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Cited by 218Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced Graph Neural Networks

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

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Cui, Hejie, Dai, Wei, Zhu, Yanqiao, Kan, Xuan, Gu, Antonio Aodong Chen, Lukemire, Joshua, Zhan, Liang, He, Lifang +2 more·2023·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2022.3218745PMC PMC10079627
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Abstract

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.

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Authors
Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A. A. C., Lukemire, J., Zhan, L., He, L., Guo, Y., & Yang, C.
Affiliations
  • Emory University (US)
  • University of California, Los Angeles (US)
  • University of Pittsburgh (US)
  • Lehigh University (US)
ORCID
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-02
Volume
42
Issue
2
Pages
493-506
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2022.3218745
PMC
PMC10079627
OpenAlex
W4307847583
Cited by
218
References
11
Citations by year
2026: 64 · 2025: 67 · 2024: 63 · 2023: 20 · 2022: 4
FWCI
20.50
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • University Research Committee of Emory University
  • GPU servers provided by the Computer Science Department, Emory University
  • National Institutes of Health (R01MH105561, R01MH118771, R01AG071243, R01MH125928, U01AG068057)
  • National Science Foundation (IIS 2045848, IIS 1837956)
  • Office of Naval Research (N00014-18-1-2009)
  • Collection of the Philadelphia Neurodevelopmental Cohort (PNC) dataset awarded to Raquel Gur (RC2MH089983)
  • Hakon Hakorson (RC2MH089924)
  • National Institutes of Health and additional federal partners (U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Keywords
brain network analysis, graph neural networks, connectome, structural connectivity, functional connectivity, benchmark, reproducibility, neuroimaging
MeSH terms
Brain, Humans, Neural Networks, Computer, Benchmarking, Neuroimaging, Connectome

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