## 2 Methodology  
### Data Collection and Pre-Processing  
The dataset analyzed in this study is provided by the Philadelphia Neurodevelopmental Cohort (PNC). PNC is a collaboration between the Children's Hospital of Philadelphia and the University of Pennsylvania, funded by the National Institute of Mental Health (NIMH) to characterize brain and behavioral interactions. The PNC consists of over 800 healthy subjects aged from 8 to 22 years. The fMRI tasks adopted in this study are emotion identification (emoid) and working memory (nback) tasks. All MRI scans were conducted using a single 3T Siemens TIM Trio whole-body scanner. During the task, participants were instructed to categorize emotions depicted in facial expressions, encompassing happiness, anger, sadness, fear, and neutrality. The total duration of the scanning session was 10.5 min. The blood oxygenation level-dependent (BOLD) signal was captured utilizing a whole-brain, single-shot, multislice, gradient-echo echoplanar sequence comprising 124 volumes (372 s) [32]. Standard pre-processing procedures were implemented utilizing SPM12 ([[http://www.filion.ucl.ac.UK/spm/software/spm12/](http://www.filion.ucl.ac.UK/spm/software/spm12/)]([http://www.filion.ucl.ac.UK/spm/software/spm12/](http://www.filion.ucl.ac.UK/spm/software/spm12/)) (accessed on 1 March 2022)), including motion correction, spatial normalization to standard MNI space, and spatial smoothing employing a 3 mm full width at half maximum Gaussian kernel [33]. We adhered to a standard pre-processing pipeline as outlined in our previous works [13; 14] using SPM12. The Power template [34] is used to divide the brain into 264 regions of interest (ROIs) to parcellate the BOLD signal and build the connectivity matrix using the Pearson correlation. Within the scope of this research, a sample of 622 subjects was selected from the overall pool of 800 participants. This specific subset was chosen due to the availability of both emotion identification (emoid) and working memory (nback) paradigms within the fMRI scan. The male and female subjects were divided into five groups, each representing a stage related to adolescent development as shown in Figure 1. The frequently used notation is listed in Table 1, and the group division information is listed in Table 2 [35]. The MGIN model was compared to other state-of-the-art algorithms, i.e., SVM, GIN [22], BrainGNN [24], M-GCN [36], MVGCN [37], MLP, and MGIN. SVM is a baseline supervised machine learning algorithm that is commonly used for classification analysis, which constructs a hyperplane in a high-dimensional space to separate data points into different classes. GIN [22] is a deep learning architecture that consists of multiple graph convolution layers followed by fully connected layers by applying a transformation to each node's features based on its neighborhood. BrainGNN [24] is a graph neural network architecture designed for brain network analysis that incorporates brain-specific prior knowledge into the neural network structure to improve the accuracy of the classification. M-GCN [36] is a multi-modal deep learning architecture that integrates functional and structural connectomics data to predict multidimensional phenotypic characterization to capture the complex relationships between brain regions at different levels of organization. MVGCN [37] is a graph convolutional network designed for multi-view data integration that can learn a shared representation across multiple views of the data and perform classification on the integrated representation. MLP is a neural network architecture that consists of multiple layers of neurons, where each neuron applies a non-linear function to the weighted sum of its inputs. MGIN is our proposed multi-modal deep learning framework used for understanding neurodevelopment.