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Dynamic Causal Modelling for evoked responses

Dynamic Causal Modelling for evoked responses . 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

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Dynamic Causal Modelling for evoked responses

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  1. Dynamic Causal Modelling for evoked responses 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. Analysis at sensor level time standard Conventional approach: Reduce evoked response to a few variables. sensors deviant sensors Alternative approach?

  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. 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

  11. 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

  12. The generative model Source dynamics 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

  13. 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 Amplitude (a.u.) neuronal (source) model David et al., NeuroImage, 2006 Time (ms)

  14. Model for auditory evoked response Garrido et al., PNAS, 2007

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

  16. 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

  17. 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

  18. 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

  19. 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

  20. Auditory evoked response Garrido et al., PNAS, 2007

  21. Auditory evoked response time (ms) time (ms) Garrido et al., PNAS, 2007

  22. Mismatch negativity 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

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

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

  25. Thanks to: Marta Garrido Jean Daunizeau Karl Friston and the FIL methods group

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