Download presentation
Presentation is loading. Please wait.
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 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
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 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
Model for mismatch negativity
Garrido et al., PNAS, 2008
11
Macro- and meso-scale macro-scale meso-scale micro-scale 11
external granular layer external pyramidal layer internal granular layer internal pyramidal layer AP generation zone synapses 11
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
states x Evoked response parameters θ data y David et al., NeuroImage, 2006 Kiebel et al., Human Brain Mapping, 2009 Input u
14
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 neuronal (source) model 14
15
Model for mismatch negativity
Garrido et al., PNAS, 2008 15
16
Spatial model Depolarisation of pyramidal cells Sensor data
Kiebel et al., NeuroImage, 2006 Daunizeau et al., NeuroImage, 2009 16
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 18
19
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
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
Garrido et al., (2007), NeuroImage
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
Group model comparison
Bayesian Model Comparison Group level log-evidence Forward (F) Backward (B) Forward and Backward (FB) subjects Garrido et al., (2007), NeuroImage
25
Evoked and induced responses
Several events can induce the activity changes of neuronal populations. These changes could be either phase-locked to the stimulus onset (i.e. evoked activities) or non phase-locked (induced responses). To extract evoked potentials, people usually apply average technique to eliminate the non-stationary components, both the noise and the induced responses. In order to obtain the induced responses, the data were projected into the time frequency domain trail by trail and then average across trails. Trends Cogn Sci Apr;3(4):
26
Time-series data in channel space Dynamic power data in source space
Modelling of induced responses Inversion of electromagnetic model L input Time-series data in channel space Dynamic power data in source space 1- Modelling: capture data tendencies/regularities But: model quality <-> predictive ability => Parameters estimation 2- 2 forms of uncertainty: - stochastic - epistemological (=> reducible) -> Definition of “noise” 3- - Model sufficiently complex -> perfect data fit but suboptimal predictions - Model too simple -> poor data fit … 4- Link modelling to some measure of the uncertainty which is propagated through the model: model relevance is related to the quality of the information we can extract from the data about the modelled system… Aim: Explain dynamic power spectrum of each source as function of power input from other sources. Chen et al., Neuroimage, 2008
27
Face data (EEG): Network of four sources
LF RF LV RV 1- Modelling: capture data tendencies/regularities But: model quality <-> predictive ability => Parameters estimation 2- 2 forms of uncertainty: - stochastic - epistemological (=> reducible) -> Definition of “noise” 3- - Model sufficiently complex -> perfect data fit but suboptimal predictions - Model too simple -> poor data fit … 4- Link modelling to some measure of the uncertainty which is propagated through the model: model relevance is related to the quality of the information we can extract from the data about the modelled system… input
28
Observed power spectra
LV RV LF RF Time (ms) observed 1- Modelling: capture data tendencies/regularities But: model quality <-> predictive ability => Parameters estimation 2- 2 forms of uncertainty: - stochastic - epistemological (=> reducible) -> Definition of “noise” 3- - Model sufficiently complex -> perfect data fit but suboptimal predictions - Model too simple -> poor data fit … 4- Link modelling to some measure of the uncertainty which is propagated through the model: model relevance is related to the quality of the information we can extract from the data about the modelled system… Frequency (Hz)
29
Single subject results: Coupling functions
LF RF RV LV RV RF 1- Modelling: capture data tendencies/regularities But: model quality <-> predictive ability => Parameters estimation 2- 2 forms of uncertainty: - stochastic - epistemological (=> reducible) -> Definition of “noise” 3- - Model sufficiently complex -> perfect data fit but suboptimal predictions - Model too simple -> poor data fit … 4- Link modelling to some measure of the uncertainty which is propagated through the model: model relevance is related to the quality of the information we can extract from the data about the modelled system… input Chen et al., Neuroimage, 2008
30
Observed and fitted power spectra
LV RV LF RF Time (ms) observed Frequency (Hz) 1- Modelling: capture data tendencies/regularities But: model quality <-> predictive ability => Parameters estimation 2- 2 forms of uncertainty: - stochastic - epistemological (=> reducible) -> Definition of “noise” 3- - Model sufficiently complex -> perfect data fit but suboptimal predictions - Model too simple -> poor data fit … 4- Link modelling to some measure of the uncertainty which is propagated through the model: model relevance is related to the quality of the information we can extract from the data about the modelled system… fitted
31
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 31
32
Thanks to: Karl Friston Marta Garrido CC Chen Jean Daunizeau and
the FIL methods group 32
Similar presentations
