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]
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]=mrintercalculate(linear_Stn,linear_Gpe,Nneur,Ttime)

%% Computing synchrony value between STN and GPe across time

% Arguments
%linear_Stn: STN Spike times (linear_S=[times,number ID])
%linear_Gpe: GPe 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=[];phistn=zeros(Nneur,Ttime);
phigpe=zeros(Nneur,Ttime);
for neur=1:Nneur
    temptime1=linear_Stn((linear_Stn(:,2)==neur));
    temptime2=linear_Gpe((linear_Gpe(:,2)==neur));
    j=1;k=1;
    for i=1:Ttime % initial transients
        if j<numel(temptime1)
            if i>=temptime1(j) && i<=temptime1(j+1)
                phistn(neur,i)=((2*pi*(i-temptime1(j)))/(temptime1(j+1)-temptime1(j)));
                if i==temptime1(j+1)
                    j=j+1;
                end
            end
        end
        
        if k<numel(temptime2)
            if i>=temptime2(k) && i<=temptime2(k+1)
                phigpe(neur,i)=((2*pi*(i-temptime2(k)))/(temptime2(k+1)-temptime2(k)));
                if i==temptime2(k+1)
                    k=k+1;
                end
            end
        end
        
    end
end

phistn1=phistn(:,:);
phigpe1=phigpe(:,:);

a=sqrt(-1);
tempMstn=sum(phistn1)/numel(phistn1);
tempMgpe=sum(phigpe1)/numel(phigpe1);
M=exp(a*((tempMstn+tempMgpe)/2));
M=M';

sumphistn=(sum(exp(a*phistn1))/neur);
sumphigpe=(sum(exp(a*phigpe1))/neur);
sumphistn=sumphistn';
sumphigpe=sumphigpe';
Rvalue=((sumphistn+sumphigpe)/2)./M;
Ravg=sum(abs(Rvalue))/numel(Rvalue);

end

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