Abstract
Fast diagnostic prediction of brain dysfunctions such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and Alzheimer's disease is important due to their prevalence in the population. A typical workflow for brain dysfunction prediction is to preprocess a brain image into a network of connected brain regions, where indicative features are extracted using simple linear or convolutional models to be used for prediction. However, due to restrictions on patient data sharing such as HIPAA rules, the number of training samples is often very limited. Even with efforts like the ABIDE initiative that aggregated brain imaging data from laboratories around the world, the subject number merely reaches around 1,000, limiting the effectiveness of data-driven models such as deep learning models.To overcome this data scarcity problem, we propose a GAN-based data augmentation technique to generate realistic brain region networks, which are used to increase the size of an existing training set so that a brain dysfunction classifier can be better trained to achieve a higher prediction accuracy. In the brain region network setting, we propose a generator that considers each brain region as an embedding, so that the connectivity between two regions can be computed using the inner product of their embeddings. This generator is trained along with a phenotype-enhanced BrainNetCNN, a domain-specific discriminator (i.e., classifier), to improve its prediction accuracy. Our embedding-based generator generates samples following the original data feature distribution (i.e., age, gender, and health condition), which improves generator quality and avoids mode collapse.Our design is generally applicable to various neuroimaging data, and experimental results obtained on two real datasets ABIDE-I and ADHD200 confirmed the effectiveness of the proposed method. Our model has been open-sourced on GitHub at https://github.com/binwsh/GAN-for-Neural-Graph.