Dynamic Causal Modelling for ERP/ERFs

Practical session

Marta Garrido and Stefan KiebelThanks to James Kilner and Karl Friston

• Hands on : application to the Mismatch Negativity (MMN)

• Demo

• Results

Outline

DCM for Evoked Responses

differences in the evoked responses

changes in effective connectivity

4,,1

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

pseudo-random auditory sequence

80% standard tones – 500 Hz

20% deviant tones – 550 Hz

time

standards deviants

Oddball paradigm

Data acquisition and processing

raw data

preprocessing

data reduction to

principal spatial

modes

(explaining most

of the variance)

• convert to matlab file

• filter

• epoch

• down sample

• artifact correction

• average

ERPs / ERFs

128 EEG scalp electrodes

mode 2

mode 1

mode 3

time (ms)

-100 -50 0 50 100 150 200 250 300 350 400-4

-3

-2

-1

0

1

2

3

4

ms

V

standardsdeviants

HEOG VEOG

a

b

c

MMN

The Mismatch Negativity (MMN) is the ERP component elicited by deviations within a

structured auditory sequence peaking at about 100 – 200 ms after change onset.

DCM specification

A1 A1

STG

input

STG

IFG

a plausible model…

modulation of effective connectivity

Forward - F

Backward - B

Both - FB

Opitz et al., 2002

Doeller et al., 2003

rIFG

rSTG

rA1lA1

lSTG

lIFG

What are the mechanisms underlying the generation of the MMN?

1 2

3 4

5

visualiseoutput

estimate the model

Matlab spm eeg

number of svd components

sources or nodes in your graph

driving inputspecify extrinsic

connections

modulatory effect

DCM.AF DCM.AB DCM.AL

DCM.B

DCM.C

Intrinsic connectionsfrom

to

choose datachoose time

window

choose polhemus file

comparemodels

Demo

Results

Forward

Backward

Intrinsic

modulation of effective connectivity

A1 A1

input

IFG

A1 A1

input

STG STG

A1 A1

input

STG STG

A1 A1

input

IFG

STG STG

A1 A1

input

A1 A1

input

STG STG

A1 A1

input

IFGIFG

STG STG

IFG

A1 A1

input

IFG

STG STG

S2

S2i S4i

S4 S5

S5i

S6

S6i

-2.9

-2.85

-2.8

-2.75

-2.7

-2.65

-2.55

x 104

S2

S2i

S4 S4iS5

S5i S6

S6i

F -

neg

ativ

e fr

ee e

nerg

ymodel space

-2.6

results Bayesian Model Comparison

Thank you