The insights from this review on improving the robustness of biomarkers discovered using GNNs can be extended to other neuroimaging modalities beyond fMRI by considering the following strategies:
Adaptation to Different Data Structures: Tailoring GNN architectures to suit the specific data structures of other neuroimaging modalities such as EEG, MEG, or PET scans. Customizing the model design to accommodate the unique characteristics of each modality can enhance the robustness of biomarker discovery.
Feature Engineering and Selection: Implementing feature engineering techniques specific to the data characteristics of each modality can help identify relevant biomarkers. Utilizing feature selection methods to extract the most informative features can improve the interpretability and reliability of the biomarkers.
Model Explainability: Incorporating explainability techniques that are suitable for the data representation of different neuroimaging modalities can provide insights into the discovered biomarkers. Ensuring that the explanations are coherent and reliable across modalities can enhance the trustworthiness of the biomarkers.
Validation and Reproducibility: Conducting rigorous validation and reproducibility studies on diverse datasets from various neuroimaging modalities can validate the generalizability of the biomarkers. Collaborative research efforts and open sharing of data and methodologies can facilitate the extension of insights to different modalities.