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Brain Signal AnalysisAdvances in Neuroelectric and Neuromagnetic Methods$
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Todd C. Handy

Print publication date: 2009

Print ISBN-13: 9780262013086

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262013086.001.0001

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Dynamic Causal Modeling for Evoked Responses

Dynamic Causal Modeling for Evoked Responses

(p.141) 6 Dynamic Causal Modeling for Evoked Responses
Brain Signal Analysis

Stefan J. Kiebel

Marta I. Garrido

Karl J. Friston

The MIT Press

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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