Machine learning

Programming language

Data science

Artificial intelligence

Bridging (networking)

Functional connectivity

Graph

Neuroscience

Modalities

Python (programming language)

Biology

Geography

Visualization

Computer science

Power graph analysis

Geodesy

Benchmark (surveying)

Theoretical computer science

Sociology

Connectomics

Computer network

Social science

Connectome

Human Connectome Project

Artificial neural network

Neuroimaging

IEEE Transactions on Medical Imaging

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.

BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks