SPM for EEG/MEGSPM for EEG/MEG SPM for EEG/MEGSPM for EEG/MEG

Wellcome Dept. of Imaging Neuroscience

University College London

Stefan KiebeStefan Kiebell

Overview: SPM5 for EEG/MEG

Statistical Parametric MappingStatistical Parametric Mapping

Spatial forward modelling/Source reconstruction

Spatial forward modelling/Source reconstruction

Dynamic Causal ModellingDynamic Causal Modelling

-voxel-based approach-Conventional analysis-Localisation of effects-Evoked responses and power

-voxel-based approach-Conventional analysis-Localisation of effects-Evoked responses and power

-Forward model important for source reconstruction and DCM-Source reconstruction localises activity in brain space

-Forward model important for source reconstruction and DCM-Source reconstruction localises activity in brain space

-Models ERP/ERF as network activity. -Explains differences between evoked responses as modulation of connectivity.

-Models ERP/ERF as network activity. -Explains differences between evoked responses as modulation of connectivity.

EEG and MEG

MEGMEG

- ~1929 (Hans Berger)- Neurophysiologists- From 10-20 clinical system to 64, 127, 256 sensors- Potential V: ~10 µV

- ~1929 (Hans Berger)- Neurophysiologists- From 10-20 clinical system to 64, 127, 256 sensors- Potential V: ~10 µV

EEGEEG

- ~1968 (David Cohen)- Physicists- From ~ 30 to more than 150 sensors- Magnetic field B: ~10-13 T

- ~1968 (David Cohen)- Physicists- From ~ 30 to more than 150 sensors- Magnetic field B: ~10-13 T

MEG@FIL

275 sensor axial gradiometer MEG system supplied by VSM medtech.

275 sensor axial gradiometer MEG system supplied by VSM medtech.

VSM medtech says VSM medtech says

Designed for unprecedented measurement accuracy, the combination of up to 275 optimum-length axial gradiometers and unique noise cancellation technology ensures the highest possible performance in some of today's most demanding urban magnetic environments.

Designed for unprecedented measurement accuracy, the combination of up to 275 optimum-length axial gradiometers and unique noise cancellation technology ensures the highest possible performance in some of today's most demanding urban magnetic environments.

MEG data

~ 50 ms~ 50 ms

rightright

leftleft

Index fIndex f Little fLittle f

Example: MEG study of finger somatotopyExample: MEG study of finger somatotopy

400 stimulations of each finger400 stimulations of each finger

[Meunier 2001]

averageaverage

. . . single trialssingle trials

event-related potential/field (ERP/ERF)

event-related potential/field (ERP/ERF)

ERP/ERF

Voxel spaces

sensor datasensor data

SPM 2DSPM 2D

SPM 3DSPM 3D

Single trial/evoked responseSingle trial/evoked response

Data (at each voxel)

Single subjectSingle subject

Trial type 1Trial type 1

Trial type iTrial type i

Trial type nTrial type n. . .. . .

. . .. . .

Multiple subjectsMultiple subjects

Subject 1Subject 1

Subject mSubject m

Subject jSubject j

. . .. . .

. . .. . .

Time

Intensity

Tim

e

single voxeltime series

single voxeltime series

Mass univariate

modelspecification

modelspecification

parameterestimation

parameterestimation

hypothesishypothesis

statisticstatistic

SPMSPM

How does SPM/EEG work?

Raw M/EEG data

Raw M/EEG data

Single trialsEpochingArtefactsFiltering

Averaging, etc.

Single trialsEpochingArtefactsFiltering

Averaging, etc.

2D - scalp2D - scalp

mass-univariateanalysis

mass-univariateanalysis

SPM{t}SPM{F}

Control of FWE

SPM{t}SPM{F}

Control of FWE

PreprocessingPreprocessing ProjectionProjection SPM5-statsSPM5-stats

3D-sourcespace

3D-sourcespace

SPM for M/EEGM/EEG data

fMRI/sMRIdata

Design matricesTime and

time-frequencycontrasts

Correctedp-values

Covariance constraints

PreprocessingPreprocessing 2D- or 3D- M/EEG data

2D- or 3D- M/EEG data

2 level hierarchical

model

2 level hierarchical

model

SPM{t}SPM{F}

SPM{t}SPM{F}

Conventional analysis: example

a1

a2

a3

a4

a5

a6

Example: difference in N170 component between trial types

Example: difference in N170 component between trial types

Average between 150 and 190 ms

Average between 150 and 190 ms

General linear model(here: 2-sample t-test)

General linear model(here: 2-sample t-test)

Tri

al t

ype

2T

rial

typ

e 1 s1

s2

s3

s1

s2

s3

PST [ms]

=

1

1Tc =

2X

2

+ 2

first levelfirst level

second levelsecond level

YIdentitymatrix

1X

Summary statistics approach

Example: difference between trial types

Example: difference between trial typesContrast: average between

150 and 190 ms

Contrast: average between 150 and 190 ms

-1 1

2nd level contrast

. . .

Gaussian Random Fields

Search volume

t59

Gaussian10mm FWHM(2mm pixels)

p = 0.05

Cluster

Control of Family-wise error

Control of Family-wise error

Worsley et al., Human Brain Mapping, 1996

Worsley et al., Human Brain Mapping, 1996

Summary

Conventional preprocessing in sensor space.

Conventional preprocessing in sensor space.

Adjustment of p-values!Adjustment of p-values!

After preprocessing, convert to voxel-space.

After preprocessing, convert to voxel-space.

Analysis of power or time data.Analysis of power or time data.

Cool source reconstruction.Cool source reconstruction.

SPM needed to get to the DCM bit.SPM needed to get to the DCM bit.