demo_evoked_lfp.py

"""
===================================
08. Record extracellular potentials
===================================

The main output of HNN simulations is the 'dipole' waveform, i.e., the net
intracellular current flowing in pyramidal cell apical dendrites. At the large
distances between cells and M/EEG sensors, this 'primary' current is the main
contributor to the measured fields. Close to the cells, the local field
potential (LFP) is the result of intracellular current leaking into the
extracellular medium through active and passive membrane channels. Under some
simplifying assumptions, we may approximate the LFP at virtual electrodes
placed in and around the HNN network model.
"""

# Authors: Nick Tolley <[email protected]>
#          Mainak Jas <[email protected]>
#          Christopher Bailey <[email protected]>

# sphinx_gallery_thumbnail_number = 3

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

###############################################################################
# We will use the default network with three evoked drives; see
# :ref:`evoked example <sphx_glr_auto_examples_plot_simulate_evoked.py>` for
# details. We'll go ahead and use the drive features defined in the parameter
# file.

import hnn_core
from hnn_core import read_params, default_network, simulate_dipole

hnn_core_root = op.dirname(hnn_core.__file__)
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
net = default_network(params, add_drives_from_params=True)

###############################################################################
# Extracellular recordings require specifying the electrode postions. It can be
# useful to visualize the cells of the network to decide on the placement of
# each electrode.
net.plot_cells()

###############################################################################
# The default network consists of an L5 and an L2 layer, within which the cell
# somas are arranged in a regular grid, and apical dendrites are aligned along
# the z-axis. We can simulate a linear array multielectrode with 100 um
# intercontact spacing [1]_ by specifying a list of (x, y, z) coordinate
# triplets. The L5 pyramidal cell somas are at z=0 um, with apical dendrites
# extending up to approximately z=2000 um. L2 pyramidal cell somas reside at
# z=1300 um, and have apical dendrites extending to z=2300 um. We'll place the
# recording array in the center of the network. By default, a value of
# 0.3 S/m is used for the constant extracellular conductivity. We'll use the
# point source approximation method for calculations.

depths = list(range(-525, 2750, 100))
electrode_pos = [(4.5, 4.5, dep) for dep in depths]
sigma, method, min_distance = 0.3, 'psa', 0.5
net.add_electrode_array('shank1', electrode_pos, sigma=sigma,
                        method=method)

###############################################################################
# The electrode arrays are stored under ``Network.rec_array`` as a dictionary
# of :class:`~hnn_core.extracellular.ElectrodeArray` objects that are now
# attached to the network and will be recorded during the simulation. Note that
# calculating the extracellular potentials requires additional computational
# resources and will thus slightly slow down the simulation.
# :ref:`Using MPI <sphx_glr_auto_examples_plot_simulate_mpi_backend.py>` will
# speed up computation considerably. Note that we will perform smoothing of the
# dipole time series during plotting (``postproc=False``)
print(net.rec_array)
net.plot_cells()

dpl = simulate_dipole(net, postproc=False)

###############################################################################
# For plotting both agregate dipole moment and LFP traces, we'll use a 10 ms
# smoothing window, after which both data can be decimated by a factor of 20
# from 40 to 2 kHz sampling rates. Note that decimation is applied in two
# steps.
trial_idx = 0
window_len = 10  # ms
decimate = [5, 4]  # from 40k to 8k to 2k
fig, axs = plt.subplots(3, 1, sharex=True, figsize=(8, 10),
                        gridspec_kw={'height_ratios': [1, 3, 1]})

# First plot the external drive statistics driving the network
net.cell_response.plot_spikes_hist(ax=axs[0], show=False)
axs[0].set_title('External drive spike event histograms')

# Then plot the aggregate dipole time series on its own axis
dpl_ax = axs[1].twinx()
dpl_color = (0.6, 0., 0.6)

dpl[trial_idx].copy().smooth(
    window_len=window_len).plot(ax=dpl_ax, decim=decimate,
                                color=dpl_color, show=False)
dpl_ax.yaxis.label.set_color(dpl_color)
dpl_ax.set_title('Aggregate dipole moment overlaid on laminar LFP traces')

voltage_offset = 300  # the spacing between individual traces
voltage_scalebar = 500  # can be different from offset
# we can assign each electrode a unique color using a linear colormap
colors = plt.get_cmap('cividis', len(electrode_pos))
net.rec_array['shank1'][trial_idx].plot(ax=axs[1],
                                        voltage_offset=voltage_offset,
                                        voltage_scalebar=voltage_scalebar,
                                        contact_labels=depths,
                                        color=colors,
                                        window_len=window_len,
                                        decim=decimate, show=False)
axs[1].grid(True, which='major', axis='x')
axs[1].set_xlabel('')
# Finally, add the spike raster to the bottom subplot
net.cell_response.plot_spikes_raster(ax=axs[2], show=False)
axs[2].set_title('Network spiking responses')
plt.show()

###############################################################################
# The strong fluctuations seen in the dipole waveform appear to correlate with
# similarly large amplitude LFP signals close to the L5 pyramidal soma layer.
#
# References
# ----------
# .. [1] Kajikawa, Y. & Schroeder, C. E. How local is the local field
#        potential? Neuron 72, 847–858 (2011).

This is great! I think it's a really good demonstration of how much LFP activity is "orthogonal" to the current dipole, with seemingly very little correspondence.

It might be worth adding ticks with 100 uV separation? While the starting LFP values are fairly uniform here, it may be hard to get a sense of scale if the x axis started in the middle of this recording.

nice! I wonder how the "sum/average LFP" looks compared to the dipole?

Edited 8 June, using commit no. 5a1a25a with plotting linear arrays. Note that compared with original version, this sets postproc=False, which yields a better comparison between dip and lfp. Expected outputs:

With minor tweaks in 80e1ae8 :

This is with the fix from #352

Finally those large fluctuations make sense: they're from L5 pyramidals!

let's see what happens once the cells are oriented correctly!

We'd already accounted for the orientation by ad hoc dimensions swaps. However, here's what this simulation looks like when the cell-to-cell distance is increased from 1 to 30 um. Definitely looks like the main current sources are in-plane with the somas. NB all other examples remain identical (to master) as long as the space constants are adjusted proportionally.

wait, so it's not a simple scaling? i'm actually a bit surprised. Do you mind posting the plots next to each other after removing the overlaid dipole (a bit distracting)?

just thinking out loud ... should we provide an API for changing the cell spacing? (separate PR)