BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
This work addresses the problem of inconsistent and non-reproducible GNN applications in neuroimaging for researchers, though it is incremental as it builds on existing GNN methods without introducing new paradigms.
The authors tackled the lack of systematic study on designing effective Graph Neural Networks (GNNs) for brain network analysis by introducing BrainGB, a benchmark that standardizes brain network construction and GNN implementation, leading to recommended general recipes for effective GNN designs based on extensive experiments across datasets.
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.