dcm for evoked responses
DESCRIPTION
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. ?. - PowerPoint PPT PresentationTRANSCRIPT
DCM for evoked responses
Ryszard Auksztulewicz
SPM for M/EEG course, 2014
?Does network XYZ explain my data better than network XY?
inputinput
?Does network XYZ explain my data better than network XY?
Which XYZ connectivity structure best explains my data?
inputinput
?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?
inputinput
?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?
input
context
?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?
input
context
?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?
input
context
?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 / ERFs1. 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
v
v
spm_fx_cmcspm_fx_erp
Inhib Inter
Spiny
Stell
Pyr
L2/3
L4
L5/6
NEURAL MASS MODEL CANONICAL MICROCIRCUIT
Pyr
Spiny
Stell
Inhib Inter
Pyr
22
3
2
437187
2
24
43
2))()()(((
pppSpSpSAHp
pp
B
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
4
3
1
2
)( 3pSAF
24
7
4
8597102
4
48
87
2))()()((
pppSpSpSAHp
pp
F
23
5
3
65476157
3
36
65
2))()()()((
pppSpSpSpSAHp
pp
B
21
1
1
23253113
1
12
21
2))()()()(((
ppCupSpSpSpSAHp
pp
F
10
7
5
6
89
)( 7pSAB
)( 3pSAF
)( 7pSAB
)( 3pSAF
)( 7pSAB
)( 7pS
U
3Lp y Output equation:
Pinotsis et al., 2012
Canonical Microcircuit Model (‘CMC’)
Bastos et al. (2012) Pinotsis et al. (2012)
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
32
5
6
89
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
4
3
1
2
10
7
5
6
89
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
4
3
1
2
)( 3pSAF
10
7
5
6
89
)( 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
4
3
1
2
)( 3pSAF
10
7
5
6
89
)( 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
4
3
1
2
)( 3pSAF
10
7
5
6
89
)( 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
4
3
1
2
)( 3pSAF
10
7
5
6
89
)( 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))()()((
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))()()((
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))()()(((
pppSpSpSAHp
pp
B
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
4
3
1
2
)( 3pSAF
24
7
4
8597102
4
48
87
2))()()((
pppSpSpSAHp
pp
F
23
5
3
65476157
3
36
65
2))()()()((
pppSpSpSpSAHp
pp
B
21
1
1
23253113
1
12
21
2))()()()(((
ppCupSpSpSpSAHp
pp
F
10
7
5
6
89
)( 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-
stim
ulus t
ime
(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-
stim
ulus t
ime
(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 2
V4
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
?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 & Blankenburg, 2013
Example #4: Parametric effects
Auksztulewicz & Blankenburg, 2013
Auksztulewicz & Blankenburg, 2013
Moran et al., 2013
Example #5: Specific CMC populations
Rubbish data
Perfect model
Rubbish results
Perfectdata
Rubbishmodel
Rubbish results
Motivate your assumptions!
Thank you!
Karl FristonGareth BarnesAndre BastosHarriet Brown
Jean DaunizeauMarta GarridoStefan Kiebel
Vladimir LitvakRosalyn Moran
Will PennyDimitris Pinotsis
Bernadette van Wijk