Ventromedial Thalamocortical Neuron (Bichler et al 2021)

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"Biophysical computer modeling of a thalamic neuron demonstrated that an increase in rebound spiking can also be accounted for by a decrease in the M-type potassium current. Modeling also showed that an increase in sag with hyperpolarizing steps found after 6-OHDA treatment could in part but not fully be accounted for by the decrease in M-type current. These findings support the hypothesis that homeostatic changes in BGMT neural properties following 6-OHDA treatment likely influence the signal processing taking place in the BG thalamocortical network in Parkinson's disease."
1 . Bichler EK, Cavarretta F, Jaeger D (2021) Changes in Excitability Properties of Ventromedial Motor Thalamic Neurons in 6-OHDA Lesioned Mice. eNeuro [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Thalamus;
Cell Type(s):
Channel(s): I A; I h; I L high threshold; I T low threshold; I K,Ca; I Sodium; I Potassium; I Na,p; I K;
Gap Junctions:
Simulation Environment: NEURON; Python;
Model Concept(s): Pathophysiology;
Search NeuronDB for information about:  I Na,p; I L high threshold; I T low threshold; I A; I K; I h; I K,Ca; I Sodium; I Potassium;
from plots import Plot, VPlot
from pynwb import NWBHDF5IO
import numpy as np
import sag_analysis as SAG


class SAGplot(Plot):
  def __init__(self, ax, markersize=7.5, linewidth=2, markeredgecolor='black', markeredgewidth=2, labelsize=10, fontweight='normal'):
    super(SAGplot, self).__init__(ax,
      xlabel='Current (pA)',
      ylabel='SAG (%)',
      legend=True)[10,60])[-25,-225]), 61, 20).tolist()), -201, -50).tolist())

  def _format_plot(self):
    super(SAGplot, self)._format_plot(){'weight' : self.fontweight, 'size' : self.labelsize}, loc=2, frameon=False, bbox_to_anchor=(0,1.15))


def plot_sag_curve(ax):
  io = NWBHDF5IO('sag.nwb', 'r')
  nwbfile =
  _sag_plot = SAGplot(ax)
  ii, ss = SAG.get_sag_curve(nwbfile, "control_%g", np.arange(0.05, 0.25, 0.05))
  _sag_plot.plot(-ii*1000, ss, color='black', label='Control')
  ii, ss = SAG.get_sag_curve(nwbfile, "km0_%g", np.arange(0.05, 0.25, 0.05))
  _sag_plot.plot(-ii*1000, ss, color='red', label='No K$_M$')
  ii, ss = SAG.get_sag_curve(nwbfile, "ih3_%g", np.arange(0.05, 0.25, 0.05))
  _sag_plot.plot(-ii*1000, ss, color='blue', label='300% I$_H$')
  ii, ss = SAG.get_sag_curve(nwbfile, "km0ih3_%g", np.arange(0.05, 0.25, 0.05))
  _sag_plot.plot(-ii*1000, ss, color='white', label='No K$_M$,300% I$_H$' )


def plot_vm_curve(ax, current=np.arange(0.05, 0.25, 0.05), stim_start=5000, stim_end=7000, xlim=[5000-50,7050], ylim=[-135,-65], key='control', color='black', title='Control'):
  io = NWBHDF5IO('sag.nwb', 'r')
  nwbfile =
  _sag_plot = VPlot(ax, title=title)
  for _current in current:
    trace = SAG.analysis.read_trace(nwbfile, "%s_%g" % (key, _current), stim_start=stim_start, stim_end=stim_end)
    _sag_plot.plot(trace['T'], trace['V'], xlim=xlim, color=color)



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