Adversarial Brain Multiplex Prediction From a Single Network for High-Order Connectional Gender-Specific Brain Mapping

Abstract

Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain regions of interest (ROIs) and give insights into gender-related connectional differences. However, to the best of our knowledge, studies on gender differences in brain connectivity were limited to investigating pairwise (i.e., low-order ) relationship ROIs, overlooking the complex high-order interconnectedness of the brain as a network. A few recent works on neurological disorder diagnosis addressed this limitation by introducing the brain multiplex, which in its shallow form, is composed of a source network intra-layer, a target intra-layer, and a convolutional inter-layer capturing the highlevel relationship between both intra-layers. However, brain multiplexes are built from at least two different brain networks, inhibiting its application to connectomic datasets with single brain networks such as functional networks. To fill this gap, we propose the first work on predicting brain multiplexes from a source network to investigate gender differences. Recently, generative adversarial networks (GANs) submerged the field of medical data synthesis. However, although conventional GANs work well on images, they cannot handle brain networks due to their non-Euclidean topological structure. Differently, in this paper, we tap into the nascent field of geometric-GANs (G-GAN) to design a deep multiplex prediction architecture comprising (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections and (ii) a conditional discriminator which classifies predicted target intra-layers by conditioning on the multiplex source intra-layers. Such architecture simultaneously learns the latent source network representation and the deep non-linear mapping from the source to target multiplex intra-layers. Our experiments on a large dataset demonstrated that predicted multiplexes significantly boost gender classification accuracy compared with source networks and identifies both low and high-order gender-specific multiplex connections.