What do you need to know about DCM for ERPs/ERFs to be able to use it?

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