  Inferring the mechanisms underlying physiological and pathological processes
in the brain from recorded electrical activity is challenging. Bayesian model
selection and dynamic causal modelling aim to identify likely biophysical
models to explain data and to fit the model parameters. Here, we use data
generated by simulations to investigate the effectiveness of Bayesian model
selection and dynamic causal modelling when applied at steady state in the
frequency domain to identify and fit Jansen-Rit models. We first investigate
the impact of the necessary assumption of linearity on the dynamics of the
Jansen-Rit model. We then apply dynamic causal modelling and Bayesian model
selection to data generated from simulations of linear neural mass models,
non-linear neural mass models, and networks of discrete spiking neurons. Action
potentials are a characteristic feature of neuronal dynamics but have not
previously been explicitly included in simulations used to test Bayesian model
selection or dynamic causal modelling. We find that the assumption of linearity
abolishes the qualitative transitions seen as a function of the connectivity
parameter in the original Jansen-Rit model. As with previous work, we find that
the recovery procedures are effective when applied to data from linear
Jansen-Rit neural mass models, however, when applying them to non-linear neural
mass models and networks of discrete spiking neurons we find that their
effectiveness is significantly reduced, suggesting caution is required when
applying these methods.
