BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks (2204.07054v3)

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.

• The paper introduces BrainGB as a benchmark that standardizes brain network analysis using graph neural networks for reproducible research.
• The authors evaluate various GNN modules and configurations across fMRI and dMRI datasets to identify optimal architectures.
• The open-source toolkit accelerates collaborative advancements in computational neuroscience and practical neuroimaging applications.

BrainGB: Benchmarking Brain Network Analysis with Graph Neural Networks

The paper "BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks" by Hejie Cui et al. addresses a crucial gap in computational neuroscience, focusing on the systematic exploration and application of Graph Neural Networks (GNNs) for brain network analysis. This research paper provides both practical and theoretical insights into leveraging GNNs for neuroimaging data, specifically addressing the complexity of human brain connectivity represented as graph-structured data.

Summary of Contributions and Findings

The authors present BrainGB, a comprehensive benchmark designed to standardize brain network analysis using GNNs. The benchmark encompasses several key contributions:

1. Infrastructure Development: BrainGB offers a modular framework that integrates various aspects of neuroimaging pipeline standardization, including both functional and structural data preprocessing. It serves as a reproducible platform with standardized settings and accessible datasets, thus bridging the gap between neuroimaging and the machine learning community.
2. Comprehensive Design Space Exploration: The paper delineates the GNN design space into four modules—node features, message passing mechanisms, attention mechanisms, and pooling strategies. An extensive range of combinations of these modules is experimentally tested to provide baseline insights for effective GNN architectures.
3. Empirical Evaluations Across Modalities and Cohorts: The authors conduct extensive experiments across diverse datasets acquired from different neuroimaging modalities (fMRI and dMRI) and cohorts. Their approach suggests practical guidelines and highlights effective GNN configurations for varied brain network analysis tasks.
4. Open-Source Availability: To facilitate adoption and collaborative development, BrainGB is released as an open-source toolkit available on GitHub. This includes detailed tutorials, model examples, and preprocessing guidance, enabling researchers to reproduce and extend the work with relative ease.

Implications and Future Directions

From a practical standpoint, BrainGB offers invaluable guidelines for designing GNN models tailored to brain connectivity studies. It suggests that the incorporation of global structural information and attention-enhanced message passing mechanisms can lead to significant improvements in predictive performance for brain network analysis. The benchmark sets a precedent for reproducibility in GNN applications, emphasizing transparent data processing, model evaluation, and collaborative research.

The paper proposes several directions for continued research:

• Neurology-driven GNN Design: Leveraging domain-specific insights to inform GNN architectures may lead to improved capture of predictive brain signals, especially for tasks requiring disease-specific interpretations.
• Cross-cohort Transfer Learning: Developing pre-training and transfer learning paradigms for GNNs may address current challenges posed by limited dataset sizes, potentially enhancing model generalization across various neuroimaging studies and disorders.

The establishment of BrainGB invites deeper discourse and exploration on the modular design of GNNs in the field of brain network analysis, with potential to drive forward the theoretical understanding and practical applications of AI in neuroscience. Through its open-source approach and collaborative framework, the paper aims to support and accelerate advancements in understanding human brain organization, cognitive functions, and neurological disorder analyses.