EEG/MEG Source Localisation

transcript

EEG/MEG Source LocalisationSPM Short Course – Wellcome Trust Centre for Neuroimaging – May 2008

Jérémie Mattout, Christophe Phillips

Jean DaunizeauGuillaume FlandinKarl FristonRik HensonStefan KiebelVladimir Litvak

OutlineEEG/MEGEEG/MEGSource localisationSource localisation

1. Introduction2. Forward model3. Inverse problem4. Bayesian inference applied to the EEG/MEG inverse problem

5. Conclusion

EEG/MEGEEG/MEGSource localisationSource localisation

invasivity

weak strong

temporal resolution (ms)1 10 102 103 104 105

MRI(a,d)

MRI EEG MEG

Introduction: EEG/MEG as Neuroimaging techniques

MEEG functionalities in SPM8 EEG/MEGEEG/MEGSource localisationSource localisation

Data Preperation

New MEEG dataformat based on “object-oriented”coding More stable

interfacing and user-friendly and a bit harder for developers

Data importation/convertion

• Import most common MEG/EEG data formats into one single data format

• Include “associated data”, e.g. electrode location and sensor setup

Data Preperation“Usual“ preprocessing

• Filtering• Re-referencing• Epoching

• Artefact and bad channel rejection

• Averaging• Displaying• …

Data Preprocessing

Scalp Data Analysis

Statistical Parametric Mapping

Dynamic Causal Modelling

Source reconstruction

Energy changes (Faces - Scrambled, p<0.01)

0.1 0.2 0.4 0.6 0.8

time (s)

0.70.50.30-0.1

-1frequ

100 200 300 400

time (ms)

Right temporal evoked signal

facesscrambled

MEG experimentof 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.

EEG/MEGEEG/MEGSource localisationSource localisation MEEG “usual” results

Change speaker…

EEG/MEG source reconstruction process

Forwardmodel

Inverseproblem

Introduction: overview

5. Conclusion

source biophysical model: current dipole

EEG/MEG source models

EquivalentCurrent

Dipoles (ECD)

Imaging orDistributed

Forward model: source space

- few dipoles withfree location and orientation

- many dipoles withfixed location and orientation

EEG/MEGEEG/MEGSource localisationSource localisation Forward model: formulation

Forwardmodel

data dipoleparameters

noiseforwardoperator

EEG/MEGEEG/MEGSource localisationSource localisation Forward model: imaging/distributed model

data dipoleamplitudes

noisegain matrix

5. Conclusion

« 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 ? »

Jacques Hadamard (1865-1963)

Inverse problem: an ill-posed problem

Inverseproblem

1. Existence2. Unicity3. Stability

EEG/MEGEEG/MEGSource localisationSource localisation Inverse problem: an ill-posed problem

Inverseproblem

EEG/MEGEEG/MEGSource localisationSource localisation Inverse problem: an ill-posed problem

Inverseproblem

Introduction of prior knowledge (regularization) is needed

EEG/MEGEEG/MEGSource localisationSource localisation Inverse problem: regularization

Data fit

Adequacywith other

modalities

Spatial and temporal priors

W = I : minimum norm

W = Δ : maximum smoothness (LORETA)

data fit prior(regularization term)

5. Conclusion

EEG/MEGEEG/MEGSource localisationSource localisation Bayesian inference: probabilistic formulation

likelihood prior

posteriorevidence

Forwardmodel

Inverseproblem posterior

likelihood

EEG/MEGEEG/MEGSource localisationSource localisation Bayesian inference: hierarchical linear model

sensor (1st) level source (2nd) level

Q : (known) variance components(λ,μ) : (unknown) hyperparameters

qeqee QQC 1

1kpkpp QQC 1

ΜJp Μ,JYp

likelihood prior

EEG/MEGEEG/MEGSource localisationSource localisation Bayesian inference: variance components

Multiple Sparse Priors(MSP)

# dipoles

Minimum Norm(IID)

Maximum Smoothness(LORETA)

kpkpp QQC 1

),0(~)( pCNJp M

EEG/MEGEEG/MEGSource localisationSource localisation Bayesian inference: graphical representation

λ1 λk

KQQJYpJYp qeeq ,,,,,,,, 1

kppk QQJpJp ,,,,, 1

likelihood

EEG/MEGEEG/MEGSource localisationSource localisation Bayesian inference: iterative estimation scheme

M-step

E-step

,maxarg)ˆ,ˆ(

),ˆ,ˆ,(

maxarg)(ˆ)(

FMJqMJq

Expectation-Maximization (EM) algorithm

)()()(

)(),(ln

)()(),(

ln)(lnMJqMJq

MJqMYpMYJp

MJqMJpMJYp

EEG/MEGEEG/MEGSource localisationSource localisation Bayesian inference: model comparison

)()()|(ln McomplexityMaccuracyMYpF

model Mi

At convergence

5. Conclusion

EEG/MEGEEG/MEGSource localisationSource localisation Conclusion: At the end of the day...

Individual reconstructions in MRI template space

Group resultsp < 0.01 uncorrectedR L

EEG/MEGEEG/MEGSource localisationSource localisation Conclusion: Summary

• Prior information is mandatory

• EEG/MEG source reconstruction:1. forward model2. inverse problem (ill-posed)

• Bayesian inference is used to:1. incorpoate such prior information…2. … and estimating their weight w.r.t the data3. provide a quantitative feedback on model adequacy

Forwardmodel

Inverseproblem

Change speaker…Again !

source biophysical model: current dipole

EEG/MEG source models

EquivalentCurrent

Dipoles (ECD)

Imaging orDistributed

Equivalent Current Dipole (ECD) solution

few dipoles with free

location and orientation

many dipoles with fixed location and orientation

EEG/MEGEEG/MEGSource localisationSource localisation ECD approach: principle

Forwardmodel

data dipoleparameters

noiseforwardoperator

but a priori fixed number of sources considered iterative fitting of the 6 parameters of each dipole

The locations s and moments w are drawn from normal distributions with precisions γs and γw.

ε is white observation noise with precision γy.

yThese are drawn from a prior gamma distribution.

wsGy )(

Dipole locations s and dipole moments w generated data using

ECD solution: variational Bayes (VB) approach

EEG/MEGEEG/MEGSource localisationSource localisation ECD solution: “classical” vs. VB approaches

“Classical” VB

Hard constraints Yes Yes

Soft constraints No Yes

Noise accommodation

No (in general)

Model comparison No YES

• can be applied to single time-slice data or average over time (MEG and EEG)

• useful for comparing several few-dipole solutions for selected time points (N100, N170, etc.)

• although not dynamic, can be used for building up intuition about underlying generators, or using as a motivation for DCM source models

• implemented in Matlab and (very soon) available in SPM8

ECD solution: when and how to apply VB-ECD?

),0(~),( CNMJp

)exp( kk ),(~ Nk

- Log-normal hyperpriors- Enforces the non-negativity of the hyperparameters- Enables Automatic Relevance Determination (ARD)

Bayesian inference: multiple sparse priors

SubjectsMRI Anatomical warping

Corticalmesh

Canonicalmesh

[Un]-normalisingspatial transformation

MNI Space

Forward model: canonical mesh

From Sensor to MRI space

MRI derived meshes

Full setup

RigidTransformation

HeadShape

SurfaceMatching

HeadShape

Forward model: coregistration

Main referencesEEG/MEGEEG/MEGSource localisationSource localisation

Friston et al. (2008) Multiple sparse priors for the M/EEG inverse problem

Kiebel et al. (2008) Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG

Mattout et al. (2007) Canonical Source Reconstruction for MEG

Daunizeau and Friston (2007) A mesostate-space model for EEG and MEG

Henson et al. (2007) Population-level inferences for distributed MEG source localization under multiple constraints: application to face-evoked fields

Friston et al. (2007) Variational free energy and the Laplace approximation

Mattout et al. (2006) MEG source localization under multiple constraints

Friston et al. (2006) Bayesian estimation of evoked and induced responses

Phillips et al. (2005) An empirical Bayesian solution to the source reconstruction problem in EEG

EEG/MEG Source Localisation

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