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EEG/MEG source reconstruction in SPM5. Jérémie Mattout / Christophe Phillips / Karl Friston. With thanks to John Ashburner, Guillaume Flandin, Rik Henson, Stefan Kiebel. Outline. Introduction - EEG/MEG inverse problem - 3D reconstruction in SPM5 I - Source model II - Data registration

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EEG/MEG source reconstruction in SPM5


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eeg meg source reconstruction in spm5

EEG/MEG source reconstructionin SPM5

Jérémie Mattout / Christophe Phillips / Karl Friston

With thanks to

John Ashburner, Guillaume Flandin, Rik Henson, Stefan Kiebel

slide2

Outline

Introduction

- EEG/MEG inverse problem

- 3D reconstruction in SPM5

I - Source model

II - Data registration

III - Head model and forward computation

IV - Inverse estimation

Demo

slide4

Introduction - EEG/MEG inverse problem

  • Jacques Hadamard (1865-1963)
    • Existence
    • Unicity
    • Stability

“Will it ever happen that mathematicians will know enough about the physiology of the brain, and neurophysiologists enough of mathematical discovery, for efficient cooperation to be possible?”

slide5

Introduction - EEG/MEG inverse problem

Forward problem (well-posed)

Y = K(J) + E

Inverse problem (ill-posed)

DataY

Current densityJ

Bayesian framework

  • incorporate multiple constraints/prior information
  • estimate the optimal contribution of those priors
  • evaluate the relevance of the priors/model

Parametric empirical Bayes

Bayesian model comparison

slide6

Introduction - 3D Reconstruction in SPM5

Preprocessing

Projection

SPM5-engine

SPM{t}

SPM{F}

EEG/MEG Raw data

2D - scalp

Mass univariate

analysis

Single Trials

- epoching

- artefacts

- filtering

- averagin

3D - brain

DCM

spm_eeg_inv_*.m

slide7

Introduction - 3D Reconstruction in SPM5

Sources

MEG data

3D Projection

‘Equivalent Current Dipoles’ (ECD)

‘Imaging’

EEG data

slide8

Introduction - 3D Reconstruction in SPM5

ECD

Imaging

(1) Source model

(3) Forward model

(2) Registration

Data

(4) Inverse method

Anatomy

slide9

Introduction - 3D Reconstruction in SPM5

D =

data: [151x2188x5 spm_file_array]

channels: [1x1 struct]

scale: [1x1 struct]

filter: [1x1 struct]

events: [1x1 struct]

reref: []

descrip: []

datatype: 'int16'

fname: 'fmbe_emer01_TCS.mat'

fnamedat: 'fmbe_emer01.dat'

Nchannels: 151

Nevents: 5

Nsamples: 2188

Radc: 625

path: [1x76 char]

inv: {1x7 cell}

modality: 'MEG'

D = spm_eeg_ldata;

Data structure

D.inv{1} =

method: 'Imaging'

mesh: [1x1 struct]

datareg: [1x1 struct]

forward: [1x1 struct]

inverse: [1x1 struct]

comment: {'MN + Smoothness'}

date: [2x11 char]

slide10

Outline

Introduction

- EEG/MEG inverse problem

- 3D reconstruction in SPM5

I - Source model

II - Data registration

III - Head model and forward computation

IV - Inverse estimation

Demo

slide11

I - Source Model (Meshes)

Compute transformation T

Individual MRI

  • wmeshTemplate_3004d.mat
  • - wmeshTemplate_4004d.mat
  • - wmeshTemplate_5004d.mat
  • - wmeshTemplate_7004d.mat

Templates

Apply inverse transformation T-1

Individual mesh

input

functions

output

  • Individual MRI
  • Template mesh
  • spatial normalization into MNI template1
  • inverted transformation applied to the template mesh2
  • inner-skull and scalp binary masks
  • cortical mesh
  • inner-skull mesh
  • scalp mesh

1Unified segmentation, J. Ashburner and K.J. Friston, NeuroImage, 2005.

2Canonical source reconstruction for EEG & MEG, J. Mattout and K.J. Friston, in preparation.

slide12

I - Source Model (Meshes)

D.inv{1} =

method: 'Imaging'

mesh: [1x1 struct]

datareg: [1x1 struct]

forward: [1x1 struct]

inverse: [1x1 struct]

comment: {'MN + Smoothness'}

date: [2x11 char]

D.inv{1}.mesh =

sMRI: [1x87 char]

nobias: [1x86 char]

def: [1x94 char]

invdef: [1x98 char]

msk_iskull: [1x92 char]

msk_scalp: [1x91 char]

msk_flags: ''

tess_ctx: [1x95 char]

Ctx_Nv: 4004

Ctx_Nf: 8000

tess_iskull: [1x108 char]

Iskull_Nv: 2002

Iskull_Nf: 4000

tess_scalp: [1x106 char]

Scalp_Nv: 2002

Scalp_Nf: 4000

CtxGeoDist: [1x101 char]

slide13

Outline

Introduction

- EEG/MEG inverse problem

- 3D reconstruction in SPM5

I - Source model

II - Data registration

III - Head model and forward computation

IV - Inverse estimation

Demo

slide14

fiducials

Rigid transformation (R,t)

II - Data Registration

fiducials

  • Landmarks (MEG/EEG)
  • ICP Surface matching (EEG)

EEG/MEG

sensor space

MRI space

input

output

  • sensor locations
  • fiducial locations
  • (in sensor & MRI space)
  • structural MRI
  • (scalp mesh)

functions

  • registered data
  • transformation matrix
  • registration of the EEG/MEG data into MRI space3

3A method for registration of 3d-shapes, P.J. Besl and N.D. McKay, IEEE Trans. Pat. Anal. And Mach. Intel., 1992.

slide15

II - Data Registration

D.inv{1} =

method: 'Imaging'

mesh: [1x1 struct]

datareg: [1x1 struct]

forward: [1x1 struct]

inverse: [1x1 struct]

comment: {'MN + Smoothness'}

date: [2x11 char]

D.inv{1}.datareg =

sens: [1x98 char]

fid: [1x94 char]

fidmri: [1x94 char]

hsp: ''

scalpvert: ''

sens_coreg: [1x104 char]

fid_coreg: [1x100 char]

hsp_coreg: ''

eeg2mri: [1x87 char]

slide16

Outline

Introduction

- EEG/MEG inverse problem

- 3D reconstruction in SPM5

I - Source model

II - Data registration

III - Head model and forward computation

IV - Inverse estimation

Demo

slide17

III - Head model & Forward computation

p

Compute for

each dipole

+

n

Forward operator

MRI space

Head model

functions

input

output

  • single sphere
  • three spheres
  • overlapping spheres
  • realistic spheres
  • sensor locations
  • cortical mesh
  • scalp mesh
  • forward operator

BrainSTorm

http://neuroimage.usc.edi/brainstorm

slide18

III - Head model & Forward computation

D.inv{1} =

method: 'Imaging'

mesh: [1x1 struct]

datareg: [1x1 struct]

forward: [1x1 struct]

inverse: [1x1 struct]

comment: {'MN + Smoothness'}

date: [2x11 char]

D.inv{1}.forward =

bst_options: [1x1 struct]

bst_channel: [1x100 char]

bst_tess: [1x97 char]

gainmat: [1x103 char]

pcagain: [1x107 char]

slide19

Outline

Introduction

- EEG/MEG inverse problem

- 3D reconstruction in SPM5

I - Source model

II - Data registration

III - Head model and forward computation

IV - Inverse estimation

Demo

slide20

IV - Parametric Empirical Bayes (Inverse)

2-level hierarchical model

Single trial

Gaussian variables

with unknown variance

Sensors

Sources

Linear parameterization of the variances

Q: variance components

: hyperparameters

slide21

IV - Parametric Empirical Bayes (Inverse)

Bayesian inference on model parameters

+

+

Model M

Maximizing the log-evidence

data fit

priors

Expectation-Maximization (EM)

E-step: maximizing F wrt J

MAP estimate

M-step: maximizing of F wrt

ReML estimate

?

Log(Bayes factor) = F1-F21

Bayesian Model Comparison

Inference

4Comparing dynamic causal models, W.D. Penny, K.E. Stephan, A. Mechelli, K. Friston, NeuroImage, 2004.

slide22

IV - Parametric Empirical Bayes (Inverse)

Evoked and induced activity

Events

s

t

t

Synchronized oscillations in time,

but not in phase with the stimulation

FT

Average

Evoked resp.

Induced resp.

-

=

slide23

data & constraints

IV - Parametric Empirical Bayes (Inverse)

Multiple trials

evoked energy

induced energy

slide24

Energy changes (Faces - Scrambled, p<0.01)

Right temporal evoked signal

45

faces

scrambled

40

3

35

2

30

frequency (Hz)

25

1

20

0

15

-1

10

400

100

200

300

-2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

time (ms)

time (s)

M170

-3

Time-frequency subspace

0

200

400

time (ms)

IV - Parametric Empirical Bayes (Inverse)

Example

MEG experiment

of Face perception4

4Electrophysiology and haemodynamic correlates of face perception, recognition and priming, R.N. Henson, Y. Goshen-Gottstein, T. Ganel, L.J. Otten, A. Quayle, M.D. Rugg, Cereb. Cortex, 2003.

slide27

IV - Parametric Empirical Bayes (Inverse)

input

functions

output

  • preprocessed data
  • - forward operator
  • mesh
  • constraints
  • - compute the MAP estimate of J1
  • compute the ReML estimate of 1
  • model evidence2,4
  • source dynamic1,2
  • power3

1An empirical Bayesian solution to the source reconstruction problem in EEG, C. Phillips, J. Mattout, M.D. Rugg, P. Maquet and K.J. Friston, NeuroImage, 2005.

2MEG source localization under multiple constraints: an extended Bayesian framework, J. Mattout, C. Phillips, M.D. Rugg and K.J. Friston, NeuroImage (in press).

3Bayesian estimation of evoked and induced responses, K.J. Friston, R.N. Henson, C. Phillips and J. Mattout, Hum. Brain Mapp. (in press).

4Variational free energy and the Laplace approximation, K.J. Friston, J. Mattout, N. Trujillo-Barreto, J. Ashburner and W. Penny (in preparation).

slide28

IV - Parametric Empirical Bayes (Inverse)

D.inv{1} =

method: 'Imaging'

mesh: [1x1 struct]

datareg: [1x1 struct]

forward: [1x1 struct]

inverse: [1x1 struct]

comment: {'MN + Smoothness'}

date: [2x11 char]

D.inv{1}.inverse =

activity: 'evoked'

contrast: [0.5000 0.5000 1 0 0]

woi: [150 190]

priors: [1x1 struct]

dim: 4004

resfile: 'fmbe_emer01_TCS_remlmat_150_190ms_evoked_11H3.mat'

LogEv: 9.8269e+003