.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_multitask_lasso.py: Multi-subject joint source localization with multi-task lasso ============================================================= The aim of this tutorial is to show how to leverage functional similarity across subjects to improve source localization with multi-task Lasso. Multi-task Lasso assumes that the exact same sources are active for all subjects at all times. This example illustrates this on the the high frequency SEF MEG dataset of (Nurminen et al., 2017) which provides MEG and MRI data for two subjects. .. code-block:: default # Author: Hicham Janati (hicham.janati@inria.fr) # # License: BSD (3-clause) import mne import os import os.path as op from mne.parallel import parallel_func from mne.datasets import hf_sef from matplotlib import pyplot as plt from groupmne import compute_group_inverse, prepare_fwds, compute_fwd Download and process MEG data ----------------------------- For this example, we use the HF somatosensory dataset [2]. We need the raw data to estimate the noise covariance since only average MEG data (and MRI) are provided in "evoked". The data will be downloaded in the same location .. code-block:: default _ = hf_sef.data_path("raw") data_path = hf_sef.data_path("evoked") meg_path = data_path + "/MEG/" data_path = op.expanduser(data_path) subjects_dir = data_path + "/subjects/" os.environ['SUBJECTS_DIR'] = subjects_dir raw_name_s = [meg_path + s for s in ["subject_a/sef_right_raw.fif", "subject_b/hf_sef_15min_raw.fif"]] def process_meg(raw_name): """Extract epochs from a raw fif file. Parameters ---------- raw_name: str. path to the raw fif file. Returns ------- epochs: Epochs instance """ raw = mne.io.read_raw_fif(raw_name) events = mne.find_events(raw) event_id = dict(hf=1) # event trigger and conditions tmin = -0.05 # start of each epoch (50ms before the trigger) tmax = 0.3 # end of each epoch (300ms after the trigger) baseline = (None, 0) # means from the first instant to t = 0 epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, baseline=baseline) return epochs epochs_s = [process_meg(raw_name) for raw_name in raw_name_s] evokeds = [ep.average() for ep in epochs_s] # compute noise covariance (takes a few minutes) noise_covs = [] for subj, ep in zip(["a", "b"], epochs_s): cov_fname = meg_path + f"subject_{subj}/sef-cov.fif" cov = mne.compute_covariance(ep[:100], tmin=None, tmax=0.) noise_covs.append(cov) f, axes = plt.subplots(1, 2, sharey=True) for ax, ev, nc, ll in zip(axes.ravel(), evokeds, noise_covs, ["a", "b"]): picks = mne.pick_types(ev.info, meg="grad") ev.plot(picks=picks, axes=ax, noise_cov=nc, show=False) ax.set_title("Subject %s" % ll, fontsize=15) plt.show() del epochs_s .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_001.png :alt: Subject a, Subject b :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_a/sef_right_raw.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 26000 ... 1735999 = 8.667 ... 578.666 secs Ready. Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_a/sef_right_raw-1.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 1736000 ... 2482999 = 578.667 ... 827.666 secs Ready. Current compensation grade : 0 2527 events found Event IDs: [1] 2527 matching events found Applying baseline correction (mode: mean) Not setting metadata Created an SSP operator (subspace dimension = 8) 8 projection items activated Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_b/hf_sef_15min_raw.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 169000 ... 1878999 = 56.333 ... 626.333 secs Ready. Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_b/hf_sef_15min_raw-1.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 1879000 ... 2892999 = 626.333 ... 964.333 secs Ready. Current compensation grade : 0 2792 events found Event IDs: [1] 2792 matching events found Applying baseline correction (mode: mean) Not setting metadata Created an SSP operator (subspace dimension = 8) 8 projection items activated Loading data for 100 events and 1051 original time points ... 0 bad epochs dropped Computing rank from data with rank=None Using tolerance 6.1e-09 (2.2e-16 eps * 306 dim * 8.9e+04 max singular value) Estimated rank (mag + grad): 298 MEG: rank 298 computed from 306 data channels with 8 projectors Created an SSP operator (subspace dimension = 8) Setting small MEG eigenvalues to zero (without PCA) Reducing data rank from 306 -> 298 Estimating covariance using EMPIRICAL Done. Number of samples used : 15100 [done] Loading data for 100 events and 1051 original time points ... 0 bad epochs dropped Computing rank from data with rank=None Using tolerance 5.3e-09 (2.2e-16 eps * 306 dim * 7.8e+04 max singular value) Estimated rank (mag + grad): 298 MEG: rank 298 computed from 306 data channels with 8 projectors Created an SSP operator (subspace dimension = 8) Setting small MEG eigenvalues to zero (without PCA) Reducing data rank from 306 -> 298 Estimating covariance using EMPIRICAL Done. Number of samples used : 15100 [done] Computing rank from covariance with rank=None Using tolerance 1.9e-13 (2.2e-16 eps * 204 dim * 4.1 max singular value) Estimated rank (grad): 201 GRAD: rank 201 computed from 204 data channels with 3 projectors Computing rank from covariance with rank=None Using tolerance 1.2e-14 (2.2e-16 eps * 102 dim * 0.52 max singular value) Estimated rank (mag): 97 MAG: rank 97 computed from 102 data channels with 5 projectors Computing rank from covariance with rank=None Using tolerance 3.3e-13 (2.2e-16 eps * 204 dim * 7.4 max singular value) Estimated rank (grad): 201 GRAD: rank 201 computed from 204 data channels with 3 projectors Computing rank from covariance with rank=None Using tolerance 8e-15 (2.2e-16 eps * 102 dim * 0.35 max singular value) Estimated rank (mag): 97 MAG: rank 97 computed from 102 data channels with 5 projectors Source and forward modeling --------------------------- To guarantee an alignment across subjects, we start by computing the source space of `fsaverage` .. code-block:: default resolution = 5 spacing = "oct%d" % resolution fsaverage_fname = op.join(subjects_dir, "fsaverage") if not op.exists(fsaverage_fname): mne.datasets.fetch_fsaverage(subjects_dir) src_ref = mne.setup_source_space(subject="fsaverage", spacing=spacing, subjects_dir=subjects_dir, add_dist=False) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Setting up the source space with the following parameters: SUBJECTS_DIR = /home/circleci/mne_data/HF_SEF/subjects/ Subject = fsaverage Surface = white Octahedron subdivision grade 5 >>> 1. Creating the source space... Doing the octahedral vertex picking... Loading /home/circleci/mne_data/HF_SEF/subjects/fsaverage/surf/lh.white... Mapping lh fsaverage -> oct (5) ... Warning: zero size triangles: [3 4] Triangle neighbors and vertex normals... Loading geometry from /home/circleci/mne_data/HF_SEF/subjects/fsaverage/surf/lh.sphere... Setting up the triangulation for the decimated surface... loaded lh.white 1026/163842 selected to source space (oct = 5) Loading /home/circleci/mne_data/HF_SEF/subjects/fsaverage/surf/rh.white... Mapping rh fsaverage -> oct (5) ... Warning: zero size triangles: [3 4] Triangle neighbors and vertex normals... Loading geometry from /home/circleci/mne_data/HF_SEF/subjects/fsaverage/surf/rh.sphere... Setting up the triangulation for the decimated surface... loaded rh.white 1026/163842 selected to source space (oct = 5) You are now one step closer to computing the gain matrix Compute forward models with a reference source space ---------------------------------------------------- the function `compute_fwd` morphs the source space src_ref to the surface of each subject by mapping the sulci and gyri patterns and computes their forward operators. Next we prepare the forward operators to be aligned across subjects .. code-block:: default subjects = ["subject_a", "subject_b"] trans_fname_s = [meg_path + "%s/sef-trans.fif" % s for s in subjects] bem_fname_s = [subjects_dir + "%s/bem/%s-5120-bem-sol.fif" % (s, s) for s in subjects] Before computing the forward operators, we make sure the coordinate transformation of the trans file provides a reasonable alignement between the different coordinate systems MEG <-> HEAD .. code-block:: default for raw_fname, trans, subject in zip(raw_name_s, trans_fname_s, subjects): raw = raw = mne.io.read_raw_fif(raw_fname) fig = mne.viz.plot_alignment(raw.info, trans=trans, subject=subject, subjects_dir=subjects_dir, surfaces='head-dense', show_axes=True, dig=True, eeg=[], meg='sensors', coord_frame='meg') n_jobs = 2 parallel, run_func, _ = parallel_func(compute_fwd, n_jobs=n_jobs) fwds_ = parallel(run_func(s, src_ref, info, trans, bem, mindist=3) for s, info, trans, bem in zip(subjects, raw_name_s, trans_fname_s, bem_fname_s)) fwds = prepare_fwds(fwds_, src_ref, copy=False) .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_002.png :alt: plot multitask lasso :class: sphx-glr-multi-img * .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_003.png :alt: plot multitask lasso :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_a/sef_right_raw.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 26000 ... 1735999 = 8.667 ... 578.666 secs Ready. Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_a/sef_right_raw-1.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 1736000 ... 2482999 = 578.667 ... 827.666 secs Ready. Current compensation grade : 0 Using subject_a-head-dense.fif for head surface. 1 BEM surfaces found Reading a surface... [done] 1 BEM surfaces read Using pyvista 3d backend. Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_b/hf_sef_15min_raw.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 169000 ... 1878999 = 56.333 ... 626.333 secs Ready. Opening raw data file /home/circleci/mne_data/HF_SEF/MEG/subject_b/hf_sef_15min_raw-1.fif... Read a total of 8 projection items: generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle generated with autossp-1.0.1 (1 x 306) idle Range : 1879000 ... 2892999 = 626.333 ... 964.333 secs Ready. Current compensation grade : 0 Using subject_b-head-dense.fif for head surface. 1 BEM surfaces found Reading a surface... [done] 1 BEM surfaces read [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 33.9s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 33.9s finished Mapping lh fsaverage -> subject_a (nearest neighbor)... Mapping rh fsaverage -> subject_a (nearest neighbor)... Mapping lh fsaverage -> subject_b (nearest neighbor)... Mapping rh fsaverage -> subject_b (nearest neighbor)... Solve the inverse problems with Multi-task Lasso ------------------------------------------------ .. code-block:: default # The Multi-task Lasso assumes the source locations are the same across # subjects for all instants i.e if a source is zero for one subject, it will # be zero for all subjects. "alpha" is a hyperparameter that controls this # structured sparsity prior. it must be set as a positive number between 0 # and 1. With alpha = 1, all the sources are 0. # We restric the time points around 20ms in order to reconstruct the sources of # the N20 response. evokeds = [ev.crop(0.019, 0.021) for ev in evokeds] stcs = compute_group_inverse(fwds, evokeds, noise_covs, method='multitasklasso', spatiotemporal=True, alpha=0.8) Let's visualize the N20 response. The stimulus was applied on the right hand, thus we only show the left hemisphere. The activation is exactly in the primary somatosensory cortex. We highlight the borders of the post central gyrus. .. code-block:: default t = 0.02 plot_kwargs = dict( hemi='lh', subjects_dir=subjects_dir, views="lateral", initial_time=t, time_unit='s', size=(800, 800), smoothing_steps=5) t_idx = stcs[0].time_as_index(t) for stc, subject in zip(stcs, subjects): g_post_central = mne.read_labels_from_annot(subject, "aparc.a2009s", subjects_dir=subjects_dir, regexp="G_postcentral-lh")[0] n_sources = [stc.vertices[0].size, stc.vertices[1].size] m = abs(stc.data[:n_sources[0], t_idx]).max() plot_kwargs["clim"] = dict(kind='value', pos_lims=[0., 0.2 * m, m]) brain = stc.plot(**plot_kwargs) brain.add_text(0.1, 0.9, "multi-subject-grouplasso (%s)" % subject, "title") brain.add_label(g_post_central, borders=True, color="green") .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_004.png :alt: plot multitask lasso :class: sphx-glr-multi-img * .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_005.png :alt: plot multitask lasso :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Reading labels from parcellation... read 1 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_a/label/lh.aparc.a2009s.annot read 0 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_a/label/rh.aparc.a2009s.annot Reading labels from parcellation... read 1 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_b/label/lh.aparc.a2009s.annot read 0 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_b/label/rh.aparc.a2009s.annot Group MNE leads to better accuracy ---------------------------------- To evaluate the effect of the joint inverse solution, we compute the individual solutions independently for each subject .. code-block:: default for subject, fwd, evoked, cov in zip(subjects, fwds_, evokeds, noise_covs): fwd_ = prepare_fwds([fwd], src_ref) stc = compute_group_inverse(fwd_, [evoked], [cov], method='multitasklasso', spatiotemporal=True, alpha=0.8)[0] stc.subject = subject g_post_central = mne.read_labels_from_annot(subject, "aparc.a2009s", subjects_dir=subjects_dir, regexp="G_postcentral-lh")[0] n_sources = [stc.vertices[0].size, stc.vertices[1].size] m = abs(stc.data[:n_sources[0], t_idx]).max() plot_kwargs["clim"] = dict(kind='value', pos_lims=[0., 0.2 * m, m]) brain = stc.plot(**plot_kwargs) brain.add_text(0.1, 0.9, "single-subject-grouplasso (%s)" % subject, "title") brain.add_label(g_post_central, borders=True, color="green") .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_006.png :alt: plot multitask lasso :class: sphx-glr-multi-img * .. image:: /auto_examples/images/sphx_glr_plot_multitask_lasso_007.png :alt: plot multitask lasso :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Mapping lh fsaverage -> subject_a (nearest neighbor)... Mapping rh fsaverage -> subject_a (nearest neighbor)... Reading labels from parcellation... read 1 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_a/label/lh.aparc.a2009s.annot read 0 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_a/label/rh.aparc.a2009s.annot Mapping lh fsaverage -> subject_b (nearest neighbor)... Mapping rh fsaverage -> subject_b (nearest neighbor)... Reading labels from parcellation... read 1 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_b/label/lh.aparc.a2009s.annot read 0 labels from /home/circleci/mne_data/HF_SEF/subjects/subject_b/label/rh.aparc.a2009s.annot References ---------- [1] Michael Lim, Justin M. Ales, Benoit R. Cottereau, Trevor Hastie, Anthony M. Norcia. Sparse EEG/MEG source estimation via a group lasso, PLOS ONE, 2017 [2] Jussi Nurminen, Hilla Paananen, & Jyrki Mäkelä. (2017). High frequency somatosensory MEG: evoked responses, FreeSurfer reconstruction [Data set]. Zenodo. http://doi.org/10.5281/zenodo.889235 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 3 minutes 17.695 seconds) **Estimated memory usage:** 1021 MB .. _sphx_glr_download_auto_examples_plot_multitask_lasso.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_multitask_lasso.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multitask_lasso.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_