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Source localization for EEG and MEG. Methods for Dummies 2006 FIL Bahador Bahrami. Before we start …. SPM5 and source localization: On-going work in progress MFD and source localization: This is the first on this topic Main references for this talk:

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source localization for eeg and meg

Source localization for EEG and MEG

Methods for Dummies 2006

FIL

Bahador Bahrami

before we start
Before we start …
  • SPM5 and source localization:
      • On-going work in progress
  • MFD and source localization:
      • This is the first on this topic
  • Main references for this talk:
      • Jeremie Mattout’s slides from SPM course
      • Slotnick S.D. chapter in Todd Handy’s ERP handbook
      • Rimona Weil’s wonderful help (thanks Rimona!)
outline
Outline
  • Theoretical
      • Source localization stated as a problem
      • Solution to the problem and their limitations
  • Practical*
      • How to prepare data
      • Which buttons to press
      • What to avoid
      • What to expect

* Subject to change along with the development of SPM 5

slide5

+

-

slide6

+

-

slide7

+

-

+

-

Any field potential vector could be consistent with an infinite number of possible dipoles

The possibilities only increase with tri-poles and quadra-poles

how do we know which one is correct

+

-

+

-

How do we know which one is correct?

We can’t. There is no correct answer.

Source localization is an ILL-DEFINED PROBLEM

We can only see which one is better

Can we find the best answer?

Only among the alternatives that you have considered.

hunting for best possible solution

MEG sensor location

MEG data

HUNTING for best possible solution

Step ONE: How does your data look like?

Source Reconstruction

Registration

hunting for best possible solution1

If

then

If

then

If

then

If

HUNTING for best possible solution

Step Two

then

FORWARD MODEL

And on and on and on and …

hunting for best possible solution2
HUNTING for best possible solution

Forward Model

Experimental DATA

Inverse Solution

Which forward solutions fit the DATA better (less error)?

hunting for best possible solution3

error

iteration

HUNTING for best possible solution

Forward

DATA

Inverse Solution

Iterative Process

Until solution stops getting better (error stabilises)

slide15

Components of the source reconstruction process

Source model

‘ECD’

‘Imaging’

Forward model

Registration

Inverse method

Data

Anatomy

recipe for source localization in spm5
Recipe for Source localization in SPM5
  • Ingredients
    • MEG converter has given you
      • .MAT data file (contains experimental data)
      • sensloc file (sensors locations)
      • sensorient (sensors orientations)
      • fidloc(fiducial locations in MEG space)
    • fidloc in MRI space (we will see shortly)
    • Structural T1 MRI scan

All in the same folder

fidloc in mri space

X

Y

Z

Nasion

Nasion

Nasion

X

Y

Z

Left Tragus

Left Tragus

Left Tragus

X

Y

Z

Right Tragus

Right Tragus

Right Tragus

fidloc in MRI space

Get these using SPM Display button

Save it as a MAT file in the same directory as the data

slide19

Components of the source reconstruction process

Forward model

Inverse solution

Source model

Registration

slide21

Source model

Compute transformation T

Individual MRI

Templates

Apply inverse transformation T-1

Individual mesh

functions

output

  • Individual MRI
  • Template mesh
  • spatial normalization into MNI template
  • inverted transformation applied to the template mesh
  • individual mesh
slide26

Scalp Mesh

iskull mesh

slide28

fiducials

fiducials

Rigid transformation (R,t)

Individual sensor space

Individual MRI space

Registration

input

functions

output

  • sensor locations
  • fiducial locations
  • (in both sensor & MRI space)
  • individual MRI
  • registration of the EEG/MEG data into individual MRI space
  • registrated data
  • rigid transformation
slide33

Model of the

head tissue properties

Individual MRI space

Foward model

Compute for

each dipole

+

K

n

Forward operator

functions

input

output

  • single sphere
  • three spheres
  • overlapping spheres
  • realistic spheres
  • sensor locations
  • individual mesh
  • forward operator K

BrainStorm

slide37

1 dipole source

per location

Y = KJ+ E

[nxt]

[nxt]

[nxp]

[pxt]

: min( ||Y – KJ||2 + λf(J) )

J

J

Inverse solution (1) - General principles

General Linear Model

Cortical mesh

n : number of sensors

p : number of dipoles

t : number of time samples

Under-determined GLM

^

Regularized solution

data fit

priors

slide38

E1 ~ N(0,Ce)

Y = KJ + E1

E2 ~ N(0,Cp)

J = 0 + E2

Ce = 1.Qe1 + … + q.Qeq

Cp = λ1.Qp1 + … + λk.Qpk

Inverse solution (2) - Parametric empirical Bayes

2-level hierarchical model

Gaussian variables

with unknown variance

Gaussian variables

with unknown variance

Sensor level

Source level

Linear parametrization of the variances

Q: variance components

(,λ): hyperparameters

slide39

Qe1 , … , Qeq

+

+

Model M

Qp1 , … , Qpk

J

K

,λ

^

J = CJKT[Ce + KCJ KT]-1Y

Inverse solution (3) - Parametric empirical Bayes

Bayesian inference on model parameters

Inference on J and (,λ)

Maximizing the log-evidence

F = log( p(Y|M) ) =  log(p(Y|J,M) ) + log( p(J|M) )dJ

data fit

priors

Expectation-Maximization (EM)

E-step: maximizing F wrt J

MAP estimate

M-step: maximizing of F wrt (,λ)

Ce + KCJKT = E[YYT]

ReML estimate

slide40

p(Y|M1)

p(Y|M2)

B12 =

Inverse solution (4) - Parametric empirical Bayes

Bayesian model comparison

Model evidence

  • Relevance of model M is quantified by its evidence p(Y|M) maximized by the EM scheme

Model comparison

  • Two models M1 and M2 can be compared by the ratio of their evidence

Bayes factor

Model selection using a

‘Leaving-one-prior-out-strategy‘

slide41

ECD approach

  • iterative forward and inverse computation

Inverse solution (5) - implementation

input

functions

output

  • preprocessed data
  • - forward operator
  • individual mesh
  • priors
  • - compute the MAP estimate of J
  • compute the ReML estimate of (,λ)
  • interpolate into individual MRI voxel-space
  • inverse estimate
  • model evidence
hunting for best possible solution4

error

iteration

HUNTING for best possible solution

Forward

DATA

Inverse Solution

Iterative Process

Until solution stops getting better (error stabilises)

types of analysis
Types of Analysis
  • Evoked
    • The evoked response is a reproducible response which occurs after each stimulation and is phase-locked with the stimulus onset.
  • Induced
    • The induced response is usually characterized in the frequency domain and contrary to the evoked response, is not phased-locked with the stimulus onset.
  • The evoked response is obtained (on the scalp) as the stimulus or event-locked average over trials. This is then the input data for the \'evoked\' case in source reconstruction.
  • One can also reconstruct the evoked power in some frequency band (over the time window), this is what is obtained when choosing \'both\' in source reconstruction.

Jeremie says:

slide51

Conclusion - Summary

MRI space

Data space

Registration

Forward model

PEB inverse solution

EEG/MEG preprocessed data

SPM

slide52

Forward model

Inverse solution

Source model

Registration

Important!

Repeated for each condition

The same for all conditions.

Therefore, only done ONCE for each subject

considerations
Considerations
  • Source localization project is still ongoing
  • Unable to incorporate prior assumptions about source (e.g., from fMRI blobs)
  • Source localization only for conditions
  • Not for contrasts
  • Source localization is a single subject analysis (no way to look at group effects)
thank you rimona
Thank you Rimona!

Thank you MFD!

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