1. Source localization
MfD 2010, 17th Feb 2010
Diana Omigie and Stjepana Kovac

2. Source localization: I. Aim / Application II. Theory
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

3. 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)

4. I Application: - focal epilepsy: spikes seizures - evoked potentials: auditory evoked potentials somatosensory evoked potentials cognitive event related potentials -

5. - IIa What is recorded EPSP Layer IV radial tangential
Lopez daSilva, 2004

6. IIb Forward problem Forward solution
How to model the surfaces i.e. the area between recording electrode and cortical generator?
Skin, CSF, skull, brain
Realistic shape – (BEM isotropic, FEM anisotropic)
Plummer, 2008

7. 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) + -

8. IIc Inverse problem Inverse solutions
Discrete 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
3D Volume image
for each timepoint

9. SPM source analysis
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

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

11. III EEG/MEG imaging pipeline
0) Load the file
Source space modeling
Data co-registration
Forward computation
Inverse reconstruction
Summarizing the results of the inverse reconstruction as an image

12. 0) Load the file

13. 1) Source space modeling
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
MRI
template

14. 1) Source space modeling
Select mesh size:
- coarse
normal
fine

15. 2) Data co-registration
Co-register
Fiducials – landmark based coregistration
Surface matching

16. 2) Data co-registration
Methods to co-register
“select” from default locations
“type” MNI coordinates directory
“click” manually each fiducial point from MRI images

17. 3) Forward computation Recommendation:
Forward Model
Recommendation:
Single shell for MEG BEM for EEG

18. 3) Forward computation

19. 4) Inverse reconstruction
Invert
Imaging
VB-ECD
Beamforming

20. 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 …

21. 4) Inverse reconstruction
TIME
Time course of the region with maximal activity
SPACE
Maximal intensity projection (MIP)

22. 5) Summarizing the results of inverse reconstruction as an image
? 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)
Window

23. 5) Summarizing the results of inverse reconstruction as an image
3D NIfTI images allow GLM
based statistical analysis
(Random field theory)

24. Sources
indicated under figures - Stavroula Kousta / Martin Chadwick (2007, MfD) - Maro Machizawa / Himn Sabir (2008, MfD)
- SPM 8 manual
BESA tutorials ( M. Scherg