MfD EEG/MEG Source Localization4th Feb 2009

Maro Machizawa

Himn Sabir

Expert: Vladimir Litvak

Inverseproblem

1. Existence2. Unicity3. Stability

1. Existence2. Unicity3. Stability

Inverseproblem

1. Existence2. Unicity3. Stability

Inverseproblem

Introduction of prior knowledge is needed

Spatio-temporal modeling

Spatio-temporal modeling – step 1Load EEG/MEG file

Spatio-temporal modeling – step 2Name the analysis (optional)

Spatio-temporal modeling – step 3Create/load meshes

Bigger the parameter, better the resolution of the results

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

Spatio-temporal modeling – step 4Coregister fiducial points with MRI

Spatio-temporal modeling – step 5Forward model

Spatio-temporal modeling – step 5Bayesian model inversion

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

Spatio-temporal modeling – step 5Invert: alternative models

The bigger the number, the better the model

-1893 -1913 -1913

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

Spatio-temporal modeling – step 6Window :

Induced: localization on each single trial then averagedEvoked: localization on already averaged data

INDUCED IMAGE

Spatio-temporal modeling – step 7Image

Group analysis: same analysis on multiple subjects

(Optional step5)Variational Bayes Equivalent Current Dipole

Optional: time-voltage display