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DCM for evoked responses

DCM for evoked responses. Ryszard Auksztulewicz SPM for M/EEG course, 2017. ?.

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DCM for evoked responses

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  1. DCM for evoked responses Ryszard Auksztulewicz SPM for M/EEG course, 2017

  2. ? 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

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

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

  5. Phillips et al., 2016

  6. 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

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

  8. 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

  9. Models

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

  11. NEURAL MASS MODEL Inhib Inter L2/3 Spiny Stell L4 Pyr L5/6 spm_fx_erp

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

  13. 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

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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  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’) Voltage change rate: f(current) Current change rate: f(voltage,current) Pinotsis et al., 2012

  24. 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

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

  26. 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

  27. 5 4 3 2 1

  28. 5 4 3 2 1 Input

  29. 5 4 3 2 1 Input

  30. 5 4 3 2 1 Input

  31. 5 4 3 2 1 Input

  32. Factor 1 5 4 3 2 1 Input

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

  34. 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

  35. Fitting DCMs to data

  36. Fitting DCMs to data H. Brown

  37. Fitting DCMs to data H. Brown

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

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

  40. 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

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

  42. 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

  43. Phillips et al., 2016

  44. ? 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

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

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

  47. Example #3: Group differences Boly et al., 2011

  48. Example #4: Factorial design & CMC Attention cf. Feldman & Friston, 2010 FORWARD PREDICTION ERROR L2/3 p x x xx L4 s m mx L5/6 A1 STG BACKWARD PREDICTIONS Bastos et al., Neuron 2012 Auksztulewicz & Friston, 2015

  49. 2x2 design: Attended vs unattended Standardvs deviant (Only trials with 2 tones) N=20 Auksztulewicz & Friston, 2015

  50. Expectation Attention Flexible factorial design Thresholded at p<.005 peak-level Corrected at a cluster-level pFWE<.05 Auksztulewicz & Friston, 2015 Contrast estimate A1E1 A1E0 A0E1 A0E0

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