Dynamic Causal Modelling for ERP/ERFs

1. Dynamic Causal Modelling for ERP/ERFs Methods for Dummies 19/03/2008 Valentina Doria Georg Kaegi

2. Classical ERP analysis time condition 1 Analyse averages over channels and select interesting peri-stimulus times channels Difference between selected data Analysis of variance (Anova), over subjects condition 2 channels Analysis at channel level. but not in brain space

3. Source reconstruction Reconstruct brain sources which generated the observed channel data Analysis at source level, but typically no model about dynamics L R 1 Selected data 0

4. New approach condition 1 Develop mechanistic model for the full data, not only for selected or averaged part Use network model condition 2 Explain differences in responses by change of a few interpretable parameters in generating network

5. Dynamic Causal ModellingforERPs/ERFs functional connectivity vs. effective connectivity causal architecture of interactions estimated by perturbing the system and measuring the response The aim of DCM is to estimate and make inferences about the coupling among brain areas, and how that coupling is influences by changes in the experimental contex. differences in the evoked responses changes in effective connectivity

6. output eq. state eq. Dynamic Causal ModellingforERPs/ERFs (II) neural mass model 3 area model Supra-granular M/EEG parameters 2 3 neuronal states Layer 4 1 Infra-granular input Intrinsic Forward Backward Lateral Extrinsic Input u David et al., 2006

7. Generative model Dynamics f Spatial forward model g states x ERP/ERF parameters θ Input u data y

8. Generative forward model:an example A1 A3 A4 Forward Backward Lateral input 4 areas, somewhere in the brain, happily working together.. A2

9. Modulation of extrinsic connectivity modulation A1 A3 A4 Forward Backward Lateral input A2 Increase in backward connection A2->A1

10. Four steps through the model Single neuronal population Single source Network of sources Spatial expression in sensors

11. Input synapses Axons Dendrites and somas uo h 0 x 0 t Neural mass model State-space model Neuronal convolution

12. inhibitory interneurons spiny stellate cells pyramidal cells Single source State equations Input Intrinsic connections neuronal (source) model

13. inhibitory interneurons spiny stellate cells pyramidal cells Extrinsic connectivity State equations Extrinsic lateral connections Extrinsic forward connections Intrinsic connections Extrinsic backward connections Output equation neuronal (source) model

14. Spatial forward model Depolarisation of pyramidal cells Sensor data Spatial model

15. Generative model Dynamics f Spatial forward model g states x ERP/ERF parameters θ Input u data y

16. Model inversion: possible? Data Can we estimate extrinsic connectivity parameters and its modulation from data? modulation A1 A3 A4 Forward Backward Lateral input Model A2

17. DCM: The basic approach Specify generative forward model (with prior distributions on unknown parameters) Data Expectation-Maximization algorithm Iterative procedure: Compute model response using current set of parameters Compare model response with data Improve parameters, if possible Output: Posterior distributions of parameters Make inferences on parameters

18. DCM specification • DCM is specified by a graph of nodes (cortical areas) and edges (connections). Differences in 2 ERPs/ERFs are explained by coupling modulations, i.e., changes in connection strength. • DCM doesn’t test all possible models. • Is crucial to build a model biologically plausible! • Different hypotheses Different models • Bayesian model comparison identifies the best model/hypothesis within the universe of models/hypothesis considered.

19. svd DCM specification – put into context mode 1 Oddball paradigm standards deviants mode 2 time pseudo-random auditory sequence 80% standard tones – 1000 Hz 20% deviant tones – 2000 Hz preprocessing mode 3 raw data • convert to matlab file • epoch • down sample • filter • artifact correction • average data reduction to principal spatial modes (explaining most of the variance) ERPs / ERFs

20. IFG STG STG A1 A1 input DCM specification – areas and connections a plausible model… • Choice of nodes/areas? • source localization, prior knowledge from literature • Choice of edges/connections? • - anatomical or functional evidence IFG A1 A1 STG STG

21. 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 DCM specification – testing different models modulation of effective connectivity

22. Forward and Backward - FB DCM output single subject IFG reconstructed responses at source level 0.93 (55%) 1.41 (99%) STG STG coupling changes probability that a change occured 1.74 (96%) 5.40 (100%) 2.41 (100%) 4.50 (100%) A1 A1 input Forward Backward standard Lateral deviant

23. Forward and Backward - FB q µ q q p ( | y ) p ( y | ) p ( ) 1 1 q µ q q q p ( | y , y ) p ( y | ) p ( y | ) p ( ) 1 2 2 1 q q µ p ( y | ) p ( | y ) 2 1 ... q µ q q q p ( | y ,..., y ) p ( y | ) p ( | y )... p ( | y ) - 1 N N N 1 1 DCM output group Parameters at group level? IFG 0.60 (100%) 1.40 (100%) STG STG 1.58 (100%) 2.65 (100%) 2.17 (100%) 17.95 (100%) A1 A1 input Forward Neumann and Lohmann, 2003 Backward Lateral

24. DCM output Penny et al., 2004 Bayesian Model Comparison DCM.F log-evidence (log-evidence normalized to the null model) add up log-evidences for group analysis subjects Forward (F) Backward (B) Forward and Backward (FB)

25. STG A1 IFG Summary • DCM models ERPs on the basis of a network of interacting cortical areas. Differences in waveforms are explained by coupling changes among these areas. • The specification of the DCM (areas and connections in the network) is a critical point. It should be biologically plausible and motivated by specific hypotheses. • DCM can be used to test different hypotheses or models of connectivity.