Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful
tool for investigating the relationship between brain function and cognitive
processes as it allows for the functional organization of the brain to be
captured without relying on a specific task or stimuli. In this paper, we
present a novel modeling architecture called BrainRGIN for predicting
intelligence (fluid, crystallized, and total intelligence) using graph neural
networks on rsfMRI derived static functional network connectivity matrices.
Extending from the existing graph convolution networks, our approach
incorporates a clustering-based embedding and graph isomorphism network in the
graph convolutional layer to reflect the nature of the brain sub-network
organization and efficient network expression, in combination with TopK pooling
and attention-based readout functions. We evaluated our proposed architecture
on a large dataset, specifically the Adolescent Brain Cognitive Development
Dataset, and demonstrated its effectiveness in predicting individual
differences in intelligence. Our model achieved lower mean squared errors and
higher correlation scores than existing relevant graph architectures and other
traditional machine learning models for all of the intelligence prediction
tasks. The middle frontal gyrus exhibited a significant contribution to both
fluid and crystallized intelligence, suggesting their pivotal role in these
cognitive processes. Total composite scores identified a diverse set of brain
regions to be relevant which underscores the complex nature of total
intelligence.

使用静息态功能磁共振成像技术 (RsfMRI)，本文提出了一种名为 BrainRGIN 的新型建模架构，通过在静态功能网络连接矩阵上使用图神经网络，预测智力 (流体、晶体和总体智力)。在青少年大脑认知发展数据集上评估了我们的模型，证明了其在预测智力个体差异方面的有效性。我们的模型在智力预测任务的均方根误差和相关性得分上均优于现有的相关图形架构和其他传统的机器学习模型。中央额叶回对流体和晶体智力都有显著的贡献，表明它们在这些认知过程中起着关键的作用。总复合分数识别出一组与大脑相关的多样区域，凸显了总智力的复杂性。