion MRI parameters, and then fusing their outputs to achieve a comprehensive parcellation.
Feature Fusion Techniques: Advanced feature fusion techniques, such as concatenation or attention mechanisms, could be employed to combine the learned features from different modalities. This would allow the model to leverage complementary information, enhancing the accuracy of brain parcellation and providing a more nuanced understanding of the relationship between brain structure and function.
Joint Learning Framework: A joint learning framework could be established where the model is trained simultaneously on all modalities. This would enable the network to learn shared representations that capture the interdependencies between structural and functional data, potentially improving the robustness of the parcellation results.
Uncertainty Estimation Across Modalities: The evidential deep learning approach used in EVENet could be adapted to estimate uncertainty across different imaging modalities. By quantifying uncertainty in the predictions derived from T1-weighted and fMRI data, the model could highlight areas where structural and functional information may conflict, guiding further investigation.
Clinical Relevance: Incorporating T1-weighted and fMRI data would enhance the clinical relevance of EVENet, allowing for more accurate assessments of brain disorders. For instance, understanding how structural abnormalities correlate with functional deficits could provide valuable insights into conditions such as schizophrenia or Alzheimer's disease.