[FieldTrip] source_recontruction with intracranial sensors
mcpiastra at libero.it
mcpiastra at libero.it
Fri Jul 5 17:47:32 CEST 2013
Dear Fieldtrip developers and users,
I have some problems with source reconstruction starting with intracranial
sensors. Schematic details are below.
Aim: do source reconstruction using BEM for the forward problem and mne for
the inverse problem.
Dataset: stereoEEG recording (50 sec)
Notation: I'll use the same nomenclature of the one in the tutorial
Since we have intracranial sensors, our aim is to build the forward model
(leadfield structure) starting from a mesh whose cortical sheet has a higher
spatial resolution. To do that we approached the problem in two ways:
1) we substituted the innermost sheet, built by ft_prepare_mesh, with the
Freesurfer cortical mesh (4180 vertices). We put dipoles on the mesh nodes and
then we built the head model and calculate the leadfield matrices (one for each
dipole). Solving the inverse problem, in source.avg.pow there were values with
order of magnitude of 10^{-30}- 10^{-20}. This is due to the fact that the
matrix vol.mat (one of the output of ft_prepare_headmodel) had values very
different if we compared the entries in correspondence to the Fieldtrip meshes
and the ones in correspondence to the Freesurfer mesh. This difference had the
order of magnitude equal to 10^{9}.
Due to this pathology, we tried a different way to compute the forward model
(following more the tutorial).
2) we built the head model using the default Fieldtrip meshes (all of the
three sheets) without substituting any sheet. (We just fixed the number of
nodes for the innermost sheet equal to the Freesurfer one.) This avoided high
differences in vol.mat.
Freesurfer mesh vertices were used to determine dipole positions, as we did
before. In this case we obtained values of source.avg.pow of the order of 10^
{-6}
In both cases we preprocessed the dataset with ft_timelockanalysis and we used
the regularization parameter lambda (for the mne method) equal to 0.2.
Our questions are:
1) what is vol.mat? Which order of magnitude should have its values? We didn't
manage to explore the building process of it. (We suppose this matrix has
electrical information interpolated through the mesh vertices.)
As consequences:
2) which is the order of magnitude expected for the values of source.avg.pow ?
And which is its unit of measure?
3) Is it correct the interpretation of: source.avg.mom and source.avg.pow as
the evolution through time of the electric dipole moment and the radiated power
of the dipole, respectively?
4) In order to apply a regularization approach to solve the inverse problem we
though that the best way was to introduce the noise covariation matrix built by
the function ft_timelockanalysis which is subsequently controlled by the lambda
parameter. Is there any criterion to choose the best lambda? Can we avoid to
pass through ft_timelockanalysis to regularize the solution?
Many thanks,
Maria Carla Piastra
PhD student in Bioengeneering c/o BioLab @ DIBRIS
University of Genova,
ITALY
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