anesthesia_bats/Ephys data/SFig5D-F.m

% plot FTCs and histograms for all data 
close all;
clear;
% FT curves and the respective classification to pure tones
aw=load('class_FT-aw'); aw=aw.class_FT;
int=load('class_FT-int'); int=int.class_FT;
chi=load('class_FT-chi'); chi=chi.class_FT;
pt=load('class_FT-pt'); pt=pt.class_FT;
pair=load('class_FT-pair'); pair=pair.class_FT;

% classification in response to natural sounds
intN=load('all_data.mat'); intN=intN.all_di;
chiN=load('chi_data.mat'); chiN=chiN.all_di;
ptN=load('pt_data.mat');   ptN=ptN.all_di;
pairN=load('pair_data.mat'); pairN=pairN.all_di;

% classification lists
awN=intN(intN(:,3)==0,1:2);
intN=intN(intN(:,3)==1,1:2);
chiN=chiN(chiN(:,3)==0,1:2);
ptN=ptN(ptN(:,3)==2,1:2);
pairN=pairN(pairN(:,3)==0,1:2);

%% create matrix to plot stack bar plots and FTC of one classification 
class_to_plot=0;
% awake data set 
combi_aw=zeros(5,3); % rows: natural class / cols: FTC class
ftc_class_aw=nan(100,18);   % rows: units / cols: FTC
n_units=aw(:,1);
n=numel(n_units);
k=1;
for i = 1:n
    unit=n_units(i);
    class_=awN(awN(:,1)==unit,2)+1;
    ftc_=aw(aw(:,1)==unit,2);
    if class_~= 0 
        combi_aw(class_,ftc_)=combi_aw(class_,ftc_)+1;  
    end
    if class_-1 == class_to_plot
        ftc_class_aw(k,:)=aw(aw(:,1)==unit,2:19);
        k=k+1;
    end
end

% anesthetized data set 
combi_int=zeros(5,3); % rows: class / cols: FTC
ftc_class_int=nan(100,18);   % rows: units / cols: FTC
n_units=int(:,1);
n=numel(n_units);
k=1;
for i = 1:n
    unit=n_units(i);
    class_=intN(intN(:,1)==unit,2)+1;
    ftc_=int(int(:,1)==unit,2);
    if class_~= 0 
        combi_int(class_,ftc_)=combi_int(class_,ftc_)+1;  
    end
    if class_-1 == class_to_plot
        ftc_class_int(k,:)=int(int(:,1)==unit,2:19);
        k=k+1;
    end
end

% paired chimeras data set 
combi_chi=zeros(5,3); % rows: class / cols: FTC
ftc_class_chi=nan(100,18);   % rows: units / cols: FTC
n_units=chi(:,1);
n=numel(n_units);
k=1;
for i = 1:n
    unit=n_units(i);
    class_=chiN(chiN(:,1)==unit,2)+1;
    ftc_=chi(chi(:,1)==unit,2);
    if class_~= 0 
        combi_chi(class_,ftc_)=combi_chi(class_,ftc_)+1;  
    end
    if class_-1 == class_to_plot
        ftc_class_chi(k,:)=chi(chi(:,1)==unit,2:19);
        k=k+1;
    end
end

% pure tones data set 
combi_pt=zeros(5,3); % rows: class / cols: FTC
ftc_class_pt=nan(100,18);   % rows: units / cols: FTC
n_units=ptN(:,1);
n=numel(n_units);
k=1;
for i = 1:n
    unit=n_units(i);
    class_=ptN(ptN(:,1)==unit,2)+1;
    ftc_=pt(pt(:,1)==unit,2);
    if class_~= 0 
        combi_pt(class_,ftc_)=combi_pt(class_,ftc_)+1;  
    end
    if class_-1 == class_to_plot
        ftc_class_pt(k,:)=pt(pt(:,1)==unit,2:19);
        k=k+1;
    end
end

% all data sets together for anesthetized data 
combi=combi_int+combi_pt; % +combi_chi  %rows: natural class / cols: FTC class
ftc_class=[ftc_class_int;ftc_class_chi;ftc_class_pt];
ftc_class = ftc_class(any(~isnan(ftc_class),2),:);


%% plotting # units awake and anesthetized
red =[0.8500 0.3250 0.0980];
blue=[0 0.4470 0.7410];
yellow=[0.9294 0.6941 0.1255];

dataset=combi_aw;

figure(1); set(gcf, 'Position',  [200, 200, 320, 180])
subplot(121)
b=bar(dataset([2,3,4,5,1],[3,1,2]),'stacked', 'FaceColor','flat');
b(1).CData =yellow; % multipeaked
b(2).CData=blue;    % LF tuned
b(3).CData=red;     % HF tuned

ylabel('# of units')
xticklabels({'ech','com','ech>com','com>ech','ech=com'})

set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);
legend('multi.','LF','HF','Location','northwest','box','off');


dataset=combi;

subplot(122)
b=bar(dataset([2,3,4,5,1],[3,1,2]),'stacked', 'FaceColor','flat');
b(1).CData =yellow; % multipeaked
b(2).CData=blue;    % LF tuned
b(3).CData=red;     % HF tuned

xticklabels({'ech','com','ech>com','com>ech','ech=com'})
ylim([0 50])
set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);

% exportgraphics(gcf,'E:\Users\User\Desktop\delay paper\anesthetized\figures_paper\class_FTC_numb.pdf',...
%     'Resolution',300','ContentType','vector','BackgroundColor','none')

%% plotting 'equally responsive' neurons % of FTC
dataset_an=combi;
dataset_aw=combi_aw;  
combi_class=[dataset_aw(1,[3,1,2]);dataset_an(1,[3,1,2])];
combi_class=combi_class./(sum(combi_class,2))*100;

figure(2); set(gcf, 'Position',  [200, 200, 60, 180])
b=bar(combi_class,'stacked', 'FaceColor','flat');
b(1).CData =yellow; % multipeaked
b(2).CData=blue;    % LF tuned
b(3).CData=red;     % HF tuned

ylabel('% of units')
xticklabels({'awake','anesthetized'})
xlim([0.2 2.8])

set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);
% legend('multi.','LF','HF','Location','northwest','box','off');

% exportgraphics(gcf,'E:\Users\User\Desktop\delay paper\anesthetized\figures_paper\class_FTC_perc.pdf',...
%     'Resolution',300','ContentType','vector','BackgroundColor','none')



%% plotting FTCs

dataset=ftc_class_aw;

lf_=dataset(:,1)==1;
hf_=dataset(:,1)==2;
mf_=dataset(:,1)==3;


x=10:5:90;
figure(3); set(gcf, 'Position',  [200, 200, 300, 180])

subplot(221)

A=dataset(lf_,2:18);

% plot(x,A','color',[0.6 0.6 0.6]); hold on
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',blue,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',blue);
alpha(.2);
xlim([8 92])
% ylim([-0.05 1.05])
text(50,0.75,['n=' num2str(size(A,1))],'fontsize',8)

% ylabel('Norm. response')
% title('\fontsize{8}LF-tuned','FontWeight', 'Normal')
set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);

subplot(222)

A=dataset(mf_,2:18);

% plot(x,A','color',[0.6 0.6 0.6]); hold on
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',yellow,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',yellow);
alpha(.2);
xlim([8 92])
% ylim([-0.05 1.05])
text(50,0.75,['n=' num2str(size(A,1))],'fontsize',8)


% title('\fontsize{8}multipeaked','FontWeight', 'Normal')
set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);


dataset=ftc_class;

lf_=dataset(:,1)==1;
hf_=dataset(:,1)==2;
mf_=dataset(:,1)==3;


x=10:5:90;

subplot(223)

A=dataset(lf_,2:18);

% plot(x,A','color',[0.6 0.6 0.6]); hold on
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',blue,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',blue);
alpha(.2);
xlim([8 92])
% ylim([-0.05 1.05])
xlabel('Freq. (kHz)')
text(50,0.75,['n=' num2str(size(A,1))],'fontsize',8)

% ylabel('Norm. response')
% title('\fontsize{8}LF-tuned','FontWeight', 'Normal')
set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);

subplot(224)

A=dataset(mf_,2:18);

% plot(x,A','color',[0.6 0.6 0.6]); hold on
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',yellow,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',yellow);
alpha(.2);
xlim([8 92])
% ylim([-0.05 1.05])
xlabel('Freq. (kHz)')
text(50,0.75,['n=' num2str(size(A,1))],'fontsize',8)

% title('\fontsize{8}multipeaked','FontWeight', 'Normal')
set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);

% exportgraphics(gcf,'E:\Users\User\Desktop\delay paper\anesthetized\figures_paper\FTC_all_eq.pdf',...
%     'Resolution',300','ContentType','vector','BackgroundColor','none')

%% plotting modulation of anesthesia on FTCs - different preparations

lf_=ftc_class_aw(:,1)==1;
mf_=ftc_class_aw(:,1)==3;
x=10:5:90;
aw_lf=ftc_class_aw(lf_,2:18);
aw_mf=ftc_class_aw(mf_,2:18);
lf_=ftc_class(:,1)==1;
mf_=ftc_class(:,1)==3;
an_lf=ftc_class(lf_,2:18);
an_mf=ftc_class(mf_,2:18);

ef_sizes_mf=nan(1,17);
ef_sizes_lf=nan(1,17);
sign_mf=nan(1,17);
sign_lf=nan(1,17);
for f=1:17
    ef_size_mf = CliffDelta(an_mf(:,f),aw_mf(:,f));  
    ef_size_lf = CliffDelta(an_lf(:,f),aw_lf(:,f)); 
    ef_sizes_mf(1,f)=ef_size_mf;
    ef_sizes_lf(1,f)=ef_size_lf;
    [p1,h1] = ranksum(an_mf(:,f),aw_mf(:,f)); 
    [p2,h2] = ranksum(an_lf(:,f),aw_lf(:,f)); 
    sign_mf(1,f)=h1;
    sign_lf(1,f)=h2;
end

figure(4); set(gcf, 'Position',  [200, 200, 320, 110])
t=tiledlayout(1,2,'TileSpacing','compact');

ax1 = nexttile;

A=aw_lf;
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',blue,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',blue);
alpha(.2);

A=an_lf;
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',red,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',red);
alpha(.2);


hold on 
for f=1:17
    if sign_lf(1,f)==1
        plot(x(f),1,'k*','MarkerSize',3)
    end
end
xlabel('freq. (kHz)')
ylabel('norm. response')


set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);

ax2 = nexttile;

A=aw_mf;
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',blue,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',blue);
alpha(.2);

A=an_mf;
y=[nanmean(A,1)-nanstd(A,1)/sqrt(size(A,1)); nanmean(A,1); nanmean(A,1)+nanstd(A,1)/sqrt(size(A,1))];
px=[x,fliplr(x)];
py=[y(1,:), fliplr(y(3,:))];
patch(px,py,1,'FaceColor',red,'EdgeColor','none'); hold on 
plot(x,y(2,:),'Color',red);
alpha(.2);

hold on 
for f=1:17
    if sign_mf(1,f)==1
        plot(x(f),1,'k*','MarkerSize',3)
    end
end
xlabel('freq. (kHz)')

set(gca, 'box', 'off')
set(gca, 'Color','none')
set(gca,'linewidth',1);set(gca,'fontsize',8);


linkaxes([ax1 ax2],'xy')
xlim([8 92])

% exportgraphics(gcf,'E:\Users\User\Desktop\delay paper\anesthetized\figures_paper\modulation_ftc_prep.pdf',...
%     'Resolution',300','ContentType','vector','BackgroundColor','none')