[Eeglablist] Combine EEG and MRI data for identification of channels of interest/connectivity analysis

[Giatsidis, Fabio]

Thank you very much Makoto! Unfortunately both EEGs and MRIs were collected
without digitizing the electrodes first, so the built-in chanlocs for the
EEG and the MNI-space for the images are all that I have and are at the
moment just independent from each other. I'll see what I can do with SPM to
"fit" the electrodes' locations, hoping that it is not a futile and/or
daunting task.

Best,
-Fabio

--------------------------------
*Fabio Giatsidis, M.D.*
Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy
Post-doctoral research fellow - Brown University - Providence, RI, USA


On Wed, Jul 25, 2018 at 3:07 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Fabio,
>
> > - is it technically possible to use MRI scans that have already been
> pre-processed into MNI space?
>
> Yes, why not. If necessary, you can back-transform to individual space.
> SPM generates these forward-backward transform matrix.
>
> > - if so, what is the best way to achieve the EEG-MRI coupling?
>
> If you mean channel locationis coregistration, you still need to
> 'digitize' i.e., measure spatial locations of the scalp electrode using a
> special devise; see also my colleague's recent work to do it using a ipad
> with a lazer scanner, https://sccn.ucsd.edu/wiki/Get_chanlocs
>
> > - does all of the above seem reasonable, or given my situation (find the
> electrodes above the area of interest and in parallel start do connectivity
> analysis in pathological recordings) it would be better to use another
> approach?
>
> Unfortunately I have rather a pessimistic opinion on EEG's spatial
> locations even with these inputs for the following reasons.
>
>    1. Head's electric forward model is still very simple. For example,
>    standard models do not have blood vessels yet (See Fiedeler et al., 2016
>    NeuroImage)
>    2. Skull's electric conductivity has unknown individual differences,
>    and it could vary up to three times (See Akalin Acar et al., 2015 or
>    around... so called 'SCALE' paper)
>    3. The results heavily depends on what algorithm you use.
>
> After all, EEG is not MRI, in the sense that with MRI you can perform MR
> microscopy, but even with 10,000 EEG channels you cannot perform EEG
> microscopy. You should think about this fundamental difference carefully if
> you haven't done so.
>
> Makoto
>
>
> On Thu, Jul 19, 2018 at 3:25 AM Giatsidis, Fabio <
> fabio_giatsidis at brown.edu> wrote:
>
>> Hello EEGLAB list,
>>
>> I have been using EEGLAB in the past few months to do some spectral
>> analysis on pathological EEGs, but I now need to couple these with the MRIs
>> of the respective patients, in order to both pinpoint/identify only the
>> electrodes above the areas of interest and start performing some
>> connectivity analysis.
>> Additional notes:
>> 1) the EEGs were recorded using EGI 128-channel HydroCel caps, and are
>> resting states only,
>> 2) the MRI scans were already converted to MNI space (and lesions marked)
>> before the EEGs were recorded,
>> 3) I cannot go back to the original images and redo the above due to
>> technical and time limitations.
>>
>> I have read a bit about NFT, Fieldtrip and SPM12, but all of these seem
>> to assume that one has to start from the raw images (which I currently
>> cannot do, see point 3).
>>
>> My questions would then be:
>> - is it technically possible to use MRI scans that have already been
>> pre-processed into MNI space?
>> - if so, what is the best way to achieve the EEG-MRI coupling?
>> - does all of the above seem reasonable, or given my situation (find the
>> electrodes above the area of interest and in parallel start do connectivity
>> analysis in pathological recordings) it would be better to use another
>> approach?
>>
>> Thank you very much for all your help!
>> Best,
>> -Fabio
>>
>> --------------------------------
>> *Fabio Giatsidis, M.D.*
>> Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy
>> Post-doctoral research fellow - Brown University - Providence, RI, USA
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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