1 / 31

Dynamic Causal Modelling for M/EEG

Dynamic Causal Modelling for M/EEG. Stefan Kiebel Wellcome Trust Centre for Neuroimaging UCL. Overview of the talk. 1 M/EEG analysis 2 Dynamic Causal Modelling 3 Bayesian model inversion 4 Examples. Overview of the talk. 1 M/EEG analysis 2 Dynamic Causal Modelling

parley
Download Presentation

Dynamic Causal Modelling for M/EEG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dynamic Causal Modelling for M/EEG Stefan Kiebel Wellcome Trust Centre for Neuroimaging UCL

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

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

  4. Electroencephalography (EEG) amplitude (μV) time trial type 1 time (ms) channels trial type 2 channels

  5. M/EEG analysis at sensor level time trial type 1 Approach: Reduce evoked response to a few variables, e.g.: The average over a few channels in peri-stimulus time. channels trial type 2 Different approach that tells us more about the neuronal dynamics of localized brain sources? channels

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

  7. Dynamic Causal Modelling Build a model for spatiotemporal data: ??? Assume that both ERPs are generated by temporal dynamics of a network of a few sources A1 A2 Describe temporal dynamics by differential equations Dynamic Causal Modelling Each source projects to the sensors, following physical laws Solve for the model parameters using Bayesian model inversion

  8. Mismatch negativity (MMN) mode 1 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)

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

  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. The generative model Source dynamics f Spatial forward model g states x Evoked response parameters θ data y Input u

  12. 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

  13. Spatial model Depolarisation of pyramidal cells Sensor data Spatial model

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

  15. Bayesian model inversion Specify generative forward model (with prior distributions of parameters) Measured data 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

  16. Model comparison: Which model is the best? Model 1 Model comparison: Select model with highest model evidence data y Model 2 ... best? Model n

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

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

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

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

  21. 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 modulation of effective connectivity

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

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

  24. Evoked and induced responses Trends Cogn Sci. 1999 Apr;3(4):151-162

  25. Modelling of induced responses Inversion of electromagnetic model L input Time-series data in channel space Dynamic power data in source space Aim: Explain dynamic power spectrum of each source as function of power input from other sources. Chen et al., Neuroimage, 2008

  26. Face data (EEG): Network of four sources LF RF LV RV input

  27. Observed power spectra LV RV LF RF Time (ms) observed Frequency (Hz)

  28. Single subject results: Coupling functions LF RF RV RF LV RV Chen et al., Neuroimage, 2008 input

  29. Observed and fitted power spectra LV RV LF RF Time (ms) observed Frequency (Hz) fitted

  30. Summary DCM combines state-equations for neural mass dynamics with spatial forward model. Differences between responses acquired under different conditions are modelled as modulation of connectivity within and between sources. Bayesian model comparison allows one to compare many different models and identify the best one. Make inference about posterior distribution of parameters (e.g., effective connectivity, location of dipoles, etc.). Many extensions to DCM for M/EEG are available in SPM8.

  31. Thanks to Karl Friston Marta Garrido CC Chen Jean Daunizeau

More Related