8.1: From MEG sensor-space data to HNN simulation

This example demonstrates how to calculate an inverse solution of the median nerve evoked response potential (ERP) in S1 from the MNE somatosensory dataset, and then simulate a biophysical model network that reproduces the observed dynamics. Note that we do not expound on how we came up with the sequence of evoked drives used in this example, rather, we only demonstrate its implementation. For those who want more background on the HNN model and the process used to articulate the proximal and distal drives needed to simulate evoked responses, see the HNN ERP tutorial. The sequence of evoked drives presented here is not part of a current publication but is motivated by prior studies [1], [2].


        # Authors: Mainak Jas 
#          Ryan Thorpe 

# sphinx_gallery_thumbnail_number = 2

Note: This specific notebook requires the installation of several other, non-HNN packages in order to run and plot MNE output properly. The next cell will install these packages into your Python environment automatically.


        %pip install -q mne nibabel ipyevents "pyvista[all]" pyvistaqt


        # Needed for certain plotting to work
import pyvista as pv
from mne.viz import set_3d_backend
set_3d_backend('notebook')
pv.set_jupyter_backend('client')

We will import the packages needed for computing the inverse solution from the MNE somatosensory dataset. MNE, and its dependency NiBabel should have been installed using the previous pip command. The somatosensory dataset can be downloaded by importing somato from mne.datasets.


        import os.path as op
import matplotlib.pyplot as plt

import mne
from mne.datasets import somato
from mne.minimum_norm import apply_inverse, make_inverse_operator

Now we set the the path of the somato dataset for subject '01'.


        data_path = somato.data_path()
subject = '01'
task = 'somato'
raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',
                    'sub-{}_task-{}_meg.fif'.format(subject, task))
fwd_fname = op.join(data_path, 'derivatives', 'sub-{}'.format(subject),
                    'sub-{}_task-{}-fwd.fif'.format(subject, task))
subjects_dir = op.join(data_path, 'derivatives', 'freesurfer', 'subjects')

Then, we load the raw data and estimate the inverse operator.


        # Read and band-pass filter the raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)
l_freq, h_freq = 1, 40
raw.filter(l_freq, h_freq)

# Identify stimulus events associated with MEG time series in the dataset
events = mne.find_events(raw, stim_channel='STI 014')

# Define epochs within the time series
event_id, tmin, tmax = 1, -.2, .17
baseline = None
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=baseline,
                    reject=dict(grad=4000e-13, eog=350e-6), preload=True)

# Compute the inverse operator
fwd = mne.read_forward_solution(fwd_fname)
cov = mne.compute_covariance(epochs)
inv = make_inverse_operator(epochs.info, fwd, cov)

There are several methods to do source reconstruction. Some of the methods such as MNE are distributed source methods whereas dipole fitting will estimate the location and amplitude of a single current dipole. At the moment, we do not offer explicit recommendations on which source reconstruction technique is best for HNN. However, we do want our users to note that the dipole currents simulated with HNN are assumed to be normal to the cortical surface. Hence, using the option pick_ori='normal' is appropriate.


        snr = 3.
lambda2 = 1. / snr ** 2
evoked = epochs.average()
stc = apply_inverse(evoked, inv, lambda2, method='MNE',
                    pick_ori="normal", return_residual=False,
                    verbose=True)

To extract the primary response in primary somatosensory cortex (S1), we create a label for the postcentral gyrus (S1) in source-space


        hemi = 'rh'
label_tag = 'G_postcentral'
label_s1 = mne.read_labels_from_annot(subject, parc='aparc.a2009s', hemi=hemi,
                                      regexp=label_tag,
                                      subjects_dir=subjects_dir)[0]

Visualizing the distributed S1 activation in reference to the geometric structure of the cortex (i.e., plotted on a structural MRI) can help us figure out how to orient the dipole. Note that in the HNN framework, positive and negative deflections of a current dipole source correspond to upwards (from deep to superficial) and downwards (from superficial to deep) current flow, respectively. Uncomment the following code to open an interactive 3D render of the brain and its surface activation (requires the pyvista python library). You should get 2 plots, the first showing the post-central gyrus label from which the dipole time course was extracted and the second showing MNE activation at 0.040 sec that resemble the following images.


        '''
Brain = mne.viz.get_brain_class()
brain_label = Brain(subject, hemi, 'white', subjects_dir=subjects_dir)
brain_label.add_label(label_s1, color='green', alpha=0.9)
stc_label = stc.in_label(label_s1)
brain = stc_label.plot(subjects_dir=subjects_dir, hemi=hemi, surface='white',
                       view_layout='horizontal', initial_time=0.04,
                       backend='pyvista')
'''

Now we extract the representative time course of dipole activation in our labeled brain region using mode='pca_flip' (see this MNE-python example_ for more details). Note that the most prominent component of the median nerve response occurs in the posterior wall of the central sulcus at ~0.040 sec. Since the dipolar activity here is negative, we orient the extracted waveform so that the deflection at ~0.040 sec is pointed downwards. Thus, the ~0.040 sec deflection corresponds to current flow traveling from superficial to deep layers of cortex.


        flip_data = stc.extract_label_time_course(label_s1, inv['src'],
                                          mode='pca_flip')
dipole_tc = -flip_data[0] * 1e9

plt.figure()
plt.plot(1e3 * stc.times, dipole_tc, 'ro--')
plt.xlabel('Time (ms)')
plt.ylabel('Current Dipole (nAm)')
plt.xlim((0, 170))
plt.axhline(0, c='k', ls=':')
plt.show()

Now, let us try to simulate the same with hnn-core. We read in the network parameters from N20.json and instantiate the network.


        import hnn_core
from hnn_core import simulate_dipole, jones_2009_model
from hnn_core import average_dipoles, JoblibBackend

hnn_core_root = op.dirname(hnn_core.__file__)
params_fname = op.join(hnn_core_root, 'param', 'N20.json')
net = jones_2009_model(params_fname)

To simulate the source of the median nerve evoked response, we add a sequence of synchronous evoked drives: 1 proximal, 2 distal, and 1 final proximal drive. In order to understand the physiological implications of proximal and distal drive as well as the general process used to articulate a sequence of exogenous drive for simulating evoked responses, see the HNN ERP tutorial_. Note that setting n_drive_cells=1 and cell_specific=False creates a drive with synchronous input across cells in the network.


        # Early proximal drive
weights_ampa_p = {'L2_basket': 0.0036, 'L2_pyramidal': 0.0039,
                  'L5_basket': 0.0019, 'L5_pyramidal': 0.0020}
weights_nmda_p = {'L2_basket': 0.0029, 'L2_pyramidal': 0.0005,
                  'L5_basket': 0.0030, 'L5_pyramidal': 0.0019}
synaptic_delays_p = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
                     'L5_basket': 1.0, 'L5_pyramidal': 1.0}

net.add_evoked_drive(
    'evprox1', mu=21., sigma=4., numspikes=1, location='proximal',
    n_drive_cells=1, cell_specific=False, weights_ampa=weights_ampa_p,
    weights_nmda=weights_nmda_p, synaptic_delays=synaptic_delays_p,
    event_seed=276)

# Late proximal drive
weights_ampa_p = {'L2_basket': 0.003, 'L2_pyramidal': 0.0039,
                  'L5_basket': 0.004, 'L5_pyramidal': 0.0020}
weights_nmda_p = {'L2_basket': 0.001, 'L2_pyramidal': 0.0005,
                  'L5_basket': 0.002, 'L5_pyramidal': 0.0020}
synaptic_delays_p = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
                     'L5_basket': 1.0, 'L5_pyramidal': 1.0}

net.add_evoked_drive(
    'evprox2', mu=134., sigma=4.5, numspikes=1, location='proximal',
    n_drive_cells=1, cell_specific=False, weights_ampa=weights_ampa_p,
    weights_nmda=weights_nmda_p, synaptic_delays=synaptic_delays_p,
    event_seed=276)

# Early distal drive
weights_ampa_d = {'L2_basket': 0.0043, 'L2_pyramidal': 0.0032,
                  'L5_pyramidal': 0.0009}
weights_nmda_d = {'L2_basket': 0.0029, 'L2_pyramidal': 0.0051,
                  'L5_pyramidal': 0.0010}
synaptic_delays_d = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
                     'L5_pyramidal': 0.1}

net.add_evoked_drive(
    'evdist1', mu=32., sigma=2.5, numspikes=1, location='distal',
    n_drive_cells=1, cell_specific=False, weights_ampa=weights_ampa_d,
    weights_nmda=weights_nmda_d, synaptic_delays=synaptic_delays_d,
    event_seed=277)

# Late distal drive
weights_ampa_d = {'L2_basket': 0.0041, 'L2_pyramidal': 0.0019,
                  'L5_pyramidal': 0.0018}
weights_nmda_d = {'L2_basket': 0.0032, 'L2_pyramidal': 0.0018,
                  'L5_pyramidal': 0.0017}
synaptic_delays_d = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
                     'L5_pyramidal': 0.1}

net.add_evoked_drive(
    'evdist2', mu=84., sigma=4.5, numspikes=1, location='distal',
    n_drive_cells=1, cell_specific=False, weights_ampa=weights_ampa_d,
    weights_nmda=weights_nmda_d, synaptic_delays=synaptic_delays_d,
    event_seed=275)

Now we run the simulation over 2 trials so that we can plot the average aggregate dipole. For a better match to the empirical waveform, set n_trials to be >=25.


        n_trials = 2
# n_trials = 25
with JoblibBackend(n_jobs=2):
    dpls = simulate_dipole(net, tstop=170., n_trials=n_trials)

Since the model is a reduced representation of the larger network contributing to the response, the model response is noisier than it would be in the net activity from a larger network where these effects are averaged out, and the dipole amplitude is smaller than the recorded data. The post-processing steps of smoothing and scaling the simulated dipole response allow us to more accurately approximate the true signal responsible for the recorded macroscopic evoked response [1], [2].


        dpl_smooth_win = 20
dpl_scalefctr = 12
for dpl in dpls:
    dpl.smooth(dpl_smooth_win)
    dpl.scale(dpl_scalefctr)

Finally, we plot the driving spike histogram, empirical and simulated median nerve evoked response waveforms, and output spike histogram.


        fig, axes = plt.subplots(3, 1, sharex=True, figsize=(6, 6),
                         constrained_layout=True)
net.cell_response.plot_spikes_hist(ax=axes[0],
                                   spike_types=['evprox', 'evdist'],
                                   show=False)
axes[1].axhline(0, c='k', ls=':', label='_nolegend_')
axes[1].plot(1e3 * stc.times, dipole_tc, 'r--')
average_dipoles(dpls).plot(ax=axes[1], show=False)
axes[1].legend(['MNE label average', 'HNN simulation'])
axes[1].set_ylabel('Current Dipole (nAm)')
net.cell_response.plot_spikes_raster(ax=axes[2])

References

[1] Jones, S. R., Pritchett, D. L., Stufflebeam, S. M., Hämäläinen, M. & Moore, C. I. Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study. J. Neurosci. 27, 10751–10764 (2007).

[2] Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT, Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data. eLife 9, e51214 (2020). https://doi.org/10.7554/eLife.51214