**5. Future directions**

Despite progress in recent years, there are lots of work to be done in developing new methods for constructing and analyzing brain networks, as well as performing group and individualized analysis. In this section, we propose some possible directions in the field of brain network research.

Network science has been used to analyze brain networks and advanced methods need to be developed to characterize the topological features of brain networks. The algebraic topological data analysis (TDA) method provides a new way to analyze the interactions between a set of nodes instead of bilateral connections. TDA could act as a complement to graph theoretical analysis in describing the topology characteristic of brain networks. More advanced network theory concepts, such as algebraic topology, have also been introduced to the analysis of brain networks [5]. Moreover, artificial neural networks and deep learning methods have been shown to be powerful in analyzing graph data. On the one hand, before network construction, models, such as Recurrent Neural Network (RNN) and Transformer, that were originally proposed to process sequential data, such as natural language and voice, can be applied to analyze the BOLD time series, both with and without preprocessing. Since the network perspective mainly models the inter-relationships between signals of spatially distinct regions, applying deep learning models directly to the time series could possibly extract information

complementary to statistical dependency, as described by functional connectivity. On the other hand, after constructing brain networks using functional connectivity, directed connectivity, or DTI fiber tracking, Graph Neural Network (GNN) or Graph Convolutional Network (GCN) could be utilized to merge these multimodal networks and combine both edge-wise features (connections) and nodal features, such as graph theory attributes. GNN was proposed to directly analyze graphs that can model relationships between nodes and perform inference on node, edge, or graph level. Applying GNN to brain networks, especially multilevel static and dynamic brain networks, could possibly extract useful features and enable multimodal information fusion.

On the application side, multiple group comparison methods have been developed. However, for clinical application, individualized diagnosis and treatment are crucial. How to transform conclusions derived from group research into individual situations is a challenging question. We define "healthy templates" as a set of methods to delineate characteristics of a healthy population. The healthy templates describe the distribution of features of healthy people and need to be built for each feature extracted from different modalities. In its most basic form, the healthy template can be a value range given a specific feature. Subjects whose feature value falls within this value range would be considered to be normal, similar to the interpretation of a blood test result. Open-source datasets are valuable resources in the construction of healthy templates. However, the site effect of MRI data is a crucial issue and multi-site data harmonization techniques need to be adopted when combining data from different scanning locations. Several methods have been proposed for harmonization but their utility remains to be tested [84, 85]. With low variance healthy templates, individualized precise treatment planning and prognosis prediction would become possible.
