AFBT GAN: enhanced explainability and diagnostic performance for cognitive decline by counterfactual generative adversarial network

Existing explanation results of functional connectivity (FC) are normally generated by using classification result labels and correlation analysis methods such as Pearson's correlation or gradient backward. However, the diagnostic model is still trained on the black box model and might lack the attention of FCs in important regions during the training. To enhance the explainability and improve diagnostic performance, providing prior knowledge on neurodegeneration-related regions when healthy subjects (HC) develop into subject cognitive decline (SCD) and mild cognitive impairment (MCI) for the diagnostic model is a key step. To better determine the neurodegeneration-related regions, we employ counterfactual reasoning to generate the target label FC matrices derived from source label FC and then subtract source label FC with target label FC. The counterfactual reasoning architecture is constructed by adaptive forward and backward transformer generative adversarial network (AFBT GAN), which is specifically designed by network property in FC and inverse patch embedding operation in the transformer. The specific design can make the model focus more on the current network correlation and employ the global insight of the transformer to reconstruct FC, which both help the generation of high-quality target label FC. The validation experiments are conducted on both clinical and public datasets, the generated attention map are both vital correlated to cognitive function and the diagnostic performance is also significant. The code is available at https://github.com/SXR3015/AFBT-GAN.