## 4 Discussion
### Limitations  
Our approach possesses several limitations that warrant consideration. First, although our model adeptly captures the intricate non-linear relationships between variables and labels, it neglects to incorporate dimensionality reduction techniques to address the diverse nature of high-dimensional datasets within the population. To remedy this deficiency, we intend to implement various dimensionality reduction methods in future iterations of our research. Second, within our graphical learning framework, the features are predicated upon the Pearson correlation coefficient, while the edge weights are derived from the cosine similarity between functional connectivity features among nodes. While these choices render our graphical network amenable to learning, we recognize the potential for improvement through the exploration of alternative feature learning and sparsity techniques, aiming to diversify edge weights and mitigate redundancy in high-order relationships. Third, in the context of our neurodevelopmental application, our study concentrates on delineating significant subnetworks and crucial ROIs contributing to sex-based distinctions. In forthcoming investigations, we plan to extend our inquiry to encompass variances in connectomes across age cohorts, developmental stages, and cognitive groupings, thereby augmenting our understanding of neurodevelopmental processes and associated biological pathways. Furthermore, despite the generalizability of our framework to analyze diverse neuroimaging data and disease contexts, it is imperative to acknowledge the potential influence of factors such as demographic heterogeneity, variability in disease phenotypes, and variations in data acquisition protocols on generalization performance. Our model may encounter challenges in extending its predictive capacity to populations characterizedby dissimilar demographic compositions, disease prevalence rates, or cultural influences not adequately represented in the training dataset. Additionally, the inherent heterogeneity of multi-modal datasets, encompassing a breadth of information types including imaging, clinical, genetic, and demographic data, poses a notable challenge in ensuring the equitable contribution and relevance of each modality across diverse populations. To address this, we propose the application of graph fusion techniques, facilitating the amalgamation of information derived from multiple graphs representing distinct modalities. This entails the exploration of methods such as learning separate embeddings for each modality and subsequently integrating them via concatenation, summation, or attention mechanisms. In the future, we aim to develop a unified representation capable of encapsulating information from all modalities concurrently [52; 53; 54; 55]. [PERSON] et al. conducted a comprehensive examination of recent advancements in theranostics tailored for MRI-guided therapy, with a particular focus on the integration of therapeutic and diagnostic facets within MRI-guided procedures. While our current research is centered on the utilization of fMRI data exclusively, the potential synergy between theranostics for MRI-guided therapy and fMRI offers several compelling prospects for both research and clinical practice. One such prospect involves harnessing fMRI for the real-time monitoring of therapeutic interventions. By amalgamating MRI-guided therapy with fMRI, investigators and clinicians can evaluate alterations in brain activity or physiological responses during treatment, thereby facilitating personalized treatment optimization. Moreover, fMRI can serve as a valuable tool for probing the underlying mechanisms of therapeutic interventions through the longitudinal monitoring of changes in brain activity patterns or connectivity subsequent to treatment.