%look at impact of previous trials on Cue and PE %Looking at the average firing rate for a given window in each of 4 %current/previous reward conditions clear all; load ('R_2R.mat'); load ('RAW.mat'); %run linear model and stats? 1 is yes, 0 is no runanalysis=1; %divide neurons up by region NAneurons=strcmp(R_2R.Ninfo(:,3),'NA'); VPneurons=strcmp(R_2R.Ninfo(:,3),'VP'); %get parameters trialsback=6; %how many trials back to look PEBaseline=R_2R.Param.BinBase+0.5; %For normalizing activity CueBaseline=[-11 -1]; CueWindow=[0 0.5]; %what window of activity is analyzed PEWindow=[-0.6 0.7]; BinDura=R_2R.Param.BinDura; bins=R_2R.Param.bins; binint=R_2R.Param.binint; binstart=R_2R.Param.binstart; %sorting bin -- which bin the neurons' activity is sorted on for heatmap(in seconds) SortBinTime=1; %seconds SortBin=(((SortBinTime-BinDura(2)/2)-binstart)/binint); %convert to bin name %reset NN=0; PEEvMeanz=0; if runanalysis==1 for i=1:length(RAW) %loops through sessions if strcmp('NA',RAW(i).Type(1:2)) | strcmp('VP',RAW(i).Type(1:2)) %only look at suc v mal sessions %events EV3=strmatch('CueP1',RAW(i).Einfo(:,2),'exact'); EV4=strmatch('CueP2',RAW(i).Einfo(:,2),'exact'); EV1=strmatch('PEP1',RAW(i).Einfo(:,2),'exact'); EV2=strmatch('PEP2',RAW(i).Einfo(:,2),'exact'); Cue=strmatch('Cue',RAW(i).Einfo(:,2),'exact'); PE=strmatch('PECue',RAW(i).Einfo(:,2),'exact'); R1=strmatch('Reward1Deliv',RAW(i).Einfo(:,2),'exact'); R2=strmatch('Reward2Deliv',RAW(i).Einfo(:,2),'exact'); %% linear model for impact of previous rewards %reset Xcue=[]; Xpe=[]; Y=[]; %set up the matrix with outcome identity for each session rewards1=cat(2,RAW(i).Erast{R1,1}(:,1),ones(length(RAW(i).Erast{R1,1}(:,1)),1)); rewards2=cat(2,RAW(i).Erast{R2,1}(:,1),zeros(length(RAW(i).Erast{R2,1}(:,1)),1)); rewards=cat(1,rewards1,rewards2); [rewards(:,1),ind]=sort(rewards(:,1)); rewards(:,2)=rewards(ind,2); %cue firstcue=find(RAW(i).Erast{Cue,1}==min(RAW(i).Erast{Cue,1}(RAW(i).Erast{Cue,1}(:,1)>rewards(trialsback),1))); %find first cue with at least 5 rewards prior for k=firstcue+1:length(RAW(i).Erast{Cue,1}(:,1)) time=RAW(i).Erast{Cue,1}(k,1); entry=find(rewards==max(rewards(rewards(:,1)CueBaseline(1)); end Bhz=basespk/(CueBaseline(1,2)-CueBaseline(1,1)); Bmean=nanmean(Bhz); Bstd=nanstd(Bhz); %get trial by trial firing rate for all cue trials [EVcell,EVn]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{Cue},CueWindow,{2});% makes trial by trial rasters for event for y= 1:EVn rewspk(y,1)=sum(EVcell{1,y}>CueWindow(1)); end Y=((rewspk(trialsback+1:end,1)/(CueWindow(1,2)-CueWindow(1,1)))-Bmean)/Bstd; %normalize the activity to baseline %true data linmdl{NN,1}=fitlm(Xcue,Y); R_2R.RewHist.LinMdlCueWeights(NN,1:trialsback)=linmdl{NN,1}.Coefficients.Estimate(2:trialsback+1)'; R_2R.RewHist.LinMdlCuePVal(NN,1:trialsback)=linmdl{NN,1}.Coefficients.pValue(2:trialsback+1)'; %shuffled YSh=Y(randperm(length(Y))); linmdlSh{NN,1}=fitlm(Xcue,YSh); R_2R.RewHist.LinMdlCueWeightsSh(NN,1:trialsback)=linmdlSh{NN,1}.Coefficients.Estimate(2:trialsback+1)'; R_2R.RewHist.LinMdlCuePValSh(NN,1:trialsback)=linmdlSh{NN,1}.Coefficients.pValue(2:trialsback+1)'; %PE rewspk=0; basespk=0; %get mean baseline firing for all PEs [Bcell1,B1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{PE},PEBaseline,{2});% makes trial by trial rasters for baseline for y= 1:B1n basespk(1,y)=sum(Bcell1{1,y}>PEBaseline(1)); end Bhz=basespk/(PEBaseline(1,2)-PEBaseline(1,1)); Bmean=nanmean(Bhz); Bstd=nanstd(Bhz); %get trial by trial firing rate for all PE trials [EVcell,EVn]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{PE},PEWindow,{2});% makes trial by trial rasters for event for y= 1:EVn rewspk(y,1)=sum(EVcell{1,y}>PEWindow(1)); end Y=((rewspk(trialsback+1:end,1)/(PEWindow(1,2)-PEWindow(1,1)))-Bmean)/Bstd; %normalize the activity to baseline %true data linmdl{NN,1}=fitlm(Xpe,Y); R_2R.RewHist.LinMdlPEWeights(NN,1:trialsback)=linmdl{NN,1}.Coefficients.Estimate(2:trialsback+1)'; R_2R.RewHist.LinMdlPEPVal(NN,1:trialsback)=linmdl{NN,1}.Coefficients.pValue(2:trialsback+1)'; %shuffled YSh=Y(randperm(length(Y))); linmdlSh{NN,1}=fitlm(Xpe,YSh); R_2R.RewHist.LinMdlPEWeightsSh(NN,1:trialsback)=linmdlSh{NN,1}.Coefficients.Estimate(2:trialsback+1)'; R_2R.RewHist.LinMdlPEPValSh(NN,1:trialsback)=linmdlSh{NN,1}.Coefficients.pValue(2:trialsback+1)'; %% stats comparing effect of current and previous reward on PE %resetting Bcell=0; EV1spk=0; EV2spk=0; EV1norm=0; EV2norm=0; %get mean baseline firing for all PE trials [Bcell1,B1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV1},PEBaseline,{2});% makes trial by trial rasters for baseline for y= 1:B1n Bcell(1,y)=sum(Bcell1{1,y}>PEBaseline(1)); end [Bcell2,B2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV2},PEBaseline,{2});% makes trial by trial rasters for baseline for z= 1:B2n Bcell(1,z+B1n)=sum(Bcell2{1,z}>PEBaseline(1)); end Bhz=Bcell/(PEBaseline(1,2)-PEBaseline(1,1)); Bmean=nanmean(Bhz); Bstd=nanstd(Bhz); %get trial by trial firing rate for post suc trials [EV1cell,EV1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV1},PEWindow,{2});% makes trial by trial rasters for event for y= 1:EV1n EV1spk(1,y)=sum(EV1cell{1,y}>PEWindow(1)); end EV1hz=EV1spk/(PEWindow(1,2)-PEWindow(1,1)); for x= 1:EV1n EV1norm(1,x)=((EV1hz(1,x)-Bmean)/Bstd); end %get trial by trial firing rate for post mal trials [EV2cell,EV2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV2},PEWindow,{2});% makes trial by trial rasters for event for y= 1:EV2n EV2spk(1,y)=sum(EV2cell{1,y}>PEWindow(1)); end EV2hz=EV2spk/(PEWindow(1,2)-PEWindow(1,1)); for x= 1:EV2n EV2norm(1,x)=((EV2hz(1,x)-Bmean)/Bstd); end PEEvMeanz(NN,1)=nanmean(EV1norm); PEEvMeanz(NN,2)=nanmean(EV2norm); %% stats comparing effect of current and previous reward on cue %resetting Bcell=0; EV1spk=0; EV2spk=0; EV1norm=0; EV2norm=0; %get mean baseline firing for all cue trials [Bcell1,B1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV3},CueBaseline,{2});% makes trial by trial rasters for baseline for y= 1:B1n Bcell(1,y)=sum(Bcell1{1,y}>CueBaseline(1)); end [Bcell2,B2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV4},CueBaseline,{2});% makes trial by trial rasters for baseline for z= 1:B2n Bcell(1,z+B1n)=sum(Bcell2{1,z}>CueBaseline(1)); end Bhz=Bcell/(CueBaseline(1,2)-CueBaseline(1,1)); Bmean=nanmean(Bhz); Bstd=nanstd(Bhz); %get trial by trial firing rate for post suc trials [EV1cell,EV1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV3},CueWindow,{2});% makes trial by trial rasters for event for y= 1:EV1n EV1spk(1,y)=sum(EV1cell{1,y}>CueWindow(1)); end EV1hz=EV1spk/(CueWindow(1,2)-CueWindow(1,1)); for x= 1:EV1n EV1norm(1,x)=((EV1hz(1,x)-Bmean)/Bstd); end %get trial by trial firing rate for post mal trials [EV2cell,EV2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV4},CueWindow,{2});% makes trial by trial rasters for event for y= 1:EV2n EV2spk(1,y)=sum(EV2cell{1,y}>CueWindow(1)); end EV2hz=EV2spk/(CueWindow(1,2)-CueWindow(1,1)); for x= 1:EV2n EV2norm(1,x)=((EV2hz(1,x)-Bmean)/Bstd); end CueEvMeanz(NN,1)=nanmean(EV1norm); CueEvMeanz(NN,2)=nanmean(EV2norm); fprintf('Neuron # %d\n',NN); end end R_2R.RewHist.PrevRewPEMeanz=PEEvMeanz; R_2R.RewHist.PrevRewCueMeanz=CueEvMeanz; end end %% which neurons to look at for stats and plotting? % Sel=R_2R.SucN | R_2R.MalN; %only look at reward-selective neurons Sel=NAneurons | VPneurons; %look at all neurons %Sel=R_2R.RewHist.LinMdlPVal(:,2)<0.1; %only neurons with significant impact of previous trial %Sel=R_2R.RewHist.LinMdlPEWeights(:,2)<-1; %only neurons with strong negative impact of previous trial %% ANOVAs %setup and run ANOVA for effects of current reward, previous reward, and region on reward firing PrevRew=cat(2,zeros(length(NAneurons),1),ones(length(NAneurons),1)); Region=cat(2,NAneurons,NAneurons); Rat=cat(2,R_2R.Ninfo(:,4),R_2R.Ninfo(:,4)); %to look at a specific selection of cells PEEvMeanz=R_2R.RewHist.PrevRewPEMeanz(Sel,:); CueEvMeanz=R_2R.RewHist.PrevRewCueMeanz(Sel,:); PrevRew=PrevRew(Sel,:); Region=Region(Sel,:); Rat=Rat(Sel,:); %cue %each region individually [~,R_2R.RewHist.PrevRewStatsCueVPSubj{1,1},R_2R.RewHist.PrevRewStatsCueVPSubj{1,2}]=anovan(reshape(CueEvMeanz(VPneurons,:),[sum(VPneurons)*2 1]),{reshape(PrevRew(VPneurons,:),[sum(VPneurons)*2 1]),reshape(Rat(VPneurons,:),[sum(VPneurons)*2 1])},'varnames',{'Previous Reward','Rat'},'random',[2],'display','off','model','full'); [~,R_2R.RewHist.PrevRewStatsCueNASubj{1,1},R_2R.RewHist.PrevRewStatsCueNASubj{1,2}]=anovan(reshape(CueEvMeanz(NAneurons,:),[sum(NAneurons)*2 1]),{reshape(PrevRew(NAneurons,:),[sum(NAneurons)*2 1]),reshape(Rat(NAneurons,:),[sum(NAneurons)*2 1])},'varnames',{'Previous Reward','Rat'},'random',[2],'display','off','model','full'); %region comparison [~,R_2R.RewHist.PrevRewStatsCueSubj{1,1},R_2R.RewHist.PrevRewStatsCueSubj{1,2}]=anovan(CueEvMeanz(:),{PrevRew(:),Region(:),Rat(:)},'varnames',{'Previous Reward','Region','Rat'},'random',[3],'nested',[0 0 0;0 0 0;0 1 0],'display','off','model','full'); %pe %each region individually [~,R_2R.RewHist.PrevRewStatsPEVPSubj{1,1},R_2R.RewHist.PrevRewStatsPEVPSubj{1,2}]=anovan(reshape(PEEvMeanz(VPneurons,:),[sum(VPneurons)*2 1]),{reshape(PrevRew(VPneurons,:),[sum(VPneurons)*2 1]),reshape(Rat(VPneurons,:),[sum(VPneurons)*2 1])},'varnames',{'Previous Reward','Rat'},'random',[2],'display','off','model','full'); [~,R_2R.RewHist.PrevRewStatsPENASubj{1,1},R_2R.RewHist.PrevRewStatsPENASubj{1,2}]=anovan(reshape(PEEvMeanz(NAneurons,:),[sum(NAneurons)*2 1]),{reshape(PrevRew(NAneurons,:),[sum(NAneurons)*2 1]),reshape(Rat(NAneurons,:),[sum(NAneurons)*2 1])},'varnames',{'Previous Reward','Rat'},'random',[2],'display','off','model','full'); %region comparison [~,R_2R.RewHist.PrevRewStatsPESubj{1,1},R_2R.RewHist.PrevRewStatsPESubj{1,2}]=anovan(PEEvMeanz(:),{PrevRew(:),Region(:),Rat(:)},'varnames',{'Previous Reward','Region','Rat'},'random',[3],'nested',[0 0 0;0 0 0;0 1 0],'display','off','model','full'); %setup and run ANOVA for effects of shuffle, trial, and region on coefficient Trial=[]; Region=[]; Rat=[]; for i=1:trialsback Trial=cat(2,Trial,i*ones(length(NAneurons),1)); Region=cat(2,Region,NAneurons); Rat=cat(2,Rat,R_2R.Ninfo(:,4)); end Trial=cat(2,Trial,Trial); Region=cat(2,Region,Region); Rat=cat(2,Rat,Rat); Shuffd=cat(2,zeros(length(NAneurons),trialsback),ones(length(NAneurons),trialsback)); %Cue Coeffs=cat(2,R_2R.RewHist.LinMdlCueWeights(:,1:trialsback),R_2R.RewHist.LinMdlCueWeightsSh(:,1:trialsback)); [~,R_2R.RewHist.LinCoeffStatsCueVPSubj{1,1},R_2R.RewHist.LinCoeffStatsCueVPSubj{1,2}]=anovan(reshape(Coeffs(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1]),{reshape(Shuffd(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1]),reshape(Trial(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1]),reshape(Rat(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1])},'varnames',{'Shuffd','Trial','Subject'},'random',[3],'display','off','model','full'); [~,R_2R.RewHist.LinCoeffStatsCueNASubj{1,1},R_2R.RewHist.LinCoeffStatsCueNASubj{1,2}]=anovan(reshape(Coeffs(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1]),{reshape(Shuffd(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1]),reshape(Trial(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1]),reshape(Rat(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1])},'varnames',{'Shuffd','Trial','Subject'},'random',[3],'display','off','model','full'); %PE Coeffs=cat(2,R_2R.RewHist.LinMdlPEWeights(:,1:trialsback),R_2R.RewHist.LinMdlPEWeightsSh(:,1:trialsback)); [~,R_2R.RewHist.LinCoeffStatsPEVPSubj{1,1},R_2R.RewHist.LinCoeffStatsPEVPSubj{1,2}]=anovan(reshape(Coeffs(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1]),{reshape(Shuffd(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1]),reshape(Trial(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1]),reshape(Rat(VPneurons,:),[sum(VPneurons)*2*(trialsback) 1])},'varnames',{'Shuffd','Trial','Subject'},'random',[3],'display','off','model','full'); [~,R_2R.RewHist.LinCoeffStatsPENASubj{1,1},R_2R.RewHist.LinCoeffStatsPENASubj{1,2}]=anovan(reshape(Coeffs(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1]),{reshape(Shuffd(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1]),reshape(Trial(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1]),reshape(Rat(NAneurons,:),[sum(NAneurons)*2*(trialsback) 1])},'varnames',{'Shuffd','Trial','Subject'},'random',[3],'display','off','model','full'); %% plotting XaxisCue=[-0.5 1]; XaxisPE=[-1 2]; Ishow=find(R_2R.Param.Tm>=XaxisCue(1) & R_2R.Param.Tm<=XaxisCue(2)); Ushow=find(R_2R.Param.Tm>=XaxisPE(1) & R_2R.Param.Tm<=XaxisPE(2)); time1=R_2R.Param.Tm(Ishow); time2=R_2R.Param.Tm(Ushow); %color map [magma,inferno,plasma,viridis]=colormaps; colormap(plasma); c=[-100 2000];ClimE=sign(c).*abs(c).^(1/4);%colormap %colors sucrose=[.95 0.55 0.15]; maltodextrin=[.9 0.3 .9]; water=[0.00 0.75 0.75]; total=[0.3 0.1 0.8]; inh=[0.1 0.021154 0.6]; exc=[0.9 0.75 0.205816]; NAc=[0.5 0.1 0.8]; VP=[0.3 0.7 0.1]; %extra colors to make a gradient sucrosem=[.98 0.8 0.35]; sucrosel=[1 1 0.4]; maltodextrinm=[1 0.75 1]; maltodextrinl=[1 0.8 1]; RD1P1=strcmp('CueP1', R_2R.Erefnames); RD1P2=strcmp('CueP2', R_2R.Erefnames); RD2P1=strcmp('PEP1', R_2R.Erefnames); RD2P2=strcmp('PEP2', R_2R.Erefnames); %% Get mean firing according to previous trial and then plot %NAc %plot suc after suc psth1=nanmean(R_2R.Ev(RD1P1).PSTHz(Sel&NAneurons,Ishow),1); sem1=nanste(R_2R.Ev(RD1P1).PSTHz(Sel&NAneurons,Ishow),1); %calculate standard error of the mean up1=psth1+sem1; down1=psth1-sem1; %plot suc after malt psth2=nanmean(R_2R.Ev(RD1P2).PSTHz(Sel&NAneurons,Ishow),1); sem2=nanste(R_2R.Ev(RD1P2).PSTHz(Sel&NAneurons,Ishow),1); %calculate standard error of the mean up2=psth2+sem2; down2=psth2-sem2; %make the plot subplot(2,4,1); hold on; title(['Cue response, NAc']) plot(time1,psth1,'Color',sucrose,'linewidth',1); plot(time1,psth2,'Color',maltodextrin,'linewidth',1); patch([time1,time1(end:-1:1)],[up2,down2(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5); patch([time1,time1(end:-1:1)],[up1,down1(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5); plot([-2 5],[0 0],':','color','k','linewidth',0.75); plot([0 0],[-2 8],':','color','k','linewidth',0.75); plot([CueWindow(1) CueWindow(1)],[-2 8],'color','b','linewidth',0.85); plot([CueWindow(2) CueWindow(2)],[-2 8],'color','b','linewidth',0.85); axis([XaxisCue(1) XaxisCue(2) min(down2)-0.15 max(up1)+0.2]); ylabel('Mean firing (z-score)'); xlabel('Seconds from cue'); legend('Post-suc','Post-mal','location','northeast'); if cell2mat(R_2R.RewHist.PrevRewStatsCueNASubj{1,1}(2,7))<0.05 plot(CueWindow(1)+(CueWindow(2)-CueWindow(1))/2,max(up2)+0.1,'*','color','k','markersize',13); end subplot(2,4,5); hold on; title('PE response, NAc'); %plot malt after suc psth3=nanmean(R_2R.Ev(RD2P1).PSTHz(Sel&NAneurons,Ushow),1); sem3=nanste(R_2R.Ev(RD2P1).PSTHz(Sel&NAneurons,Ushow),1); %calculate standard error of the mean up3=psth3+sem3; down3=psth3-sem3; %plot malt after malt psth4=nanmean(R_2R.Ev(RD2P2).PSTHz(Sel&NAneurons,Ushow),1); sem4=nanste(R_2R.Ev(RD2P2).PSTHz(Sel&NAneurons,Ushow),1); %calculate standard error of the mean up4=psth4+sem4; down4=psth4-sem4; plot(time2,psth3,'Color',sucrose,'linewidth',1); plot(time2,psth4,'Color',maltodextrin,'linewidth',1); patch([time2,time2(end:-1:1)],[up3,down3(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5); patch([time2,time2(end:-1:1)],[up4,down4(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5); plot([-2 5],[0 0],':','color','k','linewidth',0.75); plot([0 0],[-2 8],':','color','k','linewidth',0.75); plot([PEWindow(1) PEWindow(1)],[-2 8],'color','b','linewidth',0.85); plot([PEWindow(2) PEWindow(2)],[-2 8],'color','b','linewidth',0.85); axis([XaxisPE(1) XaxisPE(2) min(down3)-0.2 max(up4)+0.2]); ylabel('Mean firing (z-score)'); xlabel('Seconds from PE'); legend('Post-suc','Post-mal','location','northeast'); if cell2mat(R_2R.RewHist.PrevRewStatsPENASubj{1,1}(2,7))<0.05 plot(PEWindow(1)+(PEWindow(2)-PEWindow(1))/2,max(up3)+0.1,'*','color','k','markersize',13); end %VP %plot suc after suc psth1=nanmean(R_2R.Ev(RD1P1).PSTHz(Sel&VPneurons,Ishow),1); sem1=nanste(R_2R.Ev(RD1P1).PSTHz(Sel&VPneurons,Ishow),1); %calculate standard error of the mean up1=psth1+sem1; down1=psth1-sem1; %plot suc after malt psth2=nanmean(R_2R.Ev(RD1P2).PSTHz(Sel&VPneurons,Ishow),1); sem2=nanste(R_2R.Ev(RD1P2).PSTHz(Sel&VPneurons,Ishow),1); %calculate standard error of the mean up2=psth2+sem2; down2=psth2-sem2; %make the plot subplot(2,4,2); title(['Cue response, VP']) hold on; plot(time1,psth1,'Color',sucrose,'linewidth',1); plot(time1,psth2,'Color',maltodextrin,'linewidth',1); patch([time1,time1(end:-1:1)],[up2,down2(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5); patch([time1,time1(end:-1:1)],[up1,down1(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5); plot([-2 5],[0 0],':','color','k','linewidth',0.75); plot([0 0],[-2 8],':','color','k','linewidth',0.75); plot([CueWindow(1) CueWindow(1)],[-2 8],'color','b','linewidth',0.85); plot([CueWindow(2) CueWindow(2)],[-2 8],'color','b','linewidth',0.85); axis([XaxisCue(1) XaxisCue(2) min(down2)-0.3 max(up1)+0.3]); ylabel('Mean firing (z-score)'); xlabel('Seconds from cue'); legend('Post-suc','Post-mal','location','northeast'); if cell2mat(R_2R.RewHist.PrevRewStatsCueVPSubj{1,1}(2,7))<0.05 plot(CueWindow(1)+(CueWindow(2)-CueWindow(1))/2,max(up2)+0.1,'*','color','k','markersize',13); end subplot(2,4,6); title('PE response, VP'); hold on; %plot malt after suc psth3=nanmean(R_2R.Ev(RD2P1).PSTHz(Sel&VPneurons,Ushow),1); sem3=nanste(R_2R.Ev(RD2P1).PSTHz(Sel&VPneurons,Ushow),1); %calculate standard error of the mean up3=psth3+sem3; down3=psth3-sem3; %plot malt after malt psth4=nanmean(R_2R.Ev(RD2P2).PSTHz(Sel&VPneurons,Ushow),1); sem4=nanste(R_2R.Ev(RD2P2).PSTHz(Sel&VPneurons,Ushow),1); %calculate standard error of the mean up4=psth4+sem4; down4=psth4-sem4; plot(time2,psth3,'Color',sucrose,'linewidth',1); plot(time2,psth4,'Color',maltodextrin,'linewidth',1); patch([time2,time2(end:-1:1)],[up3,down3(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5); patch([time2,time2(end:-1:1)],[up4,down4(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5); plot([-2 5],[0 0],':','color','k','linewidth',0.75); plot([0 0],[-2 8],':','color','k','linewidth',0.75); plot([PEWindow(1) PEWindow(1)],[-2 8],'color','b','linewidth',0.85); plot([PEWindow(2) PEWindow(2)],[-2 8],'color','b','linewidth',0.85); axis([XaxisPE(1) XaxisPE(2) min(down4)-0.3 max(up3)+0.3]); ylabel('Mean firing (z-score)'); xlabel('Seconds from PE'); legend('Post-suc','Post-mal','location','northeast'); if cell2mat(R_2R.RewHist.PrevRewStatsPEVPSubj{1,1}(2,7))<0.05 plot(PEWindow(1)+(PEWindow(2)-PEWindow(1))/2,max(up3)+0.1,'*','color','k','markersize',13); end %% plot linear model coefficients %Plot all neurons Sel=NAneurons<2; %coefficients for each trial subplot(2,4,3); hold on; errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlCueWeights(Sel&NAneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlCueWeights(Sel&NAneurons,1:trialsback),1),'color',NAc); errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlCueWeights(Sel&VPneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlCueWeights(Sel&VPneurons,1:trialsback),1),'color',VP); errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlCueWeightsSh(Sel&NAneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlCueWeights(Sel&NAneurons,1:trialsback),1),'color','k'); errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlCueWeightsSh(Sel&VPneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlCueWeights(Sel&VPneurons,1:trialsback),1),'color','k'); xlabel('Trials back'); ylabel('Mean coefficient weight'); title('Linear model coefficients'); axis([0 trialsback+1 -0.5 1]); plot([-1 trialsback+1],[0 0],':','color','k','linewidth',0.75); legend('NAc','VP','Shuff'); %stats to check if VP and NAc are greater than chance R_2R.RewHist.LinCoeffCueMultComp=[]; [c,~,~,~]=multcompare(R_2R.RewHist.LinCoeffStatsCueNASubj{1,2},'dimension',[1,2],'display','off'); [d,~,~,~]=multcompare(R_2R.RewHist.LinCoeffStatsCueVPSubj{1,2},'dimension',[1,2],'display','off'); for i=1:trialsback %NAc vs shuff Cel=c(:,1)==2*(i-1)+1 & c(:,2)==2*(i-1)+2; if c(Cel,6)<0.05 R_2R.RewHist.LinCoeffCueMultComp(i,1)=1; else R_2R.RewHist.LinCoeffCueMultComp(i,1)=0; end R_2R.RewHist.LinCoeffCueMultComp(i,2)=c(Cel,2); %VP vs shuff Cel=d(:,1)==2*(i-1)+1 & d(:,2)==2*(i-1)+2; if d(Cel,6)<0.05 R_2R.RewHist.LinCoeffCueMultComp(i,3)=1; else R_2R.RewHist.LinCoeffCueMultComp(i,3)=0; end R_2R.RewHist.LinCoeffCueMultComp(i,4)=d(Cel,4); end plot([1:trialsback]-0.1,(R_2R.RewHist.LinCoeffCueMultComp(:,1)-0.5)*1.3,'*','color',NAc); %VP vs shuff plot([1:trialsback]+0.1,(R_2R.RewHist.LinCoeffCueMultComp(:,3)-0.5)*1.3,'*','color',VP); %NAc vs shuff %number of neurons with significant weights subplot(2,4,4); hold on; plot(1:trialsback,sum(R_2R.RewHist.LinMdlCuePVal(Sel&NAneurons,1:trialsback)<0.05,1)/sum(Sel&NAneurons),'color',NAc); plot(1:trialsback,sum(R_2R.RewHist.LinMdlCuePVal(Sel&VPneurons,1:trialsback)<0.05,1)/sum(Sel&VPneurons),'color',VP); plot(1:trialsback,sum(R_2R.RewHist.LinMdlCuePValSh(Sel&NAneurons,1:trialsback)<0.05,1)/sum(Sel&NAneurons),'color',NAc/3); plot(1:trialsback,sum(R_2R.RewHist.LinMdlCuePValSh(Sel&VPneurons,1:trialsback)<0.05,1)/sum(Sel&VPneurons),'color',VP/3); axis([0 trialsback+1 0 0.5]); xlabel('Trials back'); ylabel('Fraction of the population'); title('Outcome-modulated cue resp'); legend('NAc','VP','Shuff'); %Chi squared stat for each trial for i=1:trialsback [~,R_2R.RewHist.LinMdlCue2all(i,1),R_2R.RewHist.LinMdlCue2all(i,2)]=crosstab(cat(1,R_2R.RewHist.LinMdlCuePVal(Sel,i)<0.05,R_2R.RewHist.LinMdlCuePValSh(Sel,i)<0.05),cat(1,VPneurons,VPneurons+2)); [~,R_2R.RewHist.LinMdlCue2region(i,1),R_2R.RewHist.LinMdlCue2region(i,2)]=crosstab(R_2R.RewHist.LinMdlCuePVal(Sel,i)<0.05,VPneurons); end %plot([1:trialsback]-0.1,(R_2R.RewHist.LinMdlCue2all(:,2)<0.05)-0.52,'*','color',NAc); plot([1:trialsback],(R_2R.RewHist.LinMdlCue2region(:,2)<0.05&R_2R.RewHist.LinMdlCue2all(:,2)<0.05)-0.52,'*','color','k'); %% PE %coefficients for each trial subplot(2,4,7); hold on; errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlPEWeights(Sel&NAneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlPEWeights(Sel&NAneurons,1:trialsback),1),'color',NAc); errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlPEWeights(Sel&VPneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlPEWeights(Sel&VPneurons,1:trialsback),1),'color',VP); errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlPEWeightsSh(Sel&NAneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlPEWeights(Sel&NAneurons,1:trialsback),1),'color','k'); errorbar(1:trialsback,nanmean(R_2R.RewHist.LinMdlPEWeightsSh(Sel&VPneurons,1:trialsback),1),nanste(R_2R.RewHist.LinMdlPEWeights(Sel&VPneurons,1:trialsback),1),'color','k'); xlabel('Trials back'); ylabel('Mean coefficient weight'); title('Linear model coefficients'); axis([0 trialsback+1 -0.5 1]); plot([-1 trialsback+1],[0 0],':','color','k','linewidth',0.75); legend('NAc','VP','Shuff'); %stats to check if VP and NAc are greater than chance R_2R.RewHist.LinCoeffPEMultComp=[]; [c,~,~,~]=multcompare(R_2R.RewHist.LinCoeffStatsPENASubj{1,2},'dimension',[1,2],'display','off'); [d,~,~,~]=multcompare(R_2R.RewHist.LinCoeffStatsPEVPSubj{1,2},'dimension',[1,2],'display','off'); for i=1:trialsback %NAc vs shuff Cel=c(:,1)==2*(i-1)+1 & c(:,2)==2*(i-1)+2; if c(Cel,6)<0.05 R_2R.RewHist.LinCoeffPEMultComp(i,1)=1; else R_2R.RewHist.LinCoeffPEMultComp(i,1)=0; end R_2R.RewHist.LinCoeffPEMultComp(i,2)=c(Cel,2); %VP vs shuff Cel=d(:,1)==2*(i-1)+1 & d(:,2)==2*(i-1)+2; if d(Cel,6)<0.05 R_2R.RewHist.LinCoeffPEMultComp(i,3)=1; else R_2R.RewHist.LinCoeffPEMultComp(i,3)=0; end R_2R.RewHist.LinCoeffPEMultComp(i,4)=d(Cel,4); end plot([1:trialsback]-0.1,(R_2R.RewHist.LinCoeffPEMultComp(:,1)-0.5)*1.3,'*','color',NAc); %VP vs shuff plot([1:trialsback]+0.1,(R_2R.RewHist.LinCoeffPEMultComp(:,3)-0.5)*1.3,'*','color',VP); %NAc vs shuff %number of neurons with significant weights subplot(2,4,8); hold on; plot(1:trialsback,sum(R_2R.RewHist.LinMdlPEPVal(Sel&NAneurons,1:trialsback)<0.05,1)/sum(Sel&NAneurons),'color',NAc); plot(1:trialsback,sum(R_2R.RewHist.LinMdlPEPVal(Sel&VPneurons,1:trialsback)<0.05,1)/sum(Sel&VPneurons),'color',VP); plot(1:trialsback,sum(R_2R.RewHist.LinMdlPEPValSh(Sel&NAneurons,1:trialsback)<0.05,1)/sum(Sel&NAneurons),'color',NAc/3); plot(1:trialsback,sum(R_2R.RewHist.LinMdlPEPValSh(Sel&VPneurons,1:trialsback)<0.05,1)/sum(Sel&VPneurons),'color',VP/3); axis([0 trialsback+1 0 0.5]); xlabel('Trials back'); ylabel('Fraction of the population'); title('Outcome-modulated PE resp'); legend('NAc','VP','Shuff'); %Chi squared stat for each trial for i=1:trialsback [~,R_2R.RewHist.LinMdlPEX2all(i,1),R_2R.RewHist.LinMdlPEX2all(i,2)]=crosstab(cat(1,R_2R.RewHist.LinMdlPEPVal(Sel,i)<0.05,R_2R.RewHist.LinMdlPEPValSh(Sel,i)<0.05),cat(1,VPneurons,VPneurons+2)); [~,R_2R.RewHist.LinMdlPEX2region(i,1),R_2R.RewHist.LinMdlPEX2region(i,2)]=crosstab(R_2R.RewHist.LinMdlPEPVal(Sel,i)<0.05,VPneurons); end %plot([0:trialsback]-0.1,(R_2R.RewHist.LinMdlPE2all(:,2)<0.05)-0.52,'*','color',NAc); plot([1:trialsback],(R_2R.RewHist.LinMdlPEX2region(:,2)<0.05&R_2R.RewHist.LinMdlPEX2all(:,2)<0.05)-0.52,'*','color','k'); %% stats comparing PE coefficient weight in first 2 trials in selective and non-selective neurons in VP Sel=R_2R.SucN | R_2R.MalN; NSel=(R_2R.SucN | R_2R.MalN) == 0; [~,R_2R.RewHist.SelectiveHistory{1,1},R_2R.RewHist.SelectiveHistory{1,2}]=anovan(cat(1,R_2R.RewHist.LinMdlPEWeights(Sel&VPneurons,1),R_2R.RewHist.LinMdlPEWeights(NSel&VPneurons,1),R_2R.RewHist.LinMdlPEWeights(Sel&VPneurons,2),R_2R.RewHist.LinMdlPEWeights(NSel&VPneurons,2)),... {cat(1,ones(sum(Sel&VPneurons),1),zeros(sum(NSel&VPneurons),1),ones(sum(Sel&VPneurons),1),zeros(sum(NSel&VPneurons),1)),cat(1,ones(sum(VPneurons),1),zeros(sum(VPneurons),1)),... cat(1,R_2R.Ninfo(Sel&VPneurons,4),R_2R.Ninfo(NSel&VPneurons,4),R_2R.Ninfo(Sel&VPneurons,4),R_2R.Ninfo(NSel&VPneurons,4))},'varnames',{'Selective','Trial','Subject'},'random',3,'model','full','display','off'); save('R_2R.mat','R_2R');