Source localizationMfD 2010, 17th Feb 2010

Diana Omigie and Stjepana Kovac

Source localization:

I Aim / Application

II Theory

a) What is recorded (EEG / MEG)

b) Forward problem Forward solutions

c) Inverse problem Inverse solutions

d) Inverse solutions: discrete vs. distributed

III The buttons in SPM

I Aim

To find a focus of brain activity by analysing the electrical

activity recorded from surface electrodes (EEG) or SQUID

(Superconductive Quantum Interference Device; MEG)

I Application:

- focal epilepsy:

spikes

seizures

- evoked potentials:

auditory evoked potentials

somatosensory evoked potentials

cognitive event related potentials

-

IIa What is recorded

Lopez daSilva, 2004

EPSP

-

Layer IV

radial

tangential

IIb Forward problem Forward solutionHow to model the surfaces i.e. the area between

recording electrode and cortical generator?

Plummer, 2008Realistic shape – (BEM isotropic, FEM anisotropic)

Skin, CSF, skull, brain

IIc Inverse problem Inverse solutions

+-

+ -

Discrete:

- Equivalent current dipole

Distributed (differ in side constraint):

- Minimum norm

(Halmalainen & Ilmoniemi 1984)

-LORETA (Pascual-Marqui, 1994)

-MSP – multiple sparse priors (Friston, 2008)

...........

IIc Inverse problem Inverse solutionsDiscrete source analysis Distributed source analysis

Current dipole represents an extended brain area

Each current dipole represents one small brain segment

Number of sources < number of sensors Number of sources >> number of sensors

The leadfieldmatrix has more rows (number of sensors) than colums (number of sources)

The leadfieldmatrix has more colums than rows

Result:Source model and source waveforms

Result: 3D Volume imagefor each timepoint

Two aspects of source analysis are original in SPM:

- Based on Bayesian formalism: generic inversion it can

incorporate and estimate the relevance of multiple

constraints (data driven relevance estimation – Baysian

model comparison)

- The subjects specific anatomy incorporated in the

generative model of the data

SPM source analysis

III The buttons in SPM :Graphical user interface for 3D source localisation

III EEG/MEG imaging pipeline

0) Load the file

1) Source space modeling

2) Data co-registration

3) Forward computation

4) Inverse reconstruction

5) Summarizing the results of the inverse reconstruction as an

image

0) Load the file

1) Source space modeling

MRI

template

MRI – individual

head meshes (boundaries of different

head compartments)

based on the

subject’s

structural scan

Template –

SPM’s template

head model

based on the

MNI brain

1) Source space modeling

Select mesh size:

- coarse

- normal

- fine

2) Data co-registration

Co-register

Fiducials –

landmark based

coregistration

Surface matching

2) Data co-registration

Methods to co-register

– “select” from default locations

– “type” MNI coordinates directory

– “click” manually each fiducial

point from MRI images

3) Forward computation

Forward Model

Recommendation:

Single shell for MEG

BEM for EEG

3) Forward computation

4) Inverse reconstruction

Invert

Imaging

VB-ECD

Beamforming

4) Inverse reconstruction

Default – click “Standard”:

• “MSP” method will be used. MSP : Multiple Sparse Priors (Friston

et al. 2008a)

Alternatives:

• GS (greedy search: default):

– iteratively add constraints (priors)

• ARD (automatic relevance determination):

– iteratively remove irrelevant constraints

• COH (coherence):

– LORETA-like smooth prior …

4) Inverse reconstruction

TIME Time course of the region with maximal activity

SPACEMaximal intensity projection (MIP)

5) Summarizing the results of inverse reconstruction as an image

Window

? Timewindow of

interest (ms peri-

stimulus time)

? Frequency band of

interest (default 0)

? Evoked/ induced

inversion applied

either to each trial

(induced) and then

averaged or

inversion applied to

the averaged trials

(evoked)

5) Summarizing the results of inverse reconstruction as an image

3D NIfTI images allow GLM

based statistical analysis

(Random field theory)

Sources

- indicated under figures

- Stavroula Kousta / Martin Chadwick (2007, MfD)

- Maro Machizawa / Himn Sabir (2008, MfD)

- SPM 8 manual

- BESA tutorials (http://www.besa.de), M. Scherg