DCM for evoked responses
description
Transcript of DCM for evoked responses
![Page 1: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/1.jpg)
DCM for evoked responses
Harriet Brown
SPM for M/EEG course, 2013
![Page 2: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/2.jpg)
The DCM analysis pathway
![Page 3: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/3.jpg)
Collect data
Build model(s)
Fit your model parameters to
the data
Pick the best model
Make an inference
(conclusion)
The DCM analysis pathway
![Page 4: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/4.jpg)
Collect data
Build model(s)
Fit your model parameters to
the data
Pick the best model
Make an inference
(conclusion)
The DCM analysis pathway
![Page 5: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/5.jpg)
Data for DCM for ERPs
1. Downsample2. Filter (1-40Hz)3. Epoch4. Remove artefacts5. Average
![Page 6: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/6.jpg)
Collect data
Build model(s)
Fit your model parameters to
the data
Pick the best model
Make an inference
(conclusion)
The DCM analysis pathway
![Page 7: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/7.jpg)
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
![Page 8: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/8.jpg)
Models
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Standard 3-population model (‘ERP’)
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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:
![Page 11: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/11.jpg)
Canonical Microcircuit Model (‘CMC’)
![Page 12: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/12.jpg)
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
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Superficial Pyramidal Cells
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
Spiny Stellate Cells
Deep PyramidalCells
Inhibitory Interneurons
![Page 14: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/14.jpg)
Canonical Microcircuit Model (‘CMC’)
GranularLayer
Supra-granular
Layer
Infra-granular
Layer
Superficial Pyramidal Cells
Spiny Stellate Cells
Deep PyramidalCells
Inhibitory Interneurons
![Page 15: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/15.jpg)
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
![Page 16: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/16.jpg)
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
![Page 17: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/17.jpg)
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
![Page 18: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/18.jpg)
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
![Page 19: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/19.jpg)
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
![Page 20: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/20.jpg)
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
![Page 21: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/21.jpg)
Canonical Microcircuit Model (‘CMC’)
24
7
4
8597102
4
48
87
2))()()((
pppSpSpSAHp
pp
F
![Page 22: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/22.jpg)
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
![Page 23: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/23.jpg)
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:
![Page 24: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/24.jpg)
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
![Page 25: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/25.jpg)
Designing your model
Area 1 Area 2
Area 3 Area 4
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Designing your model
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5
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time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
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Designing your model
0 50 100 150 200 2500
5
10
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25
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time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
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Designing your model
0 50 100 150 200 2500
5
10
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25
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time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
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Designing your model
0 50 100 150 200 2500
5
10
15
20
25
30
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time (ms)
input
input (1)
Area 1 Area 2
Area 3 Area 4
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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
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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
![Page 32: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/32.jpg)
Collect data
Build model(s)
Fit your model parameters to
the data
Pick the best model
Make an inference
(conclusion)
The DCM analysis pathway
![Page 33: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/33.jpg)
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
![Page 34: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/34.jpg)
Fitting DCMs to data
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Fitting DCMs to data
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time (ms)
trial 1 (predicted)trial 1 (observed)trial 2 (predicted)trial 2 (observed)
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time (ms)
Observed (adjusted) 1
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channels
time
(ms)
Predicted
0 50 100 150 200 250-0.01
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time (ms)
Observed (adjusted) 2
0 50 100 150 200 250-0.01
-0.005
0
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0.01
channels
time
(ms)
Predicted
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Fitting DCMs to data
50 100 150 200-1.5
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time (ms)
trial 1 (predicted)trial 1 (observed)trial 2 (predicted)trial 2 (observed)
0 50 100 150 200 250-0.01
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time (ms)
Observed (adjusted) 1
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channels
time
(ms)
Predicted
0 50 100 150 200 250-0.01
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time (ms)
Observed (adjusted) 2
0 50 100 150 200 250-0.01
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channels
time
(ms)
Predicted
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Fitting DCMs to data
1. Check your data
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-14
time (ms)
Observed response 1
channels
peri-
stim
ulus t
ime
(ms)
Observed response 1
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time (ms)
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channels
peri-
stim
ulus t
ime
(ms)
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Fitting DCMs to data
1. Check your data
2. Check your sources
![Page 39: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/39.jpg)
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
![Page 40: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/40.jpg)
Fitting DCMs to data
1. Check your data
2. Check your sources
3. Check your model
4. Re-run model fitting
![Page 41: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/41.jpg)
Collect data
Build model(s)
Fit your model parameters to
the data
Pick the best model
Make an inference
(conclusion)
The DCM analysis pathway
![Page 42: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/42.jpg)
What questions can I ask with DCM for ERPs?
Questions about functional networks causing ERPs
Garrido et al. (2008)
![Page 43: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/43.jpg)
What questions can I ask with DCM for ERPs?
Questions about connectivity changes in different conditions or groups
Boly et al. (2011)
![Page 44: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/44.jpg)
What questions can I ask with DCM for ERPs?
Questions about the neurobiological processes underlying ERPs
mode 2
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peri-stimulus time (ms)40050 100 150 200 250 300 350-3
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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
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Area
![Page 45: DCM for evoked responses](https://reader035.fdocuments.in/reader035/viewer/2022062222/56816635550346895dd9a204/html5/thumbnails/45.jpg)
How to use DCM for ERPs well
A DCM study is only as good as its hypotheses…