NAcVP2018/MatlabScripts/k_Fig6ThreeRewards.m

%plotting 3rewards data

clear all;
load ('R_3R.mat');
load ('RAW.mat');

%get parameters
BinBase=R_3R.Param.BinBase;
BinDura=R_3R.Param.BinDura;
bins=R_3R.Param.bins;
binint=R_3R.Param.binint;
binstart=R_3R.Param.binstart;
PStatBins=0.01; %using more stringent cutoff to reduce pre-delivery noise

%which bins bound the area examined for reward-selectivity? (in seconds)
Time1=0.4; %seconds
Time2=3; %seconds
Bin1=(((Time1-BinDura(2)/2)-binstart)/binint); %convert to bin name
Bin2=(((Time2-BinDura(2)/2)-binstart)/binint); %convert to bin name

%sorting bin -- which bin the neurons' activity is sorted on for heatmap(in seconds)
SortBinTime=1.1; %seconds
SortBin=round((((SortBinTime-BinDura(2)/2)-binstart)/binint)); %convert to bin name

sucrose=[1  0.6  0.1];
maltodextrin=[.9  0.3  .9];
water=[0.00  0.75  0.75];
total=[0.3  0.1  0.8];
exc=[0 113/255 188/255];
inh=[240/255 0 50/255];

%% conduct lick analysis
global Dura Tm BSIZE Tbin
path='C:\Users\dottenh2\Documents\MATLAB\David\2Rewards Nex Files\3RLick_paper.xls';

%Main settings
BSIZE=0.01; %Do not change
Dura=[-22 20]; Tm=Dura(1):BSIZE:Dura(2);
Tbin=-0.5:0.005:0.5; %window used to determine the optimal binsize
MinNumTrials=5;
Lick=[];Lick.Ninfo={};LL=0;Nlick=0;

%Smoothing
Smoothing=1; %0 for raw and 1 for smoothing
SmoothTYPE='lowess'; %can change this between lowess and rlowess (more robust, ignores outliers more)
SmoothSPAN=50; %percentage of total data points
if Smoothing~=1, SmoothTYPE='NoSmoothing';SmoothSPAN=NaN; end

% List of events to analyze and analysis windows EXTRACTED from excel file
[~,Erefnames]=xlsread(path,'Windows','a3:a8'); % cell that contains the event names

%Finds the total number of sessions
for i=1:length(RAW)
    if strcmp('TH',RAW(i).Type(1:2))
        Nlick=Nlick+1;
        Lick.Linfo(i,1)=RAW(i).Ninfo(1,1);
    end
end
Lick.Erefnames= Erefnames;

%preallocating the result matrix
for k=1:length(Erefnames)
    Lick.Ev(k).PSTHraw(1:Nlick,1:length(Tm))=NaN(Nlick,length(Tm));
    Lick.Ev(k).BW(1:Nlick,1)=NaN;
    Lick.Ev(k).NumberTrials(1:Nlick,1)=NaN;
end

for i=1:length(RAW) %loops through sessions
    if strcmp('TH',RAW(i).Type(1:2))
        LL=LL+1; %lick session counter
        for k=1:length(Erefnames) %loops thorough the events
            EvInd=strcmp(Erefnames(k),RAW(i).Einfo(:,2)); %find the event id number from RAW
            LickInd=strcmp('Licks',RAW(i).Einfo(:,2)); %find the event id number from RAW
            if sum(EvInd)==0
                fprintf('HOWDY, CANT FIND EVENTS FOR ''%s''\n',Erefnames{k});
            end

            Lick.Ev(k).NumberTrials(LL,1)=length(RAW(i).Erast{EvInd});
            if  ~isempty(EvInd) && Lick.Ev(k).NumberTrials(LL,1)>MinNumTrials %avoid analyzing sessions where that do not have enough trials
                [PSR1,N1]=MakePSR04(RAW(i).Erast(LickInd),RAW(i).Erast{EvInd},Dura,{1});% makes collpased rasters. PSR1 is a cell(neurons)
                if ~isempty(PSR1{1}) %to avoid errors, added on 12/28 2011
                    %forcing 100ms bin size to keep it consistent across
                    %sessions (no reason is should be different for licks)
                    [PTH1,BW1,~]=MakePTH07(PSR1,repmat(N1, size(RAW(i).Erast{LickInd},1),1),{2,0,0.1});%these values force bin size to be 100ms
                    PTH1=smooth(PTH1,SmoothSPAN,SmoothTYPE)';

                    %------------- Fills the R.Ev(k) fields --------------
                    Lick.Ev(k).BW(LL,1)=BW1;
                    Lick.Ev(k).PSTHraw(LL,1:length(Tm))=PTH1;
                end
            end
        end
    end
end

Xaxis=[-2 12];
Ishow=find(Tm>=Xaxis(1) & Tm<=Xaxis(2));
time1=Tm(Ishow);
c=[-1000 7500];ClimE=sign(c).*abs(c).^(1/4);%ColorMapExc
colormap(jet);
sucrose=[1  0.6  0.1];
maltodextrin=[.9  0.3  .9];
water=[0.00  0.75  0.75];
total=[0.3  0.1  0.8];
exc=[0 113/255 188/255];
inh=[240/255 0 50/255];

Ev1=strcmp('RD1', Lick.Erefnames);
Ev2=strcmp('RD2', Lick.Erefnames);
Ev3=strcmp('RD3', Lick.Erefnames);

psth1=nanmean(Lick.Ev(Ev1).PSTHraw(:,Ishow),1);
sem1=nanste(Lick.Ev(Ev1).PSTHraw(:,Ishow),1); %calculate standard error of the mean
up1=psth1+sem1;
down1=psth1-sem1;

psthE=nanmean(Lick.Ev(Ev2).PSTHraw(:,Ishow),1);
semE=nanste(Lick.Ev(Ev2).PSTHraw(:,Ishow),1); %calculate standard error of the mean
upE=psthE+semE;
downE=psthE-semE;

psth3=nanmean(Lick.Ev(Ev3).PSTHraw(:,Ishow),1);
sem3=nanste(Lick.Ev(Ev3).PSTHraw(:,Ishow),1); %calculate standard error of the mean
up3=psth3+sem3;
down3=psth3-sem3;

%plotting
subplot(2,3,1);
hold on;
plot(time1,psth1,'Color',sucrose,'linewidth',1);
plot(time1,psthE,'Color',maltodextrin,'linewidth',1);
plot(time1,psth3,'Color',water,'linewidth',1);
patch([time1,time1(end:-1:1)],[up1,down1(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5);
patch([time1,time1(end:-1:1)],[upE,downE(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5);
patch([time1,time1(end:-1:1)],[up3,down3(end:-1:1)],water,'EdgeColor','none');alpha(0.5);
title('Mean lick rate');
%plot([-2 10],[0 0],':','color','k');
plot([0 0],[-2 8],':','color','k','linewidth',0.75);
axis([-2 12 0 7.5]);
xlabel('Seconds from reward delivery');
ylabel('Licks/s');
legend('Sucrose trials','Maltodextrin trials','Water trials');


%% bins analysis

%first and last bin aren't included because you can't compare to the previous/subsequent bin
%this axis plots the bins on their centers
xaxis=linspace(binstart+binint+BinDura(2)/2,binstart+(bins-2)*binint+BinDura(2)/2,bins-2);

for i=2:(bins-1) %no including first or last bin because can't compare to previous/subsequent bin
    
    %finds out whether firing is stronger (high excitation or lower inhibition) for 1 or 2
    for k=1:length(R_3R.Ninfo) %runs through neurons
        if R_3R.BinStatRwrd{i,1}(k).IntSig < PStatBins %if neuron is significant for this bin
            if R_3R.BinStatRwrd{i-1,1}(k).IntSig < PStatBins || R_3R.BinStatRwrd{i+1,1}(k).IntSig < PStatBins %either previous or subsequent bin must be significant
                if R_3R.BinStatRwrd{i,1}(k).R1Mean >= R_3R.BinStatRwrd{i,1}(k).R2Mean && R_3R.BinStatRwrd{i,1}(k).R1Mean >= R_3R.BinStatRwrd{i,1}(k).R3Mean %if firing is greater on sucrose trials than maltodextrin and water. if there's a tie, make it sucrose (this is highly unlikely)
                    R_3R.BinRewPref{i,1}(k,1)=1; %sucrose preferring
                elseif R_3R.BinStatRwrd{i,1}(k).R2Mean > R_3R.BinStatRwrd{i,1}(k).R1Mean && R_3R.BinStatRwrd{i,1}(k).R2Mean >= R_3R.BinStatRwrd{i,1}(k).R3Mean %if mal is greater than suc and water
                    R_3R.BinRewPref{i,1}(k,1)=2; %maltodextrin preferring
                else
                    R_3R.BinRewPref{i,1}(k,1)=3; %water preferring
                end
            else
                R_3R.BinRewPref{i,1}(k,1)=0; %if not significant in 2 consecutive bins
            end
        else
            R_3R.BinRewPref{i,1}(k,1)=0; %if no sig reward modulation
        end
        
    end
    %find how many NAc neurons have significant reward modulation in each bin
    NN1perBin(i,1)=sum(R_3R.BinRewPref{i,1}(:,1)==1); %sucrose pref
    NN2perBin(i,1)=sum(R_3R.BinRewPref{i,1}(:,1)==2); %malto pref
    NN3perBin(i,1)=sum(R_3R.BinRewPref{i,1}(:,1)==3); %malto pref
    NNperBin(i,1)=sum(R_3R.BinRewPref{i,1}(:,1)>0); %any
    %normalize to number of neurons in population
    NN1norm=NN1perBin./length(R_3R.Ninfo);
    NN2norm=NN2perBin./length(R_3R.Ninfo);
    NN3norm=NN3perBin./length(R_3R.Ninfo);
    NNnorm=NNperBin./length(R_3R.Ninfo);
    
end


%plotting number of significantly modulated neurons across time
%NAc
subplot(2,3,2);
hold on;
plot(xaxis,NNnorm(2:bins-1),'Color', total,'linewidth',1.5);
plot(xaxis,NN1norm(2:bins-1),'Color',sucrose,'linewidth',1.5);
plot(xaxis,NN2norm(2:bins-1),'Color',maltodextrin,'linewidth',1.5);
plot(xaxis,NN3norm(2:bins-1),'Color',water,'linewidth',1.5);
plot([Time1 Time1],[-1 1],':','color','k','linewidth',0.75);
plot([Time2 Time2],[-1 1],':','color','k','linewidth',0.75);
axis([xaxis(1) xaxis(end) 0 0.85]);
legend('Total','Suc > mal & wat','Mal > suc & wat','Water > suc & mal','Location','northeast')
ylabel('Fraction of population');
xlabel('Seconds from RD');
title('Reward-selective neurons');


%% plotting sucrose-selective neurons

%color map
[magma,inferno,plasma,viridis]=colormaps;
colormap(plasma);
c=[-100 2000];ClimE=sign(c).*abs(c).^(1/4);%colormap

%events we're looking at
RD1=strcmp('RD1', R_3R.Erefnames);
RD2=strcmp('RD2', R_3R.Erefnames);
RD3=strcmp('RD3', R_3R.Erefnames);

%setting up parameters
Xaxis=[-2 5];
inttime=find(R_3R.Param.Tm>=Xaxis(1) & R_3R.Param.Tm<=Xaxis(2));
paramtime=R_3R.Param.Tm(inttime);

%find all neurons with greater firing for sucrose
for i = 1:(Bin2-Bin1+1)
    %the added +1 is necessary because bin 20 is the 21st entry in the matrix
    Pref1(:,i)=R_3R.BinRewPref{Bin1+i}==1; %get neurons that have greater firing for sucrose in any of the bins bounded above
    Resp11(:,i)=Pref1(:,i)&cat(1,R_3R.BinStatRwrd{Bin1+i,1}.SucRespDir)==1; %get neurons with excitation to sucrose
    Resp12(:,i)=Pref1(:,i)&cat(1,R_3R.BinStatRwrd{Bin1+i,1}.MalRespDir)==1;%get neurons with inhibition to maltodextrin
    Resp13(:,i)=Pref1(:,i)&cat(1,R_3R.BinStatRwrd{Bin1+i,1}.WatRespDir)==-1;%get neurons with inhibition to maltodextrin
end

Sel=sum(Pref1,2)>0;  %all neurons selective in any bin
Sel1=sum(Resp11,2)>0; %all selective neurons sucrose excited in any bin
Sel3=sum(Resp12,2)>0; %all selective neurons malto inhibited in any bin
Sel6=sum(Resp13,2)>0; %all selective neurons malto inhibited in any bin

subplot(2,4,5); %heatmap of suc preferring neurons' response to sucrose
Neurons=R_3R.Ev(RD1).PSTHz(Sel,inttime); %get the firing rates of neurons of interest
SucResp=cat(1,R_3R.BinStatRwrd{SortBin+1,1}.R1Mean); %sucrose responses
TMP=SucResp(Sel); %find the magnitude of the excitations for water for this binTMP(isnan(TMP))=0; %To place the neurons with no onset/duration/peak at the top of the color-coded map
[~,SORTimg]=sort(TMP);
Neurons=Neurons(SORTimg,:); %sort the neurons by magnitude
imagesc(paramtime,[1,sum(Sel,1)],Neurons,ClimE);
title(['Sucrose trials']);
xlabel('Seconds from RD');
ylabel('Individual neurons');
hold on;
plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);


subplot(2,4,6); %heatmap of suc preferring neurons' response to maltodextrin
Neurons=R_3R.Ev(RD2).PSTHz(Sel,inttime); %get the firing rates of neurons of interest
Neurons=Neurons(SORTimg,:); %sort the neurons same as before
imagesc(paramtime,[1,sum(Sel,1)],Neurons,ClimE);
title(['Maltodextrin trials']);
xlabel('Seconds from RD');
hold on;
plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);

subplot(2,4,7); %heatmap of suc preferring neurons' response to water
Neurons=R_3R.Ev(RD3).PSTHz(Sel,inttime); %get the firing rates of neurons of interest
Neurons=Neurons(SORTimg,:); %sort the neurons same as before
imagesc(paramtime,[1,sum(Sel,1)],Neurons,ClimE);
title(['Water trials']);
xlabel('Seconds from RD');
hold on;
plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);

%plot suc preferring neurons to suc
psthE=nanmean(R_3R.Ev(RD1).PSTHz(Sel,inttime),1); 
semE=nanste(R_3R.Ev(RD1).PSTHz(Sel,inttime),1); %calculate standard error of the mean
upE=psthE+semE;
downE=psthE-semE;

%plot suc preferring neurons to malt
psth2=nanmean(R_3R.Ev(RD2).PSTHz(Sel,inttime),1); 
sem2=nanste(R_3R.Ev(RD2).PSTHz(Sel,inttime),1); %calculate standard error of the mean
up2=psth2+sem2;
down2=psth2-sem2;

%plot suc preferring neurons to water
psth3=nanmean(R_3R.Ev(RD3).PSTHz(Sel,inttime),1); 
sem3=nanste(R_3R.Ev(RD3).PSTHz(Sel,inttime),1); %calculate standard error of the mean
up3=psth3+sem3;
down3=psth3-sem3;

%plotting
subplot(2,3,3);
hold on;
plot(paramtime,psthE,'Color',sucrose,'linewidth',1);
plot(paramtime,psth2,'Color',maltodextrin,'linewidth',1);
plot(paramtime,psth3,'Color',water,'linewidth',1);
patch([paramtime,paramtime(end:-1:1)],[upE,downE(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5);
patch([paramtime,paramtime(end:-1:1)],[up2,down2(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5);
patch([paramtime,paramtime(end:-1:1)],[up3,down3(end:-1:1)],water,'EdgeColor','none');alpha(0.5);
legend('Suc trials','Mal trials','Wat trials');
plot([-2 5],[0 0],':','color','k','linewidth',0.75);
plot([0 0],[-2 8],':','color','k','linewidth',0.75);
axis([-2 5 -2 4.7]);
ylabel('Mean firing (z-score)');
title(['Suc>mal&wat (n=' num2str(sum(Sel)) ' of ' num2str(length(Sel)) ')'])
xlabel('Seconds from RD');

G = sum(Sel1&Sel3&Sel6);
F = sum(Sel3&Sel6);
E = sum(Sel1&Sel6);
D = sum(Sel1&Sel3);
A = sum(Sel1);
B = sum(Sel3);
C = sum(Sel6);

%vertical venn diagram
x = [0 0 1 1];
y1 = [C-E C-E+A C-E+A C-E];
y2 = [C-F C-F+B C-F+B C-F];
y3 = [0 C C 0];

subplot(2,35,64);
hold on;
s = patch(x,y1,sucrose);
m = patch(x,y2,maltodextrin);
w = patch(x,y3,water);
alpha(s,0.7);
alpha(w,0.5);
alpha(m,0.5);
set(gca,'xtick',[]);
ylabel('Distribution of neurons');
axis([0 1 0 C-E+A]);


%% stats on reward firing averaged together
%for simplicity, just looking at average activity in sort bin
%because I already have that data collected

SucResp=cat(1,R_3R.BinStatRwrd{SortBin+1,1}.R1Mean); %suc responses
MalResp=cat(1,R_3R.BinStatRwrd{SortBin+1,1}.R2Mean); %mal responses
WatResp=cat(1,R_3R.BinStatRwrd{SortBin+1,1}.R3Mean); %wat responses

[~,R_3R.RewRespStat{1,1},R_3R.RewRespStat{1,2}]=anovan(cat(1,SucResp(Sel),MalResp(Sel),WatResp(Sel)),{cat(1,zeros(sum(Sel),1),ones(sum(Sel),1),2*ones(sum(Sel),1)),cat(1,R_3R.Ninfo(Sel,4),R_3R.Ninfo(Sel,4),R_3R.Ninfo(Sel,4))},'varnames',{'Reward','Subject'},'random',2,'Display','off','model','full');
R_3R.RewRespStat{1,3}=multcompare(R_3R.RewRespStat{1,2},'display','off');

save('R_3R.mat','R_3R');