A new physiologically based Bayesian model for event-related brain activity

A new dynamical model for event-related brain activities is proposed, based on a decisive map of brain physical connections released recently, and coined the Connectivity Backbone (CB). In, it was pointed out that physical connections are useful predictors of activation patterns in the cortex. In this paper, we use the CB for the first time as a prior to constrain the estimation of the brain effective connections. We model them using a Bayesian multivariate autoregressive model where the prior on the parameters is modeled by a multivariate Gaussian distribution whose covariance matrix integrates the information encoded in the CB. Since the analysis of event-related brain activity requires to deal with short data segments, and calls for the simultaneous use of tens of channels, traditional approaches will fail. On the contrary, by constraining the solution space, the use of the CB as prior for high dimensional systems may succeed in providing reliable estimation.