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

5. Electroencephalography (EEG)
amplitude (μV)
time
time (ms)
standard
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 8

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. Macro- and meso-scale macro-scale meso-scale micro-scale 10
external granular
layer
external pyramidal
layer
internal granular
layer
internal pyramidal
layer
AP generation zone
synapses 10

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 states x
Evoked response
parameters θ
data y
David et al., NeuroImage, 2006
Kiebel et al., Human Brain Mapping, 2009
Input u

13. Neural mass equations and connectivity
State equations
Extrinsic lateral connections
spiny stellate cells
inhibitory interneurons
pyramidal cells
Extrinsic
forward connections
Intrinsic connections
Extrinsic backward connections
Amplitude (a.u.)
neuronal (source) model
Time (ms)
David et al., NeuroImage, 2006 13

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

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

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 17

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

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