source localization mfd 2010, 17 th feb 2010 diana omigie and stjepana kovac
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Source localization MfD 2010, 17 th Feb 2010 Diana Omigie and Stjepana Kovac. - PowerPoint PPT PresentationTRANSCRIPT
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Source localizationMfD 2010, 17th Feb 2010
Diana Omigie and Stjepana Kovac
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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
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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)
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I Application:
- focal epilepsy:
spikes
seizures
- evoked potentials:
auditory evoked potentials
somatosensory evoked potentials
cognitive event related potentials
-
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IIa What is recorded
Lopez daSilva, 2004
EPSP
-
Layer IV
radial
tangential
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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
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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)
...........
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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
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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
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III The buttons in SPM :Graphical user interface for 3D source localisation
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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
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0) Load the file
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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
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1) Source space modeling
Select mesh size:
- coarse
- normal
- fine
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2) Data co-registration
Co-register
Fiducials –
landmark based
coregistration
Surface matching
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2) Data co-registration
Methods to co-register
– “select” from default locations
– “type” MNI coordinates directory
– “click” manually each fiducial
point from MRI images
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3) Forward computation
Forward Model
Recommendation:
Single shell for MEG
BEM for EEG
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3) Forward computation
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4) Inverse reconstruction
Invert
Imaging
VB-ECD
Beamforming
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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 …
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4) Inverse reconstruction
TIME Time course of the region with maximal activity
SPACEMaximal intensity projection (MIP)
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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)
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5) Summarizing the results of inverse reconstruction as an image
3D NIfTI images allow GLM
based statistical analysis
(Random field theory)
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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