Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGBwebsite at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate the brain into various functional modules (network communities), which are critical for brain analysis. However, identifying such communities within the brain has been a nontrivial issue due to the complexity of neuronal interactions. In this work, we propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification with three main contributions. First, we offer an end-to-end transformer-based approach to learning clustering assignments. Through pairwise attention, a clustering layer, BCluster, and a transformer encoder collaboratively learn a globally shared clustering assignment that is continuously tuned to downstream tasks. BCluster enhances the model’s performance and reduces run time complexity while also providing clinical insights. Second, we propose a hierarchical structure for the clustering model, enabling the model to learn more abstract, higher-level cluster representations by combining lower-level modules. Each clustering layer is attached to a distinct readout module, which allows the model to utilize the cluster embeddings of every layer effectively. Last but not least, we redesign the attention mechanism of the transformer with stochastic noise, which enhances its cluster learning capability. We compare our model’s performance with SOTA models and perform clustering analysis with the ground truth community labels. Extensive experimental results show that with the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions.