07. Save and load T1-weighted MRI scan along with anatomical landmarks in BIDS

When working with MEEG data in the domain of source localization, we usually have to deal with aligning several coordinate systems, such as the coordinate systems of …

• the head of a study participant

• the recording device (in the case of MEG)

• the anatomical MRI scan of a study participant

The process of aligning these frames is also called coregistration, and is performed with the help of a transformation matrix, called trans in MNE.

In this tutorial, we show how MNE-BIDS can be used to save a T1 weighted MRI scan in BIDS format, and to encode all information of the trans object in a BIDS compatible way.

Finally, we will automatically reproduce our trans object from a BIDS directory.

See the documentation pages in the MNE docs for more information on source alignment and coordinate frames

Note

For this example you will need to install matplotlib and nilearn on top of your usual mne-bids installation.

# Authors: Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# License: BSD (3-clause)

We are importing everything we need for this example:

import os.path as op
import shutil as sh

import numpy as np
import matplotlib.pyplot as plt

from nilearn.plotting import plot_anat

import mne
from mne.datasets import sample
from mne.source_space import head_to_mri

from mne_bids import (write_raw_bids, BIDSPath, write_anat,
                      get_head_mri_trans, print_dir_tree)

We will be using the MNE sample data and write a basic BIDS dataset. For more information, you can checkout the respective example.

data_path = sample.data_path()
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2, 'Visual/Left': 3,
            'Visual/Right': 4, 'Smiley': 5, 'Button': 32}
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
events_data = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw-eve.fif')
output_path = op.abspath(op.join(data_path, '..', 'MNE-sample-data-bids'))
if op.exists(output_path):
    sh.rmtree(output_path)
raw = mne.io.read_raw_fif(raw_fname)
raw.info['line_freq'] = 60  # specify power line frequency as required by BIDS

sub = '01'
ses = '01'
task = 'audiovisual'
run = '01'
bids_path = BIDSPath(subject=sub, session=ses, task=task,
                     run=run, root=output_path)
write_raw_bids(raw, bids_path, events_data=events_data,
               event_id=event_id, overwrite=True)

Out:

Opening raw data file /Users/hoechenberger/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Opening raw data file /Users/hoechenberger/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/README'...

References
----------
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. http://doi.org/10.1038/sdata.2018.110


Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/participants.tsv'...

participant_id  age     sex     hand
sub-01  n/a     n/a     n/a

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/participants.json'...

{
    "participant_id": {
        "Description": "Unique participant identifier"
    },
    "age": {
        "Description": "Age of the participant at time of testing",
        "Units": "years"
    },
    "sex": {
        "Description": "Biological sex of the participant",
        "Levels": {
            "F": "female",
            "M": "male"
        }
    },
    "hand": {
        "Description": "Handedness of the participant",
        "Levels": {
            "R": "right",
            "L": "left",
            "A": "ambidextrous"
        }
    }
}

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_coordsystem.json'...

{
    "MEGCoordinateSystem": "ElektaNeuromag",
    "MEGCoordinateUnits": "m",
    "MEGCoordinateSystemDescription": "n/a",
    "HeadCoilCoordinates": {
        "NAS": [
            3.725290298461914e-09,
            0.10260561108589172,
            4.190951585769653e-09
        ],
        "LPA": [
            -0.07137660682201385,
            0.0,
            5.122274160385132e-09
        ],
        "RPA": [
            0.07526767998933792,
            0.0,
            5.587935447692871e-09
        ],
        "coil1": [
            0.032922741025686264,
            0.09897983074188232,
            0.07984329760074615
        ],
        "coil2": [
            -0.06998106092214584,
            0.06771647930145264,
            0.06888450682163239
        ],
        "coil3": [
            -0.07260829955339432,
            -0.02086828649044037,
            0.0971473976969719
        ],
        "coil4": [
            0.04996863007545471,
            -0.007233052980154753,
            0.1228904277086258
        ]
    },
    "HeadCoilCoordinateSystem": "ElektaNeuromag",
    "HeadCoilCoordinateUnits": "m"
}

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_events.tsv'...

onset   duration        trial_type      value   sample
3.6246181587150867      0.0     Auditory/Right  2       2177
4.237323479067476       0.0     Visual/Left     3       2545
4.946596485779753       0.0     Auditory/Left   1       2971
5.692498614904401       0.0     Visual/Right    4       3419
6.41342634238425        0.0     Auditory/Right  2       3852

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.4.0",
    "DatasetType": "raw",
    "Authors": [
        "Please cite MNE-BIDS in your publication before removing this (citations in README)"
    ]
}
Reading 0 ... 166799  =      0.000 ...   277.714 secs...

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_meg.json'...

{
    "TaskName": "audiovisual",
    "Manufacturer": "Elekta",
    "PowerLineFrequency": 60,
    "SamplingFrequency": 600.614990234375,
    "SoftwareFilters": "n/a",
    "RecordingDuration": 277.7136813300495,
    "RecordingType": "continuous",
    "DewarPosition": "n/a",
    "DigitizedLandmarks": false,
    "DigitizedHeadPoints": false,
    "MEGChannelCount": 306,
    "MEGREFChannelCount": 0,
    "EEGChannelCount": 60,
    "EOGChannelCount": 1,
    "ECGChannelCount": 0,
    "EMGChannelCount": 0,
    "MiscChannelCount": 0,
    "TriggerChannelCount": 9
}

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_channels.tsv'...

name    type    units   low_cutoff      high_cutoff     description     sampling_frequency      status  status_description
MEG 0113        MEGGRADPLANAR   T/m     0.10000000149011612     172.17630004882812      Planar Gradiometer      600.614990234375        good    n/a
MEG 0112        MEGGRADPLANAR   T/m     0.10000000149011612     172.17630004882812      Planar Gradiometer      600.614990234375        good    n/a
MEG 0111        MEGMAG  T       0.10000000149011612     172.17630004882812      Magnetometer    600.614990234375        good    n/a
MEG 0122        MEGGRADPLANAR   T/m     0.10000000149011612     172.17630004882812      Planar Gradiometer      600.614990234375        good    n/a
MEG 0123        MEGGRADPLANAR   T/m     0.10000000149011612     172.17630004882812      Planar Gradiometer      600.614990234375        good    n/a
Copying data files to sub-01_ses-01_task-audiovisual_run-01_meg.fif
Writing /Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_meg.fif
Closing /Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_meg.fif [done]

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/sub-01_ses-01_scans.tsv'...

filename        acq_time
meg/sub-01_ses-01_task-audiovisual_run-01_meg.fif       2002-12-03T19:01:10
Wrote /Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/sub-01_ses-01_scans.tsv entry with meg/sub-01_ses-01_task-audiovisual_run-01_meg.fif.

BIDSPath(
root: /Users/hoechenberger/mne_data/MNE-sample-data-bids
datatype: meg
basename: sub-01_ses-01_task-audiovisual_run-01_meg.fif)

Print the directory tree

print_dir_tree(output_path)

Out:

|MNE-sample-data-bids/
|--- README
|--- dataset_description.json
|--- participants.json
|--- participants.tsv
|--- sub-01/
|------ ses-01/
|--------- sub-01_ses-01_scans.tsv
|--------- meg/
|------------ sub-01_ses-01_coordsystem.json
|------------ sub-01_ses-01_task-audiovisual_run-01_channels.tsv
|------------ sub-01_ses-01_task-audiovisual_run-01_events.tsv
|------------ sub-01_ses-01_task-audiovisual_run-01_meg.fif
|------------ sub-01_ses-01_task-audiovisual_run-01_meg.json

Now let’s assume that we have also collected some T1 weighted MRI data for our subject. And furthermore, that we have already aligned our coordinate frames (using e.g., the coregistration GUI) and obtained a transformation matrix trans.

# Get the path to our MRI scan
t1_mgh_fname = op.join(data_path, 'subjects', 'sample', 'mri', 'T1.mgz')

# Load the transformation matrix and show what it looks like
trans_fname = op.join(data_path, 'MEG', 'sample',
                      'sample_audvis_raw-trans.fif')
trans = mne.read_trans(trans_fname)
print(trans)

Out:

<Transform | head->MRI (surface RAS)>
[[ 0.99930954  0.01275934  0.0348942   0.00206991]
 [ 0.00998479  0.81240475 -0.58300853  0.01130214]
 [-0.03578702  0.58295429  0.81171638 -0.02755522]
 [ 0.          0.          0.          1.        ]]

We can save the MRI to our existing BIDS directory and at the same time create a JSON sidecar file that contains metadata, we will later use to retrieve our transformation matrix trans.

# First create the BIDSPath object.
t1w_bids_path = BIDSPath(subject=sub, session=ses, root=output_path)

# We use the write_anat function
t1w_bids_path = write_anat(
    t1w=t1_mgh_fname,  # path to the MRI scan
    bids_path=t1w_bids_path,
    raw=raw,  # the raw MEG data file connected to the MRI
    trans=trans,  # our transformation matrix
    verbose=True  # this will print out the sidecar file
)
anat_dir = t1w_bids_path.directory

Out:

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/anat/sub-01_ses-01_T1w.json'...

{
    "AnatomicalLandmarkCoordinates": {
        "LPA": [
            197.25741411263368,
            153.0008593581418,
            138.5894600936019
        ],
        "NAS": [
            124.62090614299716,
            95.74083565348268,
            222.65942693440599
        ],
        "RPA": [
            50.71437932937833,
            158.24882153365422,
            140.05367187187042
        ]
    }
}

Let’s have another look at our BIDS directory

print_dir_tree(output_path)

Out:

|MNE-sample-data-bids/
|--- README
|--- dataset_description.json
|--- participants.json
|--- participants.tsv
|--- sub-01/
|------ ses-01/
|--------- sub-01_ses-01_scans.tsv
|--------- anat/
|------------ sub-01_ses-01_T1w.json
|------------ sub-01_ses-01_T1w.nii.gz
|--------- meg/
|------------ sub-01_ses-01_coordsystem.json
|------------ sub-01_ses-01_task-audiovisual_run-01_channels.tsv
|------------ sub-01_ses-01_task-audiovisual_run-01_events.tsv
|------------ sub-01_ses-01_task-audiovisual_run-01_meg.fif
|------------ sub-01_ses-01_task-audiovisual_run-01_meg.json

Our BIDS dataset is now ready to be shared. We can easily estimate the transformation matrix using MNE-BIDS and the BIDS dataset.

estim_trans = get_head_mri_trans(bids_path=bids_path)

Out:

Opening raw data file /Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_meg.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Reading events from /Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_events.tsv.
Reading channel info from /Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/meg/sub-01_ses-01_task-audiovisual_run-01_channels.tsv.
/Users/hoechenberger/Development/mne-bids/mne_bids/read.py:246: RuntimeWarning: The unit for channel(s) STI 001, STI 002, STI 003, STI 004, STI 005, STI 006, STI 014, STI 015, STI 016 has changed from V to NA.
  raw.set_channel_types(channel_type_dict)

Finally, let’s use the T1 weighted MRI image and plot the anatomical landmarks Nasion, LPA, and RPA (=left and right preauricular points) onto the brain image. For that, we can extract the location of Nasion, LPA, and RPA from the MEG file, apply our transformation matrix trans, and plot the results.

# Get Landmarks from MEG file, 0, 1, and 2 correspond to LPA, NAS, RPA
# and the 'r' key will provide us with the xyz coordinates
pos = np.asarray((raw.info['dig'][0]['r'],
                  raw.info['dig'][1]['r'],
                  raw.info['dig'][2]['r']))


# We use a function from MNE-Python to convert MEG coordinates to MRI space
# for the conversion we use our estimated transformation matrix and the
# MEG coordinates extracted from the raw file. `subjects` and `subjects_dir`
# are used internally, to point to the T1-weighted MRI file: `t1_mgh_fname`
mri_pos = head_to_mri(pos=pos,
                      subject='sample',
                      mri_head_t=estim_trans,
                      subjects_dir=op.join(data_path, 'subjects')
                      )

# Our MRI written to BIDS, we got `anat_dir` from our `write_anat` function
t1_nii_fname = op.join(anat_dir, 'sub-01_ses-01_T1w.nii.gz')

# Plot it
fig, axs = plt.subplots(3, 1)
for point_idx, label in enumerate(('LPA', 'NAS', 'RPA')):
    plot_anat(t1_nii_fname, axes=axs[point_idx],
              cut_coords=mri_pos[point_idx, :],
              title=label)
plt.show()

We can deface the MRI for anonymization by passing deface=True.

t1w_bids_path = write_anat(
    t1w=t1_mgh_fname,  # path to the MRI scan
    bids_path=bids_path,
    raw=raw,  # the raw MEG data file connected to the MRI
    trans=trans,  # our transformation matrix
    deface=True,
    overwrite=True,
    verbose=True  # this will print out the sidecar file
)
anat_dir = t1w_bids_path.directory

# Our MRI written to BIDS, we got `anat_dir` from our `write_anat` function
t1_nii_fname = op.join(anat_dir, 'sub-01_ses-01_T1w.nii.gz')

# Plot it
fig, ax = plt.subplots()
plot_anat(t1_nii_fname, axes=ax, title='Defaced')
plt.show()

Out:

Writing '/Users/hoechenberger/mne_data/MNE-sample-data-bids/sub-01/ses-01/anat/sub-01_ses-01_task-audiovisual_run-01_T1w.json'...

{
    "AnatomicalLandmarkCoordinates": {
        "LPA": [
            197.25741411263368,
            153.0008593581418,
            138.5894600936019
        ],
        "NAS": [
            124.62090614299716,
            95.74083565348268,
            222.65942693440599
        ],
        "RPA": [
            50.71437932937833,
            158.24882153365422,
            140.05367187187042
        ]
    }
}