Optically pumped magnetometer (OPM) data¶
In this dataset, electrical median nerve stimulation was delivered to the left wrist of the subject. Somatosensory evoked fields were measured using nine QuSpin SERF OPMs placed over the right-hand side somatomotor area. Here we demonstrate how to localize these custom OPM data in MNE.
import os.path as op import numpy as np import mne data_path = mne.datasets.opm.data_path() subject = 'OPM_sample' subjects_dir = op.join(data_path, 'subjects') raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif') bem_fname = op.join(subjects_dir, subject, 'bem', subject + '-5120-5120-5120-bem-sol.fif') fwd_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_sample-fwd.fif') coil_def_fname = op.join(data_path, 'MEG', 'OPM', 'coil_def.dat')
Prepare data for localization¶
First we filter and epoch the data:
raw = mne.io.read_raw_fif(raw_fname, preload=True) raw.filter(None, 90, h_trans_bandwidth=10.) raw.notch_filter(50., notch_widths=1) # Set epoch rejection threshold a bit larger than for SQUIDs reject = dict(mag=2e-10) tmin, tmax = -0.5, 1 # Find median nerve stimulator trigger event_id = dict(Median=257) events = mne.find_events(raw, stim_channel='STI101', mask=257, mask_type='and') picks = mne.pick_types(raw.info, meg=True, eeg=False) # We use verbose='error' to suppress warning about decimation causing aliasing, # ideally we would low-pass and then decimate instead epochs = mne.Epochs(raw, events, event_id, tmin, tmax, verbose='error', reject=reject, picks=picks, proj=False, decim=10, preload=True) evoked = epochs.average() evoked.plot() cov = mne.compute_covariance(epochs, tmax=0.) del epochs, raw
Opening raw data file /home/circleci/mne_data/MNE-OPM-data/MEG/OPM/OPM_SEF_raw.fif... Isotrak not found Range : 0 ... 700999 = 0.000 ... 700.999 secs Ready. Reading 0 ... 700999 = 0.000 ... 700.999 secs... Filtering raw data in 1 contiguous segment Setting up low-pass filter at 90 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 90.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 95.00 Hz) - Filter length: 331 samples (0.331 sec) Setting up band-stop filter from 49 - 51 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 49.00 - Lower transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 48.75 Hz) - Upper passband edge: 51.00 Hz - Upper transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 51.25 Hz) - Filter length: 6601 samples (6.601 sec) Trigger channel has a non-zero initial value of 256 (consider using initial_event=True to detect this event) 201 events found Event IDs:  Computing rank from data with rank=None Using tolerance 2.1e-10 (2.2e-16 eps * 9 dim * 1.1e+05 max singular value) Estimated rank (mag): 9 MAG: rank 9 computed from 9 data channels with 0 projectors Reducing data rank from 9 -> 9 Estimating covariance using EMPIRICAL Done. Number of samples used : 10251 [done]
Examine our coordinate alignment for source localization and compute a forward operator:
The Head<->MRI transform is an identity matrix, as the co-registration method used equates the two coordinate systems. This mis-defines the head coordinate system (which should be based on the LPA, Nasion, and RPA) but should be fine for these analyses.
bem = mne.read_bem_solution(bem_fname) trans = None # To compute the forward solution, we must # provide our temporary/custom coil definitions, which can be done as:: # # with mne.use_coil_def(coil_def_fname): # fwd = mne.make_forward_solution( # raw.info, trans, src, bem, eeg=False, mindist=5.0, # n_jobs=1, verbose=True) fwd = mne.read_forward_solution(fwd_fname) # use fixed orientation here just to save memory later mne.convert_forward_solution(fwd, force_fixed=True, copy=False) with mne.use_coil_def(coil_def_fname): fig = mne.viz.plot_alignment(evoked.info, trans=trans, subject=subject, subjects_dir=subjects_dir, surfaces=('head', 'pial'), bem=bem) mne.viz.set_3d_view(figure=fig, azimuth=45, elevation=60, distance=0.4, focalpoint=(0.02, 0, 0.04))
Loading surfaces... Loading the solution matrix... Three-layer model surfaces loaded. Loaded linear_collocation BEM solution from /home/circleci/mne_data/MNE-OPM-data/subjects/OPM_sample/bem/OPM_sample-5120-5120-5120-bem-sol.fif Reading forward solution from /home/circleci/mne_data/MNE-OPM-data/MEG/OPM/OPM_sample-fwd.fif... Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] 2 source spaces read Desired named matrix (kind = 3523) not available Read MEG forward solution (8196 sources, 9 channels, free orientations) Source spaces transformed to the forward solution coordinate frame Average patch normals will be employed in the rotation to the local surface coordinates.... Converting to surface-based source orientations... [done] Getting helmet for system unknown (derived from 9 MEG channel locations)
Perform dipole fitting¶
# Fit dipoles on a subset of time points with mne.use_coil_def(coil_def_fname): dip_opm, _ = mne.fit_dipole(evoked.copy().crop(0.015, 0.080), cov, bem, trans, verbose=True) idx = np.argmax(dip_opm.gof) print('Best dipole at t=%0.1f ms with %0.1f%% GOF' % (1000 * dip_opm.times[idx], dip_opm.gof[idx])) # Plot N20m dipole as an example dip_opm.plot_locations(trans, subject, subjects_dir, mode='orthoview', idx=idx)
BEM : <ConductorModel | BEM (3 layers)> MRI transform : identity Head origin : 1.3 -15.5 36.7 mm rad = 77.9 mm. Guess grid : 20.0 mm Guess mindist : 5.0 mm Guess exclude : 20.0 mm Using standard MEG coil definitions. Coordinate transformation: MRI (surface RAS) -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 Coordinate transformation: MEG device -> head 0.999800 0.015800 -0.009200 0.10 mm -0.018100 0.930500 -0.365900 16.60 mm 0.002800 0.366000 0.930600 -14.40 mm 0.000000 0.000000 0.000000 1.00 0 bad channels total Read 9 MEG channels from info 2 coil definitions read 99 coil definitions read Coordinate transformation: MEG device -> head 0.999800 0.015800 -0.009200 0.10 mm -0.018100 0.930500 -0.365900 16.60 mm 0.002800 0.366000 0.930600 -14.40 mm 0.000000 0.000000 0.000000 1.00 MEG coil definitions created in head coordinates. Decomposing the sensor noise covariance matrix... Computing rank from covariance with rank=None Using tolerance 2.2e-15 (2.2e-16 eps * 9 dim * 1.1 max singular value) Estimated rank (mag): 9 MAG: rank 9 computed from 9 data channels with 0 projectors Setting small MAG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 9 (0 small eigenvalues omitted) ---- Computing the forward solution for the guesses... Guess surface (inner skull) is in MRI (surface RAS) coordinates Filtering (grid = 20 mm)... Surface CM = ( 1.5 -15.0 35.4) mm Surface fits inside a sphere with radius 102.1 mm Surface extent: x = -73.3 ... 77.3 mm y = -100.7 ... 86.4 mm z = -42.9 ... 108.2 mm Grid extent: x = -80.0 ... 80.0 mm y = -120.0 ... 100.0 mm z = -60.0 ... 120.0 mm 1080 sources before omitting any. 543 sources after omitting infeasible sources not within 20.0 - 102.1 mm. Source spaces are in MRI coordinates. Checking that the sources are inside the surface and at least 5.0 mm away (will take a few...) Skipping interior check for 70 sources that fit inside a sphere of radius 51.5 mm Skipping solid angle check for 284 points using Qhull 299 source space points omitted because they are outside the inner skull surface. 30 source space points omitted because of the 5.0-mm distance limit. 214 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside. Go through all guess source locations... [done 214 sources] ---- Fitted : 20.0 ms, distance to inner skull : 11.8495 mm ---- Fitted : 30.0 ms, distance to inner skull : 44.7078 mm ---- Fitted : 40.0 ms, distance to inner skull : 9.0642 mm ---- Fitted : 50.0 ms, distance to inner skull : 15.3529 mm ---- Fitted : 60.0 ms, distance to inner skull : 18.6930 mm ---- Fitted : 70.0 ms, distance to inner skull : 14.6852 mm ---- Fitted : 80.0 ms, distance to inner skull : 5.2088 mm 7 time points fitted Best dipole at t=50.0 ms with 99.7% GOF
Perform minimum-norm localization¶
Due to the small number of sensors, there will be some leakage of activity to areas with low/no sensitivity. Constraining the source space to areas we are sensitive to might be a good idea.
inverse_operator = mne.minimum_norm.make_inverse_operator( evoked.info, fwd, cov, loose=0., depth=None) del fwd, cov method = "MNE" snr = 3. lambda2 = 1. / snr ** 2 stc = mne.minimum_norm.apply_inverse( evoked, inverse_operator, lambda2, method=method, pick_ori=None, verbose=True) # Plot source estimate at time of best dipole fit brain = stc.plot(hemi='rh', views='lat', subjects_dir=subjects_dir, initial_time=dip_opm.times[idx], clim=dict(kind='percent', lims=[99, 99.9, 99.99]))
Computing inverse operator with 9 channels. 9 out of 9 channels remain after picking Selected 9 channels Whitening the forward solution. Computing rank from covariance with rank=None Using tolerance 2.2e-15 (2.2e-16 eps * 9 dim * 1.1 max singular value) Estimated rank (mag): 9 MAG: rank 9 computed from 9 data channels with 0 projectors Setting small MAG eigenvalues to zero (without PCA) Creating the source covariance matrix Adjusting source covariance matrix. Computing SVD of whitened and weighted lead field matrix. largest singular value = 1.38802 scaling factor to adjust the trace = 7.35945e+18 (nchan = 9 nzero = 0) Preparing the inverse operator for use... Scaled noise and source covariance from nave = 1 to nave = 201 Created the regularized inverter The projection vectors do not apply to these channels. Created the whitener using a noise covariance matrix with rank 9 (0 small eigenvalues omitted) Applying inverse operator to "Median"... Picked 9 channels from the data Computing inverse... Eigenleads need to be weighted ... Computing residual... Explained 97.7% variance [done] Using control points [5.24807258e-11 1.55793898e-10 3.13115753e-10]
Total running time of the script: ( 0 minutes 46.388 seconds)
Estimated memory usage: 792 MB