###### Abstract
The authors contend that the proposed network architecture has the capacity to learn an injective mapping of the function \(g\), potentially endowing the model with similar capabilities to the Weisfeiler-Lehman (WL) test for graph classification tasks [22]. In contrast to existing GNNs, which utilize non-injective neighbor aggregation and thus possess limited discriminative power, GIN employs injective neighbor aggregation and is as discriminative as the WL graph isomorphism test [22]. In recent times, there has been an increasing interest in the utilization of graph-based deep learning models in diverse fMRI investigations, encompassing areas such as disease prediction [23] and biomarker identification [24]. The use of graph deep learning models proves advantageous in brain network analysis due to their ability to directly process graph-structured data as the input. This characteristic makes them well-suited for exploring and understanding the intricate connectivity patterns within the brain. [PERSON] et al. proposed the BrainGNN framework and utilized fMRI data and graph attention mechanisms to discover novel biomarkers for autism spectrum disorder [24]. Some of the limitations of the BrainGNN method include the extent of interpretability, computational complexity, and generalizability. Furthermore, [PERSON] et al. developed a novel approach for brain network analysis using contrastive learning and hierarchical signed graph pooling [25]. Their proposed method aims to capture meaningful representations of brain networks by leveraging the inherent structure and connectivity patterns in the data. Some of the limitations of the study include challenges with interpretability, computational complexity, and a lack of comparison with existing state-of-the-art deep learning techniques. In the contrastive learning approach, the authors did not discuss the interpretability of the obtained features or their clinical relevance. Understanding the underlying mechanisms driving the learned representations could provide valuable insights into brain function and pathology. Additionally, [PERSON] et al. presented BrainGB, a benchmark dataset and evaluation framework for brain network analysis using GNNs [26]. The authors addressed the need for standard evaluation in the field by providing a comprehensive benchmark that covers multiple tasks and datasets. A major limitation of the BrainGB framework is its simplified representation of brain networks, which primarily focuses on using brain regions as nodes in the constructed brain networks. However, brain connectivity is a complex and multi-modal phenomenon involving various levels of granularity and different types of connections. Additionally, the authors of BrainGB do not discuss the interpretability of the GNN models and the insights gained from their predictions [26]. Understanding the underlying factors contributing to the model's decisions as well as the meaningfulness of the learned representations would enhance the practical utility of the benchmark. Motivated by the above observations, we propose an interpretable end-to-end multi-modal graph isomorphism network (MGIN) framework to integrate multiple paradigms of fMRI, which incorporates information about signals in each brain region of interest and functional connectivity. Existing GIN methods applied to fMRI connectivity networks do not utilize data to understand the relation information both within and between modalities. Previous methods utilizing GIN only use a single modality (e.g., resting state fMRI data) for the classification tasks without discussing specific regions of interest and subnetwork structures relevant to understanding the findings. To validate the model, we applied MGIN to a large publicly accessible dataset, specifically, the Philadelphia Neurodevelopmental Cohort (PNC). Our results show that the algorithm is robust and can extract important subnetwork connections in multi-modal data.