Causality-Preserving Generative Adversarial Networks for the Diagnosis of Neurological Disorders

The brain network has proven to be an effective tool for recognising neurological disorders. However, current brain network modelling algorithms often rely on numerous assumptions when evaluating the interaction relationships between brain regions, which are frequently shown to be inaccurate. For instance, some studies assume that interactions among brain regions are linear. Additionally, some research posits that other brain regions do not influence the evaluation outcomes when assessing causal effects among paired brain regions. To address this challenge, we propose an innovative method for modelling brain networks that estimates interaction relationships among brain regions from a causal perspective. Notably, during the evaluation process, this method comprehensively considers all relevant brain regions rather than examining individual relationships in isolation. To validate the efficacy of our approach, we conducted extensive experimental verifications using publicly available datasets. The experimental results indicate that our proposed brain network modelling method not only demonstrates superior performance in the dementia identification task but also successfully identifies several new biomarkers, providing novel perspectives and insights for dementia research.