Models of effective connectivity & Dynamic Causal Modelling (DCM)
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Models of effective connectivity &Dynamic Causal Modelling (DCM)
Klaas Enno Stephan
Laboratory for Social & Neural Systems Research Institute for Empirical Research in EconomicsUniversity of Zurich
Functional Imaging Laboratory (FIL)Wellcome Trust Centre for NeuroimagingUniversity College London
Methods & Models for fMRI data analysis in neuroeconomics 09 December 2009
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Overview
• Brain connectivity: types & definitions– anatomical connectivity– functional connectivity– effective connectivity
• Psycho-physiological interactions (PPI)
• Dynamic causal models (DCMs)– DCM for fMRI: Neural and hemodynamic levels– Parameter estimation & inference
• Applications of DCM to fMRI data– Design of experiments and models– Some empirical examples and simulations
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Connectivity
A central property of any system
Communication systems Social networks(internet) (Canberra, Australia)
FIgs. by Stephen Eick and A. Klovdahl;see http://www.nd.edu/~networks/gallery.htm
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Structural, functional & effective connectivity
• anatomical/structural connectivity= presence of axonal connections
• functional connectivity = statistical dependencies between regional time series
• effective connectivity = causal (directed) influences between neurons or neuronal populations
Sporns 2007, Scholarpedia
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Anatomical connectivity
• neuronal communication via synaptic contacts
• visualisation by tracing techniques
• long-range axons “association fibres”
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Diffusion-weighted imaging
Parker & Alexander, 2005, Phil. Trans. B
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Parker, Stephan et al. 2002, NeuroImage
Diffusion-weighted imaging of the
cortico-spinal tract
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Why would complete knowledge of anatomical connectivity not be enough
to understand how the brain works?
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Connections are recruited in a context-dependent fashion
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
Synaptic strengths are context-sensitive: They depend on spatio-temporal patterns
of network activity.
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Connections show plasticity
• critical for learning
• can occur both rapidly and slowly
• NMDA receptors play a critical role
• NMDA receptors are regulated by modulatory neurotransmitters like dopamine, serotonine, acetylcholine
• synaptic plasticity = change in the structure and transmission properties of a chemical synapse
Gu 2002, Neuroscience
NMDAreceptor
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Short-term plasticity
• NMDAR-independent– e.g. synaptic depression– non-inactivating sodium
channels
• NMDAR-dependent– phosphorylation of
AMPARs– modulation of EPSPs at
NMDARs by DA, ACh, 5HT (gating)
Reynolds et al. 2001, Nature
Tsodyks & Markram 1997, PNAS
pea
k P
SP
(m
V)
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Different approaches to analysing functional connectivity
• Seed voxel correlation analysis
• Eigen-decomposition (PCA, SVD)
• Independent component analysis (ICA)
• any other technique describing statistical dependencies amongst regional time series
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Seed-voxel correlation analyses
• Very simple idea:
– hypothesis-driven choice of a seed voxel → extract reference
time series
– voxel-wise correlation with time series from all other voxels in the brain
seed voxel
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Drug-induced changes in functional connectivity
Finger-tapping task in first-episode schizophrenic patients:
voxels that showed changes in functional connectivity (p<0.005) with the left ant. cerebellum after medication with olanzapineStephan et al. 2001, Psychol. Med.
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Does functional connectivity not simply correspond to co-activation in
SPMs?No, it does not - see the fictitious example on the right:
Here both areas A1 and A2 are correlated identically to task T, yet they have zero correlation among themselves:
r(A1,T) = r(A2,T) = 0.71butr(A1,A2) = 0 !
task T regional response A2regional response A1
Stephan 2004, J. Anat.
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Pros & Cons of functional connectivity analyses
• Pros:– useful when we have no experimental control over
the system of interest and no model of what caused the data (e.g. sleep, hallucinatons, etc.)
• Cons:– interpretation of resulting patterns is difficult /
arbitrary
– no mechanistic insight into the neural system of interest
– usually suboptimal for situations where we have a priori knowledge and experimental control about the system of interestmodels of effective connectivity necessary
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For understanding brain function mechanistically, we need models of effective connectivity, i.e.
models of causal interactions among neuronal populations.
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Some models for computing effective connectivity from fMRI data
• Structural Equation Modelling (SEM) McIntosh et al. 1991, 1994; Büchel & Friston 1997; Bullmore et al. 2000
• regression models (e.g. psycho-physiological interactions, PPIs)Friston et al. 1997
• Volterra kernels Friston & Büchel 2000
• Time series models (e.g. MAR, Granger causality)Harrison et al. 2003, Goebel et al. 2003
• Dynamic Causal Modelling (DCM)bilinear: Friston et al. 2003; nonlinear: Stephan et al. 2008
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Overview
• Brain connectivity: types & definitions– anatomical connectivity– functional connectivity– effective connectivity
• Psycho-physiological interactions (PPI)
• Dynamic causal models (DCMs)– DCM for fMRI: Neural and hemodynamic levels– Parameter estimation & inference
• Applications of DCM to fMRI data– Design of experiments and models– Some empirical examples and simulations
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Psycho-physiological interaction (PPI)
We can replace one main effect in the GLM by the time series of an area that shows this main effect.
E.g. let's replace the main effect of stimulus type by the time series of area V1:
Task factorTask A Task B
Sti
m 1
Sti
m 2
Sti
mu
lus
fact
or
TA/S1 TB/S1
TA/S2 TB/S2
e
βVTT
βV
TT y
BA
BA
3
2
1
1 )(
1
)(
e
βVTT
βV
TT y
BA
BA
3
2
1
1 )(
1
)(
e
βSSTT
βSS
TT y
BA
BA
321
221
1
)( )(
)(
)(
e
βSSTT
βSS
TT y
BA
BA
321
221
1
)( )(
)(
)(
GLM of a 2x2 factorial design:
main effectof task
main effectof stim. type
interaction
main effectof taskV1 time series main effectof stim. typepsycho-physiologicalinteraction
Friston et al. 1997, NeuroImage
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V1V1
attention
no attention
V1 activity
V5 a
ctiv
ity
SPM{Z}
time
V5 a
ctiv
ity
Friston et al. 1997, NeuroImageBüchel & Friston 1997, Cereb. Cortex
V1 x Att.V1 x Att.
=
V5V5
V5V5
Attention
Attentional modulation of V1→V5
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PPI: interpretation
Two possible interpretations
of the PPI term:
V1
Modulation of V1V5 by attention
Modulation of the impact of attention on V5 by V1
V1V1 V5V5 V1
V5V5
attentionattention
V1V1
attentionattention
e
βVTT
βV
TT y
BA
BA
3
2
1
1 )(
1
)(
e
βVTT
βV
TT y
BA
BA
3
2
1
1 )(
1
)(
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Two PPI variants
• "Classical" PPI:
– Friston et al. 1997, NeuroImage
– depends on factorial design
– in the GLM, physiological time series replaces one experimental factor
– physio-physiological interactions: two experimental factors are replaced by physiological time series
• Alternative PPI:
– Macaluso et al. 2000, Science
– interaction term is added to an existing GLM
– can be used with any design
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Is the red letter left or right from the midline of the word?
group analysis (random effects),n=16, p<0.05 corrected
analysis with SPM2
group analysis (random effects),n=16, p<0.05 corrected
analysis with SPM2
Task-driven lateralisation
letter decisions > spatial decisions
time
•••
Does the word contain the letter A or not?
spatial decisions > letter decisions
Stephan et al. 2003, Science
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Bilateral ACC activation in both tasks –but asymmetric connectivity !
IPS
IFG
Left ACC left inf. frontal gyrus (IFG):increase during letter decisions.
Right ACC right IPS:increase during spatial decisions.
left ACC (-6, 16, 42)
right ACC (8, 16, 48)
spatial vs letterdecisions
letter vs spatialdecisions
group analysisrandom effects (n=15)
p<0.05, corrected (SVC)
Stephan et al. 2003, Science
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PPI single-subject example
bVS= -0.16
bL=0.63
Signal in left ACC
Sig
nal
in
lef
t IF
G
bL= -0.19
Sig
nal
in
rig
ht
ant.
IP
S
Signal in right ACC
bVS=0.50
Left ACC signal plotted against left IFG
spatialdecisions
letterdecisions
letterdecisions
spatialdecisions
Right ACC signal plotted against right IPS
Stephan et al. 2003, Science
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PPI for event-related fMRI requires deconvolution
Gitelman et al. 2003, NeuroImage
(A HRF) (B HRF) (A B) HRF
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Pros & Cons of PPIs• Pros:
– given a single source region, we can test for its context-dependent connectivity across the entire brain
– easy to implement
• Cons:– very simplistic model:
only allows to model contributions from a single area
– ignores time-series properties of data
– application to event-related data relies deconvolution procedure (Gitelman et al. 2003, NeuroImage)
– operates at the level of BOLD time series
sometimes very useful, but limited causal interpretability;in most cases, we need more powerful models
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Overview
• Brain connectivity: types & definitions– anatomical connectivity– functional connectivity– effective connectivity
• Psycho-physiological interactions (PPI)
• Dynamic causal models (DCMs)– DCM for fMRI: Neural and hemodynamic levels– Parameter estimation & inference
• Applications of DCM to fMRI data– Design of experiments and models– Some empirical examples and simulations
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Dynamic causal modelling (DCM)
• DCM framework was introduced in 2003 for fMRI by Karl Friston, Lee Harrison and Will Penny (NeuroImage 19:1273-1302)
• part of the SPM software package
• currently more than 100 published papers on DCM
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),,( uxFdt
dx
Neural state equation:
Electromagneticforward model:
neural activityEEGMEGLFP
Dynamic Causal Modeling (DCM)
simple neuronal modelcomplicated forward model
complicated neuronal modelsimple forward model
fMRIfMRI EEG/MEGEEG/MEG
inputs
Hemodynamicforward model:neural activityBOLD
Stephan & Friston 2007, Handbook of Brain Connectivity
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LGleft
LGright
RVF LVF
FGright
FGleft
LG = lingual gyrusFG = fusiform gyrus
Visual input in the - left (LVF) - right (RVF)visual field.x1 x2
x4x3
u2 u1
1 11 1 12 2 13 3 12 2
2 21 1 22 2 24 4 21 1
3 31 1 33 3 34 4
4 42 2 43 3 44 4
x a x a x a x c u
x a x a x a x c u
x a x a x a x
x a x a x a x
Example: a linear system of dynamics in visual cortex
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Example: a linear system of dynamics in visual cortex
LG = lingual gyrusFG = fusiform gyrus
Visual input in the - left (LVF) - right (RVF)visual field.
state changes
effectiveconnectivity
externalinputs
systemstate
inputparameters
11 12 131 1 12
21 22 242 2 121
31 33 343 3 2
42 43 444 4
0 0
0 0
0 0 0
0 0 0
a a ax x c
a a ax x uc
a a ax x u
a a ax x
x Ax Cu
},{ CA
LGleft
LGright
RVF LVF
FGright
FGleft
x1 x2
x4x3
u2 u1
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Extension: bilinear dynamic system
LGleft
LGright
RVF LVF
FGright
FGleft
x1 x2
x4x3
u2 u1
CONTEXTu3
( )
1
( )m
jj
j
x A u B x Cu
(3)11 12 131 1 1212
121 22 242 2 21
3 2(3)31 33 343 334
342 43 444 4
0 0 00 0 0
0 0 00 0 0 0
0 0 0 00 0 0
0 0 0 00 0 0 0
a a ax x cbu
a a ax x cu u
a a ax xbu
a a ax x
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intrinsic connectivity
direct inputs
modulation ofconnectivity
Neural state equation CuxBuAx jj )( )(
u
xC
x
x
uB
x
xA
j
j
)(
hemodynamicmodelλ
x
y
integration
BOLDyyy
activityx1(t)
activityx2(t) activity
x3(t)
neuronalstates
t
drivinginput u1(t)
modulatoryinput u2(t)
t
Stephan & Friston (2007),Handbook of Brain Connectivity
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Bilinear DCM
CuxBuAdt
dx m
i
ii
1
)(
Bilinear state equation:
driving input
modulation
...)0,(),(2
0
uxux
fu
u
fx
x
fxfuxf
dt
dxTwo-dimensional Taylor series (around x0=0, u0=0):
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-
x2
stimuliu1
contextu2
x1
+
+
-
-
-+
u1
Z1
u2
Z2
2 1
(2)
2121 1111
22
212 222
0 0
0 00
x Ax u B x Cu
x ua cx u x
x uab
b
2 1
(2)
2121 1111
22
212 222
0 0
0 00
x Ax u B x Cu
x ua cx u x
x uab
b
Example: context-dependent decay u1
u2
x2
x1
Penny, Stephan, Mechelli, Friston NeuroImage (2004)
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DCM parameters = rate constants
dxax
dt 0( ) exp( )x t x at
The coupling parameter a thus describes the speed ofthe exponential change in x(t)
0
0
( ) 0.5
exp( )
x x
x a
Integration of a first-order linear differential equation gives anexponential function:
/2lna
00.5x
a/2ln
Coupling parameter a is inverselyproportional to the half life of z(t):
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The problem of hemodynamic convolution
Goebel et al. 2003, Magn. Res. Med.
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Hemodynamic forward models are important for connectivity analyses of fMRI data
David et al. 2008, PLoS Biol.
Granger causality
DCM
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sf
tionflow induc
(rCBF)
s
v
stimulus functions
v
q q/vvEf,EEfqτ /α
dHbchanges in
100 )( /αvfvτ
volumechanges in
1
f
q
)1(
fγsxs
signalryvasodilato
u
s
CuxBuAdt
dx m
j
jj
1
)(
t
neural state equation
1
3.4
111),(
3
002
001
32100
k
TEErk
TEEk
vkv
qkqkV
S
Svq
hemodynamic state equationsf
Balloon model
BOLD signal change equation
},,,,,{ h},,,,,{ h
important for model fitting, but of no interest for statistical inference
• 6 hemodynamic parameters:
• Computed separately for each area (like the neural parameters) region-specific HRFs!
The hemodynamic model in DCM
Friston et al. 2000, NeuroImageStephan et al. 2007, NeuroImage
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0 2 4 6 8 10 12 14
0
0.2
0.4
0 2 4 6 8 10 12 14
0
0.5
1
0 2 4 6 8 10 12 14
-0.6
-0.4
-0.2
0
0.2
RBMN
, = 0.5
CBMN
, = 0.5
RBMN
, = 1
CBMN
, = 1
RBMN
, = 2
CBMN
, = 2sf
tionflow induc
(rCBF)
s
v
stimulus functions
v
q q/vvEf,EEfqτ /α
dHbchanges in
100 )( /αvfvτ
volumechanges in
1
f
q
)1(
fγsxs
signalryvasodilato
u
s
CuxBuAdt
dx m
j
jj
1
)(
t
neural state equation
1
3.4
111),(
3
002
001
32100
k
TEErk
TEEk
vkv
qkqkV
S
Svq
hemodynamic state equations
f
Balloon model
BOLD signal change equation
The hemodynamic model in DCM
Stephan et al. 2007, NeuroImage
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5 10 15 20 25 30 35 40
5
10
15
20
25
30
35
40
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
A
B
C
h
ε
How interdependent are neural and hemodynamic parameter estimates?
Stephan et al. 2007, NeuroImage
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Bayesian statistics
)()|()|( pypyp )()|()|( pypyp posterior likelihood ∙ prior
)|( yp )|( yp )(p )(p
Bayes theorem allows one to formally incorporate prior knowledge into computing statistical probabilities.
In DCM: empirical, principled & shrinkage priors.
The “posterior” probability of the parameters given the data is an optimal combination of prior knowledge and new data, weighted by their relative precision.
new data prior knowledge
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sf (rCBF)induction -flow
s
v
f
stimulus function u
modelled BOLD response
vq q/vvf,Efqτ /α1)(
dHbin changes
/αvfvτ 1
in volume changes
f
q
)1(
signalry vasodilatodependent -activity
fγszs
s
)(xy )(xy eXuhy ),(
observation model
hidden states{ , , , , }z x s f v q
state equation( , , )z F x u
parameters
},{
},...,{
},,,,{1
nh
mn
h
CBBA
• Combining the neural and hemodynamic states gives the complete forward model.
• An observation model includes measurement error e and confounds X (e.g. drift).
• Bayesian parameter estimation by means of a Levenberg-Marquardt gradient ascent, embedded into an EM algorithm.
• Result:Gaussian a posteriori parameter distributions, characterised by mean ηθ|y and covariance Cθ|y.
Overview:parameter estimation
ηθ|y
neural stateequation
( )jjx A u B x Cu ( )j
jx A u B x Cu
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• Gaussian assumptions about the posterior distributions of the parameters
• Use of the cumulative normal distribution to test the probability that a certain parameter (or contrast of parameters cT ηθ|y) is above a chosen threshold γ:
• By default, γ is chosen as zero ("does the effect exist?").
Inference about DCM parameters:Bayesian single-subject analysis
cCc
cp
yT
yT
N
cCc
cp
yT
yT
N
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Bayesian single subject inference
LGleft
LGright
RVFstim.
LVFstim.
FGright
FGleft
LD|RVF
LD|LVF
LD LD
0.34 0.14
-0.08 0.16
0.13 0.19
0.01 0.17
0.44 0.14
0.29 0.14
Contrast:Modulation LG right LG links by LD|LVFvs.modulation LG left LG right by LD|RVF
p(cT>0|y) = 98.7%
Stephan et al. 2005, Ann. N.Y. Acad. Sci.
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Inference about DCM parameters: Bayesian fixed-effects group analysis
Because the likelihood distributions from different subjects are independent, one can combine their posterior densities by using the posterior of one subject as the prior for the next:
)|()...|()|(),...,|(
...
)|()|(
)()|()|(),|(
)()|( )|(
111
12
1221
11
ypypypyyp
ypyp
pypypyyp
pypyp
NNN
)|()...|()|(),...,|(
...
)|()|(
)()|()|(),|(
)()|( )|(
111
12
1221
11
ypypypyyp
ypyp
pypypyyp
pypyp
NNN
1,...,|
1|
1|,...,|
1
1|
1,...,|
11
1
NiiN
iN
yy
N
iyyyy
N
iyyy
CC
CC
1,...,|
1|
1|,...,|
1
1|
1,...,|
11
1
NiiN
iN
yy
N
iyyyy
N
iyyy
CC
CC
Under Gaussian assumptions this is easy to compute:
groupposterior covariance
individualposterior covariances
groupposterior mean
individual posterior covariances and means
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Inference about DCM parameters:group analysis (classical)
• In analogy to “random effects” analyses in SPM, 2nd level analyses can be applied to DCM parameters:
Separate fitting of identical models for each subject
Separate fitting of identical models for each subject
Selection of bilinear parameters of interestSelection of bilinear
parameters of interest
one-sample t-test:
parameter > 0 ?
one-sample t-test:
parameter > 0 ?
paired t-test: parameter 1 > parameter 2 ?
paired t-test: parameter 1 > parameter 2 ?
rmANOVA: e.g. in case of
multiple sessions per subject
rmANOVA: e.g. in case of
multiple sessions per subject
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Overview
• Brain connectivity: types & definitions– anatomical connectivity– functional connectivity– effective connectivity
• Psycho-physiological interactions (PPI)
• Dynamic causal models (DCMs)– DCM for fMRI: Neural and hemodynamic levels– Parameter estimation & inference
• Applications of DCM to fMRI data– Design of experiments and models– Some empirical examples and simulations
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Any design that is good for a GLM of fMRI data.
What type of design is good for DCM?
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GLM vs. DCM
DCM tries to model the same phenomena as a GLM, just in a different way:
It is a model, based on connectivity and its modulation, for explaining experimentally controlled variance in local responses.
No activation detected by a GLM → inclusion of this region in a DCM is useless!
Stephan 2004, J. Anat.
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Multifactorial design: explaining interactions with DCM
Task factorTask A Task B
Sti
m 1
Sti
m 2
Sti
mu
lus
fact
or
TA/S1 TB/S1
TA/S2 TB/S2
X1 X2
Stim2/Task A
Stim1/Task A
Stim 1/Task B
Stim 2/Task B
GLM
X1 X2
Stim2
Stim1
Task A Task B
DCM
Let’s assume that an SPM analysis shows a main effect of stimulus in X1 and a stimulus task interaction in X2.
How do we model this using DCM?
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Stim 1Task A
Stim 2Task A
Stim 1Task B
Stim 2Task B
Simulated data
X1
X2
+++X1 X2
Stimulus 2
Stimulus 1
Task A Task B
+++++
++++
– –
Stephan et al. 2007, J. Biosci.
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Stim 1Task A
Stim 2Task A
Stim 1Task B
Stim 2Task B
plus added noise (SNR=1)
X1
X2
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Example studies of DCM for fMRI• DCM now an
established tool for fMRI & M/EEG analysis
• >100 studies published, incl. high-profile journals
• combinations of DCM with computational models
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Is the red letter left or right from the midline of the word?
group analysis (random effects),n=16, p<0.05 corrected
analysis with SPM2
group analysis (random effects),n=16, p<0.05 corrected
analysis with SPM2
Task-driven lateralisation
letter decisions > spatial decisions
time
•••
Does the word contain the letter A or not?
spatial decisions > letter decisions
Stephan et al. 2003, Science
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Theories on inter-hemispheric integration during lateralised
tasksInformation transfer
(for left-lateralised task)Inhibition/CompetitionHemispheric recruitment
LVF RVF
T
T
T
T+
−
−
T
T
+
+
Predictions:modulation by task conditional on visual fieldasymmetric connection strengths
Predictions:modulation by task onlynegative & symmetricconnection strengths
Predictions:modulation by task onlypositive & symmetricconnection strengths
|LVF
|RVF
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LGleft
LGright
FGright
FGleft
RVF LVF
B
A
Bcond
Bind
LD
VF
VF LD Bind Bcond
intra
inter16 models
LGleft
LGright
FGright
FGleft
LD
RVF
LVF
LGleft
LGright
RVFstim.
LVFstim.
FGright
FGleft
LD
LD,RVF
LD|RVF
LD
LD,LVF
LD|LVF
VF
LD
Bind
Bcond
LD
RVF
LVF
LD|RVF
LD|LVF
VF LD Bind BcondD
C
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LGleft
LGright
RVFstim.
LVFstim.
FGright
FGleft
LD|RVF
LD|LVF
LD LD
0.25 0.04
0.03 0.03
0.12 0.02
0.02 0.02
0.36 0.06
0.16 0.05
Left lingual gyrus(LG)
-12,-64,-4
Left fusiform gyrus(FG)
-44,-52,-18
Right fusiform gyrus(FG)
38,-52,-20
Right lingual gyrus(LG)
14,-68,-2
mean parameter estimates SE (n=12)
significant modulation (p<0.05, uncorrected)non-significant modulation
significant modulation (p<0.05, Bonferroni-corrected)LD>SD masked incl. with RVF>LVF
p<0.05 cluster-level corrected(p<0.001 voxel-level cut-off)
LD>SD, p<0.05 cluster-level corrected(p<0.001 voxel-level cut-off)
p<0.01 uncorrected
LD>SD masked incl. with LVF>RVFp<0.05 cluster-level corrected(p<0.001 voxel-level cut-off)
Ventral stream & letter decisions
Stephan et al. 2007, J. Neurosci.
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MOGleft
LGleft
LGright
RVFstim.
LVFstim.
FGright
FGleft
LD|RVF
LD|LVF
LD LD
0.20 0.04
0.06 0.02
0.00 0.01
0.01 0.01
0.27 0.06
0.11 0.03
MOGright
0.00 0.04
0.01 0.03
0.07 0.02
0.01 0.01
Ventral stream & letter decisions
LD>SD, p<0.05 cluster-level corrected(p<0.001 voxel-level cut-off)
Left MOG-38,-90,-4
mean parameter estimates SE (n=12)
significant modulation (p<0.05, uncorrected)non-significant
significant modulation (p<0.05, corrected)
Left FG-44,-52,-18
Right MOG-38,-94,0
p<0.01 uncorrected
Left LG-12,-70,-6
Left LG-14,-68,-2
LD>SD masked incl. with RVF>LVFp<0.05 cluster-level corrected(p<0.001 voxel-level cut-off)
LD>SD masked incl. with LVF>RVFp<0.05 cluster-level corrected
(p<0.001 voxel-level cut-off)
Right FG38,-52,-20
Stephan et al. 2007, J. Neurosci.
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-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5 6 7 8 9 10 11 12
Subjects
MA
P e
sti
ma
teleft to right
right to left
Asymmetric modulation of LG callosal connections is
consistent across subjects
Stephan et al. 2007, J. Neurosci.
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Fixation cross
Auditory
Auditory VisualFixation cross
Time (ms)
0 200
400
600
800
1000
1200
2000 ± 500
or
Visual
“Distractor”Target
“Distractor”
Target
1400
Incidental learning of audio-visual associations
Hypothesis: Incidental learning of this relation is reflected by prediction-error dependent changes in connectivity between auditory and visual areas.
80%
80%
den Ouden et al. 2009, Cereb. Cortex
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Incidental learning of audio-visual associations
2x2x2 factorial design (differential classical conditioning)
Tone 1 (T1)present absent
pre
sent
abse
nt
Vis
ual st
imu
lus
40%
10%
10%
40%
Tone 2 (T2)present absent
pre
sent
abse
nt
Vis
ual st
imu
lus
10%
40%
40%
10%
CS+: Tone 1 predicts presence of VSCS-: Tone 2 predicts absence of VS
p(VS|T1) = 0.8p(VS|T1) = 0.2
p(VS|T2) = 0.2p(VS|T2) = 0.8
50% trials with auditory stimuli,50% trials with visual stimuli
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Rescorla-Wagner model of associative learning
p < 0.05, correctedrandom effects, n=16
- V1 & PUT increasingly activate the more surprising the visual outcome is
- V1 & PUT increasingly deactivate the more expected the visual outcome is
Rescorla-Wagner learningcurve (=0.075):
predictionoutcomeprediction
During learning, predictive tones
V1PUT
0 100 200 300 400 500 600 700 8000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Trial
As
so
cia
tio
n
CS+ A+
CS+ A-
CS- A+
CS- A-
A+V+ A+V- A-V+ A-V- A+V+ A+V- A-V+ A-V--2
-1.5
-1
-0.5
0
0.5
1
Bet
a (%
sig
nal
ch
ang
e)
ME Visual
den Ouden et al. 2009, Cereb. Cortex
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AUDCTX
VISCTX
auditorystimuli
visualstimuliprediction error
(computational model)
A very simple neurocomputational model
Learning changed the coupling strength of the A1V1 connection by +2% (for unexpected VS) and -8% (for expected VS).
0.1
-0.01
A1 V1
aud. input (A+) vis. input (V+)
CS+ (V+ vs. V-)
A+V+ A+V- A-V+ A-V- A+V+ A+V- A-V+ A-V--2
-1.5
-1
-0.5
0
0.5
1
Bet
a (%
sig
nal
ch
ang
e)
ME Visual
A+V+ A+V- A-V+ A-V- A+V+ A+V- A-V+ A-V--0.5
0
0.5
1
1.5
2
2.5
3
Bet
a (%
sig
nal
ch
ang
e)
ME Auditory
CS+
CS-
0.10
-0.01
p = 0.028
den Ouden et al. 2009, Cereb. Cortex
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Thank you