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 4 Bayesian model inversion 5 Examples

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

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)

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

Analysis at sensor level time standard Conventional approach: Reduce evoked response to a few variables. sensors deviant sensors Alternative approach?

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

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

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)

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

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

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

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

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

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

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

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

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

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

Auditory evoked response Garrido et al., PNAS, 2007

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

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

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

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

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