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Resting-state functional magnetic resonance imaging (rsfMRI) is a powerfultool for investigating the relationship between brain function and cognitiveprocesses as it allows for the functional organization of the brain to becaptured without relying on a specific task or stimuli. In this paper, wepresent a novel modeling architecture called BrainRGIN for predictingintelligence (fluid, crystallized, and total intelligence) using graph neuralnetworks on rsfMRI derived static functional network connectivity matrices.Extending from the existing graph convolution networks, our approachincorporates a clustering-based embedding and graph isomorphism network in thegraph convolutional layer to reflect the nature of the brain sub-networkorganization and efficient network expression, in combination with TopK poolingand attention-based readout functions. We evaluated our proposed architectureon a large dataset, specifically the Adolescent Brain Cognitive DevelopmentDataset, and demonstrated its effectiveness in predicting individualdifferences in intelligence. Our model achieved lower mean squared errors andhigher correlation scores than existing relevant graph architectures and othertraditional machine learning models for all of the intelligence predictiontasks. The middle frontal gyrus exhibited a significant contribution to bothfluid and crystallized intelligence, suggesting their pivotal role in thesecognitive processes. Total composite scores identified a diverse set of brainregions to be relevant which underscores the complex nature of totalintelligence.

Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data