what do you need to know about dcm for erps/erfs to be able to use it?
TRANSCRIPT
What do you need to know about DCM for ERPs/ERFs to be able
to use it?
Dynamic Causal Modelling for ERPs/ERFs (I)
differences in the evoked responses
changes in effective connectivity
functional connectivity vs. effective connectivity
causal architecture of interactions
The aim of DCM is to estimate and make inferences about
the coupling among brain areas, and how that coupling is
influences by changes in the experimental contex.
estimated by perturbing the system and
measuring the response
neural mass model
Layer 4
Supra-granular
Infra-granular
IntrinsicForward
BackwardLateral
Input u
1
32
3 area model
,,.
uxftx
),( xgy
state eq.
output eq.
Extrinsic
M/EEGneuronal states
parameters
input
David et al., 2006
Dynamic Causal Modelling for ERPs/ERFs (II)
DCM specification (I)
• DCM is specified by a graph of nodes (cortical areas) and edges
(connections). Differences in 2 ERPs/ERFs are explained by coupling
modulations, i.e., changes in connection strength.
• DCM doesn’t test all possible models.
• Is crucial to build a model biologically plausible!
• Different hypotheses Different models
• Bayesian model comparison identifies the best model/hypothesis within
the universe of models/hypothesis considered.
pseudo-random auditory sequence
80% standard tones – 1000 Hz
20% deviant tones – 2000 Hz
time
standards deviants
Oddball paradigm
DCM specification (II) – put into contextmode 1
mode 2
mode 3
svd
raw data
preprocessing
data reduction to
principal spatial
modes
(explaining most
of the variance)
• convert to matlab file
• epoch
• down sample
• filter
• artifact correction
• average
ERPs / ERFs
A1 A1
STG
input
STG
IFG
A1A1
STGSTG
IFG
a plausible model…
DCM specification (III) – areas and connections
Choice of nodes/areas?
- source localization, prior knowledge from literature
Choice of edges/connections?
- anatomical or functional evidence
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
DCM specification (IV) – testing different models
A1 A1
STG
ForwardBackward
Lateral
input
Forward and Backward - FB
STG
IFG
2.4
1 (
10
0%
) 4.5
0 (1
00
%) 5
.40
(1
00
%) 1
.74
(96
%)
1.4
1 (
99
%)
standard
deviant
0.9
3 (5
5%
)
DCM output (I) single subject
reconstructed responses at source
level
coupling changes
probability that a change occured
1,| Nyp
subN
iii
1
1
subN
ii
1
A1 A1
STG
ForwardBackward
Lateral
input
Forward and Backward - FB
STG
IFG
2.1
7 (
10
0%
) 17
.95
(10
0%
) 2.6
5 (
10
0%
) 1.5
8 (1
00
%)
0.6
0 (
10
0%
) 1.4
0 (1
00
%)
group
Neumann and Lohmann, 2003
)|()...|()|(),...,|(...
)|()|( )()|()|(),|(
)()|( )|(
111
12
1221
11
ypypypyyp
ypyppypypyyp
pypyp
NNN
DCM output (II)
Parameters at group level?
lo
g-e
vid
en
ce
(log
-evi
denc
e no
rmal
ized
to
the
nu
ll m
ode
l) Bayesian Model Comparison
subjects
Forward (F)
Backward (B)
Forward and Backward (FB)
fmyp ln
)(lnlnln jiij mypmypB
subN
sisiNsub mypmyyyp
121 )(ln|,...,,ln
Penny et al., 2004
DCM output (III)
DCM.F
add up log-evidences for group analysis
Summary
• DCM models ERPs on the basis of a network of interacting cortical areas. Differences in waveforms are explained by coupling changes among these areas.
• The specification of the DCM (areas and connections in the network) is a critical point. It should be biologically plausible and motivated by specific hypotheses.
• DCM can be used to test different hypotheses or models of connectivity.
STGA1 IFG