School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China.
School of Mathematics, Northwest University, Xi'an 710127, People's Republic of China.
J Neural Eng. 2021 Jul 15;18(4). doi: 10.1088/1741-2552/ac0f4d.
. Brain connectivity network supports the information flow underlying human cognitions and should reflect the individual variability in human cognitive behaviors. Various studies have utilized brain connectivity to predict individual differences in human behaviors. However, traditional studies viewed brain connectivity network as a one-dimensional vector, a method which neglects topological properties of brain connectivity network.. To utilize these topological properties, we proposed that graph neural network (GNN) which combines graph theory and neural network can be adopted. Different from previous node-driven GNNs that parameterize on the node feature transformation, we designed an edge-driven GNN named graph propagation network (GPN) that parameterizes on the information propagation within brain connectivity network.Edge-driven GPN outperforms various baseline models such as node-driven GNN and traditional partial least square regression in predicting the individual total cognition based on the resting-state functional connectome. GPN also reveals a directed network topology encoding the information flow, indicating that higher-order association cortices such as dorsolateral prefrontal, inferior frontal and inferior parietal cortices are responsible for the information integration underlying total cognition.. These results suggest that edge-driven GPN can better explore topological structures of brain connectivity network and can serve as a new method to associate brain connectome and human behaviors.
脑连接网络支持人类认知的信息流动，应该反映人类认知行为的个体差异。许多研究利用脑连接来预测人类行为的个体差异。然而，传统的研究将脑连接网络视为一维向量，这种方法忽略了脑连接网络的拓扑性质。为了利用这些拓扑性质，我们提出可以采用图神经网络（GNN），它将图论和神经网络结合在一起。与以前的基于节点特征变换的节点驱动 GNN 不同，我们设计了一种称为图传播网络（GPN）的边驱动 GNN，它基于脑连接网络内的信息传播进行参数化。边驱动 GPN 在基于静息态功能连接体预测个体总认知方面优于各种基线模型，如节点驱动 GNN 和传统的偏最小二乘回归。GPN 还揭示了一个有向网络拓扑结构，编码信息流，表明更高阶的联合皮层，如背外侧前额叶、额下回和顶下小叶，负责总认知的信息整合。这些结果表明，边驱动 GNN 可以更好地探索脑连接网络的拓扑结构，并可以作为一种新的方法来将脑连接组与人类行为联系起来。
