Effect of cortical D1 receptor sensitivity on working memory maintenance (Reneaux & Gupta 2018)

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Accession:240382
Alterations in cortical D1 receptor density and reactivity of dopamine-binding sites, collectively termed as D1 receptor-sensitivity in the present study, have been experimentally shown to affect the working memory maintenance during delay-period. However, computational models addressing the effect of D1 receptor-sensitivity are lacking. A quantitative neural mass model of the prefronto-mesoprefrontal system has been proposed to take into account the effect of variation in cortical D1 receptor-sensitivity on working memory maintenance during delay. The model computes the delay-associated equilibrium states/operational points of the system for different values of D1 receptor-sensitivity through the nullcline and bifurcation analysis. Further, to access the robustness of the working memory maintenance during delay in the presence of alteration in D1 receptor-sensitivity, numerical simulations of the stochastic formulation of the model are performed to obtain the global potential landscape of the dynamics.
Reference:
1 . Reneaux M, Gupta R (2018) Prefronto-cortical dopamine D1 receptor sensitivity can critically influence working memory maintenance during delayed response tasks PLOS ONE 13(5):e0198136
Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism: Prefrontal cortex (PFC);
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): D1;
Gene(s):
Transmitter(s): Dopamine;
Simulation Environment: MATLAB;
Model Concept(s): Bifurcation; Stochastic simulation; Working memory; Schizophrenia;
Implementer(s): Reneaux, Melissa [reneauxm5 at gmail.com]; Gupta, Rahul [gupta.sbt at gmail.com];
Search NeuronDB for information about:  D1; Dopamine;
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Scripts
README.txt
Bifurcation.m
Nullcline.m
Stochastic.m
                            
% This program generates the equilibrium state attained by the mesocortical
% circuit for a particular value of the free parameters R_DA and D1Rsens
% when run for a sufficiently long time with the inclusion of 
% Additive Noise using the Euler Maruyama method 

% The resultant values obtained for the variables is further used 
% for the analysis of the Potential Landscape

D1Rsens=3; % D1R sensitivity  (A.U.)                         FIX THIS VALUE
R_DA=1000*0.00576;% Dopamine Releasability per seconds       FIX THIS VALUE

Ti=0;% seconds
Tf=300; % Sufficiently long time for equilibrium             FIX THIS VALUE
N=(Tf*1000)+1;
dt=(Tf-Ti)/(N-1);
Sqrt_dt=sqrt(dt);
clearvars Ti Tf;

% Constants
c1=0.009852;% no units
c2=0.018259;% no units
c3=0.001052;% no units
c4=9.375000;% no units

% Synaptic Weights
WPP_0=8.5077e03;% Hz per second
WIP=5.1613e03;% Hz per second
WPI_0=6.4570e03;% Hz per second
WPD=3.2790e03;%Hz per second

% Time Constants
tauPN=0.02;% second
tauIN_0=0.0068;% second
tauDN=0.01;% second
tauDA=0.8;% second

% Basal activities of the various neuronal populations and basal DA
% concentration in cortex
aPN_b=3;% Hz
aIN_b=9;% Hz
aDN_b=3;% Hz
DA_b=0.2;% nM

N1=1000000;% No. of samples in the ensemble

% These Initial values are the unstable equlibrium points of the variables
% which have been obtained from the Nullcline.m program


% Initial conditions
aPN=5.484567;% Unstable fixed-point value                    FIX THIS VALUE
aIN=9.213290;% Unstable fixed-point value                    FIX THIS VALUE
aDN=3.802494;% Unstable fixed-point value                    FIX THIS VALUE
DA=0.203906;% Unstable fixed-point value                     FIX THIS VALUE
D1Ract=0.109780;% Unstable fixed-point value                 FIX THIS VALUE

aPN=aPN*ones(N1,1);
aIN=aIN*ones(N1,1);
aDN=aDN*ones(N1,1);
DA=DA*ones(N1,1);
D1Ract=D1Ract*ones(N1,1);

daPN=aPN-aPN_b;
daIN=aIN-aIN_b;
daDN=aDN-aDN_b;
dDA=DA-DA_b;

% Noise Intensities associated with the aPN,aIN and aDN populations together with DA concentration respectively
Sigma_1=0.76125;
Sigma_2=0.08215;
Sigma_3=0.14256;
Sigma_4=0.0008;
    
for i=1:N-1
        f1=dt*(-(daPN./tauPN)+(WPP_0*(0.12*D1Ract+0.68).*tanh(c1*daPN))-(WIP*tanh(c2*daIN)));
        f2=dt*(-(daIN./(tauIN_0*(0.24*D1Ract+0.26)))+(WPI_0*(0.12*D1Ract+0.68).*tanh(c1*daPN)));
        f3=dt*(-(daDN./tauDN)+(WPD*tanh(c1*daPN)));
        f4=dt*(-(dDA./tauDA)+(R_DA*tanh(c3*daDN)));
            
        aPN=aPN+f1+(Sqrt_dt*Sigma_1*randn(N1,1));
        aIN=aIN+f2+(Sqrt_dt*Sigma_2*randn(N1,1));
        aDN=aDN+f3+(Sqrt_dt*Sigma_3*randn(N1,1));
        DA=DA+f4+(Sqrt_dt*Sigma_4*randn(N1,1));
            
        D1Ract=D1Rsens*tanh(c4*DA);      
end


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