Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

Abstract

Brain connectivity plays an important role in determining the brain region's function. Previous researchers proposed that the brain region's function is characterized by that region's input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region's function. To overcome this problem, we proposed that a brain region's function is characterized by that region's multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA's face activation and revealed a hierarchical network for the face processing of rFFA.

Keywords: connectivity–function relationship; fusiform face function; graph neural network; individual prediction; multi-hops connectivity.