Mechanisms underlying different onset patterns of focal seizures (Wang Y et al 2017)

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Accession:226074
"Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types { low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. ..."
Reference:
1 . Wang Y, Trevelyan AJ, Valentin A, Alarcon G, Taylor PN, Kaiser M (2017) Mechanisms underlying different onset patterns of focal seizures PLoS 13(5):e1005475
Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Epilepsy; Beta oscillations; Gamma oscillations; Oscillations; Activity Patterns; Spatio-temporal Activity Patterns;
Implementer(s): Wang, Yujiang [yujiang.wang at newcastle.ac.uk];
/
WangYetAl2017
lib
ConnLocGaussian.m *
ConnPatchyRemOverlap.m *
convolve2.m *
distSheet.m *
distTorus.m *
exindex.m *
FilterEEG.m
Gaussian.m *
GaussianLocConnFunc.m
generatePatchesOverlap.m *
getDelayMatrix.m
getDelayMatrixserial.m
getNoise.m
getParam.m *
getParamDelay.m
KLDiv.m
makeCellCluster.m *
makeCellClusterToroidal.m *
MayColourMap.mat *
meanMacroCol.m *
ODEsheet.m
ODEsheetStim.m
plotVideo.m
runSheet.m *
runSheetDelay.m *
runSheetDelayRamp.m
runSheetPRamp.m *
Sigm.m *
                            
function Y=runSheetPRamp(y0,parameters)

%this is an euler solver with a noise term as subcortical input, which
%takes a ramp in P.


nsq=parameters.n^2;
h=parameters.h;


SigThresh=parameters.SigThresh;
SigSteepness=parameters.SigSteepness;
tauPy=parameters.tauPy;
tauInh=parameters.tauInh;


neq = length(y0);
Nl = size(parameters.NValue,2);
Y = zeros(neq,Nl);

Y(:,1) = y0;
for i = 1:Nl-1 
    Py=Y(1:nsq,i);
    Inh=Y(nsq+1:2*nsq,i);
    
    
    dPydt     =(-Py     + Sigm(parameters.Py2Py*Py      - parameters.Inh2Py*Inh + parameters.PyInput(:,i) + parameters.NValue(:,i),  SigThresh,SigSteepness))./tauPy;
    dInhdt    =(-Inh    + Sigm(parameters.Py2Inh*Py                  + parameters.InhInput,                SigThresh,SigSteepness))./tauInh;

    Y(:,i+1) = Y(:,i) + h*[dPydt;dInhdt];
  
  
end
Y = single(Y)';

end

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