UCLN: Toward the Causal Understanding of Brain Disorders With Temporal Lag Dynamics.

Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful tool for exploring interactions among brain regions. A growing body of research is actively investigating various computational approaches for estimating causal effects among brain regions. Compared to traditional methods, causal relationship reveals the causal influences among distinct brain regions, offering a deeper understanding of brain network dynamics.

Deep Causality Variational Autoencoder Network for Identifying the Potential Biomarkers of Brain Disorders.

Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e.

A Structure-Guided Effective and Temporal-Lag Connectivity Network for Revealing Brain Disorder Mechanisms.

Brain network provides important insights for the diagnosis of many brain disorders, and how to effectively model the brain structure has become one of the core issues in the domain of brain imaging analysis. Recently, various computational methods have been proposed to estimate the causal relationship (i.e.