  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.
