Data preparation and coregistration after data acquisition
Author: Hadi Zaatiti hadi.zaatiti@nyu.edu
In this notebook, we shall do the initial data processing of the raw data generated by an MEG experiment. Prepare the following files to go through this notebook: Prerequisits: Obtained from the laser scan:
Headscan basic surface .txt
Headscan points .txt
Obtained from the KIT-MEG machine:
Marker measurement (x2) .mrk
MEG recording con.
Environment: Have MNE with all dependencies installedc From these files we will n .create .fif files, the base file format for MNE
In a terminal, run the following command, the following is an example on Windows CMD terminal.
[1]:
%%cmd
mne kit2fiff
Microsoft Windows [Version 10.0.22631.3447]
(c) Microsoft Corporation. All rights reserved.
(mne-1.6.1_0) C:\Users\hz3752\PycharmProjects\meg-pipeline\docs\source\4-pipeline\notebooks>mne kit2fiff
********************************************************************************
WARNING: Imported VTK version (9.3) does not match the one used
to build the TVTK classes (9.2). This may cause problems.
Please rebuild TVTK.
********************************************************************************
(mne-1.6.1_0) C:\Users\hz3752\PycharmProjects\meg-pipeline\docs\source\4-pipeline\notebooks>
Place the two .mrk in the Source marker 1 and Source marker 2, then in Sources, place the .con in Data, the head scan .txt in Digitizer head shape and the head scan points .txt in Digitizer Fiducials
You should now see the head scan, markers and scan points. Press the SAVE FIFF to save all the data within a .fiff.
Let us now load the .fiff within python and check its structure of the .fif
[2]:
%matplotlib inline
import mne
import matplotlib.pyplot as plt
raw = mne.io.read_raw_fif(r'C:\Users\hz3752\PycharmProjects\mne_bids_pipeline\data\meg\sub-01_01-eyes-closed-raw.fif')
raw.plot(duration = 15)
plt.show()
print(raw.info.get_channel_types())
plt.show()
Opening raw data file C:\Users\hz3752\PycharmProjects\mne_bids_pipeline\data\meg\sub-01_01-eyes-closed-raw.fif...
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[2], line 5
2 import mne
3 import matplotlib.pyplot as plt
----> 5 raw = mne.io.read_raw_fif(r'C:\Users\hz3752\PycharmProjects\mne_bids_pipeline\data\meg\sub-01_01-eyes-closed-raw.fif')
8 raw.plot(duration = 15)
9 plt.show()
File C:\ProgramData\mne-python\1.6.1_0\Lib\site-packages\mne\io\fiff\raw.py:543, in read_raw_fif(fname, allow_maxshield, preload, on_split_missing, verbose)
502 @fill_doc
503 def read_raw_fif(
504 fname, allow_maxshield=False, preload=False, on_split_missing="raise", verbose=None
505 ):
506 """Reader function for Raw FIF data.
507
508 Parameters
(...)
541 are updated accordingly.
542 """
--> 543 return Raw(
544 fname=fname,
545 allow_maxshield=allow_maxshield,
546 preload=preload,
547 verbose=verbose,
548 on_split_missing=on_split_missing,
549 )
File <decorator-gen-179>:12, in __init__(self, fname, allow_maxshield, preload, on_split_missing, verbose)
File C:\ProgramData\mne-python\1.6.1_0\Lib\site-packages\mne\io\fiff\raw.py:105, in Raw.__init__(self, fname, allow_maxshield, preload, on_split_missing, verbose)
103 next_fname = fname
104 while next_fname is not None:
--> 105 raw, next_fname, buffer_size_sec = self._read_raw_file(
106 next_fname, allow_maxshield, preload, do_check_ext
107 )
108 do_check_ext = False
109 raws.append(raw)
File <decorator-gen-180>:12, in _read_raw_file(self, fname, allow_maxshield, preload, do_check_ext, verbose)
File C:\ProgramData\mne-python\1.6.1_0\Lib\site-packages\mne\io\fiff\raw.py:187, in Raw._read_raw_file(self, fname, allow_maxshield, preload, do_check_ext, verbose)
185 check_fname(fname, "raw", endings)
186 # filename
--> 187 fname = str(_check_fname(fname, "read", True, "fname"))
188 ext = os.path.splitext(fname)[1].lower()
189 whole_file = preload if ".gz" in ext else False
File <decorator-gen-0>:12, in _check_fname(fname, overwrite, must_exist, name, need_dir, verbose)
File C:\ProgramData\mne-python\1.6.1_0\Lib\site-packages\mne\utils\check.py:263, in _check_fname(fname, overwrite, must_exist, name, need_dir, verbose)
261 raise PermissionError(f"{name} does not have read permissions: {fname}")
262 elif must_exist:
--> 263 raise FileNotFoundError(f'{name} does not exist: "{fname}"')
265 return fname
FileNotFoundError: fname does not exist: "C:\Users\hz3752\PycharmProjects\mne_bids_pipeline\data\meg\sub-01_01-eyes-closed-raw.fif"
Coregistration after KIT2FIFF
Coregistration involves aligning the MEG sensor axis, with the axis of the MRI headscan.
Different transformations can be applied during this coregistration:
Scaling: making the head bigger or smaller to adjust to the volume of the system
linear transformation: this involves translating the head scan to match the center of the MEG helmet
rotation
Non-linear transformations are not used in MEG coregistration.
You have MRI anatomical data of the participant
In this situation, the participant has had his head scanned in the MRI. Get the MRI ID of the participant and cross check it in the .csv file on NYU BOX, since the ID of the participants in MEG is different than the one in MRI.
T1w scans are needed and provided as input to the HPC brainsegmentation freesurfer pipeline. The MRI Lab team will provide you with the complete segmentation output folder, however this will not contain the BEM/FEM model needed for source localization.
At this point you can follow the tutorial on computing the BEM prior to proceeding.
We are now ready to coregister the participant head scan with the MEG sensors positions.
You can run the following script to launch a mne coregistration GUI
[ ]:
import mne
from PyQt5.QtWidgets import QApplication
import sys
app = QApplication.instance() # checks if QApplication already exists
if not app: # create QApplication if it doesnt exist
app = QApplication(sys.argv)
mne.gui.coregistration(
inst=r'C:\Users\hz3752\PycharmProjects\mne_bids_pipeline\data\meg\Sub-0037\sub-01_01-eyes-closed-raw.fif',
subject='Sub-0037',
subjects_dir=r'C:\Users\hz3752\PycharmProjects\mne_bids_pipeline\data\anat\outputs\PostFreeSurfer\T1w', # contains a sub-folder for subject
head_high_res=True,
)
app.exec_()
bem directory. In such case you will now need to set the fiducialsthe Nasion
the LPA: the left pre-aucular
the RPA: the right pre-aucular
Set each one of them and then save the fiducials. A file called sub-id-fiducials.fif is then created in the bem directory At this point you will see the digitized headscan from the laser scan, with the MRI headshape.
Apply the Fit fiducials and then the Fit ICp algorithms to match respectivaly the HPI coils markers and headscan with the three points previously.
Perform any additional translation, rotation that can seem needed to have the best alignment.
You do not have the MRI anatomical data of the participant
(Add part how to use fsaverage, to match a generic headshape to the positions of the HPI coil markers)
Conclusion
We now have the necesary .fif files to perform the source localization. You can now proceed to the source localization notebook.
[ ]: