# Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer

arXiv: https://arxiv.org/abs/2009.11166 · HTML: https://mlsanity.com/paper/2009.11166/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations](https://mlsanity.com/paper/2104.03895/) (2021)
- [Fast Temporal Wavelet Graph Neural Networks](https://mlsanity.com/paper/2302.08643/) (2023)
- [Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory](https://mlsanity.com/paper/2110.11237/) (2021)
- [Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping](https://mlsanity.com/paper/2204.05110/) (2022)
- [StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis](https://mlsanity.com/paper/2110.04279/) (2021)
- [A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline Timepoint](https://mlsanity.com/paper/2109.07739/) (2021)
- [Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification](https://mlsanity.com/paper/2209.06005/) (2022)
- [Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification](https://mlsanity.com/paper/2212.07790/) (2022)
- [Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data](https://mlsanity.com/paper/2401.01383/) (2024)
- [Reservoir-Based Graph Convolutional Networks](https://mlsanity.com/paper/2603.24131/) (2026)

## Abstract (arXiv)

Foreseeing the brain evolution as a complex highly inter-connected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regions of interest (ROIs) in health and disease. Interestingly, brain graph evolution models remain almost absent in the literature. Here we design an adversarial brain network normalizer for representing each brain network as a transformation of a fixed centered population-driven connectional template. Such graph normalization with respect to a fixed reference paves the way for reliably identifying the most similar training samples (i.e., brain graphs) to the testing sample at baseline timepoint. The testing evolution trajectory will be then spanned by the selected training graphs and their corresponding evolution trajectories. We base our prediction framework on geometric deep learning which naturally operates on graphs and nicely preserves their topological properties. Specifically, we propose the first graph-based Generative Adversarial Network (gGAN) that not only learns how to normalize brain graphs with respect to a fixed connectional brain template (CBT) (i.e., a brain template that selectively captures the most common features across a brain population) but also learns a high-order representation of the brain graphs also called embeddings. We use these embeddings to compute the similarity between training and testing subjects which allows us to pick the closest training subjects at baseline timepoint to predict the evolution of the testing brain graph over time. A series of benchmarks against several comparison methods showed that our proposed method achieved the lowest brain disease evolution prediction error using a single baseline timepoint. Our gGAN code is available at http://github.com/basiralab/gGAN.