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DCM for evoked responses Harriet Brown SPM for M/EEG course, 2013
DCM for evoked responses
Harriet Brown
SPM for M/EEG course, 2013
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
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
1. Downsample2. Filter (1-40Hz)3. Epoch4. Remove artefacts5. Average
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
Standard 3-population model (‘ERP’)
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:
Canonical Microcircuit Model (‘CMC’)
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
Superficial Pyramidal Cells
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
Spiny Stellate Cells
Deep PyramidalCells
Inhibitory Interneurons
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
Superficial Pyramidal Cells
Spiny Stellate Cells
Deep PyramidalCells
Inhibitory Interneurons
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
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
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
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
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
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
Canonical Microcircuit Model (‘CMC’)
24
7
4
8597102
4
48
87
2))()()((
pppSpSpSAHp
pp
F
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
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:
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
Designing your model
Area 1 Area 2
Area 3 Area 4
Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
35
time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
35
time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
35
time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
35
time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
35
time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
35
time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
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
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
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
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
Fitting DCMs to data
1. Check your data
2. Check your sources
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
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
Collect data
Build model(s)
Fit your model parameters to
the data
Pick the best model
Make an inference
(conclusion)
The DCM analysis pathway
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?
Questions about the neurobiological processes underlying ERPs
mode 2
50 100 150 200 250 300 350 400-3
-2
-1
0
1
2
3x 10-3 mode 1
peri-stimulus time (ms)40050 100 150 200 250 300 350-3
-2
-1
0
1
2
3x 10-3 mode 2
peri-stimulus time (ms)
50 100 150 200 250 300 350 400-3
-2
-1
0
1
2
3x 10 mode 1
peri-stimulus time (ms)
-3
50 100 150 200 250 300 350 400-3
-2
-1
0
1
2
3x 10
peri-stimulus time (ms)
-3
Deep Pyramidal Cell gain changed
Superficial Pyramidal Cell gain changed
Par
amet
er v
alue
V4 IPL Area 18 SOG-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Area
How to use DCM for ERPs well
A DCM study is only as good as its hypotheses…
![United States Patent [19] 5,938,688 · Hinman et al., “Depth Evoked Potential and Single Unit Correlates of Vertex Midlatency Auditory Evoked Responses,” Brain Research, 264:57—67](https://static.documents.pub/doc/80x56/5f74697eb013c00c2b73061c/united-states-patent-19-5938688-hinman-et-al-aoedepth-evoked-potential-and.jpg)
