###### Abstract  
_Background_: A fundamental grasp of the variability observed in healthy individuals holds paramount importance in the investigation of neuropsychiatric conditions characterized by sex-related phenotypic distinctions. Functional magnetic resonance imaging (fMRI) serves as a meaningful tool for discerning these differences. Among deep learning models, graph neural networks (GNNs) are particularly well-suited for analyzing brain networks derived from fMRI blood oxygen level-dependent (BOLD) signals, enabling the effective exploration of sex differences during adolescence. _Method_: In the present study, we introduce a multi-modal graph isomorphism network (MGIN) designed to elucidate sex-based disparities using fMRI task-related data. Our approach amalgamates brain networks obtained from multiple scans of the same individual, thereby enhancing predictive capabilities and feature identification. The MGIN model adopts pinpoints crucial subnetworks both within and between multi-task fMRI datasets. Moreover, it offers interpretability through the utilization of GNNExplainer, which identifies pivotal sub-network graph structures contributing significantly to sex group classification. _Results_: Our findings indicate that the MGIN model outperforms competing models in terms of classification accuracy, underscoring the benefits of combining two fMRI paradigms. Additionally, our model discerns the most significant sex-related functional networks, encompassing the default mode network (DMN), visual (VIS) network, cognitive (CNG) network, frontal (FRNT) network, salience (SAL) network, subcortical (SUB) network, and sensorimotor (SM) network associated with hand and mouth movements. Remarkably, the MGIN model achieves superior sex classification accuracy when juxtaposed with other state-of-the-art algorithms, yielding a noteworthy 81.67% improvement in classification accuracy. _Conclusion_: Our model's superiority emanates from its capacity to consolidate data from multiple scans of subjects within a proven interpretable framework. Beyond its classification prowess, our model guides our comprehension of neurodevelopment during adolescence by identifying critical subnetworks of functional connectivity. deep learning; graph neural network; interpretability; multi-modality; multi-paradigm; sex differences +
Footnote †: journal: Applied  
is a neuroimaging method that measures the hemodynamic changes in regional brain oxygenation. By enhancing the field's understanding of sex differences in healthy individuals, we can develop a more thorough understanding of neurodevelopmental disorders that exhibit phenotypic differences between sexes [10; 11]. Machine learning-based methods, such as graph neural networks (GNNs), can be employed in the context of fMRI data to proficiently unveil new biomarkers that underlie developmental differences in sex, age, and cognition [12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. The graph isomorphism network (GIN) is a specialized type of graph neural network that has been shown to achieve superior discriminative power among GNNs for classification tasks. Ultimately, the node embeddings acquired through the GIN classifier serve the purpose of generating the embedding for an entire graph. Additionally, GIN is regarded as a dual representation of the convolutional neural network [22]. In the realm of graph theory, the graph isomorphism problem is centered on the determination of whether two graphs exhibit topological equivalence. Two graphs are considered isomorphic if a node mapping exists that preserves node adjacencies, signifying that an edge connecting a pair of nodes in the first graph is mirrored in the second graph in the same manner. Essentially, isomorphic graphs share identical connectivity and vary solely due to node permutation. The network enacts the aggregate and combine functions by summing the node features. For the graph-level readout, the embedded node features from each layer are summated and subsequently concatenated to yield the ultimate graph feature.