Neuro-imaging data analysis

Abstract

Brain network analysis using graph neural networks (GNNs) has become a powerful method for modelling neuroimaging data such as fMRI. However, class imbalance remains a major challenge in real-world datasets, where some diagnostic groups are significantly underrepresented. This issue is often overlooked in existing GNN-based models, limiting their robustness and clinical relevance.

In this project, we introduce an oversampling strategy specifically tailored for brain connectivity graphs. Our method integrates biologically informed augmentation steps, including interpolation, symmetry enforcement, noise removal, Laplacian smoothing and edge symmetry correction. Experiments on two benchmark datasets, ADNI and PPMI, demonstrate strong improvements across key evaluation metrics. Confusion matrix analysis shows more balanced class-wise performance, while saliency mapping highlights neurologically meaningful regions (ROI) in line with existing literature. The proposed approach offers a practical and interpretable solution to address class imbalance, with a strong potential to generalize to other neuroimaging datasets and clinical applications.