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

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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]
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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 [Rvalue,Ravg]=mrcalculate(linear_S,Nneur,Ntime)

%% Computing synchrony value across time

% Arguments
%linear_S: Spike times (linear_S=[times,number ID])
%Nneur: Number of neurons
%Ntime: Simulation time

% Output
%Rvalue: Synchrony value across time
%Ravg: Average synchrony value

% References
%Pinsky PF, Rinzel J (1995) Synchrony measures for biological neural networks. Biol Cybern 73:129–137.

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

%%
Rvalue=[];phi=[];

phi=3000*ones(Nneur,Ntime-1);
for neur=1:Nneur
    temptime=linear_S((linear_S(:,2)==neur));
    % temptime =[4    12    21    30    60    78   100   117   126   163   503   652   797   857   940   943];
    j=1;
    
    while j<numel(temptime)
        for i=temptime(j):1:temptime(j+1)-1
            phi(neur,i)=(2*pi*(i-temptime(j)))/(temptime(j+1)-temptime(j));
        end
        j=j+1;
    end
end
a=sqrt(-1);
tempM=sum(phi)/numel(phi);
M=exp(a*tempM);
Rvalue=((sum(exp(a*phi))/neur))./M;
Ravg=sum(abs(Rvalue))/numel(Rvalue);

end