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MfD EEG/MEG Source Localization4th Feb 2009
Maro Machizawa
Himn Sabir
Expert: Vladimir Litvak
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Inverseproblem
1. Existence2. Unicity3. Stability
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1. Existence2. Unicity3. Stability
Inverseproblem
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1. Existence2. Unicity3. Stability
Inverseproblem
Introduction of prior knowledge is needed
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Spatio-temporal modeling
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Spatio-temporal modeling – step 1Load EEG/MEG file
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Spatio-temporal modeling – step 2Name the analysis (optional)
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Spatio-temporal modeling – step 3Create/load meshes
Bigger the parameter, better the resolution of the results
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Spatio-temporal modeling – step 4Coregister fiducial points with MRI
• Choose either of methods to coregister– “select” from default locations (at FIL)– “type” MNI coordinates directory– “click” manually each fiducial point from MRI images
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Spatio-temporal modeling – step 4Coregister fiducial points with MRI
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Spatio-temporal modeling – step 5Forward model
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Spatio-temporal modeling – step 5Bayesian model inversion
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Spatio-temporal modeling – step 5Invert: alternative models
• GS (greedy search: default): – iteratively add constraints (priors)
• ARD (automatic relevance determination): – iteratively remove irrelevant constraints
• COH (coherence): – LORETA-like smooth prior
• IID (independent identically distributed): – minimum norm
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Spatio-temporal modeling – step 5Invert: alternative models
The bigger the number, the better the model
-1893 -1913 -1913
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Spatio-temporal modeling – step 5Invert: visualization options
1 digit (ms): map on that time(ms)
2 digits (ms): video during the period
3 digits (x y z): max. voxel on that MNI coordinate
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Spatio-temporal modeling – step 6Window :
Induced: localization on each single trial then averagedEvoked: localization on already averaged data
INDUCED IMAGE
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Spatio-temporal modeling – step 7Image
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Group analysis: same analysis on multiple subjects
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(Optional step5)Variational Bayes Equivalent Current Dipole
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Optional: time-voltage display