A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007)

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Accession:93422
"A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson’s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers. ... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn’t lead to the co-contraction of antagonist motor units." See below readme and papers for more and details.
Reference:
1 . Cutsuridis V, Perantonis S (2006) A neural network model of Parkinson's disease bradykinesia. Neural Netw 19:354-74 [PubMed]
2 . Cutsuridis V (2006) Neural Model of Dopaminergic Control of Arm Movements in Parkinson's Disease Bradykinesia. Artificial Neural Networks - ICANN 2006, Lecture Notes in Computer Science, Part 1, LNCS 4131, Kollias S et al., ed. pp.583
3 . Cutsuridis V (2007) Does abnormal spinal reciprocal inhibition lead to co-contraction of antagonist motor units? A modeling study. Int J Neural Syst 17:319-27 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): Dopaminergic Receptor;
Gene(s):
Transmitter(s): Dopamine;
Simulation Environment: MATLAB;
Model Concept(s): Pathophysiology; Parkinson's;
Implementer(s): Cutsuridis, Vassilis [vcutsuridis at gmail.com];
Search NeuronDB for information about:  Dopaminergic Receptor; Dopamine;
function y = Nplots(t0,tf,t,y,rates,om)

NumFig = 1;
om = om(1);

figure(NumFig)
clf
set(NumFig,'Color',[1 1 1])
subplot(3,2,1)
plot(t,rates(:,1))
title('GO signal')
v = axis;
% axis([0 40 v(3) v(4)])
axis([0 v(2) v(3) v(4)])
axis tight
subplot(3,2,2)
plot(t,y(:,1))
hold on
plot(t,max(y(:,2),0),'--')
v = axis;
% axis([0 20 v(3) v(4)])
axis([0 v(2) v(3) v(4)])
axis tight
title('DV')
subplot(3,2,3)
plot(t,y(:,3))
hold on
plot(t,max(y(:,4),0),'--')
v = axis;
% axis([0 20 v(3) v(4)])
axis([0 tf v(3) v(4)])
% axis tight
title('PPV')
subplot(3,2,4)
plot(t,rates(:,10))
hold on
plot(t,rates(:,11),'--')
v = axis;
% axis([0 20 0 0.08])
axis([0 tf 0.006 v(4)])
% axis tight
title('DVV')
xlabel('Time (ms)')
subplot(3,2,5)
plot(t,rates(:,12))
v = axis;
% axis([0 20 0.03 0.07])
axis([0 v(2) 0.006 v(4)])
axis tight
title('P')
xlabel('Time (ms)')

NumFig = NumFig + 1;

figure(NumFig)
clf;
set(NumFig,'Color',[1 1 1])
subplot(3,2,1)
plot(t,y(:,11))
hold on
plot(t,y(:,12), 'r--')
v = axis;
% axis([0 60 v(3) v(4)])
axis([t0 tf 0 v(4)])
plot([om om],[0 v(4)],'r--')
title('alpha-MN')
subplot(3,2,2)
plot(t,y(:,21))
hold on
plot(t,y(:,22),'--')
v = axis;
% axis([0 60 v(3) v(4)])
axis([t0 tf v(3) v(4)])
plot([om om],[v(3) v(4)],'r--')
title('IaIN')
subplot(3,2,3)
plot(t,y(:,9))
hold on
plot(t,y(:,10), '--')
v = axis;
% axis([0 60 v(3) v(4)])
axis([t0 tf v(3) v(4)])
plot([om om],[v(3) v(4)],'r--')
title('Renshaw cell')
subplot(3,2,4)
plot(t,y(:,25))
hold on
plot(t,y(:,26),'--')
v = axis;
% axis([0 60 -0.01 v(4)])
axis([t0 tf v(3) v(4)])
plot([om om],[v(3) v(4)],'r--')
title('IbIN')
subplot(3,2,5)
plot(t,rates(:,13))
hold on
v = axis;
% axis([0 60 v(3) v(4)])
axis([t0 tf v(3) v(4)])
plot(t,rates(:,14),'--')
title('Renshaw recruitment rate')
subplot(3,2,6)
plot(t,y(:,23))
hold on
plot(t,y(:,24),'--')
v = axis;
% axis([0 60 0 v(4)])
axis([t0 tf v(3) 0.7])
plot([om om],[v(3) v(4)],'r--')
title('Spindle response')


NumFig = NumFig + 1;

figure(NumFig)
clf;
set(NumFig,'Color', [1 1 1])
subplot(2,2,1)
plot(t,y(:,13))
hold on
plot(t,y(:,14),'--')
axis tight
title('Static gamma-MN')
subplot(2,2,2)
plot(t,y(:,15))
hold on
plot(t,y(:,16),'--')
axis tight
title('Intrafusal static')
subplot(2,2,3)
plot(t,y(:,17))
hold on
plot(t,y(:,18),'--')
v = axis;
axis([0 tf 0 v(4)])
% axis tight
title('Dynamic gamma-MN')
subplot(2,2,4)
plot(t,y(:,19))
hold on
plot(t,y(:,20),'--')
v = axis;
axis([0 tf 0 v(4)])
% axis tight
title('Intrafusal dynamic')

NumFig = NumFig + 1;

figure(NumFig)
clf;
set(NumFig,'Color', [1 1 1])
subplot(3,1,1)
plot(t,y(:,5))
hold on
plot(t,y(:,6),'--')
axis tight
title('Contractile muscle state')
subplot(3,1,2)
plot(t,rates(:,6))
hold on
plot(t,rates(:,7), '--')
axis tight
title('Contraction rate (beta)')
subplot(3,1,3)
plot(t,rates(:,8))
hold on
plot(t,rates(:,9), '--')
axis tight
title('Number of contractile fibers (B)')
xlabel('Time (ms)')

NumFig = NumFig + 1;

figure(NumFig)
clf
set(NumFig,'Color',[1 1 1])
subplot(2,2,1)
plot(t,y(:,7))
hold on
v = axis;
% axis([0 60 -0.01 v(4)])
axis([t0 tf 0. v(4)])
plot([om om],[v(3) v(4)],'r--')
title('Position')
subplot(2,2,2)
plot(t,y(:,8))
hold on
v = axis;
% axis([0 60 -0.03 v(4)])
axis([t0 tf v(3) v(4)])
plot([om om],[v(3) v(4)],'r--')
title('Velocity')
subplot(2,2,3)
plot(t,rates(:,2))
hold on
plot(t,rates(:,3), '--')
axis tight
title('Length')
subplot(2,2,4)
plot(t,rates(:,4))
hold on
plot(t,rates(:,5), '--')
v = axis;
axis([t0 tf 0 v(4)])
plot([om om],[v(3) v(4)],'r--')
title('Force')

NumFig = NumFig + 1;

figure(NumFig)
clf
set(NumFig,'Color',[1 1 1])
subplot(2,2,1)
plot(t,rates(:,10))
hold on
v = axis;
axis([0 60 0.01 v(4)])
plot([om om],[0.0 v(4)],'r--')
title('DVV flexion')
subplot(2,2,3)
hold on
plot(t,rates(:,11))
v = axis;
axis([0 60 0.005 0.03])
plot([om om],[0.0 0.03],'r--')
title('DVV extension')
xlabel('Time (ms)')
subplot(2,2,2)
plot(t,rates(:,12))
hold on
v = axis;
axis([0 60 0.04 v(4)])
plot([om om],[0.04 v(4)],'r--')
title('P flexion')
subplot(2,2,4)
plot(t,rates(:,12))
hold on
v = axis;
axis([0 60 0.04 v(4)])
plot([om om],[0.04 v(4)],'r--')
title('P extension')
xlabel('Time (ms)')

NumFig = NumFig + 1;
y = NumFig;