1. DCM for evoked responses Harriet Brown SPM for M/EEG course, 2013

2. The DCM analysis pathway

3. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway

4. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway

5. Data for DCM for ERPs 1.Downsample 2.Filter (1-40Hz) 3.Epoch 4.Remove artefacts 5.Average

6. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway

7. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway ‘hardwired’ model features

8. Models

9. Standard 3-population model (‘ERP’)

10. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Output equation:

11. Canonical Microcircuit Model (‘CMC’)

12. Granular Layer Supra- granular Layer Infra- granular Layer

13. Superficial Pyramidal Cells Superficial Pyramidal Cells Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

14. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

15. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

16. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

17. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

18. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

19. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

20. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

21. Canonical Microcircuit Model (‘CMC’)

22. Granular Layer Supra- granular Layer Infra- granular Layer

23. Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Output equation:

24. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway ‘hardwired’ model features

25. Designing your model Area 1Area 2 Area 3 Area 4

26. Designing your model 050100150200250 0 5 10 15 20 25 30 35 time (ms) input input (1) Area 1Area 2 Area 3 Area 4

27. Designing your model 050100150200250 0 5 10 15 20 25 30 35 time (ms) input input (1) Area 1Area 2 Area 3 Area 4

28. Designing your model 050100150200250 0 5 10 15 20 25 30 35 time (ms) input input (1) Area 1Area 2 Area 3 Area 4

29. Designing your model 050100150200250 0 5 10 15 20 25 30 35 time (ms) input input (1) Area 1Area 2 Area 3 Area 4

30. Designing your model 050100150200250 0 5 10 15 20 25 30 35 time (ms) input input (1) Area 1Area 2 Area 3 Area 4

31. Designing your model 050100150200250 0 5 10 15 20 25 30 35 time (ms) input input (1) Area 1Area 2 Area 3 Area 4

32. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway

33. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway fixed parameters

34. Fitting DCMs to data

37. 1.Check your data

38. Fitting DCMs to data 1.Check your data 2.Check your sources

39. 1.Check your data 2.Check your sources 3.Check your model Model 1 V4 IPL A19 OFC V4 IPL A19 OFC V4 IPL Model 2 V4 IPL Fitting DCMs to data

40. 1.Check your data 2.Check your sources 3.Check your model 4.Re-run model fitting

41. Collect data Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) The DCM analysis pathway

42. What questions can I ask with DCM for ERPs? Questions about functional networks causing ERPs Garrido et al. (2008)

43. What questions can I ask with DCM for ERPs? Questions about connectivity changes in different conditions or groups Boly et al. (2011)

44. What questions can I ask with DCM for ERPs? Questions about the neurobiological processes underlying ERPs mode 2 50100150200250300350400 -3 -2 0 1 2 3 x 10 -3 mode 1 peri-stimulus time (ms) 400 50100150200250300350 -3 -2 0 1 2 3 x 10 -3 mode 2 peri-stimulus time (ms) 50100150200250300350400 -3 -2 0 1 2 3 x 10 mode 1 peri-stimulus time (ms) -3 50100150200250300350400 -3 -2 0 1 2 3 x 10 peri-stimulus time (ms) -3 Deep Pyramidal Cell gain changed Superficial Pyramidal Cell gain changed Parameter value V4IPLArea 18SOG -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 Area

45. How to use DCM for ERPs well A DCM study is only as good as its hypotheses…