OO-NVU - Version 3.2

Python implementation of the NVU model.

Contains the following pathways:

• Excitatory/inhibitory neuron dynamics based on Wilson & Cowan (1972)
• GABA and NPY release from the neuron (inhibitory only)
• K+ pathway via K+ release from the neuron into the SC
• Nitric oxide pathway via glutamate release from the neuron into the SC
• Astrocytic calcium pathway via glutamate release from the neuron into the SC
• TRPV4 calcium channel on the astrocytic endfoot
• 20-HETE and Arachidonic Acid dynamics in the astroycte and SMC

How to Run

The code is run in Python. All functions are commented. If using Spyder it is recommended that you set the following preferences:

• To have plots open in new windows rather than in the console so they can be edited/saved, go to Preferences->IPython Console->Graphics and set Graphics Backend to Automatic
• To show all variables in Variable explorer (including figures and data objects), go to Preferences->Variable Explorer and turn off Exclude unsupported data types

The code is laid out as follows:

• To solve the system and plot variables use the script run.py
• To change any model parameters see the file parameters.py which is loaded as a module in run.py (via import parameters as p)
• To adjust any algebraic variables see the file algebraic_variables.py
• To adjust any differential equations see the file ODEsystem.py
• To adjust model functions (e.g. time dependent input functions like P(t) or Q(t)) see the file model_functions.py
• To adjust initial conditions see the file ICs.py
• When adding any new state variables to ODEsystem.py these must also be added to indices.py and state_variable_names.py
• The normalised hemodynamics variables are defined in normalised_hemo.py (solved separately from ODEsystem.py as they depend on the steady state conditions)
• Two custom plotting functions are defined in plotting_functions.py for plotting either state or algebraic variables either on the same graph or in subplots; examples of use are shown in run.py
• Experimental data stored as mat files in the subfolder ./mat_files can be extracted and plotted using custom functions defined in import_mat_files.py; examples of use are shown in run.py

Note that time is in milliseconds.

For further information on the NVU model refer to the following papers:

• Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 1–24. https://doi.org/10.1016/S0006-3495(72)86068-5
• Mathias, E., Kenny, A., Plank, M. J., & David, T. (2018). Integrated models of neurovascular coupling and BOLD signals: Responses for varying neural activations. NeuroImage, 174(March), 69–86. http://doi.org/10.1016/j.neuroimage.2018.03.010
• Kenny, A., Plank, M. J., & David, T. (2018). The role of astrocytic calcium and TRPV4 channels in neurovascular coupling. Journal of Computational Neuroscience, 44(1), 97–114. http://doi.org/10.1007/s10827-017-0671-7
• Dormanns, K., Brown, R. G. G., & David, T. (2016). The role of nitric oxide in neurovascular coupling. Journal of Theoretical Biology, 394, 1–17. http://doi.org/10.1016/j.jtbi.2016.01.009
• Dormanns, K., van Disseldorp, E. M. J., Brown, R. G., & David, T. (2015). Neurovascular coupling and the influence of luminal agonists via the endothelium. Journal of Theoretical Biology, 364, 49–70. http://doi.org/10.1016/j.jtbi.2014.08.029
• Farr, H., & David, T. (2011). Models of neurovascular coupling via potassium and EET signalling. Journal of Theoretical Biology, 286(1), 13–23. http://doi.org/10.1016/j.jtbi.2011.07.006