Head/MRI alignment with CTF MEG 275 - Forward solution - Sources reconstruction

šŸ’¬ Support & Discussions
1

Hello, I am reaching out after several unsuccessful attempts to realign head and MRI coordinates to my CTF 275 MEG recordings with the objective of performing a sources reconstruction. I think my issue might be somehow related to #4727.

I donā€™t have access to the individualized MRI scans of my subjects, hence at first Iā€™ve tried to perform the sources reconstruction based either on a pre-made head model (e.g. ā€˜fsaverageā€™) or lately, on a sphere head model ( i.e. based on the the 3 cardinal fiducial points plus 29 other extra digitization points from my raw files) assuming it was easier to align it in space.

The steps I normally use for my datasets consist of a standard Pre-processing pipeline (e.g. Filtering, Finding events, Making a montage, Choosing Picks (meg + ref_meg), Epoching, Auto-reject, ICA), to later proceed to do the sources reconstruction. Nonetheless, Iā€™ve got stuck several times trying to create the forward solution always with the same error message:
ā€œFound N sensors inside the outermost sphere shell / scalp surfaceā€

Here are some images of how the plot_alignment outputs look like at different runs:

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In my last attempts I have thought that the problem was based on the origin coordinates I was passing to the make_sphere_model commnad through fit_sphere_to_headshape. Therefore, I have tried to copy the ā€œorigin device coordinatesā€ (i.e. output in my bash window) manually to the sphere hoping that was going to work, but it was not the case. I have also tried to then look for those origin coordinates of my different frames of reference (e.g. head/MEG), but I havenā€™t been able to pull the coordinates from: mne-python/constants.py at main Ā· mne-tools/mne-python Ā· GitHub

I have also tried to empirically calculate the real MEG origin coordinates from data.info[ā€˜dev_head_tā€™][ā€˜transā€™], hoping that would help me to compensate manually for the RunTimeWarning that says that (x,y) fit is more than 20 mm from head frame origin, but I was not quite sure on how to accurately compensate spatially those 20 mm that are mismatching.

Long story short, I feel I am running in circles from one possibility to another just in order to overcome what it seems to be a simple geometric problem. Thus, any advice to move forward (pun intended) in my analysis would be very much appreciated.

Thanks in advance for your inputs.

Rob

  • MNE version: 1.0.3
  • operating system: CentOS Linux 7
2

Hello @RobertoFelipeSG and welcome to the forum!

Such a pity that our forum doesnā€™t allow me to grant you an award for that :joy:

Do you think you could share your recording (actually, a super short snippet would suffice, as basically weā€™ll only need the info structure)? I could then try to take a look.

You can anonymize the metadata via e.g. raw.anonymize() before sharing.

Best wishes,
Richard

PS
Iā€™ve never seen anyone do source estimation on MEG data without individual MRIs. For EEG, it is quite common, but for MEG ā€¦ apparently not. Are you sure youā€™ll really want to go this route?

3

Dear @richard, thanks for your reply and your willingness to help.

I am glad you appreciated my joke. Who said science and comedy couldnā€™t co-exist, am I right?

I sent to your personal email one of my raw anonymous datasets and its corresponding pre-processed file. Please let me know if this is sufficient or if there is anything else I can help with to overcome this issue.

P.S. I am fully aware this is not a common practice. I have done EEG source reconstruction with averaged brains before and now I would have liked to implement now a more accurate approach for this MEG dataset. However, to the best of knowledge, we donā€™t have the complete set of MRI scans corresponding to all the subjects as this was a dataset acquired pre-pandemic. Aside from the lack of spatial accuracy, what else in your opinion would I be facing if I decide to go analyze my data this way?