The Time Lag in Local Field Potential Signals for the Development of its Bayesian Belief Network

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. Using a set comprised of three rats Local Field Potentials, a network model was constructed from data using the Bayesian Network method, considering and not taking into account the time delay of communication among brain areas recorded in this study. To this end, there was the appliance of the Delayed Mutual Information method to identify the lag between Local Field Potentials and K2 Score to compare the models. 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 main contribution of this study is to incorporate the minor delays inside the Bayesian Network method, achieved by applying the Delayed Mutual Information method before its application. That supports the neurophysiology dynamics considering an essential feature in the suggested methodology to support the functional connectivity analysis among brain areas.