## 1 Introduction  
Trauma is a phenomenon of experiencing psychologically, emotionally, or physically overwhelming events, affecting 70-90% of individuals at some point in their lifetime [1]. While universal, the impact of trauma is particularly profound during childhood and adolescence, critical periods of brain growth and reorganization. Developmental vulnerabilities in emotion regulation and stress coping mechanisms make children more susceptible to trauma's long-term effects, including disrupted neurodevelopment and maladaptive behaviors [2, 3]. Childhood trauma has been linked to the development of post-traumatic stress disorder (PTSD), anxiety disorders, and depression later in life [4]. These findings underscore the importance of understanding the underlying brain mechanisms to facilitate early interventions [5]. Magnetic Resonance Imaging (MRI) has been a cornerstone for exploring structural brain changes in trauma-exposed individuals [6]. Studies have revealed associations between childhood trauma and alterations in brain regions, e.g., a thinner superior frontal gyrus (SFG) cortex and thicker right caudal middle frontal gyrus and left isthmus cingulate [7]. Additionally, distinct gray matter volume patterns in the prefrontal, cerebellar, limbic, and sensory networks have been identified [8]. Analyzing MRI data poses unique challenges due to its multi-dimensional nature, requiring computational approaches to extract meaningful insights [9]. Traditional machine learning techniques, such as Support Vector Machines (SVMs) and Principal Component Analysis (PCA), have been widely used in neuroimaging studies. SVMs are particularly effective in both linear and non-linear classification tasks [10], while PCA aids in reducing dimensionality by identifying components that explain the largest variance in the data [11]. However, these methods also face limitations in capturing the intricate, non-linear, and irregular relationships inherent in brain imaging data [12]. Deep learning methods, particularly convolutional neural networks (CNNs), have shown promise in learning high-dimensional features from image-based data [13]. CNNs are commonly applied to tasks such as image classification and segmentation, but their grid-based architecture struggles to effectively handle the irregular patterns and non-Euclidean relationships present in brain imaging data [14]. This limitation has spurred interest in more flexible models like graph neural networks (GNNs), which excel in modeling complex, non-linear structures and relationships in data [15]. GNNs have emerged as a powerful alternative, combining the feature-learning capabilities of neural networks with the flexibility to handle graph-structured data [16]. Unlike CNNs, GNNs can directly model the relational and irregular patterns found in neuroimaging, such as connections between brain regions or irregular spatial distributions [14]. GNNs also share theoretical advantages with kernel-based models like SVMs but provide additional capacity for learning hierarchical representations of structural data [17; 18]. Recent studies have reviewed GNN-based methods in network neuroscience, highlighting applications such as disease classification, missing brain graph synthesis, and biomarker discovery, emphasizing their potential in advancing the diagnosis of neurological disorders and population graph integration [19; 20]. For example, BrainGNN has been proposed as a framework to analyze fMRI data, leveraging ROI-aware graph convolutions and salient ROI-selection mechanisms to outperform traditional fMRI analysis methods in identifying biomarkers for Autism Spectrum Disorder (ASD) and task decoding in Human Connectome Project (HCP) data [20]. Despite these strengths, GNN applications in neuroimaging still remain limited and have not been applied to examine the structural changes related to childhood trauma and related psychopathologies.