Analyzing connections between brain regions of interest (ROI) is vital to
detect neurological disorders such as autism or schizophrenia. Recent
advancements employ graph neural networks (GNNs) to utilize graph structures in
brains, improving detection performances. Current methods use correlation
measures between ROI's blood-oxygen-level-dependent (BOLD) signals to generate
the graph structure. Other methods use the training samples to learn the
optimal graph structure through end-to-end learning. However, implementing
those methods independently leads to some issues with noisy data for the
correlation graphs and overfitting problems for the optimal graph. In this
work, we proposed Bargrain (balanced graph structure for brains), which models
two graph structures: filtered correlation matrix and optimal sample graph
using graph convolution networks (GCNs). This approach aims to get advantages
from both graphs and address the limitations of only relying on a single type
of structure. Based on our extensive experiment, Bargrain outperforms
state-of-the-art methods in classification tasks on brain disease datasets, as
measured by average F1 scores.

本研究提出了 Bargrain，它通过使用图卷积网络模拟了两种图结构：过滤后的相关矩阵和最优样本图，旨在既得益于这两种图结构，又解决了仅依赖单一结构的限制。大量实验证明，Bargrain 在脑部疾病数据集的分类任务中，以平均 F1 分数为衡量指标，胜过了现有方法。