Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity

Megi Isallari (Lead / Corresponding author), Islem Rekik

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, brain graph super-resolution is still poorly investigated because of the complex nature of non-Euclidean graph data. In this paper, we propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs with N' nodes (i.e., anatomical regions of interest (ROIs)) from low-resolution (LR) graphs with N nodes where N<N'. First, we formalize our GSR problem as a node feature embedding learning task. Once the HR nodes' embeddings are learned, the pairwise connectivity strength between brain ROIs can be derived through an aggregation rule based on a novel Graph U-Net architecture. While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology. Second, inspired by graph spectral theory, we break the symmetry of the U-Net architecture by super-resolving the low-resolution brain graph structure and node content with a GSR layer and two graph convolutional network layers to further learn the node embeddings in the HR graph. Third, to handle the domain shift between the ground-truth and the predicted HR brain graphs, we incorporate adversarial regularization to align their respective distributions. Our proposed AGSR-Net framework outperformed its variants for predicting high-resolution functional brain graphs from low-resolution ones. Our AGSR-Net code is available on GitHub at

Original languageEnglish
Article number102084
Number of pages11
JournalMedical Image Analysis
Early online date21 Apr 2021
Publication statusPublished - Jul 2021


  • Graph super-resolution
  • Brain connectivity
  • Graph node embedding
  • Graph neural network
  • Spectral upsampling
  • Adversarial learning

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity'. Together they form a unique fingerprint.

Cite this