  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 BrainGB website 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.
