Huntington`s disease model (Gambazzi et al. 2010)

 Download zip file 
Help downloading and running models
"Although previous studies of Huntington’s disease (HD) have addressed many potential mechanisms of striatal neuron dysfunction and death, it is also known based on clinical findings that cortical function is dramatically disrupted in HD. With respect to disease etiology, however, the specific molecular and neuronal circuit bases for the cortical effects of mutant huntingtin (htt) have remained largely unknown. In the present work we studied the relation between the molecular effects of mutant htt fragments in cortical cells and the corresponding behavior of cortical neuron microcircuits using a novel cellular model of HD. We observed that a transcript-selective diminution in activity-dependent BDNF expression preceded the onset of a synaptic connectivity deficit in ex vivo cortical networks, which manifested as decreased spontaneous collective burst-firing behavior measured by multi-electrode array substrates. Decreased BDNF expression was determined to be a significant contributor to network-level dysfunction, as shown by the ability of exogenous BDNF to ameliorate cortical microcircuit burst firing. The molecular determinants of the dysregulation of activity-dependent BDNF expression by mutant htt appear to be distinct from previously elucidated mechanisms, as they do not involve known NRSF/REST-regulated promoter sequences, but instead result from dysregulation of BDNF exon IV and VI transcription. These data elucidate a novel HD-related deficit in BDNF gene regulation as a plausible mechanism of cortical neuron hypoconnectivity and cortical function deficits in HD. Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications. A simple mathematical model is proposed to interpret the observations and to explore the impact of specific synaptic dysfunctions on network activity. Interestingly, the model predicts a decrease in synaptic connectivity to be an early effect of mutant huntingtin in cortical neurons, supporting the hypothesis of decreased, rather than increased, synchronized cortical firing in HD."
1 . Gambazzi L, Gokce O, Seredenina T, Katsyuba E, Runne H, Markram H, Giugliano M, Luthi-Carter R (2010) Diminished activity-dependent brain-derived neurotrophic factor expression underlies cortical neuron microcircuit hypoconnectivity resulting from exposure to mutant huntingtin fragments. J Pharmacol Exp Ther 335:13-22 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Gap Junctions:
Simulation Environment: C or C++ program;
Model Concept(s): Pathophysiology; Huntington's;
Implementer(s): Giugliano, Michele [mgiugliano at];
% (Extended) Mean-Field Network Simulations
% Lausanne, June 3rd 2008 - Michele Giugliano, PhD.

% please change directory so that 'pwd' returns '.../giugliano'

addpath matlab;

clear all;				% all variables of the workspace are cleared
OUT  = [];				% a data structure for the output is prepared
figure(1); clf			% one figure is invoked and cleared
figure(25);clf			% another figure is invoked and cleared

N    = 100;				% Let's simulate the (extended) mean field behavior of 100 excitatory IeF neurons
dsim = 1000000;			% for 1000 seconds (i.e. 1000'000 msec)
C    = 0.3;				% where neurons are pairwise connected with a probability of 30 out of 100
J    = 22;				% where the mean synaptic efficacy, i.e. the peak EPSC is 22 pA / synapse
m    = 5;				% where the background synaptic release contributes with a tonic component
s    = 60;				% as well as with a fluctuating component
U    = 0.5;				% and where synapses are short-term plastic, with a presynaptic probability of release of 50%

% The whole idea behind using a scripted language is to make a **parameter exploration**
% Let's say that we want to see the effect of the probability of connection, C, then 'C' is our parameter (i.e. 'par')
for par =0.1:0.025:0.5,
 C = par;   
 cmd = sprintf('!./meanfield %f %f %f %f %f %f %f', dsim, N, C, J, m, s, U);		% see the Readme.txt file and meanfield.c
 eval(cmd);																			% type 'help eval' in Matlab: it is a powerful cmd

 figure(1); hold on;

 c      = load('simulation_results/data.x');										% data.x contains the R(t) - see the paper
 c(:,1) = c(:,1)/1000.;																% the first column being time (in msec)
 [shapes, events] = extract_spike(c(:,1), c(:,2), c(:,2), 100, 50, 300, 300, 0);	% I use a peak-detection utility to extract 'burst' times and shapes!!

 M = length(shapes);																% How many events were exracted? M
 if (M>1)							% see how 'extract_spike.m' is coded and you'll understand that 
 SH = []; SH = shapes{1,2};			% what follows is a way to have the 'average' shape of the peak
 rrr = 0;							%
 for k=2:length(shapes),
  if (length(shapes{k,2})~=length(SH)),
   rrr = rrr + 1;
   SH = SH + shapes{k,2};
   %plot(shapes{k,1}, shapes{k,2},'k'); drawnow; hold on;
 SH  = SH / (length(shapes) - rrr);

 kkk = find(SH==max(SH));
 SH(kkk) = SH(kkk-1);
 plot(shapes{1,1}, SH/max(SH),'k'); drawnow; hold off; %title(num2str(D)); 
 %plot(shapes{1,1}, SH,'k'); drawnow; hold off; %title(num2str(D)); 
 %set(gca, 'XLim', [-2000, 2000], 'YLim', [0 400]); %pause;
 %set(gca, 'XLim', [-300, 300], 'YLim', [0 1]); %pause;
 PBs = events(:,1);
 IBI = PBs(2:end) - PBs(1:end-1);
 OUT = [OUT; par, mean(IBI), std(IBI)/mean(IBI)]


%plot(c(:,1), c(:,2), events(:,1), events(:,2), 'ro'); title(P(i).name(6:end-2)); %set(gca, 'XLim', [1500 2500]);

if (~isempty(OUT))
figure(25); hold on; subplot(2,1,1);
plot(OUT(:,1), OUT(:,2),'o'); set(gca, 'YLim', [0 30]);
subplot(2,1,2); hold on;  
plot(OUT(:,1), OUT(:,3),'o'); set(gca, 'YLim', [0 1.3]);

qq = plot(OUT(4:end,1), OUT(4:end,2),'-o'); 
set(qq, 'Color', [0 0 0], 'MarkerEdgeCOlor', [0 0 0], 'MarkerFaceColor', [0 0 0])
set(qq, 'LineWidth', 2, 'MarkerSize',15);
set(gca, 'YLim', [0 30], 'XLim', [0.2 0.55]);
qq = plot(OUT(4:end,1), OUT(4:end,3),'-^'); set(gca, 'YLim', [0 1.3]);
set(qq, 'Color', [0 0 0], 'MarkerEdgeCOlor', [0 0 0], 'MarkerFaceColor', [0 0 0])
set(qq, 'LineWidth', 2, 'MarkerSize',15);
set(gca, 'YLim', [0 1.2], 'XLim', [0.2 0.55]);

print(gcf, '-loose', '-depsc2', 'IBI.eps');
% OUT = sortrows(OUT,1);
% PP = plot(OUT(1:end-5,1)/10000, OUT(1:end-5,2), '-ko'); set(gca, 'YLim', [0.5 1.2], 'XLim', [0 2.5])
% set(PP(1), 'LineWidth', 3, 'MarkerFaceColor', [0 0 0], 'MarkerSize', 10);
% set(gca, 'FontName', 'Arial', 'FontSize', 20);
% set(gca, 'YTick', [0.6 0.8 1 1.2]);
% set(gca, 'Box', 'off', 'YAxisLocation', 'left');
% xlabel('{\Delta}', 'FontName', 'Arial', 'FontSize', 25);
% ylabel('mean IBI [s]', 'FontName', 'Arial', 'FontSize', 25);
% print(gcf, '-loose', '-depsc2', 'mIBI.eps');
% XXX = [0.7 0.7 0.7];
% PP = plot(OUT(1:end-5,1)/10000, OUT(1:end-5,3), '-k^'); set(gca, 'YLim', [0. 1], 'XLim', [0 2.5])
% set(PP(1), 'LineWidth', 3, 'MarkerFaceColor', XXX, 'MarkerEdgeColor', XXX, 'Color', XXX, 'MarkerSize', 10);
% set(gca, 'FontName', 'Arial', 'FontSize', 20);
% set(gca, 'YTick', [0 0.25 0.5 0.75 1]);
% set(gca, 'Box', 'off', 'YAxisLocation', 'right');
% xlabel('{\Delta}', 'FontName', 'Arial', 'FontSize', 25);
% ylabel('cv IBI', 'FontName', 'Arial', 'FontSize', 25);
% print(gcf, '-loose', '-depsc2', 'cvIBI.eps');

Loading data, please wait...