A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms
This work addresses brain disorder diagnosis by improving connectivity modeling, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of modeling brain networks for diagnosing disorders by proposing an effective temporal-lag neural network (ETLN) that simultaneously infers causal relationships and temporal-lag values between brain regions, with evaluation on the ADNI database showing effectiveness.
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., effective connectivity) between brain regions. Compared with traditional correlation-based methods, effective connectivity can provide the direction of information flow, which may provide additional information for the diagnosis of brain diseases. However, existing methods either ignore the fact that there is a temporal-lag in the information transmission across brain regions, or simply set the temporal-lag value between all brain regions to a fixed value. To overcome these issues, we design an effective temporal-lag neural network (termed ETLN) to simultaneously infer the causal relationships and the temporal-lag values between brain regions, which can be trained in an end-to-end manner. In addition, we also introduce three mechanisms to better guide the modeling of brain networks. The evaluation results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method.