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Fig7F.m
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Fig7F.m
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%choosing the anatomical measure here
%options are:
%'primary' for primary input similarity; Figure 7F middle
%'secondary' for secondary input similarity; Figure 7F right
%'invlambda' for sum of inverse shortest path; Figure 7F left
Measure='primary';
%loading the connectome / network map
load Neuro279_EJ.mat; %matrix containing gap junctions
load Neuro279_Syn.mat; %matrix containing chemical synapses
load Order279.mat; %array containing the neuron order of matrices above
c=Neuro279_Syn; %chemical synapse network
g=Neuro279_EJ; %gap junction network
cg=c+g; %combined network
cg_t=double(cg>0); %combined network - unweighted
c_t=double(c>0); %chemical synapse network - unweighted
g_t=double(g>0); %gap junction network - unweighted
%calculating all possible neuronal pairs within the connectome
comb_pairs=nchoosek(1:size(Neuro279_EJ,1),2);%
load Uzel_WT.mat; %loading WT whole-brain imaging datasets
%loading multiple hub neuron inhibition datasets
load AVAAVERIMPVC_His.mat;
load AVBRIBAIB_His.mat;
%%
%generating Correlation Matrix of the connectome averaged across all WT
%Datasets.
[AvgCorrMatr, ~, ~]= CorrelationHeatmaps2022(Uzel_WT,'derivs','Correlation',0,Order279);
%calculating the absolute correlation coefficients of all neuronal pairs
clear Values
for i=1:size(comb_pairs,1)
Values(i,1)=abs(AvgCorrMatr(comb_pairs(i,1),comb_pairs(i,2)));
end
%
%calculating secondary input similarity (cosine similarity) for all pairs in unweighted network
[secon_intact]=calculateSIP_t(cg_t,comb_pairs,'cos');
%calculating primary input similarity (cosine similarity) for all pairs in unweighted network
[prim_intact]=calculatePIP(cg_t,comb_pairs,'cos');
%calculating sum of inverse shortest paths for all pairs in unweighted network
[inverseLambda_intact]=calculateLinv_lite6(c_t,g_t,comb_pairs);
Conditions={'AVAAVERIMPVC','AVBRIBAIB'}; %conditions represent all multiple hub neuron inhibition lines
for kkk=1:length(Conditions);
Condition=Conditions{kkk};
clear Neuronstobezeroed Input_InhibitionDataset
%below is to exclude the inhibited neurons from the analysis.
switch Condition
case 'AVAAVERIMPVC'
Neuronstobezeroed={'AVAL','AVAR','AVEL','AVER','PVCL','PVCR','RIML','RIMR'};
Input_InhibitionDataset=AVAAVERIMPVC_His;
case 'AVBRIBAIB'
Neuronstobezeroed={'AVBL','AVBR','RIBL','RIBR','AIBL','AIBR'};
Input_InhibitionDataset=AVBRIBAIB_His;
end
clear Neuronstobezeroed_num
for i=1:length(Order279);
for j=1:length(Neuronstobezeroed);
if strcmp(Neuronstobezeroed(j), Order279{i});
Neuronstobezeroed_num(j)=i;
end
end
end
%generating Correlation Matrix of the connectome averaged across all
%designated inhibition datasets
[temp_extended_corr_matr, ~, ~]= CorrelationHeatmaps2022(Input_InhibitionDataset,'derivs','Correlation',0,Order279);
%generating perturbed networks with the removal of specified neurons
temp_cg=perturb_matrix(cg,Order279,Neuronstobezeroed);
temp_c=perturb_matrix(c,Order279,Neuronstobezeroed);
temp_g=perturb_matrix(g,Order279,Neuronstobezeroed);
%below is to exclude the inhibited neurons from the analysis.
temp_extended_corr_matr(:,Neuronstobezeroed_num)=nan;
temp_extended_corr_matr(Neuronstobezeroed_num,:)=nan;
%rename the WT average correlation matrix
temp_extended_corr_matr_correspondingWT=AvgCorrMatr;
%below is to exclude the inhibited neurons from the analysis (WT values this time).
temp_extended_corr_matr_correspondingWT(:,Neuronstobezeroed_num)=nan;
temp_extended_corr_matr_correspondingWT(Neuronstobezeroed_num,:)=nan;
%calculating the absolute correlation coefficients of all neuronal
%pairs in inhibition and WT datasets
clear tempHisValues tempWTValues
for i=1:size(comb_pairs,1)
tempHisValues(i,1)=abs(temp_extended_corr_matr(comb_pairs(i,1),comb_pairs(i,2)));
tempWTValues(i,1)=abs(temp_extended_corr_matr_correspondingWT(comb_pairs(i,1),comb_pairs(i,2)));
end
%calculating the selected measure for all pairs
if strcmp(Measure,'secondary')
[temp_second]=calculateSIP_t(temp_cg,comb_pairs,'cos');
elseif strcmp(Measure,'primary')
[temp_prim]=calculatePIP(temp_cg,comb_pairs,'cos');
elseif strcmp(Measure,'invlambda')
[temp_Linv]=calculateLinv(temp_c,temp_g,comb_pairs,6);
end
%calculating the percent change in the selected measure due to in
%silico perturbation of the network
clear temp_PercentChange
for i=1:size(comb_pairs,1)
if strcmp(Measure,'secondary')
temp_PercentChange(i)=abs(secon_intact(i)-temp_second(i))/secon_intact(i);
elseif strcmp(Measure,'primary')
temp_PercentChange(i)=abs(prim_intact(i)-temp_prim(i))/prim_intact(i);
elseif strcmp(Measure,'invlambda')
temp_PercentChange(i)=abs(inverseLambda_intact(i)-temp_Linv(i))/inverseLambda_intact(i);
end
NeuronPairs(i,1:2)=[comb_pairs(i,1),comb_pairs(i,2)];
end
%saving the results from multiple network hub inhibition lines
%within the for loop
if strcmp(Measure,'secondary')
CCV{kkk}=[tempWTValues,tempHisValues,secon_intact,temp_second,temp_PercentChange',NeuronPairs];
elseif strcmp(Measure,'primary')
CCV{kkk}=[tempWTValues,tempHisValues,prim_intact,temp_prim,temp_PercentChange',NeuronPairs];
elseif strcmp(Measure,'invlambda')
CCV{kkk}=[tempWTValues,tempHisValues,inverseLambda_intact,temp_Linv,temp_PercentChange',NeuronPairs];
end
end
%concatenating the results from multiple network hub inhibition lines
MultiHubResults=[];
for i=1:numel(CCV);
MultiHubResults=[MultiHubResults;CCV{i}];
end
%filtering out the neuron pairs that were not recorded within both datasets
Filtered_Results=MultiHubResults(find(~isnan(MultiHubResults(:,1)) | ~isnan(MultiHubResults(:,2))),:);
PercentChange=Filtered_Results(:,5); %percent changes in the selected measure of the recorded neuronal pairs
PooledWTCorVal=Filtered_Results(:,1); %WT correlation values of the recorded neuronal pairs
PooledDataCorVal=Filtered_Results(:,2); %Inhibition correlation values of the recorded neuronal pairs
%% plotting the histogram for both perturbed and unaffected pairs (according
%to percent change)
figure;
subplot(2,1,1)
thresh1=0.1; %threshold for the classification of perturbed and unaffected pairs
histogram(abs(PooledWTCorVal(find(PercentChange>=thresh1))),0:0.025:1,'Normalization','Probability','FaceAlpha',0.5);
hold on;
histogram(abs(PooledDataCorVal(find(PercentChange>=thresh1))),0:0.025:1,'Normalization','Probability','FaceAlpha',0.5);
ylim([0 0.18])
xlabel('Absolute Correlation Coefficient')
ylabel('Fraction')
set(gca,'FontSize',14)
title('Perturbed Pairs (>10%)')
%
subplot(2,1,2)
histogram(abs(PooledWTCorVal(find(PercentChange<thresh1))),0:0.025:1,'Normalization','Probability','FaceAlpha',0.5);
hold on;
histogram(abs(PooledDataCorVal(find(PercentChange<thresh1))),0:0.025:1,'Normalization','Probability','FaceAlpha',0.5);
ylim([0 0.18])
xlabel('Absolute Correlation Coefficient')
ylabel('Fraction')
set(gca,'FontSize',14)
title('Unaffected Pairs (<10%)')