Task-guided Generative Adversarial Networks for Synthesizing and Augmenting Structural Connectivity Matrices for Connectivity-Based Prediction

Abstract

Recent machine learning techniques have improved the modeling of complex dependencies between brain connectivity and cognitive/behavioral traits, facilitating connectome-based predictions. However, they typically require large datasets. While large open datasets like the Human Connectome Project have offered significant benefits to connectomics research, collecting such large data remains a challenge due to the financial cost and time. To address this issue, we propose Task-guided GAN II, a novel data augmentation method leveraging generative adversarial networks (GANs) to enhance the sample size from limited datasets for connectome-based prediction tasks. Distinguishing from previous approaches, our method incorporates a task- guided branch within the conventional Wasserstein GAN framework, specifically designed to synthesize structural connectivity matrices. It aims to effectively augment data and improve the prediction accuracy of human cognitive traits by capturing more task-directed features within the data. We evaluated the effectiveness of data augmentation using Task-guided GAN II in predicting fluid intelligence utilizing the NIMH Health Research Volunteer Dataset. Our results demonstrate that data augmentation with Task-guided GAN II not only improves prediction accuracy but also ensures that its latent space effectively captures correlations between structural connectivity and cognitive outcomes. Our method would be beneficial in leveraging small datasets for human connectomics research.