Dynamic Causal Modeling (DCM) is a technique designed to assess the effective connectivity in the brain, i.e. the influence one neuronal system exerts over another. The central idea behind DCM is to treat the brain as a deterministic nonlinear dynamical system that is subject to inputs, and produces outputs. DCM for EEG uses neural mass model to explain source activity and to build a forward model that predicts scalp-recorded response, based on a particular underlying network structure. Further analysis is done by selecting, using the Bayesian inference, among the competing hypotheses (models) the one that is best to explain the data. We used DCM approach to find a plausible model for ERPs recorded for standard and deviant stimuli in visual oddball task, and to evaluate the reproducibility of this model over a set of individual recordings. The model that best explained the data and gave reproducible results was the one that allowed the changes in strength of forward connections. These results are compatible with the DCM for auditory oddball experiment by other authors.

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technique erps brain bayesian inference connectivity deterministic nonlinear dynamical system source activity auditory oddball eeg causal modeling dcm neural mass model inputs standard and deviant stimuli in visual oddball task scalprecorded response

M G, Sharaev E V, Mnatsakanian,.[Dynamic causal modeling of brain electrical responses elicited by simple stimuli in visual oddball paradigm].. 64 (6),627-38.

M G, Sharaev E V, Mnatsakanian,"[Dynamic causal modeling of brain electrical responses elicited by simple stimuli in visual oddball paradigm]." 64

M G, Sharaev E V, Mnatsakanian,"[Dynamic causal modeling of brain electrical responses elicited by simple stimuli in visual oddball paradigm]."