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Dynamic Causal Modelling for ERP/ERFs

Dynamic Causal Modelling for ERP/ERFs. Practical session Stefan Kiebel and Rosalyn Moran. DCM for Evoked Responses. 4. 3. STG. STG. functional connectivity vs. effective connectivity. causal architecture of interactions. estimated by perturbing the system and measuring the response. 2.

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Dynamic Causal Modelling for ERP/ERFs

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  1. Dynamic Causal Modelling for ERP/ERFs Practical session Stefan Kiebel and Rosalyn Moran

  2. DCMforEvoked Responses 4 3 STG STG functional connectivity vs. effective connectivity causal architecture of interactions estimated by perturbing the system and measuring the response 2 1 A1 A1 input modulation of effective connectivity 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 context. differences in the evoked responses changes in effective connectivity

  3. 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)

  4. 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

  5. Forward - F Both - FB Backward - B Motivation for MMN model plausible models… Opitz et al., 2002 4 3 STG STG lA1 rA1 rSTG lSTG 2 1 A1 A1 input Doeller et al., 2003 modulation of effective connectivity

  6. Matlab spm eeg choose time window choose data number of spatial components sources or nodes in your graph from DCM.AF DCM.AB DCM.AL specify extrinsic connections driving input to DCM.C Intrinsic connections modulations DCM.B estimate the model visualise output

  7. DCM specification – testing different models Forward and Forward - F Backward - B Backward - FB STG STG STG STG STG STG STG A1 A1 A1 A1 A1 A1 input input input Forward Forward Forward Backward Backward Backward Lateral Lateral Lateral modulation of effective connectivity

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

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