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Dynamic Causal Modelling for ERP/ERFs

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Presentation on theme: "Dynamic Causal Modelling for ERP/ERFs"— Presentation transcript:

1 Dynamic Causal Modelling for ERP/ERFs
Practical session Marta Garrido and Stefan Kiebel Thanks to James Kilner and Karl Friston

2 Hands on : application to the Mismatch Negativity (MMN)
Outline Hands on : application to the Mismatch Negativity (MMN) Demo Results

3 estimated by perturbing the system and measuring the response
DCM for Evoked Responses functional connectivity vs. effective connectivity causal architecture of interactions estimated by perturbing the system and measuring the response 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. differences in the evoked responses changes in effective connectivity

4 Data acquisition and processing
mode 1 Data acquisition and processing Oddball paradigm standards deviants mode 2 pseudo-random auditory sequence 80% standard tones – 500 Hz 20% deviant tones – 550 Hz time preprocessing mode 3 raw data convert to matlab file filter epoch down sample artifact correction average data reduction to principal spatial modes (explaining most of the variance) 128 EEG scalp electrodes ERPs / ERFs time (ms)

5 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. b 4 standards deviants 3 MMN HEOG VEOG 2 a 1 V m -1 -2 -3 -4 -100 -50 50 100 150 200 250 300 350 400 ms c

6 What are the mechanisms underlying the generation of the MMN?
DCM specification a plausible model… Forward - F Backward - B Both - FB 5 IFG 3 4 STG STG Opitz et al., 2002 lIFG rIFG lA1 rA1 lSTG rSTG 1 2 A1 A1 input Doeller et al., 2003 modulation of effective connectivity

7 Intrinsic connections
Matlab spm eeg choose time window choose data number of svd components choose polhemus file sources or nodes in your graph DCM.AF DCM.AB DCM.AL to specify extrinsic connections driving input DCM.C modulatory effect Intrinsic connections from DCM.B estimate the model compare models visualise output

8 Demo

9 ERPs (channels) ERPs (sources)

10 Coupling (A) Coupling (B)

11 Coupling (C) Input Spatial overview

12 Dipoles Response

13 Model Comparison

14 Results

15 DCM specification – testing different models
STG Forward Backward Lateral input - F B Forward and FB IFG modulation of effective connectivity

16 results Bayesian Model Comparison log-evidence group level subjects
Forward (F) Backward (B) Forward and Backward (FB)

17 modulation of effective connectivity
S2 S4 S5 S6 A1 input IFG STG A1 input IFG STG A1 input STG A1 input Forward Backward Intrinsic S2i S4i S5i IFG A1 input STG S6i IFG A1 input STG A1 input STG modulation of effective connectivity A1 input

18 F - negative free energy
results Bayesian Model Comparison 4 model space x 10 -2.55 S5i S6 -2.6 S4 S4i -2.65 S5 S6i -2.7 F - negative free energy -2.75 -2.8 S2i -2.85 -2.9 S2

19 Thank you

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