Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.
Bishal Thapaliya, Esra Akbas, Jiayu Chen, Ram Sapkota, Bhaskar Ray, Pranav Suresh, Vince D Calhoun, Jingyu Liu
Author Information
Bishal Thapaliya: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA. Electronic address: bthapaliya16@gmail.com.
Esra Akbas: Department of Computer Science, Georgia State University, Atlanta, USA.
Jiayu Chen: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA.
Ram Sapkota: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA.
Bhaskar Ray: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA.
Pranav Suresh: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA.
Vince D Calhoun: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.
Jingyu Liu: Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA.
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. Our GitHub implementation is publicly available on https://github.com/bishalth01/BrainRGIN/.
