Dynamic Causal Modeling for Evoked Responses
Dynamic Causal Modeling for Evoked Responses
This chapter describes the dynamic causal modeling (DCM) equations, demonstrates how the ensuing model is inverted using Bayesian techniques, and reports the use of Bayesian priors to derive better magnetoencephalography/electoencephalography (EEG) models. It discusses the current DCM algorithms and some promising future developments, and explores the EEG data acquired under a mismatch negativity paradigm. The three plausible models defined under a given architecture and dynamics are examined. The chapter shows that evoked responses, due to bilateral sensory input (e.g., visually or auditory), could be analyzed using DCMs with symmetry priors.
Keywords: dynamic causal modeling, Bayesian techniques, magnetoencephalography, electoencephalography, mismatch negativity, evoked responses, bilateral sensory input
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