1. J. Daunizeau Motivation, Brain and Behaviour group, ICM, Paris, France Wellcome Trust Centre for Neuroimaging, London, UK Dynamic Causal Modelling for EEG/MEG: principles

2. Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics 4 Bayesian inference 5 Conclusion

3. Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics 4 Bayesian inference 5 Conclusion

4. Introduction structural, functional and effective connectivity structural connectivity = presence of axonal connections functional connectivity = statistical dependencies between regional time series effective connectivity = causal (directed) influences between neuronal populations ! connections are recruited in a context-dependent fashion O. Sporns 2007, Scholarpedia structural connectivityfunctional connectivityeffective connectivity

5. u1u1 u 1 X u 2 A B u2u2 u1u1 A B u2u2 u1u1 localizing brain activity: functional segregation effective connectivity analysis: functional integration Introduction from functional segregation to functional integration « Where, in the brain, did my experimental manipulation have an effect? » « How did my experimental manipulation propagate through the network? » ?

6. neural states dynamics Electromagnetic observation model: spatial convolution simple neuronal model realistic observation model realistic neuronal model simple observation model fMRI EEG/MEG inputs Introduction DCM: evolution and observation mappings Hemodynamic observation model: temporal convolution

7. Introduction DCM: a parametric statistical approach DCM: model structure 1 2 4 3  24 u likelihood DCM: Bayesian inference model evidence: parameter estimate: priors on parameters

8. Introduction DCM for EEG-MEG: auditory mismatch negativity S-D: reorganisation of the connectivity structure rIFG rSTG rA1 lSTG lA1 rIFG rSTG rA1 lSTG lA1 standard condition (S) deviant condition (D) t ~ 200 ms … … S SS D S S S S D S sequence of auditory stimuli Daunizeau, Kiebel et al., Neuroimage, 2009

9. response or time (ms) 02004006008002000 Put PPAFFA PMd P(outcome|cue) PMdPutPPAFFA auditory cue visual outcome cue-independent surprise cue-dependent surprise Den Ouden, Daunizeau et al., J. Neurosci., 2010 Introduction DCM for fMRI: audio-visual associative learning

10. Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics 4 Bayesian inference 5 Conclusion

11. 12 3 12 3 12 3 time Dynamical systems theory motivation 12 3 u

12. Dynamical systems theory exponentials

13. Dynamical systems theory initial values and fixed points

14. Dynamical systems theory time constants

15. Dynamical systems theory matrix exponential

16. Dynamical systems theory eigendecomposition of the Jacobian

17. Dynamical systems theory dynamical modes

18. Dynamical systems theory spirals

20. Dynamical systems theory spiral state-space

21. Dynamical systems theory embedding

22. Dynamical systems theory kernels and convolution

23. Dynamical systems theory summary Motivation: modelling reciprocal influences System stability and dynamical modes can be derived from the system’s Jacobian: o D>0: fixed points o D>1: spirals o D>1: limit cycles (e.g., action potentials) o D>2: metastability (e.g., winnerless competition) Link between the integral (convolution) and differential (ODE) forms limit cycle (Vand Der Pol)strange attractor (Lorenz)

24. Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics 4 Bayesian inference 5 Conclusion

25. Neural ensembles dynamics DCM for M/EEG: systems of neural populations GolgiNissl internal granular layer internal pyramidal layer external pyramidal layer external granular layer mean-field firing ratesynaptic dynamics macro-scalemeso-scalemicro-scale EP EI II

26. Neural ensembles dynamics DCM for M/EEG: from micro- to meso-scale S(x)H(x) : post-synaptic potential of j th neuron within its ensemble mean-field firing rate mean membrane depolarization (mV) mean firing rate (Hz) membrane depolarization (mV) ensemble density p(x) S(x)

27. Neural ensembles dynamics DCM for M/EEG: synaptic dynamics time (ms) membrane depolarization (mV) post-synaptic potential IPSP EPSP

28. Neural ensembles dynamics DCM for M/EEG: intrinsic connections within the cortical column GolgiNissl internal granular layer internal pyramidal layer external pyramidal layer external granular layer spiny stellate cells inhibitory interneurons pyramidal cells intrinsic connections

29. Neural ensembles dynamics DCM for M/EEG: from meso- to macro-scale lateral (homogeneous) density of connexions local wave propagation equation (neural field): 0 th -order approximation: standing wave

30. Neural ensembles dynamics DCM for M/EEG: extrinsic connections between brain regions extrinsic forward connections spiny stellate cells inhibitory interneurons pyramidal cells extrinsic backward connections extrinsic lateral connections

31. Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics 4 Bayesian inference 5 Conclusion

32. Bayesian inference forward and inverse problems forward problem likelihood inverse problem posterior distribution

33. Bayesian inference the electromagnetic forward problem

34. Bayesian paradigm deriving the likelihood function - Model of data with unknown parameters: e.g., GLM: - But data is noisy: - Assume noise/residuals is ‘small’: → Distribution of data, given fixed parameters: f

35. Likelihood: Prior: Bayes rule: Bayesian paradigm likelihood, priors and the model evidence generative model m

36. Principle of parsimony : « plurality should not be assumed without necessity » “Occam’s razor” : model evidence p(y|m) space of all data sets y=f(x) x Bayesian inference model comparison Model evidence:

37. free energy : functional of q mean-field: approximate marginal posterior distributions: Bayesian inference the variational Bayesian approach

38. 12 3 u Bayesian inference DCM: key model parameters state-state coupling input-state coupling input-dependent modulatory effect

39. Bayesian inference model comparison for group studies m1m1 m2m2 differences in log- model evidences subjects fixed effect random effect assume all subjects correspond to the same model assume different subjects might correspond to different models

40. Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics 4 Bayesian inference 5 Conclusion

41. Conclusion back to the auditory mismatch negativity S-D: reorganisation of the connectivity structure rIFG rSTG rA1 lSTG lA1 rIFG rSTG rA1 lSTG lA1 standard condition (S) deviant condition (D) t ~ 200 ms … … S SS D S S S S D S sequence of auditory stimuli

42. Conclusion DCM for EEG/MEG: variants  DCM for steady-state responses  second-order mean-field DCM  DCM for induced responses  DCM for phase coupling 0100200300 0 50 100 150 200 250 0100200300 -100 -80 -60 -40 -20 0 0100200300 -100 -80 -60 -40 -20 0 time (ms) inputdepolarization time (ms) auto-spectral density LA auto-spectral density CA1 cross-spectral density CA1-LA frequency (Hz) 1 st and 2d order moments

43. Suitable experimental design: –any design that is suitable for a GLM –preferably multi-factorial (e.g. 2 x 2) e.g. one factor that varies the driving (sensory) input and one factor that varies the modulatory input Hypothesis and model: –define specific a priori hypothesis –which models are relevant to test this hypothesis? –check existence of effect on data features of interest –there exists formal methods for optimizing the experimental design for the ensuing bayesian model comparison [Daunizeau et al., PLoS Comp. Biol., 2011] Conclusion planning a compatible DCM study

44. Many thanks to: Karl J. Friston (UCL, London, UK) Will D. Penny (UCL, London, UK) Klaas E. Stephan (UZH, Zurich, Switzerland) Stefan Kiebel (MPI, Leipzig, Germany)