Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019)

 Download zip file 
Help downloading and running models
Accession:244384
"... A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD (Parkinson's disease) therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc (Substantia Nigra pars compacta) cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress’ variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model."
Reference:
1 . Muddapu VR, Mandali A, Chakravarthy VS, Ramaswamy S (2019) A Computational Model of Loss of Dopaminergic Cells in Parkinson's Disease Due to Glutamate-Induced Excitotoxicity. Front Neural Circuits 13:11 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Basal ganglia; Subthalamic Nucleus;
Cell Type(s): Abstract Izhikevich neuron;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; Gaba; NMDA;
Gene(s):
Transmitter(s): Dopamine; Glutamate; Gaba;
Simulation Environment: MATLAB;
Model Concept(s): Deep brain stimulation; Parkinson's;
Implementer(s): Muddapu, Vignayanandam R. [vignan.0009 at gmail.com]; Chakravarthy, Srinivasa V. [schakra at iitm.ac.in];
Search NeuronDB for information about:  AMPA; NMDA; Gaba; Dopamine; Gaba; Glutamate;
function  [ph,edges,r] = psth(times, binsize, fs, ntrials, triallen, plotflag, varargin)
% PSTH Computes the peri-stimulus time histogram from spike times.
% The routine plots the trial averaged spike rate as a function of time.
% H = PSTH(TIMES, BINSIZE, FS,NTRIALS,TRIALLEN)
% H = PSTH(TIMES, BINSIZE, FS,NTRIALS,TRIALLEN ,AXESHANDLE)
% TIMES - spike times (samples)
% BINSIZE - binwidth (ms) - 1000ms
% FS - sampling rate (hz)
% NTRIALS - number of trials
% TRIALLEN - length of a trial (samples)
% PLOTFLAG - 0/1 (no plot/plot)
% H - plot handle
%
% An example:
% %spike times can be specified in continuous time 
% %here we have 3 trials and a trial length of 1000 samples
% t = [10, 250, 900, 1300, 1600, 2405, 2900];
%
% %the same spike times can also be specified per trial
% t2 =[10, 250, 900, 300, 600, 405, 900];
% r = psth(t,10,1000,3,1000) ;
% r2 = psth(t2,10,1000,3,1000);
%
% Author: Rajiv Narayan
% askrajiv@gmail.com
% Boston University, Boston, MA

h_color ='k';
nin=nargin;

narginchk(6,7);

switch(nin)
 
 case 6 %no plot handle
%   figure;
%   h=gca;
  
 case 7
  if(ishandle(varargin{1}))
    h=varargin{1};
  else
    error('Invalid Plot handle');
  end

end

%Compute PSTH        
lastBin = binsize * ceil((triallen-1)*(1000/(fs*binsize)));
edges = 0 : binsize : lastBin;	
x = (mod(times-1,triallen)+1)*(1000/fs);
r = (histc(x,edges)*1000) / (ntrials*binsize);
ph=0;
% ph=bar(edges(1:end-1),r(1:end-1),'histc');

if plotflag==1
%Plot histogram
figure;
h=gca;
axes(h);
ph=bar(edges(1:end-1),r(1:end-1),'histc');
set(ph,'edgecolor',h_color,'facecolor',h_color);
end
end



Loading data, please wait...