DCM for evoked responses

Ryszard Auksztulewicz

SPM for M/EEG course, 2018

?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?

input

context

Collect data

Build model(s)

Fit your model parameters to

the data

Pick the best model

Make an inference

(conclusion)

The DCM analysis pathway

Collect data

Build model(s)

Fit your model parameters to

the data

Pick the best model

Make an inference

(conclusion)

The DCM analysis pathway

Data for DCM for ERPs / ERFs

1. Downsample2. Filter (e.g. 1-40Hz)3. Epoch4. Remove artefacts5. Average

• Per subject• Grand average

6. Plausible sources• Literature / a priori• Dipole fitting / 3D source

reconstruction

Collect data

Build model(s)

Fit your model parameters to

the data

Pick the best model

Make an inference

(conclusion)

The DCM analysis pathway

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

Models

Kiebel et al., 2008

Neuronal (source) model

spm_fx_erp

InhibInter

SpinyStell

Pyr

L2/3

L4

L5/6

NEURAL MASS MODEL

Canonical Microcircuit Model (‘CMC’)

Bastos et al. (2012) Pinotsis et al. (2012)

mv

xv

spm_fx_cmcspm_fx_erp

InhibInter

SpinyStell

Pyr

L2/3

L4

L5/6

NEURAL MASS MODEL CANONICAL MICROCIRCUIT

Pyr

SpinyStell

InhibInter

Pyr

Canonical Microcircuit Model (‘CMC’)

Supra-granular

Layer

Infra-granular

Layer

GranularLayer

Superficial Pyramidal Cells

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

3γ2γ

5γ

6γ

8γ9γ

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

4γ

3γ

1γ

2γ

10γ

7γ

5γ

6γ

8γ9γ

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

4γ

3γ

1γ

2γ

)( 3pSAF

10γ

7γ

5γ

6γ

8γ9γ

)( 7pSAB

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

4γ

3γ

1γ

2γ

)( 3pSAF

10γ

7γ

5γ

6γ

8γ9γ

)( 7pSAB

)( 3pSAF

)( 7pSAB

)( 3pSAF

)( 7pSAB

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

4γ

3γ

1γ

2γ

)( 3pSAF

10γ

7γ

5γ

6γ

8γ9γ

)( 7pSAB

)( 3pSAF

)( 7pSAB

)( 3pSAF

)( 7pSAB

U

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

4γ

3γ

1γ

2γ

)( 3pSAF

10γ

7γ

5γ

6γ

8γ9γ

)( 7pSAB

)( 3pSAF

)( 7pSAB

)( 3pSAF

)( 7pSAB

)( 7pS

U

Superficial Pyramidal Cells

Spiny Stellate Cells

Deep PyramidalCells

Inhibitory Interneurons

Pinotsis et al., 2012

24

7

4

8597102

4

48

87

2))()()((ττ

γγτ

pppSpSpSAHp

pp

F −−−−=

=

!

! Voltage change rate: f(current)Current change rate: f(voltage,current)

Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’)

24

7

4

8597102

4

48

87

2))()()((ττ

γγτ

pppSpSpSAHp

pp

F −−−−=

=

!

!

David et al., 2006; Pinotsis et al., 2012

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

Canonical Microcircuit Model (‘CMC’)

22

3

2

437187

2

24

43

2))()()(((ττ

γγτ

pppSpSpSAHp

pp

B −−−+−=

=

!

!

Canonical Microcircuit Model (‘CMC’)

GranularLayer

Supra-granular

Layer

Infra-granular

Layer

4γ

3γ

1γ

2γ

)( 3pSAF

24

7

4

8597102

4

48

87

2))()()((ττ

γγτ

pppSpSpSAHp

pp

F −−−−=

=

!

!

23

5

3

65476157

3

36

65

2))()()()((ττ

γγγτ

pppSpSpSpSAHp

pp

B −−−+−−=

=

!

!

21

1

1

23253113

1

12

21

2))()()()(((ττ

γγγτ

ppCupSpSpSpSAHp

pp

F −−−−−=

=

!

!

10γ

7γ

5γ

6γ

8γ9γ

)( 7pSAB

)( 3pSAF

)( 7pSAB

)( 3pSAF

)( 7pSAB

)( 7pS

U

3Lp y =

Pinotsis et al., 2012

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

2 1

4 3

5

2 1

4 3

5

Input

2 1

4 3

5

Input

2 1

4 3

5

Input

2 1

4 3

5

Input

2 1

4 3

5

Input

Factor 1

2 1

4 3

5

Input

Factor 1 Factor 2

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

Fitting DCMs to data

Fitting DCMs to data

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 1

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 2

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 3

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 4

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 5

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 6

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 7

50 100 150 200-1.5

-1

-0.5

0

0.5

1

1.5mode 8

time (ms)

trial 1 (predicted)trial 1 (observed)trial 2 (predicted)trial 2 (observed)

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

time (ms)

Observed (adjusted) 1

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

channels

time (

ms)

Predicted

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

time (ms)

Observed (adjusted) 2

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

channels

time (

ms)

Predicted

H. Brown

Fitting DCMs to data

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 1

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 2

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 3

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 4

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 5

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 6

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 7

50 100 150 200-1.5

-1

-0.5

0

0.5

1mode 8

time (ms)

trial 1 (predicted)trial 1 (observed)trial 2 (predicted)trial 2 (observed)

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

time (ms)

Observed (adjusted) 1

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

channels

time (

ms)

Predicted

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

time (ms)

Observed (adjusted) 2

0 50 100 150 200 250-0.01

-0.005

0

0.005

0.01

channels

time (

ms)

Predicted

H. Brown

Fitting DCMs to data

1. Check your data

0 50 100 150 200-5

0

5x 10

-14

time (ms)

Observed response 1

channels

peri-

stimu

lus tim

e (ms

)

Observed response 1

50 100 150 200 250

0

50

100

150

200

0 50 100 150 200-5

0

5x 10

-14

time (ms)

Observed response 2

channels

peri-

stimu

lus tim

e (ms

)

Observed response 2

50 100 150 200 250

0

50

100

150

200

H. Brown

Fitting DCMs to data

1. Check your data

2. Check your sources

H. Brown

1. Check your data

2. Check your sources

3. Check your model

Model 1

V4

IPLA19

OFC

V4

IPLA19

OFC

V4

IPL

Model 2V4

IPL

H. Brown

Fitting DCMs to data

Fitting DCMs to data

1. Check your data

2. Check your sources

3. Check your model

4. Re-run model fitting

H. Brown

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

Friston et al., 2016

?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?

input

context

Garrido et al., 2008

Example #1: Architecture of MMN

Garrido et al., 2007

Example #2: Role of feedback connections

Boly et al., 2011

Example #3: Group differences

Auksztulewicz & Friston, 2015

Example #4: Factorial design & CMC

Bastos et al., Neuron 2012

Attentioncf. Feldman & Friston, 2010

L2/3

L4

L5/6

smx

mν

xx

FORWARD PREDICTION ERROR

BACKWARD PREDICTIONS

A1 STG

xνxν

p

2x2 design:Attended vs unattendedStandard vs deviant(Only trials with 2 tones)

N=20

Auksztulewicz & Friston, 2015

Flexible factorial designThresholded at p<.005 peak-levelCorrected at a cluster-level pFWE<.05

Cont

rast

est

imat

e

Attention Expectation

Auksztulewicz & Friston, 2015

Flexible factorial designThresholded at p<.005 peak-levelCorrected at a cluster-level pFWE<.05

Cont

rast

est

imat

e

A1E1 A1E0 A0E1 A0E0

Auksztulewicz & Friston, 2015

inputinput

inputinput

InhInt

input

SP

input

Connectivity structure

Extrinsic modulation

Intrinsic modulation

Auksztulewicz & Friston, 2015

Example #5: Same paradigm, different data

Phillips et al., 2016

Example #5: Same paradigm, different data

Phillips et al., 2016

Example #6: Hierarchical modelling

Rosch et al., 2017

Evoked response potentials at Fz

Friston et al., 2016

Example #6: Hierarchical modelling

Rosch et al., 2017

Rubbish data

Perfect model

Rubbish results

Perfectdata

Rubbishmodel

Rubbish results

Motivate your assumptions!

Thank you!

Karl FristonGareth BarnesAndre BastosHarriet Brown

Hayriye CagnanJean DaunizeauMarta GarridoStefan Kiebel

Vladimir LitvakRosalyn Moran

Will PennyDimitris PinotsisRichard Rosch

Bernadette van Wijk