Generative AI Empower Addiction-Related Brain Circuits Detection via Graph Diffusion-Infused Adversarial Learning

The study of the nicotine addiction mechanism is of great significance in both nicotine withdrawal and brain science. The detection of addiction-related brain circuitry using functional magnetic resonance imaging (fMRI) is a critical step in studying this mechanism. However, it is challenging to accurately estimate addiction-related brain circuitry due to the low signal-to-noise ratio of fMRI and the issue of small sample size. In this work, a graph diffusion-infused adversarial learning (GDAL) network is proposed to capture addiction-related brain circuitry accurately. The GDAL combines the graph convolution method with the diffusion model so that the model can fully capture addiction-related brain circuitry in non-Euclidean space. The diffusion reconstruction module (DRM) is designed to reconstruct the brain network to maintain the consistency of sample distribution in the latent space so that the brain circuitry can be detected more accurately. The proposed model reduces the search space by improving the conditional guidance of the DRM so that the model can better understand the latent distribution for the issue of small sample size. The experimental results demonstrate the effectiveness of the proposed method.