Time-Delay-Aware Causal Autoencoder for Neurological Disorder Recognition

Brain networks have become a crucial approach for characterizing interactions among brain regions, offering a powerful tool for understanding the pathological mechanisms underlying neurological disorders. In recent years, numerous brain network estimation methods have been developed based on correlation-based metrics. However, these approaches can only reflect statistical dependencies between brain regions. To address this limitation, various methods have been proposed to estimate causal effects, i.e., the direction of information flow, among brain regions. Unfortunately, many existing approaches assume uniform information transmission delays across different brain regions, which can introduce estimation bias and ultimately reduce the accuracy of neurological disorder identification. To address this issue, we propose a Time-Delay-Aware Causal Autoencoder (TDCA) that jointly estimates causal effects and transmission delays across brain regions. Furthermore, we introduce two constraints to ensure that the constructed brain network complies with established neurophysiological principles. Experimental results on two publicly datasets demonstrate the effectiveness and superiority of the proposed method.