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;
%% Script for computing FFT for STN and GPe

% Arguments
%Spike times (linear_S=[times,number ID])

% Output
%FFT plots

%%
%Created on 2016
%@author: Vignayanandam R. Muddapu (CNS@IIT-Madras)

%%

% Number of neurons
%STN -> Mandali2015
nSTN=32; % (nSTNxnSTN network size)
Mstn=nSTN;
Nstn=nSTN;
Pstn=Mstn*Nstn;

%GPe -> Mandali2015
nGPe=32; % (nGPexnGPe network size)
Mgpe=nGPe;
Ngpe=nGPe;
Pgpe=Mgpe*Ngpe;

%SNc -> Cullen2015
nSNc=8; % (nSNcxnSNc network size)
Msnc=nSNc;
Nsnc=nSNc;
Psnc=Msnc*Nsnc;

% Time parameters
dt=0.1; % Timestep
tspan=0:dt:dur;
Ttime=numel(tspan);

sec=0.001;
binsize=100; % msec
fs=10000;
temp1=ST_psth(stn_firings);
temp2=ST_psth(gpe_firings);
temp3=ST_psth(snc_firings);
% Trade-off b/w binsize and desired results
[ph1,ed1,r1]=psth(temp1,binsize,fs,Pstn,Ttime,0); %225-binsize
[ph2,ed2,r2]=psth(temp2,binsize,fs,Pgpe,Ttime,0);
[ph3,ed3,r3]=psth(temp3,binsize,fs,Psnc*2,Ttime,0);

fig81=figure(81);
set(fig81, 'Position', [5, 50, 1920, 955]);
subplot(311)
plot(ed1,r1,'b');
% xlim([0 numel(ed1)]);
title('STN')
refline([0 mean(r1)]);
I11=strcat(num2str(mean(r1)));legend(I11);
subplot(312)
plot(ed2,r2,'r')
% xlim([0 numel(ed2)]);
title('GPe')
refline([0 mean(r2)]);
I11=strcat(num2str(mean(r2)));legend(I11);
subplot(313)
plot(ed3,r3,'k')
% xlim([0 numel(ed2)]);
title('SNc')
refline([0 mean(r3)]);
I11=strcat(num2str(mean(r3)));legend(I11);
f811=strcat('PSTH_',filename0);
saveas(fig81,f811,'png');

Loading data, please wait...