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Abstract Purpose The objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality. Methods There is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats’ local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison. Results Bayesian network depicted the probabilistic relationship among rat’s brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results. Conclusion The primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology.