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DCM for evoked responses

DCM for evoked responses. Ryszard Auksztulewicz SPM for M/EEG course, 2014. ?. Does network XYZ explain my data better than network XY?. input. input. ?. Does network XYZ explain my data better than network XY? Which XYZ connectivity structure best explains my data?. input. input. ?.

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DCM for evoked responses

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  1. DCM for evoked responses Ryszard Auksztulewicz SPM for M/EEG course, 2014

  2. ? Does network XYZ explain my data better than network XY? input input

  3. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data? input input

  4. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion? input input

  5. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections? context input

  6. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which neural populations are affected by contextual factors? context input

  7. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which neural populations are affected by contextual factors?Which connections determine observed frequency coupling? context input

  8. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which neural populations are affected by contextual factors?Which connections determine observed frequency coupling?How changing a connection/parameter would influence data? context input

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

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

  11. Data for DCM for ERPs / ERFs • Downsample • Filter (e.g. 1-40Hz) • Epoch • Remove artefacts • Average • Per subject • Grand average • Plausible sources • Literature / a priori • Dipole fitting / 3D source reconstruction

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

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

  14. Models

  15. Neuronal (source) model Kiebel et al., 2008

  16. NEURAL MASS MODEL CANONICAL MICROCIRCUIT Pyr Inhib Inter L2/3 xv Spiny Stell Spiny Stell mv L4 Inhib Inter Pyr Pyr L5/6 spm_fx_erp spm_fx_cmc

  17. Canonical Microcircuit Model (‘CMC’) Output equation: Supra-granular Layer Granular Layer Infra-granular Layer Pinotsis et al., 2012

  18. Canonical Microcircuit Model (‘CMC’) Bastos et al. (2012) Pinotsis et al. (2012)

  19. Canonical Microcircuit Model (‘CMC’) Supra- granular Layer Granular Layer Infra-granular Layer

  20. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  21. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  22. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  23. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  24. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  25. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  26. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  27. Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

  28. Canonical Microcircuit Model (‘CMC’) Voltage change rate: f(current) Current change rate: f(voltage,current) Pinotsis et al., 2012

  29. Canonical Microcircuit Model (‘CMC’) Voltage change rate: f(current) Current change rate: f(voltage,current) H, τKernels: pre-synaptic inputs -> post-synaptic membrane potentials [ H: max PSP; τ: rate constant ] S Sigmoid operator: PSP -> firing rate David et al., 2006; Pinotsis et al., 2012

  30. Canonical Microcircuit Model (‘CMC’) Supra-granular Layer Granular Layer Infra-granular Layer Pinotsis et al., 2012

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

  32. 5 4 3 2 1

  33. 5 4 3 2 1 Input

  34. 5 4 3 2 1 Input

  35. 5 4 3 2 1 Input

  36. 5 4 3 2 1 Input

  37. Factor 1 5 4 3 2 1 Input

  38. Factor 1 Factor 2 5 4 3 2 1 Input

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

  40. Fitting DCMs to data

  41. Fitting DCMs to data H. Brown

  42. Fitting DCMs to data H. Brown

  43. Fitting DCMs to data • Check your data H. Brown

  44. Fitting DCMs to data • Check your data • Check your sources H. Brown

  45. OFC OFC A19 IPL A19 IPL V4 V4 Model 1 Fitting DCMs to data • Check your data • Check your sources • Check your model IPL IPL V4 V4 Model 2 H. Brown

  46. Fitting DCMs to data • Check your data • Check your sources • Check your model • Re-run model fitting H. Brown

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

  48. ? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which connections/populations are affected by contextual factors? context input

  49. Example #1: Architecture of MMN Garrido et al., 2008

  50. Example #2: Role of feedback connections Garrido et al., 2007

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