1. DCM for evoked responses
Ryszard Auksztulewicz
SPM for M/EEG course, 2016

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
Data should be processed like this to leave ERPs for each condition

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
I’m going to talk about these models here

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

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

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

13. CANONICAL MICROCIRCUIT
NEURAL MASS MODEL
CANONICAL MICROCIRCUIT
Pyr
L2/3
Inhib Inter 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, τ Kernels: 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
In theory this should take 1s of work and 45mins drinking coffee.

36. Fitting DCMs to data H. Brown
You should end up with something like this. This is nicely fitted data because…..
H. Brown

37. Fitting DCMs to data H. Brown
But sometimes this happens. This is poorly fitted data because…
H. Brown

38. Fitting DCMs to data Check your data H. Brown
Do you have a clear ERP which differs between conditions? If not, check your artefact rejection and other sources of noise in the data.
H. Brown

39. Fitting DCMs to data Check your data Check your sources H. Brown
Are they in locations of greatest source activity as identified by source localisation? Does the fit improve a lot if you allow the source locatiosn to be optimised?
H. Brown

40. Fitting DCMs to data Check your data Check your sources
Model 1 V4
IPL
A19
OFC
Fitting DCMs to data
Check your data
Check your sources
Check your model
e.g. does the model have enough explanatory power or enough areas? What about the input?
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
Sometimes you know the model is sound but a few datasets are being stubborn. Might be due to the free energy landscape. If you’re fitting individual subject data, try re-initialising with the posteriors form a previous subject.
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 A1
STG
L5/6
BACKWARD PREDICTIONS
Bastos et al., Neuron 2012
Auksztulewicz & Friston, 2015

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

50. Attention Expectation
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

51. Auksztulewicz & Friston, 2015
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

52. Connectivity structure
input
input
Extrinsic modulation
input
input
Intrinsic modulation SP
Inh Int
input
input

53. xv mv xx mx Superficial pyramidal cells:
signal prediction errors to higher areas
Brown & Friston, 2010
Feldman & Friston, 2013
Inhibitory interneurons:
prediction errors of hidden states
Bastos et al., 2012
attentional effects of ACh -> depression of I.I. activity
Xiang et al., 1998
Buia and Tiesinga, 2006
attentional gamma spike-LFP phase locking
Vinck et al., 2013 xv mv xx mx
SPM CMC

55. Auksztulewicz & Friston, 2015

56. Motivate your assumptions!
Rubbish
data
Perfect
model
Rubbish results
Perfect
data
Rubbish
model
Rubbish results

57. Thank you! Karl Friston Gareth Barnes Andre Bastos Harriet Brown
Jean Daunizeau
Marta Garrido
Stefan Kiebel
Vladimir Litvak
Rosalyn Moran
Will Penny
Dimitris Pinotsis
Bernadette van Wijk