.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_tutorials/tutorial_04_sliding_win_model_comparison.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_tutorials_tutorial_04_sliding_win_model_comparison.py: Sliding time window laminar model comparison using free energy ============================================================== This tutorial demonstrates how to perform laminar inference of event-related responses in a sliding time window using model comparison based on free energy as a metric of model fit, described in [Bonaiuto et al., 2021, Laminar dynamics of high amplitude beta bursts in human motor cortex](https://doi.org/10.1016/j.neuroimage.2021.118479). A temporal Gaussian function is simulated at a particular cortical location in various layers. Source reconstruction is performed on the whole time window using the Empirical Bayesian Beamformer on the simulated sensor data using a forward model based on the multilayer mesh as a localizer. This is used to select priors on each layer mesh for a sliding time window model comparison using free energy. .. GENERATED FROM PYTHON SOURCE LINES 9-13 Setting up the simulations -------------------------- Simulations are based on an existing dataset, which is used to define the sampling rate, number of trials, duration of each trial, and the channel layout. .. GENERATED FROM PYTHON SOURCE LINES 13-50 .. code-block:: default import os import shutil import numpy as np import nibabel as nib import matplotlib.pyplot as plt import tempfile from lameg.invert import coregister, invert_ebb, load_source_time_series from lameg.laminar import sliding_window_model_comparison from lameg.simulate import run_dipole_simulation from lameg.surf import interpolate_data from lameg.util import get_surface_names from lameg.viz import show_surface, color_map import spm_standalone # Subject information for data to base the simulations on subj_id = 'sub-104' ses_id = 'ses-01' # Fiducial coil coordinates nas = [0.9662503311032098, 108.83514306876269, 1.6712361927090313] lpa = [-74.28671169006893, 20.15061014698176, -29.849056272705948] rpa = [76.02110531729883, 18.9467849625573, -25.779407159603114] # Data file to base simulations on data_file = os.path.join( '../test_data', subj_id, 'meg', ses_id, 'spm/pspm-converted_autoreject-sub-104-ses-01-001-btn_trial-epo.mat' ) spm = spm_standalone.initialize() .. GENERATED FROM PYTHON SOURCE LINES 51-52 For source reconstructions, we need an MRI and a surface mesh. The simulations will be based on a forward model using the multilayer mesh, and the model comparison will use each layer mesh .. GENERATED FROM PYTHON SOURCE LINES 52-76 .. code-block:: default # Native space MRI to use for coregistration mri_fname = os.path.join('../test_data', subj_id, 'mri/s2023-02-28_13-33-133958-00001-00224-1.nii' ) # Mesh to use for forward model in the simulations multilayer_mesh_fname = os.path.join('../test_data', subj_id, 'surf/multilayer.11.ds.link_vector.fixed.gii') # Load multilayer mesh and compute the number of vertices per layer mesh = nib.load(multilayer_mesh_fname) n_layers = 11 verts_per_surf = int(mesh.darrays[0].data.shape[0]/n_layers) # Get name of each mesh that makes up the layers of the multilayer mesh - these will be used for the source # reconstruction layer_fnames = get_surface_names( n_layers, os.path.join('../test_data', subj_id, 'surf'), 'link_vector.fixed' ) # Inflated meshes for plotting ds_inflated = nib.load(os.path.join('../test_data', subj_id, 'surf', 'inflated.ds.gii')) orig_inflated = nib.load(os.path.join('../test_data', subj_id, 'surf', 'inflated.gii')) .. GENERATED FROM PYTHON SOURCE LINES 77-78 We're going to copy the data file to a temporary directory and direct all output there. .. GENERATED FROM PYTHON SOURCE LINES 78-99 .. code-block:: default # Extract base name and path of data file data_path, data_file_name = os.path.split(data_file) data_base = os.path.splitext(data_file_name)[0] # Where to put simulated data tmp_dir = tempfile.mkdtemp() # Copy data files to tmp directory shutil.copy( os.path.join(data_path, f'{data_base}.mat'), os.path.join(tmp_dir, f'{data_base}.mat') ) shutil.copy( os.path.join(data_path, f'{data_base}.dat'), os.path.join(tmp_dir, f'{data_base}.dat') ) # Construct base file name for simulations base_fname = os.path.join(tmp_dir, f'{data_base}.mat') .. GENERATED FROM PYTHON SOURCE LINES 100-101 Invert the subject's data using the multilayer mesh. This step only has to be done once - this is just to compute the forward model that will be used in the simulations .. GENERATED FROM PYTHON SOURCE LINES 101-128 .. code-block:: default # Patch size to use for inversion (in this case it matches the simulated patch size) patch_size = 5 # Number of temporal modes to use for EBB inversion n_temp_modes = 4 # Coregister data to multilayer mesh coregister( nas, lpa, rpa, mri_fname, multilayer_mesh_fname, base_fname, spm_instance=spm ) # Run inversion [_,_] = invert_ebb( multilayer_mesh_fname, base_fname, n_layers, patch_size=patch_size, n_temp_modes=n_temp_modes, spm_instance=spm ) .. GENERATED FROM PYTHON SOURCE LINES 129-132 Simulating a signal on the pial surface --------------------------------------- We're going to simulate 200ms of a Gaussian with a dipole moment of 5nAm and a width of 25ms .. GENERATED FROM PYTHON SOURCE LINES 132-148 .. code-block:: default # Strength of simulated activity (nAm) dipole_moment = 8 # Temporal width of the simulated Gaussian signal_width=.025 # 25ms # Sampling rate (must match the data file) s_rate = 600 # Generate 200ms of a Gaussian at a sampling rate of 600Hz (to match the data file) time=np.linspace(0,.2,121) zero_time=time[int((len(time)-1)/2+1)] sim_signal=np.exp(-((time-zero_time)**2)/(2*signal_width**2)).reshape(1,-1) plt.plot(time,dipole_moment*sim_signal[0,:]) plt.xlabel('Time (s)') plt.ylabel('Amplitude (nAm)') .. GENERATED FROM PYTHON SOURCE LINES 149-152 .. image:: ../_static/tutorial_04_sim_signal.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 154-155 We need to pick a location (mesh vertex) to simulate at .. GENERATED FROM PYTHON SOURCE LINES 155-175 .. code-block:: default # Vertex to simulate activity at sim_vertex=24585 pial_ds_mesh_fname = os.path.join('../test_data', subj_id, 'surf', 'pial.ds.link_vector.fixed.gii') pial_ds_mesh = nib.load(pial_ds_mesh_fname) pial_coord = pial_ds_mesh.darrays[0].data[sim_vertex,:] pial_mesh_fname = os.path.join('../test_data', subj_id, 'surf', 'pial.gii') pial_mesh = nib.load(pial_mesh_fname) cam_view = [152, 28, 15, 3.5, 26, 38.5, 0, 0, 1] plot = show_surface( pial_mesh, opacity=1, coords=pial_coord, coord_size=2, camera_view=cam_view ) .. GENERATED FROM PYTHON SOURCE LINES 176-179 .. image:: ../_static/tutorial_04_sim_location.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 181-182 We'll simulate a 5mm patch of activity with -5 dB SNR at the sensor level. The desired level of SNR is achieved by adding white noise to the projected sensor signals .. GENERATED FROM PYTHON SOURCE LINES 182-207 .. code-block:: default # Simulate at a vertex on the pial surface pial_vertex = sim_vertex # Orientation of the simulated dipole pial_unit_norm = mesh.darrays[2].data[pial_vertex,:] prefix = f'sim_{sim_vertex}_pial_' # Size of simulated patch of activity (mm) sim_patch_size = 5 # SNR of simulated data (dB) SNR = -10 # Generate simulated data pial_sim_fname = run_dipole_simulation( base_fname, prefix, pial_vertex, sim_signal, pial_unit_norm, dipole_moment, sim_patch_size, SNR, spm_instance=spm ) .. GENERATED FROM PYTHON SOURCE LINES 208-211 Localizer inversion ------------------- Now we'll run a source reconstruction using the multilayer mesh, extract the signal in the pial layer, and select a prior based on the peak. .. GENERATED FROM PYTHON SOURCE LINES 211-243 .. code-block:: default [_,_,MU] = invert_ebb( multilayer_mesh_fname, pial_sim_fname, n_layers, patch_size=patch_size, n_temp_modes=n_temp_modes, return_mu_matrix=True, spm_instance=spm ) layer_vertices = np.arange(verts_per_surf) layer_ts, time, ch_names = load_source_time_series( pial_sim_fname, mu_matrix=MU, vertices=layer_vertices ) # Layer peak m_layer_max = np.max(np.mean(layer_ts,axis=-1),-1) prior = np.argmax(m_layer_max) sim_coord = ds_inflated.darrays[0].data[sim_vertex,:] prior_coord = ds_inflated.darrays[0].data[prior,:] print(f'Simulated vertex={sim_vertex}, Prior vertex={prior}') print('Simulated coordinate') print(sim_coord) print('Prior coordinate') print(prior_coord) .. GENERATED FROM PYTHON SOURCE LINES 244-245 We can see that the prior is the same as the location we simulated at .. GENERATED FROM PYTHON SOURCE LINES 245-274 .. code-block:: default # Interpolate for display on the original inflated surface interpolated_data = interpolate_data(orig_inflated, ds_inflated, m_layer_max) # Plot colors and camera view max_abs = np.max(np.abs(m_layer_max)) c_range = [-max_abs, max_abs] cam_view=[289, 32, -19, 3.5, 29, 26, 0, 0, 1] # Plot peak colors,_ = color_map( interpolated_data, "RdYlBu_r", c_range[0], c_range[1] ) plot = show_surface( orig_inflated, vertex_colors=colors, info=True, camera_view=cam_view, coords=prior_coord, coord_size=2, coord_color=[0,0,255] ) .. GENERATED FROM PYTHON SOURCE LINES 275-278 .. image:: ../_static/tutorial_04_localizer.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 280-283 Sliding time window model comparison (pial - white matter) ---------------------------------------------------------- Now we can run sliding time window model comparison between source models based on the pial and white matter surfaces using free energy. Specifically, we'll look at the difference in free energy between the two models (pial - white matter), in sliding and overlapping windows of 16ms. The free energy difference (pial - white matter) should be positive (more model evidence for the pial surface model) because we simulated activity on the pial surface. .. GENERATED FROM PYTHON SOURCE LINES 283-314 .. code-block:: default # Number of temporal models for sliding time window inversion sliding_n_temp_modes = 4 # Size of sliding window (in ms) win_size = 50 # Whether or not windows overlap win_overlap = True # Run sliding time window model comparison between the first layer (pial) and the last layer (white matter) [Fs,wois] = sliding_window_model_comparison( prior, nas, lpa, rpa, mri_fname, [layer_fnames[0], layer_fnames[-1]], pial_sim_fname, spm_instance=spm, invert_kwargs={ 'patch_size': patch_size, 'n_temp_modes': sliding_n_temp_modes, 'win_size': win_size, 'win_overlap': win_overlap, } ) # Plot difference in free energy over time (pial minus white) - this should be positive plt.plot(np.mean(wois,axis=-1), Fs[0,:]-Fs[1,:]) plt.xlabel('Time (ms)') plt.ylabel(r'$\Delta$F') .. GENERATED FROM PYTHON SOURCE LINES 315-318 .. image:: ../_static/tutorial_04_pial_sim_results.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 320-323 White matter surface simulation with pial - white matter sliding time window model comparison --------------------------------------------------------------------------------------------- Let's simulate the same pattern of activity, in the same location, but on the white matter surface. This time, sliding time window model comparison should yield greater model evidence for the white matter surface, and therefore the difference in free energy (pial - white matter) should be negative. .. GENERATED FROM PYTHON SOURCE LINES 323-394 .. code-block:: default # Simulate at the corresponding vertex on the white matter surface white_vertex = (n_layers-1)*verts_per_surf+sim_vertex prefix = f'sim_{sim_vertex}_white_' # Generate simulated data white_sim_fname = run_dipole_simulation( base_fname, prefix, white_vertex, sim_signal, pial_unit_norm, dipole_moment, sim_patch_size, SNR, spm_instance=spm ) # Localizer [_,_,MU] = invert_ebb( multilayer_mesh_fname, white_sim_fname, n_layers, patch_size=patch_size, n_temp_modes=n_temp_modes, return_mu_matrix=True, spm_instance=spm ) layer_vertices = np.arange(verts_per_surf) layer_ts, time, _ = load_source_time_series( white_sim_fname, mu_matrix=MU, vertices=layer_vertices ) # Layer peak m_layer_max = np.max(np.mean(layer_ts,axis=-1),-1) prior = np.argmax(m_layer_max) prior_coord = ds_inflated.darrays[0].data[prior,:] print(f'Simulated vertex={sim_vertex}, Prior vertex={prior}') print('Simulated coordinate') print(sim_coord) print('Prior coordinate') print(prior_coord) # Run sliding time window model comparison between the first layer (pial) and the last layer (white matter) [Fs,wois] = sliding_window_model_comparison( prior, nas, lpa, rpa, mri_fname, [layer_fnames[0], layer_fnames[-1]], white_sim_fname, spm_instance=spm, invert_kwargs={ 'patch_size': patch_size, 'n_temp_modes': sliding_n_temp_modes, 'win_size': win_size, 'win_overlap': win_overlap, } ) # Plot difference in free energy over time (pial minus white) - this should be negative plt.plot(np.mean(wois,axis=-1), Fs[0,:]-Fs[1,:]) plt.xlabel('Time (ms)') plt.ylabel(r'$\Delta$F') .. GENERATED FROM PYTHON SOURCE LINES 395-398 .. image:: ../_static/tutorial_04_white_sim_results.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 401-404 Simulation in each layer with sliding time window model comparison across layers -------------------------------------------------------------------------------- That was sliding time window model comparison with two candidate models: one based on the white matter surface, and one on the pial. Let's now simulate on each layer, and for each simulation, run sliding time window model comparison across all layers. We'll turn off SPM visualization here. .. GENERATED FROM PYTHON SOURCE LINES 404-476 .. code-block:: default # Now simulate at the corresponding vertex on each layer, and for each simulation, run sliding window model # comparison across all layers all_layerF = [] for l in range(n_layers): print(f'Simulating in layer {l}') l_vertex = l*verts_per_surf+sim_vertex prefix = f'sim_{sim_vertex}{l}_' l_sim_fname = run_dipole_simulation( base_fname, prefix, l_vertex, sim_signal, pial_unit_norm, dipole_moment, sim_patch_size, SNR, spm_instance=spm ) # Localizer [_,_,MU] = invert_ebb( multilayer_mesh_fname, l_sim_fname, n_layers, patch_size=patch_size, n_temp_modes=n_temp_modes, return_mu_matrix=True, viz=False, spm_instance=spm ) layer_vertices = np.arange(verts_per_surf) layer_ts, time, _ = load_source_time_series( l_sim_fname, mu_matrix=MU, vertices=layer_vertices ) # Layer peak m_layer_max = np.max(np.mean(layer_ts,axis=-1),-1) prior = np.argmax(m_layer_max) prior_coord = ds_inflated.darrays[0].data[prior,:] print(f'Simulated vertex={sim_vertex}, Prior vertex={prior}') print('Simulated coordinate') print(sim_coord) print('Prior coordinate') print(prior_coord) [Fs,wois] = sliding_window_model_comparison( prior, nas, lpa, rpa, mri_fname, layer_fnames, l_sim_fname, viz=False, spm_instance=spm, invert_kwargs={ 'patch_size': patch_size, 'n_temp_modes': sliding_n_temp_modes, 'win_size': win_size, 'win_overlap': win_overlap, } ) all_layerF.append(Fs) all_layerF = np.squeeze(np.array(all_layerF)) .. GENERATED FROM PYTHON SOURCE LINES 477-478 For each simulation, we can plot the free energy for all models relative to the worst model within a central time window. The layer model with the highest free energy should correspond to the layer that the activity was simulated in. .. GENERATED FROM PYTHON SOURCE LINES 478-513 .. code-block:: default # Average free energy within small time window in center of the epoch woi_t = np.mean(wois,axis=-1) woi_idx = np.where((woi_t>=-20) & (woi_t<=20))[0] m_all_layerF = np.mean(all_layerF[:,:,woi_idx],axis=2) col_r = plt.cm.cool(np.linspace(0,1, num=n_layers)) plt.figure(figsize=(10,4)) # For each simulation, plot the mean free energy of each layer model relative to that of the worst # model for that simulation plt.subplot(1,2,1) for l in range(n_layers): layerF = m_all_layerF[l,:] plt.plot(layerF-np.min(layerF), label=f'{l}', color=col_r[l,:]) plt.legend() plt.xlabel('Eval layer') plt.ylabel(r'$\Delta$F') # For each simulation, find which layer model had the greatest free energy plt.subplot(1,2,2) peaks=[] for l in range(n_layers): layerF = m_all_layerF[l,:] layerF = layerF-np.min(layerF) pk = np.argmax(layerF) peaks.append(pk) plt.plot(peaks) plt.xlim([-0.5,10.5]) plt.ylim([-0.5,10.5]) plt.plot([0,10],[0,10],'k--') plt.xlabel('Sim layer') plt.ylabel(r'Peak $\Delta$F') plt.tight_layout() .. GENERATED FROM PYTHON SOURCE LINES 514-517 .. image:: ../_static/tutorial_04_results.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 517-538 .. code-block:: default # Normalization step norm_layerF = np.zeros(m_all_layerF.shape) for l in range(n_layers): norm_layerF[l,:] = m_all_layerF[l,:] - np.min(m_all_layerF[l,:]) # Transpose for visualization im=plt.imshow(norm_layerF.T, cmap='Spectral_r') # Find the indices of the max value in each column max_indices = np.argmax(norm_layerF, axis=1) # Plot an 'X' at the center of the square for each column's maximum for idx, max_idx in enumerate(max_indices): plt.text(idx, max_idx, 'X', fontsize=12, ha='center', va='center', color='black', weight='bold') plt.xlabel('Simulated layer', fontsize=14) plt.ylabel('Evaluated layer', fontsize=14) cb=plt.colorbar(im) cb.set_label(r'$\Delta F$', fontsize=14) .. GENERATED FROM PYTHON SOURCE LINES 539-542 .. image:: ../_static/tutorial_04_results_matrix.png :width: 800 :alt: .. GENERATED FROM PYTHON SOURCE LINES 544-549 .. code-block:: default spm.terminate() # Delete simulation files shutil.rmtree(tmp_dir) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.000 seconds) .. _sphx_glr_download_auto_tutorials_tutorial_04_sliding_win_model_comparison.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: tutorial_04_sliding_win_model_comparison.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: tutorial_04_sliding_win_model_comparison.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_