Optical stimulation of a channelrhodopsin-2 positive pyramidal neuron model (Foutz et al 2012)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:153196
A computational tool to explore the underlying principles of optogenetic neural stimulation. This "light-neuron" model consists of theoretical representations of the light dynamics generated by a fiber optic in brain tissue, coupled to a multicompartment cable model of a cortical pyramidal neuron (Hu et al. 2009, ModelDB #123897) embedded with channelrhodopsin-2 (ChR2) membrane dynamics. Simulations predict that the activation threshold is sensitive to many of the properties of ChR2 (density, conductivity, and kinetics), tissue medium (scattering and absorbance), and the fiber-optic light source (diameter and numerical aperture). This model system represents a scientific instrument to characterize the effects of optogenetic neuromodulation, as well as an engineering design tool to help guide future development of optogenetic technology.
Reference:
1 . Foutz TJ, Arlow RL, McIntyre CC (2012) Theoretical principles underlying optical stimulation of a channelrhodopsin-2 positive pyramidal neuron. J Neurophysiol 107:3235-45 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon; Channel/Receptor; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s): I K; I M; I K,Ca; I Sodium; I Calcium; Channelrhodopsin (ChR);
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Detailed Neuronal Models; Action Potentials; Parameter sensitivity; Intracortical Microstimulation; Neuromodulation;
Implementer(s): MacIntyre, CC [ccm4 at case.edu]; Foutz, Thomas J [tfoutz at uw.edu]; Arlow, Richard L [richard.arlow at case.edu];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; I K; I M; I K,Ca; I Sodium; I Calcium; Channelrhodopsin (ChR);
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>

# <headingcell level=1>

# Introduction

# <markdowncell>

# Foutz, T. J., Arlow, R. L., & McIntyre, C. C. (2012). Theoretical principles underlying optical stimulation of a channelrhodopsin-2 positive pyramidal neuron. Journal of Neurophysiology, 107(12), 3235–3245. doi:10.1152/jn.00501.2011

# <markdowncell>

# This folder contains the core source files used to generate the data and figures in the manuscript. The below example script is an ipython notebook file which can be used to generate Figure 2.

# <headingcell level=1>

# Dependencies

# <markdowncell>

# These files were run on Mac OS 10.9 using Python 2.7.6 64-bit installed using Canopy 1.3. Canopy is a package from Enthought, who provides free licenses to academic users, and it includes all the dependencies other than NEURON (Mayavi, Matplotlib, numpy). NEURON was installed as a python library using the following installation bash script:
#     
#     sudo mkdir /nrn
#     sudo chown $USER:staff /nrn
#     cd /nrn
#     hg clone http://www.neuron.yale.edu/hg/neuron/iv
#     hg clone http://www.neuron.yale.edu/hg/neuron/nrn
#     cd /nrn/iv
#     ./build.sh
#     ./configure --prefix=/nrn CFLAGS='-arch x86_64' CXXFLAGS='-arch x86_64'
#     make
#     make install
#     make clean
#     cd /nrn
#     cd /nrn/nrn
#     make clean
#     ./build.sh
#     ./configure --prefix=/nrn --with-iv=/nrn PYLIBLINK='-framework Python' PYLIB='-framework Python' --with-nrnpython=dynamic CC='clang' CXX='clang++' CFLAGS='-O3 -Wno-return-type -Wno-implicit-function-declaration -Wno-implicit-int' CXXFLAGS='-O3 -Wno-return-type -Wno-implicit-function-declaration -Wno-implicit-int'
#     make
#     sudo make install
#     make clean
#     cd /nrn/nrn/src/nrnpython
#     python setup.py install

# <headingcell level=1>

# Abstract

# <markdowncell>

# Theoretical principles un- derlying optical stimulation of a channelrhodopsin-2 positive pyrami- dal neuron. J Neurophysiol 107: 3235–3245, 2012. First published March 21, 2012; doi:10.1152/jn.00501.2011.—Optogenetics is an emerging field of neuromodulation that permits scaled, millisecond temporal control of the membrane dynamics of genetically targeted cells using light. Optogenetic technology has revolutionized neuro- science research; however, numerous biophysical questions remain on the optical and neuronal factors impacting the modulation of neural activity with photon-sensitive ion channels. To begin to address such questions, we developed a computational tool to explore the under- lying principles of optogenetic neural stimulation. This “light-neuron” model consists of theoretical representations of the light dynamics generated by a fiber optic in brain tissue, coupled to a multicompart- ment cable model of a cortical pyramidal neuron embedded with channelrhodopsin-2 (ChR2) membrane dynamics. Simulations re- vealed that the large energies required to generate an action potential are primarily due to the limited conductivity of ChR2, and that the major determinants of stimulation threshold are the surface area of illuminated cell membrane and proximity to the light source. Our results predict that the activation threshold is sensitive to many of the properties of ChR2 (density, conductivity, and kinetics), tissue me- dium (scattering and absorbance), and the fiber-optic light source (diameter and numerical aperture). We also illustrate the impact of redistributing the ChR2 expression density (uniform vs. nonuniform) on the activation threshold. The model system developed in this study represents a scientific instrument to characterize the effects of opto- genetic neuromodulation, as well as an engineering design tool to help guide future development of optogenetic technology.

# <headingcell level=1>

# Import NEURON library

# <codecell>

from neuron import h
h.load_file("stdrun.hoc")
h.cvode_active(1)

# <headingcell level=1>

# Instantiate cell, stimulator and simulation classes

# <markdowncell>

# Cell model is taken from *Distinct contributions of Na(v)1.6 and Na(v)12 in action potential initiation and backpropagation*, Hu *et al.*, Nat Neurosci (2009). For details regarding the cell and optical fiber models, see source code and manuscript.

# <codecell>

from classes import Hu, Optrode, Sim
cell = Hu()
optrode = Optrode(h.soma)
sim = Sim(cell,optrode,output_filename='csv/distance_threshold.csv')

# <headingcell level=1>

# Run simulation

# <markdowncell>

# This example recreates the data for Figure 2c in the manuscript. It determines the threhold for generating an action potential at a range of distances from the cell body, and for a range of fiber diameters. It takes about XXX minutes on my macbook pro laptop. The output is already in the csv folder. Uncomment this section if you desire to regenerate the results on your hardwar, or to adjust the parameters.

# <codecell>

#distances=xrange(100, 2000 + 50, 50)
#fiber_diameters = [0.1,0.2,0.4]
#params   = [{'Distance (um)':d,
#             'Fiber Optic Diameter (mm)':f} 
#            for d in distances 
#            for f in fiber_diameters]
#sim.main(params)

# <headingcell level=1>

# Plot results

# <markdowncell>

# iPython notebooks can plot matplotlib inline if desired using the following *magic* command

# <codecell>

%pylab inline

# <markdowncell>

# Use matplotlib to display the distance versus threshold results.

# <codecell>

from matplotlib import pyplot
from classes import Data
from functions import make_legend

# Simulation
ds = Data('csv/distance_threshold.orig.csv')
ds.sort('Distance (um)')

# Strength-Distance
fig = pyplot.figure(figsize=(3.5,2.5))
styles = iter(['b--','g-.','r'])
for f in [0.1,0.2,0.4]:
    ds.set_slice(ds.data['Fiber Optic Diameter (mm)']==f)
    pyplot.semilogy(ds.slice['Distance (um)'],ds.slice['Threshold (W/cm2)'],
                    styles.next(),
                    label='%.1f' % f)
pyplot.plot([1400], [38.0], '*', color='0.5',label='Aravanis (0.2)') # Aravanis data point
pyplot.xlabel('Distance (um)')
pyplot.ylabel('Threshold (W/cm2)')
pyplot.xlim([0,2000])

# <headingcell level=1>

# Visualize Simulation Rig

# <markdowncell>

# 2D visualization of the neuron geometry can be done in matplotlib using the cell's *plot* method. The *plot* method generates the cell's trajectory with a color overlay determined by an arbitrary input function that is run for each NEURON section. In this example, the input function returns the section's intracellular voltage.

# <codecell>

from matplotlib import pyplot
plot_func = lambda sec:sec.v
h.run()
cell.plot(plot_func)
pyplot.show()

# <markdowncell>

# 3D visualization of the neuron geometry requires Mayavi, a 3d visualization library based on VTK. It is done with a *display* method. Again, the color is determined by an arbitary function run on each NEURON section (here I use the same function as above). The optical fiber can also be represented using a *display* method.

# <codecell>

cell.display(plot_func)
optrode.display(bounds=[[-1000,1000],[-1000,1000],[0,2000]])

# <headingcell level=1>

# Move Neuron

# <markdowncell>

# To move the neuron in space, just use the *move* method. Here we move by 50 um along each axis. Here we stop the simulation during the light pulse at time 4 ms so that we can plot the irradiance of the section.

# <codecell>

from mayavi import mlab
mlab.figure()
cell.move([50,50,50])
h.tstop = 4
h.run()
plot_func = lambda sec:sec.irradiance_chanrhod
cell.display(plot_func)
optrode.display(bounds=[[-1000,1000],[-1000,1000],[0,2000]])

# <headingcell level=1>

# Further Questions

# <markdowncell>

# Please see the source code and manuscript for more information regarding the model and simulation design. Feel free to contact the authors if additional code resources are needed.


Loading data, please wait...