Eduardo Mart

1
Eduardo Martínez Montes Neurophysics Department
Cuban Neuroscience Center
Source Localization for the EEG and MEG
2
(No Transcript)
3
EEG generators

• EEG reflects the electrical activity of neuronal
  masses, with spatial and temporal synchrony.
• Primary Current Density (PCD). Macroscopic
  temporal and spatial average of current density
  produced by Postsinaptic Potentials.

4
Direct Problem

• Main difficulties
• . Geometry . Inhomogeneity . Anisotropy

5
Direct Problem

• POTENTIAL
• Maxwell equations
• Boundary conditions
• 2nd Green Identity
• Fredholm Eq. 2nd type
• Drawbacks
• . Prior Model for DCP
• . Sensitivity to conductivity ratios

Nunez, 1981 Riera and Fuentes, 1998
6
Inverse Problem of the EEG/MEG
7
Different Approaches

• Dipolar - local minima, ad hoc number of
  dipoles, spread act.

Christoph et al., 2004
8
Whats wrong with IS methods?
1- Ghost Sources
2- Bias in the estimation of deep sources
9
New methodology

• Based on Bayesian Approach
• Aims to reduce the appearance of ghost sources
• Aims to overcome the bias on the estimation of
  the deep sources.
• Bayesian Model Averaging (BMA)
• Trujillo et al., 2004.

10
MN Methods Tikhonov vs Bayes
11
Why Bayes?

• Offers a natural way for introducing prior
  information in terms of probabilities
• It is easy to construct very complicated models
  from much simpler ones

12
Bayesian FrameworkFirst Level
13
Why Bayes Again?

• It accounts for uncertainty about model form by
  weighting the conditional posterior densities
  according to the posterior probabilities of each
  model.

14
Model Uncertainty
15
Bayesian FrameworkSecond Level
Given
16
Models and Dimensionality
For 69 compartments
17
What we need to do
1- Measure the influence of anatomical brain
areas in the generation of the EEG/MEG data under
consideration
2- Summarize this information in order to obtain
realistic posterior estimates of the electric
activity inside the brain
18
Simulations (OW)
TRUE
LORETA
BMA
19
Simulations
20
Previous Studies about Visual Steady-State
responses

• A strong source has been reported in the primary
  visual cortex located in the medial region of the
  occipital hemispheric pole.
• A second frontal source has also been observed
  and has been associated with the
  electroretinogram.
• Some authors have predicted the activation of the
  thalamus, but it has not been yet detected with
  none of the inverse methods available.

21
Visual Steady-State Response
22
Steady-State Somatosensorial
23
Steady-State Auditivo
24
Conclusions

• A new Bayesian inverse solution method based on
  model averaging is proposed
• The new method shows less blurring and
  significantly less ghost sources than previous
  approaches
• The new approach shows that the EEG might contain
  enough information for estimating deep sources
  even in the presence of cortical ones.

25
Ongoing Research

• Extension of the methodology to include
  spatial-temporal constraints
• Use connectivity constraints for solving the
  EEG/MEG inverse problem
• Estimation of causal models using the anatomical
  connectivity as prior information

26
References

• Nunez P., (1981) Electrics Fields of the Brain.
  New York Oxford Univ. Press.
• Riera JJ, Fuentes ME (1998). Electric lead field
  for a piecewise homogeneous volume conductor
  model of the head. IEEE Trans Biomed Eng 45746
  753.
• Christoph M. Michel, Micah M. Murray, Göran
  Lantz, Sara Gonzalez, Laurent Spinelli, Rolando
  Grave de Peralta, (2004). EEG source imaging.
  Clinical Neurophysiology, 115, 21952222.

• N.J. Trujillo-Barreto, L. Melie-García, E.
  Cuspineda, E. Martínez, P.A. Valdés-Sosa.
  Bayesian Inference and Model Averaging in EEG/MEG
  Imaging abstract. Presented at the 9th
  International Conference on Functional Mapping of
  the Human Brain, June 19-22, 2003, New York, NY.
  Available on CD-Rom in NeuroImage, Vol. 19, No.
  2.
• N.J. Trujillo-Barreto, E. Palmero, L. Melie, E.
  Martinez. MCMC for Bayesian Model Averaging in
  EEG/MEG Imaging abstract. Presented at the 9th
  International Conference on Functional Mapping of
  the Human Brain, June 19-22, 2003, New York, NY.
  Available on CD-Rom in NeuroImage, Vol. 19, No.
  2.
• N.J. Trujillo-Barreto, E. Aubert-Vázquez, P.A.
  Valdés-Sosa, (2004). Bayesian Model Averaging in
  EEG/MEG imaging. NeuroImage, 21 13001319.