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Dynamic Causal Modelling of

Evoked Responses in EEG/MEG

Wellcome Dept. of Imaging Neuroscience

University College London

Stefan KiebelOlivier David

Karl Friston

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Motivation

The Mismatch Negativity (MMN) is the evoked EEG

response component elicited by deviations within a

structured auditory sequence.

It peaks at about 100 200 ms after change stimulusonset.

What are the mechanisms that generate the MMN?

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Mismatch negativity paradigm

time!

standards deviants

14 subjects15 min, 128 EEG channelspseudo-random sequence

80% standard tones 1000 Hz20% deviant tones 2000 Hz

Oddball paradigm

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Sensor data

MMN!

20 V!ERP standards!ERP deviants!deviants - standards!

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Two hypotheses

What are the mechanisms that generate the MMN?

Adaptation: 2 differentially adaptive sources

in STG generate the MMN. These sources are

the same that generate the N1 (prominentauditory component peaking around 100 ms).

Predictive Coding: The MMN reflects a

failure to suppress prediction error. This canbe explained quantitatively in terms of

coupling changes among cortical areas.

How can we test these hypotheses?

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A1! A1!

STG!

STG!

IFG!Forward!Backward!

Lateral!

input

MMN!

ERP standards!ERP deviants!deviants - standards!

Dynamic Causal Modelling!

Garrido et al., in preparation

If we assume that MMN shows

evidence of predictive coding

scheme, can we explain the MMN

by a modulation of connectivity?

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Theory

So how does DCM for

evoked responses work then?

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Forward model

Magnetic fieldInteractions

between areas

Sensor data Current densityNeuronal

activity

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Inverse problems

Source

reconstruction

Effective

connectivity

DCM

Sensor data Current densityNeuronal

activity

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Dynamicsf

ERP/ERF

Input u

Spatial forward modelg

Generative model

),( xgy =),,( uxfx =

datay

parameters

statesx

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Neural mass modelNeuronal assembly

Time [ms]

( ) ( )( )( )

( )( )vvrS

vS

tvStm

+

=

=

0

0

exp1

v[mV]

Mean firing rate

m(t)

Mean membrane

potential

v(t)

Mean firing rate

m(t)

mh