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DCM for ERP/ERF: theory and practice

DCM for ERP/ERF: theory and practice. Giovanna Moretto and Friederike Sch üür. ?. ?. Dynamical Causal Modelling. A sophisticated technique to investigate effective connectivity of the brain for fMRI and EEG / MEG data:. EEG / MEG data :

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DCM for ERP/ERF: theory and practice

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  1. DCM for ERP/ERF: theory and practice Giovanna Moretto and Friederike Schüür

  2. ? ? Dynamical Causal Modelling A sophisticated technique to investigate effective connectivity of the brain for fMRI and EEG / MEG data: EEG / MEG data: The goal of DCM is to explain evoked responses as the output of an interacting network consisting of a few areas that receive an input stimulus.

  3. Terminology: effective connectivity? Functional specialisation: Identification of a particular brain region with a specific function. Functional integration: Identifying interactions among specialised neural populations & how these depend on the context. Functional connectivity: Is defined as correlations between remote neuro-physiological events. Effective connectivity: Refers explicitly to the influence that one neuronal system exerts over another, either at a synaptic (i.e.synaptic efficacy) or population level.

  4. Seed-voxel correlation analysis Eigenimage analysis Independent component analysis Psycho-physiological interactions Structural Equation Modelling Psycho-physiological interactions Kalman Filtering Volterra Series Dynamical Causal Modelling (DCM) Different analyses for different purposes … Functional Connectivity Effective Connectivity

  5. Only about Evoked Responses for an EEG data set (also possible for steady state responses and induced responses). Principle for e.g. induced responses is highly similar as well as DCM for MEG data sets. Outline • Introduction Example: Mismatch Negativity (EEG) • Dynamical Causal Model • Single Source • Network of Sources • Spatial Expression in Sensors • Model Inversion • Example in detail: Mismatch Negativity (EEG) • DCM in SPM

  6. Introduction Example: Mismatch Negativity Oddball paradigm standards deviants time pseudo-random auditory sequence 80% standard tones – 500 Hz 20% deviant tones – 550 Hz data SPM • convert to matlab file • filter • epoch • down sample • artifact correction • average ERPs of 12 subjects, 2 conditions (standard + deviant) raw data preprocessing 128 EEG scalp electrodes

  7. 4 standards deviants 3 MMN 2 1 V m 0 -1 -2 -3 -4 -100 -50 0 50 100 150 200 250 300 350 400 ms Grand Mean (average over subjects) DCM: 1) Models the difference between two evoked responses … 2) … as a modulation of some of the inter-aereal connections.

  8. A1 A2 How can the MMN be explained? Build a model to test hypotheses: Assume that both ERPs are generated by temporal dynamics of a few sources Describe temporal dynamics by differential equations Dynamic Causal Modelling Each source projects to the sensors, following physical laws Solve for the model‘s parameters using Bayesian model inversion

  9. IFG IFG STG STG STG STG A1 A1 A1 A1 input input Why? What are we interested in … ? 1.41 (99%) 0.93 (55%) First, to find the best model … Second, to determine the coupling parameters … 2.41 (100%) 5.40 (100%) 4.50 (100%) 1.74 (96%)

  10. The Theory in more detail … Overview Single Source Network of Sources Spatial Expression in Sensors Model Inversion

  11. inhibitory interneurons spiny stellate cells pyramidal cells Single Source State equations Input Intrinsic connections see Jansen & Rit (1995) and David & Friston (2003) neuronal (source) model

  12. In more detail … Overview Single Source Network of Sources Spatial Expression in Sensors Model Inversion

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

  14. Overview Single Source Network of Sources Spatial Expression in Sensors Model Inversion

  15. Depolarisation of pyramidal cells Spatial model Sensor data Spatial Forward Model Default: Each area that is part of the model is modeled by one equivalent current dipole (ECD).

  16. Overview Single Source Network of Sources Spatial Expression in Sensors Model Inversion

  17. Observed (adjusted) 1 Predicted 6 6 4 4 2 2 0 0 -2 -2 -4 -4 input -6 -6 -8 -8 0 50 100 150 200 250 0 50 100 150 200 250 time (ms) time (ms) Model Inversion Data Predicted data We need to estimate the extrinsic connectivity parameters and their modulation from data.

  18. DCM: Model Inversion Data Predicted data (model) 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

  19. The Practical Part … The buttons … But actually, the practical part of DCM still involves a lot of theory, as we will see …

  20. 4 standards deviants 3 MMN 2 1 V m 0 -1 -2 -3 -4 -100 -50 0 50 100 150 200 250 300 350 400 ms Back to the example: MMN Oddball paradigm standards deviants time Before the DCM: B. Make required assumptions based on already existing literature … e.g. for the location of the sources. A. Collect, pre-process, and average EEG data.

  21. MMN could be generated by a temporofrontal network (Doeller et al. 2003; Opitz et al. 2002). “We argue that the right IFG mediates auditory deviance detection in case of low discriminability between a sensory memory trace and auditory input. This prefrontal mechanism might be part of top-down modulation of the deviance detection system in the STG.” Assumptions …

  22. STG A1 IFG Assumptions … MMN could be generated by a temporofrontal network (Doeller et al. 2003; Opitz et al. 2002). • Assumed Sources: • Left A1 • Right A1 • Left STG • Right STG • Right IFG Find the coordinates of the sources … (in mm in MNI coordinates).

  23. DCM specification … IFG STG STG Opitz et al., 2002 rIFG lA1 rA1 rSTG lSTG A1 A1 input Doeller et al., 2003 modulation of effective connectivity

  24. Alternative Models for Comparison … IFG IFG IFG Forward and Forward - F Backward - B Backward - FB STG STG STG STG STG STG A1 A1 A1 A1 A1 A1 input input input Forward Forward Forward Backward Backward Backward Lateral Lateral Lateral modulation of effective connectivity

  25. Finally … SPM! DCM for Evoked Responses Also for steady-state responses (SSR) and induces responses (IND) …

  26. Choose time window Trial indices Choose nr. of components

  27. How to spatially model ER Sources’ coordinates Onset time for modelling Sources’ names

  28. IFG STG STG A1 A1 input modulation of effective connectivity e.g. from left A1 to left STG Specify extrinsic connections Input to Modulatory effect Intrinsic connections from Invert DCM

  29. Wait …

  30. Coupling B Posterior means for gain modulations Probability ≠ prior means

  31. IFG IFG STG STG STG STG A1 A1 A1 A1 input input Why? What are we interested in … ? 1.41 (99%) 0.93 (55%) First, to find the best model … Second, to determine the coupling parameters … 2.41 (100%) 5.40 (100%) 4.50 (100%) 1.74 (96%)

  32. Forward (F) Backward (B) Forward and Backward (FB) Log evidence = accuracy - complexity

  33. IFG IFG STG STG STG STG A1 A1 A1 A1 input input Why? What are we interested in … ? 1.41 (99%) 0.93 (55%) First, to find the best model … Second, to determine the coupling parameters … 2.41 (100%) 5.40 (100%) 4.50 (100%) 1.74 (96%)

  34. right A1 0.8 0.6 0.4 0.2 IFG 0 -0.2 50 100 150 200 STG STG A1 A1 input modulation of effective connectivity trial 1 (pop. 1) trial 2 (pop. 1) trial 1 (pop. 2) trial 2 (pop. 2) trial 1 (pop. 3) trial 2 (pop. 3) ERP (sources) … Activity of interneuron populations Activity of pyramidal cells

  35. Observed (adjusted) 1 Predicted 6 6 4 4 2 2 0 0 -2 -2 -4 -4 -6 -6 -8 -8 0 50 100 150 200 250 0 50 100 150 200 250 time (ms) time (ms) Observed (adjusted) 2 Predicted 6 6 4 4 2 2 0 0 -2 -2 -4 -4 -6 -6 -8 -8 0 50 100 150 200 250 0 50 100 150 200 250 time (ms) time (ms) Response … Decided not to model early responses condition 1 condition 2

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

  37. Conclusions • DCM is a sophisticated technique to investigate effective connectivity • Combines a biologically plausible neuronal mass model with a spatial forward model to generate a predicted data set • Allows us to estimate connectivity parameters & how they are modulated between conditions • And to compute the model evidence in order to single out the best model of the ones proposed. • Underlying theory is complex, but SPM analysis is comparatively simple. • But: requires a lot of previous knowledge. • DCM is not a method to do ERP source reconstruction but knowledge about possible sources is a prerequisite for applying DCM to a data set. • DCM is not exploratory!

  38. References: Jansen BH, Rit VG, (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics 73:357–366 David O, Friston KJ (2003). A neural mass model for MEG/EEG: coupling and neuronal dynamics. Neuroimage 20:1743–1755 Kiebel SJ, Garrido MI, Moran RJ, Friston KJ (2008). Dynamic causal modeling for EEG and MEG. Cognitive Neurodynamics (2008) 2:121–136 SPM8 Manual:http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf

  39. Special Thanks to Rosalyn Moran

  40. Thanks for your attention …. Any questions?

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