Cross-Atlas Brain Connectivity Mapping with Dual-Conditional Diffusion Model

The open neuroimaging datasets provided by researchers offer a wealth of samples for scientific research, enhancing reproducibility and accelerating new scientific discoveries. However, due to privacy concerns and the costs of data management, researchers often release data that has been processed using atlases. Nevertheless, releasing such data has some limitations, especially in the field of connectomics. Different studies may use different atlases, leading to brain connectivity data that is not directly comparable across studies. Additionally, since there is no universally accepted standard atlas, researchers have to compromise on atlas selection, which may not meet the needs of all studies. To address these limitations, we propose a cross-atlas brain connectivity mapping framework based on a dual-conditional diffusion model, which can generate brain connectivity corresponding to a target atlas given only the brain connectivity corresponding to an original atlas. We introduce the first deep learning framework for cross-atlas brain connectivity mapping and demonstrate its effectiveness through experiments. We also validate the effectiveness of the dual-conditional diffusion model through ablation experiments, showing that adding additional conditional information provides a richer source of guidance.