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Dynamic Causal Modelling for ERP/ERFs

Dynamic Causal Modelling for ERP/ERFs. Practical session Marta Garrido and Stefan Kiebel Thanks to James Kilner and Karl Friston. Outline. Hands on : application to the Mismatch Negativity (MMN) Demo Results. DCM for Evoked Responses.

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Dynamic Causal Modelling for ERP/ERFs

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

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

  3. DCMforEvoked 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. mode 1 Data acquisition and processing Oddball paradigm standards deviants mode 2 time pseudo-random auditory sequence 80% standard tones – 500 Hz 20% deviant tones – 550 Hz 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 1 a V m 0 -1 -2 -3 -4 -100 -50 0 50 100 150 200 250 300 350 400 ms c

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

  7. 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 Intrinsic connections modulatory effect from DCM.B compare models estimate the model visualise output

  8. Demo

  9. Results

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

  11. 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 S2i -2.8 -2.85 -2.9 S2

  12. Thank you

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