## 4 Discussion
Clinically, this suggests that trauma-related neurodevelopmental changes may predispose children to specific cognitive and emotional challenges, underscoring the importance of early interventions to mitigate these effects [34]. Prior studies on trauma and brain structure have mainly reported findings such as reduced amygdala volume [35] and smaller gray matter volumes in regions like the left superior parietal lobule and right superior frontal gyrus [36]. These studies largely relied on visual inspection of MRI images, focusing on differences in white and gray matter volumes. In contrast, this study utilized GNNs to analyze connectivity density across brain regions, providing new and meaningful results. Our findings extend from the previous findings by highlighting the regions (such as the posterior cerebellum) that have been previously implicated in trauma-related brain changes. This aligns with existing literature [37], affirming the validity of our GNN-based approach. The findings of this study hold significance for future research on the roles of specific brain regions in response to trauma, disease, or other experiences. By successfully applying GNNs, we were able to move beyond traditional CNNs or machine learning models, which may overlook the irregular and relational nature of brain image data [14], and demonstrate the applicability of GNNs for brain imaging research. Furthermore, this study suggests that GNNs can uncover meaningful insights beyond the traditionally discussed frontal lobe regions [38], highlighting the importance of other brain areas. Nevertheless, this study has limitations. The relatively small sample size and the generic GNN model architecture used in this analysis may restrict the generalizabilityof the results. In addition, the patient group had heterogeneous presentations of trauma exposure (physical abuse, sexual abuse, neglect, etc.) as well as comorbid psychopathologies (internalizing and externalizing). While this study focused on trauma exposure as the primary variable, other factors such as substance use or medication use were not controlled and should be considered in future research. While this study highlights structural differences in the brain, the neurodevelopmental changes in these differences could not be examined due to the cross-sectional design. Future research should explore the neuro-developmental trajectory of the observed structural changes. Also explore more tailored GNN architectures and training methods that align with the unique characteristics of brain image data. Additionally, combining GNNs with complementary methods, such as functional connectivity analysis, may provide a more comprehensive understanding of brain networks. While this study used an unsupervised approach to analyze group differences, future studies could validate the effectiveness of GNNs by incorporating supervised learning with defined target variables. Expanding on these directions will further establish GNNs as a robust tool for brain imaging research. As this study represents an early-stage exploration, it should be considered a pilot study, and more comprehensive longitudinal research is needed to better understand the causal relationships between trauma exposure and brain connectivity changes.