![Page 1: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/1.jpg)
Dynamic Causal Modellingfor EEG and MEG
Stefan Kiebel
Max Planck Institute forHuman Cognitive and Brain Sciences
Leipzig, Germany
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Overview of the talk
1 M/EEG analysis
2 Dynamic Causal Modelling – Motivation
3 Dynamic Causal Modelling – Generative model
4 Bayesian model inversion
5 Examples
![Page 3: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/3.jpg)
Overview of the talk
1 M/EEG analysis
2 Dynamic Causal Modelling – Motivation
3 Dynamic Causal Modelling – Generative model
4 Bayesian model inversion
5 Examples
![Page 4: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/4.jpg)
pseudo-random auditory sequence
80% standard tones – 500 Hz
20% deviant tones – 550 Hz
time
standards deviants
Mismatch negativity (MMN)
time (ms)
μV
Paradigm
Raw data(e.g., 128 sensors)
Preprocessing (SPM8)
Evoked responses(here: single sensor)
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Electroencephalography (EEG)
time
sens
ors
sens
ors
standard
deviant
time (ms)
amplitude (μV)
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M/EEG analysis at sensor levelse
nsor
sse
nsor
s
standard
deviant
time
Conventional approach: Reduce evoked response to a few
variables.
Alternative approach that tellsus about communication among
brain sources?
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Overview of the talk
1 M/EEG analysis
2 Dynamic Causal Modelling – Motivation
3 Dynamic Causal Modelling – Generative model
4 Bayesian model inversion
5 Examples
![Page 8: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/8.jpg)
Electroencephalography (EEG)
time (ms)
amplitude (μV)
Modelling aim: Explain all data with few
parameters
How?Assume data are caused
by few communicating brain sources
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Connectivity models
A1 A1
STG
Input (stimulus)
STG
Conventional analysis: Which regions are involved in task?
A1 A1
STG
Input (stimulus)
STG
DCM analysis: How do regions communicate?
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Model for mismatch negativity
Garrido et al., PNAS, 2008
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Macro- and meso-scale
internal granularlayer
internal pyramidallayer
external pyramidallayer
external granularlayer
AP generation zone synapses
macro-scale meso-scale micro-scale
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Overview of the talk
1 M/EEG analysis
2 Dynamic Causal Modelling – Motivation
3 Dynamic Causal Modelling – Generative model
4 Bayesian model inversion
5 Examples
![Page 13: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/13.jpg)
The generative model
),,( uxfx
Source dynamics f
states x
parameters θ
Input u
Evoked response
data y
),( xgy
Spatial forward model g
David et al., NeuroImage, 2006Kiebel et al., Human Brain Mapping, 2009
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Neural mass equations and connectivity
Extrinsicforward
connections
spiny stellate
cells
inhibitory interneurons
pyramidal cells
4 3
214
014
41
2))()((
ee
LF
e
e xxCuxSIAA
Hx
xx
1 2)( 0xSAF
)( 0xSAL
)( 0xSABExtrinsic backward connections
Intrinsic connections
neuronal (source) model
Extrinsic lateral connections
State equations
,,uxfx
0x
278
038
87
2))()((
ee
LB
e
e xxxSIAA
Hx
xx
236
746
63
225
1205
52
650
2)(
2))()()((
iii
i
ee
LB
e
e
xxxS
Hx
xx
xxxSxSAA
Hx
xx
xxx
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Model for mismatch negativity
Garrido et al., PNAS, 2008
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Spatial model
0x
LL
Depolarisation ofpyramidal cells
Spatial model
Sensor data y
Kiebel et al., NeuroImage, 2006Daunizeau et al., NeuroImage, 2009
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Overview of the talk
1 M/EEG analysis
2 Dynamic Causal Modelling – Motivation
3 Dynamic Causal Modelling – Generative model
4 Bayesian model inversion
5 Examples
![Page 18: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/18.jpg)
Bayesian model inversion
Evoked responsesSpecify generative forward model
(with prior distributions of parameters)
Expectation-Maximization algorithm
Iterative procedure: 1. Compute model response using current set of parameters
2. Compare model response with data3. Improve parameters, if possible
1. Posterior distributions of parameters
2. Model evidence )|( myp
),|( myp
Friston, PLoS Comp Biol, 2008
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Model selection: Which model is the best?
)|( 1mypModel 1
data y
Model 2
...
Model n
)|( 2myp
)|( nmypbest?
Model selection:
Selectmodel with
highestmodel
evidence
),|( 1myp
),|( 2myp
),|( nmyp
)|( imyp
Fastenrath et al., NeuroImage, 2009Stephan et al., NeuroImage, 2009
best?
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Overview of the talk
1 M/EEG analysis
2 Dynamic Causal Modelling – Motivation
3 Dynamic Causal Modelling – Generative model
4 Bayesian model inversion
5 Examples
![Page 21: Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany](https://reader036.vdocuments.us/reader036/viewer/2022081603/56649f585503460f94c7cf83/html5/thumbnails/21.jpg)
Mismatch negativity (MMN)
Garrido et al., PNAS, 2008
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Mismatch negativity (MMN)
Garrido et al., PNAS, 2008
time (ms) time (ms)
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A1 A1
STG STG
ForwardBackward
Lateral
STG
input
A1 A1
STG STG
ForwardBackward
Lateral
input
A1 A1
STG
ForwardBackward
Lateral
input
Forward - F Backward - BForward and
Backward - FB
STG
IFGIFGIFG
modulation of effective connectivity
Another (MMN) example
Garrido et al., (2007), NeuroImage
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Bayesian Model Comparison
Forward (F)
Backward (B)
Forward and Backward (FB)
subjects
lo
g-ev
iden
ce
Group level
Group model comparison
Garrido et al., (2007), NeuroImage
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Summary
DCM enables testing hypotheses about how brain sources communicate.
DCM is based on a neurobiologically plausible generative model of evoked responses.
Differences between conditions are modelled as modulation of connectivity.
Inference: Bayesian model selection
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Thanks to: Karl FristonMarta GarridoCC ChenJean Daunizeauand the FIL methods group