# BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification

arXiv: https://arxiv.org/abs/2103.08494 · HTML: https://mlsanity.com/paper/2103.08494/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)

## Built on by (most cited first, 8 of 8)

- [Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis](https://mlsanity.com/paper/2406.03002/) (2024)
- [Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation](https://mlsanity.com/paper/2212.05316/) (2022)
- [D-CoRP: Differentiable Connectivity Refinement for Functional Brain Networks](https://mlsanity.com/paper/2405.18658/) (2024)
- [SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation](https://mlsanity.com/paper/2501.07055/) (2025)
- [Predicting conversion of mild cognitive impairment to Alzheimer's disease](https://mlsanity.com/paper/2203.04725/) (2022)
- [Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma](https://mlsanity.com/paper/2508.09593/) (2025)
- [Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks](https://mlsanity.com/paper/2410.11064/) (2024)
- [Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay](https://mlsanity.com/paper/2604.14259/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks. Structural connectivity matrices between separated brain regions are constructed using tractography on diffusion MRI data. The BrainNetGAN model is trained to generate fake brain connectivity matrices, which are expected to reflect latent distribution of the real brain network data. Finally, a convolutional neural network classifier is proposed for binary dementia classification. Numerical results show that the binary classification performance in the testing set was improved using the BrainNetGAN augmented dataset. The proposed methodology allows quick synthesis of an arbitrary number of augmented connectivity matrices and can be easily transferred to similar classification tasks.