[Mne_analysis] DICS_wrong_hemisphere_reconstruction {Disarmed}

Edoardo Pinzuti edoardo.pinzuti at gmail.com
Tue Dec 8 09:42:37 EST 2020
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I investigated a little more about the wrong bemforming reconstruction (it
was the same with different beamforming methods).
I imported the source model and the bem from mne to fieldtrip but I load
the grad structure directly from Fiedltrip and as you can see in the
mne_.jpg they don t have the same coordinates. Then I took the  inverse of
the transformation matrix  ctf_head_t and applied to the headmodel and
sourcemodel to get cft_aligned.jpg. I finally performed the DICS
beamoforming with  exactly the same CSD data and I get the peak on the
frontal right hemisphere this time, like I was trying to replicate.

On the other hand if I apply a different transformation matrix to the grad
( to bring everything aligned with the imported headmodel and
sourcemodel from mne) the peak is on the frontal left. So I guess I have a
problem with the coordinates and there is not flipping or other issue in
the source model or beamforming method.

Should I  bring everything to ctf coordinates in mne ? do you know why this


Il giorno mer 2 dic 2020 alle ore 22:05 Eric Larson <larson.eric.d at gmail.com>
ha scritto:

>         External Email - Use Caution
> Do you have another idea where there could be a flip right/ left ? mri
>> conventions, coordinates and so on
> 1. To check these I would use `plot_alignment` in MNE-Python to check to
> make sure everything is defined properly. Then if you use the same
> src/trans/bem in make_forward_solution it should all be correct.
> 2. It's possible we are hitting some rank bug, see for example *MailScanner
> has detected a possible fraud attempt from "secure-web.cisco.com" claiming
> to be* https://secure-web.cisco.com/1cBG9Qj0aLSXRpVoGDVMMyACDINZF046vX7j97rJcvG6RmvH5jCsdZdxkW9L9MHUFobqICT5KoW8MZQrl2qHV8JjWE5RiMEvpHFHFsW0WKRkyUTt7mTlkXEspXxwnkmjyPf_IW7gz-TnhjH9lJkqk2hUj3xH2X2FbmMpRJD08R7c0-Bnw897_KH-dpZ2eDIHJcYKo6x_fQSS4AMpuM37Dby9s88rRt1xTzY4PLTOaIDLTvFCKn6Ud4ly6qZ3LcC9Op0KvPSnyV8SyodxB8Q3yJQ/https%3A%2F%2Fgithub.com%2Fmne-tools%2Fmne-python%2Fpull%2F8594
> <https://secure-web.cisco.com/1o_lH-k4rHSYPhDzjd__shBfyihVFumwSV_W9W_OMpoll5YYDcxtValRvb9okGVShJsvTRZCV6hb00YuaKjRtY-iOGJ3UoWZUpZ-sdLKjb4NlS51yYibeBLjxDpYR8YHYFgdj8D7b7UBs_NcHgjB2mGiF6YU_LykyrQMfZxUsLXBxZm5XvNZJoeOUimYbUdIFjPCJYD8x2jC1QNM1uPhqxnZsey0r8j7ToEQaxHBPbfQgZimyQvYW3T2JOGFQNGTSidhSMqzFkqKwTkLEIgm4ng/https%3A%2F%2Fgithub.com%2Fmne-tools%2Fmne-python%2Fpull%2F8594>
> 3. You might need to use reduce_rank=True as in this example
> <https://secure-web.cisco.com/13MsdCox5KdzEpkrHMoAukWFSeMSbQzGuM0nRfQFGr7oaJ8uyROq_2nFM41F8P2DmnxF03hvqeB8I0Ao6OUFluoTL9Mtka3zYJqHIp8K7eRUGxWXuR5BskgNzlfNwDJ6UK8oEb3tjXAaNlPBQy3X43EbHnN0_YKdHKGexmausx9AJqM_zI3M8iTdyon8qNz_IBnSjLgtOEpgAxgt2Uz4R7JQe4ebaLWv7xbMCT5XU6BZH953ICan7D2pyaeQVFeFQOgG_0jSTj5bNrIUrrEWNHg/https%3A%2F%2Fmne.tools%2Fdev%2Fauto_examples%2Finverse%2Fplot_dics_source_power.html%23sphx-glr-auto-examples-inverse-plot-dics-source-power-py>.
> It's possible FT reduces rank by default and we do not, so make sure
> parameters match.
> 4. Can you also try doing `real_filter=True` to see if it helps? We are
> looking into orientation selection for the complex filters currently, it
> seems like FT produces better results for the complex.
> Eric
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