Presentation on theme: "Dynamic Causal Modelling for ERP/ERFs Practical session Marta Garrido and Stefan Kiebel Thanks to James Kilner and Karl Friston."— Presentation transcript:
Dynamic Causal Modelling for ERP/ERFs Practical session Marta Garrido and Stefan Kiebel Thanks to James Kilner and Karl Friston
Hands on : application to the Mismatch Negativity (MMN) Demo Results Outline
DCM for Evoked Responses differences in the evoked responses changes in effective connectivity functional connectivity vs. effective connectivity causal architecture of interactions The aim of DCM is to estimate and make inferences about the coupling among brain areas, and how that coupling is influences by changes in the experimental contex. estimated by perturbing the system and measuring the response
pseudo-random auditory sequence 80% standard tones – 500 Hz 20% deviant tones – 550 Hz time standardsdeviants Oddball paradigm Data acquisition and processing raw data preprocessing data reduction to principal spatial modes (explaining most of the variance) convert to matlab file filter epoch down sample artifact correction average ERPs / ERFs 128 EEG scalp electrodes mode 2 mode 1 mode 3 time (ms)
ms V standards deviants HEOGVEOG a b c MMN The Mismatch Negativity (MMN) is the ERP component elicited by deviations within a structured auditory sequence peaking at about 100 – 200 ms after change onset.
DCM specification A1 STG input STG IFG a plausible model… modulation of effective connectivity Forward - F Backward - B Both - FB Opitz et al., 2002 Doeller et al., 2003 rIFG rSTG rA1lA1 lSTG lIFG What are the mechanisms underlying the generation of the MMN?
visualise output estimate the model Matlabspm eeg number of svd components sources or nodes in your graph driving input specify extrinsic connections modulatory effect DCM.A F DCM.A B DCM.A L DCM.B DCM.C Intrinsic connections from to choose data choose time window choose polhemus file compare models