[Eeglablist] Group analysis with dipole fitting results from custom head model

[Delorme, Arnaud]

Dear Noelle,

> Here are the steps I used to create a custom 5-layer FEM head model from a subject MRI using Fieldtrip:
> 1) MRI normalized to MNI space

This part is good. This can also be done with SPM with a user graphic interface.

> 2) MRI resliced using ft_volumereslice

In SPM this part is not necessary (a new volume in MNI space is saved).

> 3) MRI realigned to the CTF coordinate system by marking fiducials (nas, lhj, rhj; same fiducials used to coregister EEG electrode locations) and using ft_volumerealign

The MRI should be in MNI space so this step should not be necessary because DIPFIT can handle electrode and MRI alignment. In fact, DIPFIT will mainly work in MNI space for this type of application, and has not been tested in CTF space.

> 4)MRI segmented and used to create mesh (ft_volumesegment, ft_prepare_mesh)

Very good.

> 5) Electrodes co-registered to MRI based on fiducials (nas, lhj, rhj) using ft_electroderealign

Once you have the mesh, you can use DIPFIT to align electrodes with the mesh.
DIPFIT menu “Head model and settings”
- Select the mesh
- Select the MRI

> 6) Created head model and source model (ft_prepare_headmodel, ft_prepare_sourcemodel, ft_prepare_vol_sens)

This can be done in DIPFIT. 
DIPFIT (4.2) menu “Distributed source leadfield matrix"

> 7) Computed leadfield matrix  (ft_prepare_leadfield)

See above

> Dipole results:
> Currently, the resulting dipoles seem to be localized accurately when plotted on the subject MRI, but do not appear to be oriented correctly when plotted with the template MRI (avg152t1.mat) as the background image.

Dipole orientation will vary in each subject, and it is fine.
EEGLAB will allow you to cluster dipoles based on location (and optionally orientation).

Overall, you pipeline seems OK, except the alignment with CTF coordinates, which I think is an unnecessary complication (DIPFIT will align the head model and channel coordinate for you). The result should be the same though.

Arno