Dynamic Causal Modelling using Chen-Fliess Expansion

Dynamic causal modelling (DCM) provides a powerful framework for studying dynamics of large neural populations by using neural mass model, a set of differential equations. Although DCM has been increasingly developed into a useful clinical tool in the fields of computational psychiatry and neurology, inferring the hidden neuronal states in the model with neurophysiological data is still challenging. Many existing approaches, based on a bilinear approximation to the neural mass model, can mis-specify the model and thus compromise their accuracy. In this talk, we will introduce Chen-Fliess expansion for the neural mass model. The Chen-Fliess expansion is a type of Taylor series that converts the problem of estimating differential equations into a problem of estimating ill-posed nonlinear regression. We develop a maximum likelihood estimation based on the Chen-Fliess approximation. Both simulations and real data analysis are conducted to evaluate the proposed approach.

Dynamic causal modelling

Neural differential equations

Chen-Fliess expansion

Maximum likelihood estimation

Hidden state model

Computational psychiatry and neurology