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Dynamic Causal Modelling for EEG and MEG

Dynamic Causal Modelling for EEG and MEG. Stefan Kiebel. Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany. Overview of the talk. 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion

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Dynamic Causal Modelling for EEG and MEG

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  1. Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany

  2. Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion 5 Examples

  3. Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion 5 Examples

  4. Mismatch negativity (MMN) standards deviants Paradigm time pseudo-random auditory sequence 80% standard tones – 500 Hz 20% deviant tones – 550 Hz Preprocessing (SPM8) Raw data (e.g., 128 sensors) Evoked responses (here: single sensor) μV time (ms)

  5. Electroencephalography (EEG) amplitude (μV) time standard time (ms) sensors deviant sensors

  6. M/EEG analysis at sensor level time standard Conventional approach: Reduce evoked response to a few variables. sensors deviant Alternative approach that tells us about communication among brain sources? sensors

  7. Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion 5 Examples

  8. Electroencephalography (EEG) amplitude (μV) time (ms) Modelling aim: Explain all data with few parameters How? Assume data are caused by few communicating brain sources

  9. Connectivity models Conventional analysis: Which regions are involved in task? DCM analysis: How do regions communicate? STG STG STG STG A1 A1 A1 A1 Input (stimulus) Input (stimulus)

  10. Model for mismatch negativity Garrido et al., PNAS, 2008

  11. synapses AP generation zone Macro- and meso-scale macro-scale meso-scale micro-scale external granular layer external pyramidal layer internal granular layer internal pyramidal layer

  12. Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion 5 Examples

  13. The generative model Source dynamics f Spatial forward model g Evoked response states x parameters θ data y David et al., NeuroImage, 2006 Kiebel et al., Human Brain Mapping, 2009 Input u

  14. inhibitory interneurons spiny stellate cells pyramidal cells Neural mass equations and connectivity State equations Extrinsic lateral connections Extrinsic forward connections Intrinsic connections Extrinsic backward connections neuronal (source) model

  15. Model for mismatch negativity Garrido et al., PNAS, 2008

  16. Spatial model Depolarisation of pyramidal cells Sensor data Spatial model Kiebel et al., NeuroImage, 2006 Daunizeau et al., NeuroImage, 2009

  17. Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion 5 Examples

  18. Bayesian model inversion Specify generative forward model (with prior distributions of parameters) Evoked responses Expectation-Maximization algorithm Iterative procedure: • Compute model response using current set of parameters • Compare model response with data • Improve parameters, if possible • Posterior distributions of parameters • Model evidence Friston, PLoS Comp Biol, 2008

  19. Model selection: Which model is the best? best? Model 1 Model selection: Select model with highest model evidence data y Model 2 ... best? Model n Fastenrath et al., NeuroImage, 2009 Stephan et al., NeuroImage, 2009

  20. Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling – Motivation 3 Dynamic Causal Modelling – Generative model 4 Bayesian model inversion 5 Examples

  21. Mismatch negativity (MMN) Garrido et al., PNAS, 2008

  22. Mismatch negativity (MMN) time (ms) time (ms) Garrido et al., PNAS, 2008

  23. Another (MMN) example IFG IFG IFG 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 Garrido et al., (2007), NeuroImage modulation of effective connectivity

  24. Forward (F) Backward (B) Forward and Backward (FB) Group model comparison Bayesian Model Comparison Group level log-evidence subjects Garrido et al., (2007), NeuroImage

  25. Summary DCM enables testing hypotheses about how brain sources communicate. DCM is based on a neurobiologically plausible generative model of evoked responses. Differences between conditions are modelled as modulation of connectivity. Inference: Bayesian model selection

  26. Thanks to: Karl Friston Marta Garrido CC Chen Jean Daunizeau and the FIL methods group

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