09_DCM_ERP

DCM for evoked responses
Harriet Brown
SPM for M/EEG course, 2013
The DCM analysis pathway
The DCM analysis pathway
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
The DCM analysis pathway
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
Data for DCM for ERPs
1.
2.
3.
4.
5.
Downsample
Filter (1-40Hz)
Epoch
Remove artefacts
Average
The DCM analysis pathway
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
The DCM analysis pathway
‘hardwired’
model features
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
Models
Standard 3-population model (‘ERP’)
Canonical Microcircuit Model (‘CMC’)
B
A S ( p7 )
Output equation:
y  Lp 3
p 5  p 6
p 6 
H3
3
F
(  A S ( p 7 )   5 S ( p1 )   6 S ( p 7 )   4 S ( p 5 )) 
B
5
Supragranular
Layer
2 p6
3

B
A S ( p3 )
p5
A S ( p7 )
3
2
p 3  p 4
4
p 4 
3
H2
2
((  A S ( p 7 )   8 S ( p1 )   7 S ( p 3 )) 
B
2
8
9
6
p 2 
H1
1
(( A S ( p 3 )   1 S ( p1 )   3 S ( p 5 )   2 S ( p 3 ) Cu ) 
F
2 p2
1

1
p1

2
7
p 1  p 2
Granular
Layer
2 p4
2
1
U
Infragranular
Layer
F
p 7  p 8
p 8 
H4
4
( A S ( p 2 )   10 S ( p 7 )   9 S ( p 5 )) 
F
F
A S ( p3 )
2 p8
4

p7
4
2
 10
A S ( p3 )
B
A S ( p7 )

p3
2
2
S ( p7 )
Canonical Microcircuit Model (‘CMC’)
Canonical Microcircuit Model (‘CMC’)
Supragranular
Layer
Granular
Layer
Infragranular
Layer
Canonical Microcircuit Model (‘CMC’)
Inhibitory Interneurons
Superficial Pyramidal
Cells
Supragranular
Layer
Granular
Layer
Spiny Stellate Cells
Infragranular
Layer
Deep Pyramidal
Cells
Canonical Microcircuit Model (‘CMC’)
Inhibitory Interneurons
Superficial Pyramidal
Cells
Supragranular
Layer
Granular
Layer
Spiny Stellate Cells
Infragranular
Layer
Deep Pyramidal
Cells
Canonical Microcircuit Model (‘CMC’)
Inhibitory Interneurons
5
Supragranular
Layer
Granular
Layer
3
9
6
Superficial Pyramidal
Cells
8
Spiny Stellate Cells
Infragranular
Layer
Deep Pyramidal
Cells
2
Canonical Microcircuit Model (‘CMC’)
Inhibitory Interneurons
5
Supragranular
Layer
Granular
Layer
4
3
9
6
Superficial Pyramidal
Cells
2
8
Spiny Stellate Cells
Infragranular
Layer
Deep Pyramidal
Cells
 10
7
1
Canonical Microcircuit Model (‘CMC’)
F
A S ( p3 )
Inhibitory Interneurons
5
Supragranular
Layer
Granular
Layer
4
3
9
6
Superficial Pyramidal
Cells
2
8
7
1
Spiny Stellate Cells
Infragranular
Layer
Deep Pyramidal
Cells
 10
B
A S ( p7 )
Canonical Microcircuit Model (‘CMC’)
B
A S ( p7 )
F
Inhibitory Interneurons
5
Supragranular
Layer
Granular
Layer
4
3
6
A S ( p7 )
Superficial Pyramidal
Cells
2
8
9
B
A S ( p3 )
7
1
Spiny Stellate Cells
Infragranular
Layer
F
Deep Pyramidal
Cells
F
A S ( p3 )
 10
A S ( p3 )
B
A S ( p7 )
Canonical Microcircuit Model (‘CMC’)
B
A S ( p7 )
F
Inhibitory Interneurons
5
Supragranular
Layer
Granular
Layer
4
3
6
A S ( p7 )
Superficial Pyramidal
Cells
2
8
9
B
A S ( p3 )
7
1
Spiny Stellate Cells
U
Infragranular
Layer
F
Deep Pyramidal
Cells
F
A S ( p3 )
 10
A S ( p3 )
B
A S ( p7 )
Canonical Microcircuit Model (‘CMC’)
B
A S ( p7 )
F
Inhibitory Interneurons
5
Supragranular
Layer
Granular
Layer
4
3
6
A S ( p7 )
Superficial Pyramidal
Cells
2
8
9
B
A S ( p3 )
7
1
Spiny Stellate Cells
U
Infragranular
Layer
F
Deep Pyramidal
Cells
F
A S ( p3 )
 10
A S ( p3 )
B
A S ( p7 )
S ( p7 )
Canonical Microcircuit Model (‘CMC’)
p 7  p 8
p 8 
H4
4
( A S ( p 2 )   10 S ( p 7 )   9 S ( p 5 )) 
F
2 p8
4

p7

2
4
Canonical Microcircuit Model (‘CMC’)
B
A S ( p7 )
p 5  p 6
p 6 
H3
3
F
(  A S ( p 7 )   5 S ( p1 )   6 S ( p 7 )   4 S ( p 5 )) 
B
5
Supragranular
Layer
2 p6
3

B
A S ( p3 )
p5
A S ( p7 )
3
2
p 3  p 4
4
p 4 
3
H2
2
((  A S ( p 7 )   8 S ( p1 )   7 S ( p 3 )) 
B
2
8
9
6
p 2 
H1
1
(( A S ( p 3 )   1 S ( p1 )   3 S ( p 5 )   2 S ( p 3 ) Cu ) 
F
2 p2
1

1
p1

2
7
p 1  p 2
Granular
Layer
2 p4
2
1
U
Infragranular
Layer
F
p 7  p 8
p 8 
H4
4
( A S ( p 2 )   10 S ( p 7 )   9 S ( p 5 )) 
F
F
A S ( p3 )
2 p8
4

p7
4
2
 10
A S ( p3 )
B
A S ( p7 )

p3
2
2
S ( p7 )
Canonical Microcircuit Model (‘CMC’)
B
A S ( p7 )
Output equation:
y  Lp 3
p 5  p 6
p 6 
H3
3
F
(  A S ( p 7 )   5 S ( p1 )   6 S ( p 7 )   4 S ( p 5 )) 
B
5
Supragranular
Layer
2 p6
3

B
A S ( p3 )
p5
A S ( p7 )
3
2
p 3  p 4
4
p 4 
3
H2
2
((  A S ( p 7 )   8 S ( p1 )   7 S ( p 3 )) 
B
2
8
9
6
p 2 
H1
1
(( A S ( p 3 )   1 S ( p1 )   3 S ( p 5 )   2 S ( p 3 ) Cu ) 
F
2 p2
1

1
p1

2
7
p 1  p 2
Granular
Layer
2 p4
2
1
U
Infragranular
Layer
F
p 7  p 8
p 8 
H4
4
( A S ( p 2 )   10 S ( p 7 )   9 S ( p 5 )) 
F
F
A S ( p3 )
2 p8
4

p7
4
2
 10
A S ( p3 )
B
A S ( p7 )

p3
2
2
S ( p7 )
The DCM analysis pathway
‘hardwired’
model features
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
Designing your model
Area 1
Area 2
Area 3
Area 4
Designing your model
input
35
input (1)
30
25
20
15
10
5
0
0
50
100
150
200
250
time (ms)
Area 1
Area 2
Area 3
Area 4
Designing your model
input
35
input (1)
30
25
20
15
10
5
0
0
50
100
150
200
250
time (ms)
Area 1
Area 2
Area 3
Area 4
Designing your model
input
35
input (1)
30
25
20
15
10
5
0
0
50
100
150
200
250
time (ms)
Area 1
Area 2
Area 3
Area 4
Designing your model
input
35
input (1)
30
25
20
15
10
5
0
0
50
100
150
200
250
time (ms)
Area 1
Area 2
Area 3
Area 4
Designing your model
input
35
input (1)
30
25
20
15
10
5
0
0
50
100
150
200
250
time (ms)
Area 1
Area 2
Area 3
Area 4
Designing your model
input
35
input (1)
30
25
20
15
10
5
0
0
50
100
150
200
250
time (ms)
Area 1
Area 2
Area 3
Area 4
The DCM analysis pathway
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
The DCM analysis pathway
fixed
parameters
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
Fitting DCMs to data
Fitting DCMs to data
Predicted
Observed (adjusted) 1
0.01
0.01
0.005
time (ms)
0.005
0
-0.005
-0.01
mode 1
-0.005
0
50
100
150
time (ms)
200
250
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-1.5
-1.5
50
100
150
200
50
100
mode 3
0
-0.01
mode 2
1.5
0
50
100
150
channels
200
250
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-1.5
-1.5
100
150
200
50
100
mode 5
Observed (adjusted) 2
Predicted
0.01
0.01
0.005
0.005
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
50
100
150
200
-1.5
50
time (ms)
mode 7
0
-0.005
-0.01
0
-0.005
0
50
100
150
time (ms)
200
250
-0.01
0
50
100
150
channels
200
250
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
50
100
150
200
100
150
200
mode 8
1.5
-1.5
150
mode 6
1.5
-1.5
200
mode 4
1.5
50
150
200
-1.5
trial 1 (predicted)
trial 1 (observed)
trial 2 (predicted)
trial 2 (observed)
50
100
150
time (ms)
200
Fitting DCMs to data
mode 2
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
Predicted
Observed (adjusted) 1
0.01
0.005
0.005
time (ms)
0.01
0
-0.005
-0.01
mode 1
1
-1.5
50
100
150
200
-1.5
50
100
mode 3
0
200
mode 4
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-0.005
0
50
100
150
time (ms)
200
-0.01
250
0
50
100
150
channels
200
250
-1.5
50
100
150
200
-1.5
50
100
mode 5
Observed (adjusted) 2
150
200
mode 6
1
1
0.5
0.5
0
0
-0.5
-0.5
Predicted
0.01
0.01
0.005
-1
-1
-1.5
-1.5
50
100
150
200
50
100
150
200
0.005
time (ms)
mode 7
0
-0.005
-0.01
150
0
mode 8
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
trial 1 (predicted)
trial 1 (observed)
trial 2 (predicted)
trial 2 (observed)
-0.005
0
50
100
150
time (ms)
200
250
-0.01
0
50
100
150
channels
200
250
-1.5
50
100
150
200
-1.5
50
100
150
time (ms)
200
Fitting DCMs to data
5
x 10
-14
Observed response 1
Observed response 1
0
0
100
150
-5
5
200
0
x 10
50
-14
100
time (ms)
150
200
50
Observed response 2
100
150
channels
200
250
Observed response 2
0
50
peri-stimulus time (ms)
1. Check your data
peri-stimulus time (ms)
50
0
100
150
-5
200
0
50
100
time (ms)
150
200
50
100
150
channels
200
250
Fitting DCMs to data
1. Check your data
2. Check your sources
Fitting DCMs to data
OFC
A19
IPL
1. Check your data
2. Check your sources
OFC
A19
V4
IPL
V4
Model 1
3. Check your model
IPL
IPL
V4
V4
Model 2
Fitting DCMs to data
1. Check your data
2. Check your sources
3. Check your model
4. Re-run model fitting
The DCM analysis pathway
Build model(s)
Fit your model
parameters to
the data
Collect data
Pick the best
model
Make an
inference
(conclusion)
What questions can I ask with DCM for
ERPs?
Questions about
functional networks
causing ERPs
Garrido et al. (2008)
What questions can I ask with DCM for
ERPs?
Questions about
connectivity changes in
different conditions or
groups
Boly et al. (2011)
What questions can I ask with DCM for
ERPs?
mode 1
-3
3x 10
2
2
1
1
0
0
-1
-1
-2
-2
mode 2
-3
50 100 150 200 250 300 350 400 -3 50 100 150 200 250 300 350 400
peri-stimulus time (ms)
peri-stimulus time (ms)
-3
-3
mode 1
mode 2
3 x 10
3 x 10
Superficial
Pyramidal Cell
gain changed
2
2
1
1
0
0
-1
-1
-2
-2
-3
50 100 150 200 250 300 350 400 -3
peri-stimulus time (ms)
50 100 150 200 250 300 350 400
peri-stimulus time (ms)
0.25
Parameter value
Questions about the
neurobiological
processes underlying
ERPs
Deep
Pyramidal Cell
gain changed
-3
3x 10
0.2
0.15
0.1
0.05
0
-0.05
-0.1
V4
IPL
Area Area 18
SOG
How to use DCM for ERPs well
A DCM study is only as good as its hypotheses…

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