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 rsim=ConnLocGaussian(n,distfunc,sigmaG)
% Calculates whole connectivity matrix
%this is a version that is slightly quicker than the  GaussianLocConnFunc
%at higher n>100...does the same though!
% ARGS:
% n = length and width of the grid;
% distfunc = @distSheet (to calculate euclidian dist)
% steepness = steepness of the function #conn/#all.possible.conn over
% distance
% RETURNS:
% rsim = sparse connectivity matrix

%check if matlabpool is open and if not then open it
if  matlabpool('size') == 0
    matlabpool
end

% width of the square
nsub=n^2;


%lay out coordinates
[coordx,coordy] = meshgrid(1:n,1:n);
coorx=reshape(coordx,nsub,1);
coory=reshape(coordy,nsub,1);

% Calculate the locations of the sparse elements
%for i=1:nsub
% make sparse matrix

rsim=sparse(nsub,nsub);
for i=1:nsub
    % call distfinc (i.e. sheet, torus etc
    distM=(distfunc([coorx(i) coory(i)],[coorx coory]));                    %gets all the distances from all other points to current point
    p=exp(-distM.^2./(2*sigmaG^2));%generate gaussian
    p(distM==0)=0;%no self connection
    p=p/max(p);%renormalise
    pindex=find(p>0.022);%cut off unconnected ones
    pV=rand(size(pindex));
    pConn=p(pindex)>pV;
    
    
    indTo=pindex(pConn);
    indTo=indTo(indTo~=i);
    
    rsim=rsim+sparse(indTo,i,1,nsub,nsub);

    
end

rsim(rsim>0)=1;

end


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