Topology-Guided Cyclic Brain Connectivity Generation using Geometric Deep Learning

Abubakhari Sserwadda, Islem Rekik (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review


Background: There is a growing need for analyzing medical data such as brain connectomes. However, the unavailability of large-scale training samples increases risks of model over-fitting. Recently, deep learning (DL) architectures quickly gained momentum in synthesizing medical data. However, such frameworks are primarily designed for Euclidean data (eg., images), overlooking geometric data (eg., brain connectomes). A few existing geometric DL works that aimed to predict a target brain connectome from a source one primarily focused on domain alignment and were agnostic to preserving the connectome topology.

New Method: To address the above limitations, firstly, we adapt the graph translation generative adversarial network (GT GAN) architecture to brain connectomic data. Secondly, we extend the baseline GT GAN to a cyclic graph translation (CGT) GAN, allowing bidirectional brain network translation between the source and target views. Finally, to preserve the topological strength of brain regions of interest (ROIs), we impose a topological strength constraint on the CGT GAN learning, thereby introducing CGTS GAN architecture.

Comparison with existing methods: We compared CGTS with graph translation methods and its ablated versions.

Results: Our deep graph network outperformed the baseline comparison method and its ablated versions in mean squared error (MSE) using multiview autism spectrum disorder connectomic dataset.

Conclusion: We designed a topology-aware bidirectional brain connectome synthesis framework rooted in geometric deep learning, which can be used for data augmentation in clinical diagnosis.

Original languageEnglish
Article number108988
JournalJournal of Neuroscience Methods
Early online date4 Nov 2020
Publication statusE-pub ahead of print - 4 Nov 2020


  • Brain connectome generation
  • Geometric deep learning
  • Cyclic adversarial graph translation
  • Topological strength

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