Excessive beta oscillations in Parkinson's disease (Pavlides et al. 2015)

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Accession:184491
" ... Understanding the generation of beta oscillations is important to improve treatments for Parkinson’s disease. Competing theories exist for how these oscillations are generated in the affected brain circuits, which include the motor cortex and a set of subcortical nuclei called the basal ganglia. This paper suggests two hypotheses for the generation of beta oscillations. The first hypothesis is that beta oscillations are generated in the motor cortex, and the basal ganglia resonate to the cortical input. The second hypothesis additionally proposes that feedback from the basal ganglia to cortex is critically important for the presence of the oscillations. We show that the models can successfully account for a wide range of experimental data concerning the presence of beta oscillations in Parkinson’s disease."
Reference:
1 . Pavlides A, Hogan SJ, Bogacz R (2015) Computational Models Describing Possible Mechanisms for Generation of Excessive Beta Oscillations in Parkinson's Disease. PLoS Comput Biol 11:e1004609 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Subthalamic Nucleus; Basal ganglia;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Parkinson's; Beta oscillations; Activity Patterns; Oscillations;
Implementer(s): Pavlides, Alex ;
  
				README
				======
Date: 
27/08/2015

This is the readme for models associated with the paper:

Alex Pavlides, S. John Hogan and Rafal Bogacz. (2015) Computational
models describing possible mechanisms for generation of excessive beta
oscillations in Parkinson's disease. PLOS Computational Biology

Description: 
This set of Matlab files produce a figure showing Results of simulations of the resonance model (panels A and B) and feedback model (panels C and D). A, C) Each of the six panels shows the activity of the STN, GPe and cortical populations as a function of time. The labels to the left indicate if a row shows simulations of an intact model, or a model with particular connections blocked. In simulations in panel A the following parameters were used: wSG =4.87, wGS=1.33, wCS=9.98, wSC=8.93, wGG=0.53, wCC=6.17, C=172.18, Str=8.46, TCC=4.65, tau_E=11.59, tau_I=13.02, B_E=17.85, B_I=9.87, M_E=75.77 and M_I=205.72. In simulations in panel C the following values were used: wSG=2.56, wGS=3.22, wCS=6.60, wSC= 0.00, wGG=0.90, wCC=3.08, C=277.94, Str=40.51, TCC=7.74, tau_E=11.69, tau_I=10.45, B_E=3.62, B_I=7.18, M_E=71.77 and M_I=276.39. B, D) The comparison between experimental and simulated statistics of the oscillations.

File list:
main.m
minfunction.m
model_eqs.m
generate_fig.m
frenquency.m
peakdet.m
rotateXLabels.m

Data:
weights.mat


Operating instructions:
Ensure files and data are availabe in Matlab. To run type 'main' at the command line. The figure should be generated. 


Contact: If there are any questions please contact Dr Rafal Bogacz by email: rafal.bogacz@ndcn.ox.ac.uk

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