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DCM for evoked responses

Explore Dynamic Causal Modeling (DCM) for studying neural connectivity using neuronal mass models. Learn how to build, fit, and infer models from ERP/ERF data, understanding neural circuitry and network dynamics.

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DCM for evoked responses

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  1. DCM for evoked responses Ryszard Auksztulewicz SPM for M/EEG course, 2015

  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. 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

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

  7. 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

  8. Models

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

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

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

  12. 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

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

  14. 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

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

  23. 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

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

  25. 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

  26. 5 4 3 2 1

  27. 5 4 3 2 1 Input

  28. 5 4 3 2 1 Input

  29. 5 4 3 2 1 Input

  30. 5 4 3 2 1 Input

  31. Factor 1 5 4 3 2 1 Input

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

  33. 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

  34. Fitting DCMs to data

  35. Fitting DCMs to data H. Brown

  36. Fitting DCMs to data H. Brown

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

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

  39. 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

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

  41. 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

  42. ? 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

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

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

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

  46. 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

  47. 2x2 design: Attended vs unattended Standardvs deviant (Only trials with 2 tones) N=20 Auksztulewicz & Friston, 2015

  48. 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

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

  50. Connectivity structure input input Extrinsic modulation input input Intrinsic modulation SP InhInt input input

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