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;
/
Scripts
README.txt
Bifurcation.m
Nullcline.m
Stochastic.m
                            
%% NULLCLINE PLOT
% This program generates the aPN and D1Ract nullcline plot

%% PARAMETERS
% Free Parameters
R_DA=0.00576e3;%nM per second                               FIX THIS VALUE
D1Rsens=3;% D1 Receptor Sensitivity (A.U.)                  FIX THIS VALUE

N=25001;% Number of discrete points

aPN_b=3;% basal pyramidal neuron activity in Hz

aPN=linspace(aPN_b,25,N); 
D1Ract=linspace(0,2,N);

aPN_eqm=zeros(1,N);

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

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


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

daPN=aPN-aPN_b;% Hz

%% aPN NULLCLINE

for i=1:length(D1Ract)
    
    Neg=(daPN./tauPN)+WIP*tanh(c2*(tauIN_0*(0.24*D1Ract(i)+0.26))*(WPI_0*(0.12*D1Ract(i)+0.68))*(tanh(c1*daPN)));
    Pos=(WPP_0*(0.12*D1Ract(i)+0.68))*tanh(c1*daPN);
    
    A_PN=Pos-Neg;
    clearvars Pos Neg;
    Y_pos=find(A_PN>=0);
    Y=numel(Y_pos);
    
    if Y>1
    aPN_eqm(i)=daPN(Y_pos(end));                       % Tells us the position number, As Y(1)=2 The point is one number behind
    end                                             % Here we know only 2 points of intersection exists 0 and the point being calculated
    
end

aPN_nullcline=aPN_eqm+aPN_b;

%% D1Ract NULLCLINE

Index_temp=max(find(D1Ract<=D1Rsens-0.001));                % To keep infinity in check
D1Ract_temp=D1Ract(1:Index_temp);

D1Ract_nullcline=((1/c1)*atanh((1/(c3*tauDN*WPD))*atanh((1/(c4*tauDA*R_DA))*atanh(D1Ract_temp/D1Rsens))))+aPN_b; % values of aPN

%% PLOT

plot(D1Ract,aPN_nullcline,'b');
hold on;box off;
plot(D1Ract_temp,D1Ract_nullcline,'g')
axis([0 2 0 25])
xlabel('D1 Receptor Activation D1R_{act} (A.U.)','FontWeight','bold','FontSize',9);
ylabel('Activity of Pyramidal population a_{PN} (Hz)','FontWeight','bold','FontSize',9);
title('Equilibrium points for D1R_{sens} 3','FontWeight','bold','FontSize',9);

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