ZMT has shared various images/movies showing the propagation of action potentials on nerve geometry. For example, https://zmt.swiss/assets/images/sim4life/framework/neuros3.jpg or https://www.youtube.com/watch?v=g_PwOyVjNt0 at 3:00 minutes.
What are the general steps after the neuronal titration simulation to setup these kind of visualizations? How do I overlay the time varying neuronal simulation results on top of the geometry in the 3D window?
The steps to create the spherical highlight at the site of initiation similar as seen in the youtube video would also be nice.
Thank you for your help.
Dear @JKM ,
There are a few steps that needs to done before running the neuronal simulations. First, you should create as many Line Sensors as many axons/neurons you have. If you want to visualize the propagation of transmembrane potential over time, this is already the default quantity to show. You need to setup as many time steps N you want to visualize. If your simulation runs for M seconds, the interval between time frames will be M/N.
After you run the simulation, you'll find new sensors with the same name of the neuron/axon they belong to. They contain the time-domain surface plot of the transmembrane potential. You can visualize them using the Surface Viewer->Animation.
A few suggestions:
I hope this helps!
A related question: Might you have any suggestions for making the surface animation more obvious?
Animations with myelinated axons have a small node region with changes in potential sandwiched between large insulated regions that don't change. The visualization can become difficult to see (e.g. when projecting the animation on a screen in a lighted room) due to the small size of the nodes relative to the insulated sections, and I was wondering if someone may have established some methods to improve the visualization? Perhaps creating dummy results swapping the potential of the nodes and myelinated regions, that while not technically correct could be easier to visualize, or some other scheme.
Thank you for the help.
I know the issue, however we prefer not to change the visualization method, as the data in the line sensors can be extracted to calculate other quantities of interest. The issue that you experience is for the myelinated fibers using any MRG parameterization (Motor, Small and Motor and Sensor MRG fibers). For Sweeney, Rat, Senn and the unmyelinated Sundt fiber model, the issue does not exist.
All the best!