Source localization MfD 2010, 17 th Feb 2010 Diana Omigie and Stjepana Kovac
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Transcript of Source localization MfD 2010, 17 th Feb 2010 Diana Omigie and Stjepana Kovac
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