  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.
