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Copy pathdrgCaImAnBatchMultiSessionLicksv2.m
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drgCaImAnBatchMultiSessionLicksv2.m
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function drgCaImAnBatchMultiSessionLicksv2(choiceBatchPathName,choiceFileName)
%This code does a lick analysis for sessions where we record the behavior and licks
%for optogenetic modulation of behavior/licks by turning MLIs with
%optogenetics
% Needs a choices file such as
% drgCaImAn_multichoices_PVhM4Di_all_files_03262020.m
% Needs the output files from drgCaImAn_batch_dropc_no_microscope.m
%
%Used display_choice=2
warning('off')
close all
clear all
display_choice=2;
lick_threshold=80; %This is the threshold to exclude the runs where Ming was adding water manually, this was set at 20
t_odor_on=0;
t_odor_off=4;
min_trials=20; %Number of trials for the sliding window for percent correct
tic
if nargin==0
[choiceFileName,choiceBatchPathName] = uigetfile({'drgCaImAn_multichoices*.m'},'Select the .m file with all the choices for analysis');
end
fprintf(1, ['\ndrgCaImAnBatchMultiSessionLicks run for ' choiceFileName '\n\n']);
addpath(choiceBatchPathName)
eval(['handles=' choiceFileName(1:end-2) ';'])
handles.choiceFileName=choiceFileName;
handles.choiceBatchPathName=choiceBatchPathName;
caimanhandles=handles;
mice_included=unique(caimanhandles.caimandr_choices.mouse);
for ii_mouse=1:length(mice_included)
if mice_included(ii_mouse)~=0
for filNum=1:caimanhandles.caimandr_choices.no_files
if caimanhandles.caimandr_choices.mouse(filNum)~=0
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_smtrials=0;
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_sptrials=0;
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).splicks=zeros(179,1000);
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).smlicks=zeros(179,1000);
end
end
for hM4DNo=1:4
caimanhandles.per_mouse(mice_included(ii_mouse)).hM4D(hM4DNo).no_sptrials=0;
caimanhandles.per_mouse(mice_included(ii_mouse)).hM4D(hM4DNo).no_smtrials=0;
end
end
end
% szwins=size(caimanhandles.caimandr_choices.wins);
%Read the files and calculate the dF/F in each window
per_file_lick_freq_sp=[];
per_file_lick_freq_sm=[];
per_file_lick_log10_p_val=[];
no_files_included=0;
per_file_hM4D=[];
per_file_mouse=[];
per_file_trial_window=[];
per_file_decision_time=[];
per_file_decision_time_reinf=[];
per_file_fractional_lick_time_on_sp=[];
per_file_fractional_lick_time_on_sm=[];
for filNum=1:caimanhandles.caimandr_choices.no_files
num_odor_trials=0;
epochs_per_trial=[];
num_odor_trials_dFF=0;
all_lda_events=[];
all_lda_events_miss_FA=[];
all_opto_on_per_trial=[];
lick_times=[];
no_licks=[];
dLickTraces=[];
handles_outs.lick_slopes=[];
handles_outs.dFF_slopes=[];
handles_outs.no_lick_slopes=0;
handles_outs.no_dFF_slopes=0;
handles_outs.no_vels=0;
handles_outs.velocities=[];
handles_outs.no_accels=0;
handles_outs.acceleration=[];
handles_outs.epochs_per_trial=[];
handles_outs.perCorr=[];
%Variables for optical flow
skip_ii=19;
baseline_ii=200;
%Read the CalmAn_batch file
if isfile([caimanhandles.caimandr_choices.pathName{filNum} caimanhandles.caimandr_choices.fileName{filNum}])
load([caimanhandles.caimandr_choices.pathName{filNum} caimanhandles.caimandr_choices.fileName{filNum}])
if no_odor_trials>20
first_num_odor_trials(filNum)=num_odor_trials+1;
for trNo=1:no_odor_trials
%Save epoch
num_odor_trials=num_odor_trials+1;
all_lda_events{num_odor_trials}=lda_event{trNo};
all_opto_on_per_trial(num_odor_trials)=opto_on_per_trial(trNo);
if epoch_per_trial(trNo)==6
%Hit
epochs_per_trial(1,num_odor_trials)=1;
epochs_per_trial(2:4,num_odor_trials)=0;
all_lda_events_miss_FA(num_odor_trials)=1;
if sum(which_trial_Hit==trNo)>0
%Save the licks for this trial
this_Hitii_lick=find(which_trial_Hit==trNo,1);
these_Hitii_lick_times=[];
these_Hitii_lick_times=Hit_lick_times(this_Hitii_lick,1:Hit_no_lick_times(this_Hitii_lick));
if ~isempty(these_Hitii_lick_times)
lick_times(num_odor_trials,1:length(these_Hitii_lick_times))=these_Hitii_lick_times;
no_licks(num_odor_trials)=length(these_Hitii_lick_times);
else
no_licks(num_odor_trials)=0;
end
dLickTraces(num_odor_trials,:)=dHit_lick_traces(this_Hitii_lick,:);
epochs_per_trial_dFF(num_odor_trials)=1;
end
end
if epoch_per_trial(trNo)==7
%Miss
epochs_per_trial(2,num_odor_trials)=1;
epochs_per_trial(1,num_odor_trials)=0;
epochs_per_trial(3:4,num_odor_trials)=0;
all_lda_events_miss_FA(num_odor_trials)=2;
if sum(which_trial_Miss==trNo)>0
%Save lick times
this_Missii_lick=find(which_trial_Miss==trNo,1);
these_Missii_lick_times=[];
these_Missii_lick_times=Miss_lick_times(this_Missii_lick,1:Miss_no_lick_times(this_Missii_lick));
if ~isempty(these_Missii_lick_times)
lick_times(num_odor_trials,1:length(these_Missii_lick_times))=these_Missii_lick_times;
no_licks(num_odor_trials)=length(these_Missii_lick_times);
else
no_licks(num_odor_trials)=0;
end
dLickTraces(num_odor_trials,:)=dMiss_lick_traces(this_Missii_lick,:);
epochs_per_trial_dFF(num_odor_trials)=2;
end
end
if epoch_per_trial(trNo)==8
%FA
epochs_per_trial(3,num_odor_trials)=1;
epochs_per_trial(1:2,num_odor_trials)=0;
epochs_per_trial(4,num_odor_trials)=0;
all_lda_events_miss_FA(num_odor_trials)=4;
if sum(which_trial_FA==trNo)>0
%Save lick times
this_FAii_lick=find(which_trial_FA==trNo,1);
these_FAii_lick_times=[];
these_FAii_lick_times=FA_lick_times(this_FAii_lick,1:FA_no_lick_times(this_FAii_lick));
if ~isempty(these_FAii_lick_times)
lick_times(num_odor_trials,1:length(these_FAii_lick_times))=these_FAii_lick_times;
no_licks(num_odor_trials)=length(these_FAii_lick_times);
else
no_licks(num_odor_trials)=0;
end
dLickTraces(num_odor_trials,:)=dFA_lick_traces(this_FAii_lick,:);
epochs_per_trial_dFF(num_odor_trials)=3;
end
end
if epoch_per_trial(trNo)==9
%CR
epochs_per_trial(4,num_odor_trials)=1;
epochs_per_trial(1:3,num_odor_trials)=0;
all_lda_events_miss_FA(num_odor_trials)=3;
if sum(which_trial_CR==trNo)>0
%Save lick times
this_CRii_lick=find(which_trial_CR==trNo,1);
these_CRii_lick_times=[];
these_CRii_lick_times=CR_lick_times(this_CRii_lick,1:CR_no_lick_times(this_CRii_lick));
if ~isempty(these_CRii_lick_times)
lick_times(num_odor_trials,1:length(these_CRii_lick_times))=these_CRii_lick_times;
no_licks(num_odor_trials)=length(these_CRii_lick_times);
else
no_licks(num_odor_trials)=0;
end
dLickTraces(num_odor_trials,:)=dCR_lick_traces(this_CRii_lick,:);
epochs_per_trial_dFF(num_odor_trials)=4;
end
end
end
%Calculate percent correct
sliding_window=min_trials; %Trials for determination of behavioral performance
min_precent_high_beh=80; %Minimum percent correct for good behavior blocks
max_percent_low_beh=65;
perCorr=[];
%Note: Because this is a reversal I am moving the window for calculation of perCorr to the right by nine points
for ii=1:num_odor_trials-sliding_window+1
no_Hits=sum(epochs_per_trial(1,ii:ii+sliding_window-1)==1);
no_CRs=sum(epochs_per_trial(4,ii:ii+sliding_window-1)==1);
perCorr(ii+sliding_window-1)=100*(no_Hits+no_CRs)/sliding_window;
end
perCorr(1:sliding_window)=perCorr(sliding_window+1);
%Note, this is here so that perCorr=0 is included in the 0-10 % bin.
perCorr(perCorr==0)=0.00001;
%Plot percent correct vs trial
close all
figNo=1;
try
close(figNo)
catch
end
hFig1 = figure(figNo);
set(hFig1, 'units','normalized','position',[.25 .65 .5 .25])
jj_low=find(perCorr<max_percent_low_beh);
plot(jj_low,perCorr(jj_low),'ob')
hold on
jj_high=find(perCorr>min_precent_high_beh);
plot(jj_high,perCorr(jj_high),'or')
jj_mid=find((perCorr<=min_precent_high_beh)&(perCorr>=max_percent_low_beh));
plot(jj_mid,perCorr(jj_mid),'o','MarkerEdgeColor',[0.7 0.7 0.7],'MarkerFaceColor',[0.7 0.7 0.7])
hold on
plot([0 num_odor_trials],[50 50],'-k')
% %Draw the boundaries of each file
% for filNum=2:caimanhandles.caimandr_choices.no_files
% % plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[0 100],'-k')
% if isfield(caimanhandles.caimandr_choices,'start_reversal')
% if caimanhandles.caimandr_choices.start_reversal==filNum
% plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[0 100],'-k','LineWidth',4)
% text(first_num_odor_trials(filNum)+2,80,'Reversal','Color','k','FontSize',18)
% end
% end
% if isfield(caimanhandles.caimandr_choices,'start_gogo')
% if caimanhandles.caimandr_choices.start_gogo==filNum
% plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[0 100],'-k','LineWidth',4)
% text(first_num_odor_trials(filNum)+2,80,'Go-go','Color','k','FontSize',18)
% end
% end
% if isfield(caimanhandles.caimandr_choices,'start_session')
% if sum(caimanhandles.caimandr_choices.start_session==filNum)>0
% plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[0 100],'-k')
% % text(first_num_odor_trials(filNum)+2,80,'Reversal','Color','k','FontSize',18)
% end
% end
% end
title(['Percent correct vs. trial number ' choiceFileName(18:end-2)])
xlabel('Trial number')
ylabel('Percent correct')
ylim([0 100])
%Save the percent correct
caimanhandles.per_file(filNum).file_processed=1;
caimanhandles.per_file(filNum).perCorr=perCorr;
caimanhandles.per_file(filNum).jj_low=jj_low;
caimanhandles.per_file(filNum).jj_high=jj_high;
caimanhandles.per_file(filNum).jj_mid=jj_mid;
caimanhandles.per_file(filNum).no_odor_trials=no_odor_trials;
%Now plot the intertrial interval to see whether laser on changes the
%subsequent ITI
ITI=per_trial_odor_on_time(2:end)-per_trial_odor_on_time(1:end-1);
ITItrials=[1:no_odor_trials-1];
figNo=figNo+1;
try
close(figNo)
catch
end
hFig1 = figure(figNo);
set(hFig1, 'units','normalized','position',[.25 .65 .5 .25])
%Plot ITI for trials with no previous laser
plot(ITItrials(opto_on_per_trial(1:end-1)==0),ITI(opto_on_per_trial(1:end-1)==0),'ob')
hold on
plot(ITItrials(opto_on_per_trial(1:end-1)==1),ITI(opto_on_per_trial(1:end-1)==1),'or')
title('ITI')
xlabel('Trial No')
legend('No laser','Laser')
ylabel('ITI (sec)')
%Save ITI
caimanhandles.per_file(filNum).ITI=ITI;
if ~isfield(caimanhandles.caimandr_choices,'start_reversal')
caimanhandles.caimandr_choices.start_reversal=200;
end
%Now
% for winNo=1:szwins(1)
% handles_sig.win(winNo).ii_for_sig=0;
% end
if caimanhandles.caimandr_choices.start_reversal>length(first_num_odor_trials)
%This is a forward run
total_trial_windows=3;
supertitle_description{1}=' percent correct <65';
supertitle_description{2}=' percent correct >=65&<80';
supertitle_description{3}=' percent correct >=80';
else
%Forward and reverse
total_trial_windows=2;
supertitle_description{1}=' trials before reversal';
supertitle_description{2}=' trials after reversal at end of the session';
end
trial_window_description{1}='percent correct <65';
trial_window_description{2}='percent correct >=65&<80';
trial_window_description{3}='percent correct >=80';
for no_trial_windows=1:total_trial_windows
caimanhandles.per_file(filNum).trial_window_processed(no_trial_windows)=0;
dFF_trial_mask=[];
lick_excluded_trials=[];
jj=0;
events_miss_FA=[];
which_trials_in_PCA=[];
if caimanhandles.caimandr_choices.start_reversal>length(first_num_odor_trials)
% fprintf(1, '\n\nPCA processed for dF/F for trials before reversal \n');
pct_windows=[45 65;65 80;75 100.1];
for ii=1:num_odor_trials
if sum((lick_times(ii,1:no_licks(ii))>=t_odor_on)&(lick_times(ii,1:no_licks(ii))<=t_odor_off))<lick_threshold
lick_excluded_trials(ii)=0;
if (perCorr(ii)>=pct_windows(no_trial_windows,1))&(perCorr(ii)<pct_windows(no_trial_windows,2))
dFF_trial_mask(ii)=1;
jj=jj+1;
handles_par(no_trial_windows).perCorr(jj)=perCorr(ii);
events{jj,1}=all_lda_events{ii};
events_miss_FA(jj)=all_lda_events_miss_FA(ii);
which_trials_in_PCA(jj)=ii;
else
dFF_trial_mask(ii)=0;
end
else
if (perCorr(ii)>=pct_windows(no_trial_windows,1))&(perCorr(ii)<pct_windows(no_trial_windows,2))
lick_excluded_trials(ii)=1;
else
lick_excluded_trials(ii)=0;
end
dFF_trial_mask(ii)=0;
end
end
else
if no_trial_windows==1
%Forward trials
fprintf(1, '\n\nLDA processed for dF/F for trials before reversal \n');
for ii=1:num_odor_trials
if sum((lick_times(ii,1:no_licks(ii))>=t_odor_on)&(lick_times(ii,1:no_licks(ii))<=t_odor_off))<lick_threshold
lick_excluded_trials(ii)=0;
if (trial_dFF(ii)>=1)&(trial_dFF(ii)<=first_num_odor_trials(caimanhandles.caimandr_choices.start_reversal)-1)
dFF_trial_mask(ii)=1;
jj=jj+1;
handles_par(no_trial_windows).perCorr(jj)=perCorr(ii);
events{jj,1}=all_lda_events{ii};
else
dFF_trial_mask(ii)=0;
end
else
dFF_trial_mask(ii)=0
if (trial_dFF(ii)>=1)&(trial_dFF(ii)<=first_num_odor_trials(caimanhandles.caimandr_choices.start_reversal)-1)
lick_excluded_trials(ii)=1;
else
lick_excluded_trials(ii)=0;
end
end
end
else
%Trials at end of reversal
fprintf(1, '\n\nLDA processed for dF/F for trials after reversal \n');
for ii=1:num_odor_trials_dFF
if sum((lick_times(ii,1:no_licks(ii))>=t_odor_on)&(lick_times(ii,1:no_licks(ii))<=t_odor_off))<lick_threshold
lick_excluded_trials(ii)=0;
if (trial_dFF(ii)>=max(trial_dFF)-100)
dFF_trial_mask(ii)=1;
jj=jj+1;
handles_par(no_trial_windows).perCorr(jj)=perCorr(ii);
events{jj,1}=all_lda_events{ii};
else
dFF_trial_mask(ii)=0;
end
else
if (trial_dFF(ii)>=max(trial_dFF)-100)
lick_excluded_trials(ii)=1;
else
lick_excluded_trials(ii)=0;
end
dFF_trial_mask(ii)=0;
end
end
end
end
if sum(dFF_trial_mask)>0
%Do lick analysis
%dt_lick=0.3;
%First plot the lick frequency
dt_lick=1/6; %6 Hz
time_licks=([1:ceil((dt_after+dt_before)/dt_lick)]*dt_lick)-dt_before;
delta_t_gauss=2; %seconds
no_conv_points_lick=ceil(delta_t_gauss/(time_licks(2)-time_licks(1)));
no_conv_points_dFF=ceil(delta_t_gauss/(1/6));
%Figure out the lick threshold to exclude trials wheren Ming
%gave the animal water manually during the odor on
all_licks_per_dt_per_trial=zeros(num_odor_trials,ceil((dt_after+dt_before)/dt_lick));
for trial_no=1:num_odor_trials
for ii_lick=1:no_licks(trial_no)
all_licks_per_dt_per_trial(trial_no, ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=all_licks_per_dt_per_trial(trial_no, ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
end
%
% hit_odor_lick_freq=zeros(1,sum(all_lda_events_miss_FA==1));
% hit_odor_lick_freq(1,:)=sum(all_licks_per_dt_per_trial(all_lda_events_miss_FA==1,(time_licks>=t_odor_on)&(time_licks<=t_odor_off)),2)/(t_odor_off-t_odor_on);
%
% lick_threshold=mean(hit_odor_lick_freq)+2*std(hit_odor_lick_freq);
% lick_threshold=20;
% lick_threshold=200;
%Calculate the lick frequency for S+ and S-
Splick_freq=zeros(1,ceil((dt_after+dt_before)/dt_lick));
Smlick_freq=zeros(1,ceil((dt_after+dt_before)/dt_lick));
sp_trno=0;
sm_trno=0;
for trial_no=1:num_odor_trials
if dFF_trial_mask(trial_no)==1
%if sum((lick_times(trial_no,1:no_licks(trial_no))>=t_odor_on)&(lick_times(trial_no,1:no_licks(trial_no))<=t_odor_off))<lick_threshold
if strcmp(all_lda_events{trial_no},'S+')
%S+
for ii_lick=1:no_licks(trial_no)
Splick_freq( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=Splick_freq( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
sp_trno=sp_trno+1;
else
%S-
for ii_lick=1:no_licks(trial_no)
Smlick_freq( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=Smlick_freq( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
sm_trno=sm_trno+1;
end
%end
end
end
Splick_freq=(Splick_freq/(sp_trno*dt_lick));
Smlick_freq=(Smlick_freq/(sm_trno*dt_lick));
Splick_freq_no_conv=Splick_freq;
Smlick_freq_no_conv=Smlick_freq;
%Convolve lick_freq using a window of 0.9 sec
% no_conv_points=3;
% conv_win=ones(1,no_conv_points);
%Convolve lick_freq using a Gaussian window
conv_win=gausswin(no_conv_points_lick);
Splick_freq=conv(Splick_freq,conv_win,'same')/sum(conv_win);
Smlick_freq=conv(Smlick_freq,conv_win,'same')/sum(conv_win);
%Plot the lick frequency
figNo=figNo+1;
try
close(figNo)
catch
end
figure(figNo)
hold on
lfreqmax=20;
p1=plot(time_licks,Smlick_freq,'-b','LineWidth',2);
p2=plot(time_licks,Splick_freq,'-r','LineWidth',2);
%Odor on markers
plot([0 0],[-1 lfreqmax],'-k')
odorhl=plot([0 mean(delta_odor)],[-0.5 -0.5],'-k','LineWidth',5);
plot([mean(delta_odor) mean(delta_odor)],[-1 lfreqmax],'-k')
%Reinforcement markers
plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)],[-1 lfreqmax],'-r')
reinfhl=plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[-0.5 -0.5],'-r','LineWidth',5);
plot([mean(delta_odor_on_reinf_on)+mean(delta_reinf) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[-1 lfreqmax],'-r')
switch no_trial_windows
case 1
title(['Lick frequency for percent correct <65']);
case 2
title(['Lick frequency for percent correct >=65&<80']);
case 3
title(['Lick frequency for percent correct >80']);
end
xlabel('Time (sec)')
ylabel('Lick frequency (Hz)')
legend([p1 p2],'S+','S-')
xlim([-10 20])
ylim([-1 lfreqmax])
caimanhandles.per_file(filNum).trial_window_processed(no_trial_windows)=1;
caimanhandles.per_file(filNum).trial_window(no_trial_windows).time_licks=time_licks;
caimanhandles.per_file(filNum).trial_window(no_trial_windows).Smlick_freq=Smlick_freq;
caimanhandles.per_file(filNum).trial_window(no_trial_windows).Splick_freq=Splick_freq;
caimanhandles.delta_odor_on_reinf_on=mean(delta_odor_on_reinf_on);
caimanhandles.delta_reinf=mean(delta_reinf);
caimanhandles.delta_odor=mean(delta_odor);
%If there were a subset of trials with light on for optogenetics
%show the frequency with light on and off
if (sum(all_opto_on_per_trial==1)>0)&(sum(all_opto_on_per_trial==0)>0)
%Calculate the lick frequency for S+ and S- with light on or
%off
Splick_freq_on=zeros(1,ceil((dt_after+dt_before)/dt_lick));
Splick_freq_off=zeros(1,ceil((dt_after+dt_before)/dt_lick));
Smlick_freq_on=zeros(1,ceil((dt_after+dt_before)/dt_lick));
Smlick_freq_off=zeros(1,ceil((dt_after+dt_before)/dt_lick));
sp_trno_on=0;
sp_trno_off=0;
sm_trno_on=0;
sm_trno_off=0;
for trial_no=1:num_odor_trials
if dFF_trial_mask(trial_no)==1
if all_opto_on_per_trial(trial_no)==1
if strcmp(all_lda_events{trial_no},'S+')
%S+
for ii_lick=1:no_licks(trial_no)
Splick_freq_on( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=Splick_freq_on( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
sp_trno_on=sp_trno_on+1;
else
%S-
for ii_lick=1:no_licks(trial_no)
Smlick_freq_on( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=Smlick_freq_on( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
sm_trno_on=sm_trno_on+1;
end
else
if strcmp(all_lda_events{trial_no},'S+')
%S+
for ii_lick=1:no_licks(trial_no)
Splick_freq_off( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=Splick_freq_off( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
sp_trno_off=sp_trno_off+1;
else
%S-
for ii_lick=1:no_licks(trial_no)
Smlick_freq_off( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))=Smlick_freq_off( ceil((lick_times(trial_no,ii_lick)+dt_before)/dt_lick))+1;
end
sm_trno_off=sm_trno_off+1;
end
end
end
end
if sp_trno_on>0
Splick_freq_on=(Splick_freq_on/(sp_trno_on*dt_lick));
end
if sp_trno_off>0
Splick_freq_off=(Splick_freq_off/(sp_trno_off*dt_lick));
end
if sm_trno_on>0
Smlick_freq_on=(Smlick_freq_on/(sm_trno_on*dt_lick));
end
if sm_trno_off>0
Smlick_freq_off=(Smlick_freq_off/(sm_trno_off*dt_lick));
end
%Convolve lick_freq using a Gaussian window
conv_win=gausswin(no_conv_points_lick);
Splick_freq_on=conv(Splick_freq_on,conv_win,'same')/sum(conv_win);
Smlick_freq_on=conv(Smlick_freq_on,conv_win,'same')/sum(conv_win);
Splick_freq_off=conv(Splick_freq_off,conv_win,'same')/sum(conv_win);
Smlick_freq_off=conv(Smlick_freq_off,conv_win,'same')/sum(conv_win);
%Plot the lick frequency for light on and off
figNo=figNo+1;
try
close(figNo)
catch
end
figure(figNo)
hold on
lfreqmax=20;
p1=plot(time_licks,Smlick_freq_off,'-','LineWidth',2,'Color',[0.7 0.7 1]);
p2=plot(time_licks,Smlick_freq_on,'-b','LineWidth',2);
p3=plot(time_licks,Splick_freq_off,'-','LineWidth',2,'Color',[1 0.7 0.7]);
p4=plot(time_licks,Splick_freq_on,'-r','LineWidth',2);
%Odor on markers
plot([0 0],[-1 lfreqmax],'-k')
odorhl=plot([0 mean(delta_odor)],[-0.5 -0.5],'-k','LineWidth',5);
plot([mean(delta_odor) mean(delta_odor)],[-1 lfreqmax],'-k')
%Reinforcement markers
plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)],[-1 lfreqmax],'-r')
reinfhl=plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[-0.5 -0.5],'-r','LineWidth',5);
plot([mean(delta_odor_on_reinf_on)+mean(delta_reinf) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[-1 lfreqmax],'-r')
switch no_trial_windows
case 1
title(['Lick frequency for percent correct <65']);
case 2
title(['Lick frequency for percent correct >=65&<80']);
case 3
title(['Lick frequency for percent correct >80']);
end
xlabel('Time (sec)')
ylabel('Lick frequency (Hz)')
legend([p1 p2 p3 p4],'S- light off','S- light on','S+ light off','S+ light on')
xlim([-10 20])
ylim([-1 lfreqmax])
end
%Now plot the p value for the difference in licks between S+ and S-
%Get lick p values
try
trials_used=10;
no_pvals=0;
p_val_Sp_Sm=[];
time_p_lick=[];
fractional_lick_time_on_sp=zeros(1,length(time_licks));
fractional_lick_time_on_sm=zeros(1,length(time_licks));
for ii=1:length(time_licks)-1
sp_trno=0;
sm_trno=0;
this_Sp=[];
this_Sm=[];
for trial_no=1:num_odor_trials
% if sum((lick_times(trial_no,1:no_licks(trial_no))>=t_odor_on)&(lick_times(trial_no,1:no_licks(trial_no))<=t_odor_off))<lick_threshold
if dFF_trial_mask(trial_no)==1
if strcmp(all_lda_events{trial_no},'S+')
%S+
sp_trno=sp_trno+1;
this_Sp(sp_trno,1)=0;
for ii_lick=1:no_licks(trial_no)
if (lick_times(trial_no,ii_lick)>=time_licks(ii))&(lick_times(trial_no,ii_lick)<time_licks(ii+1))
this_Sp(sp_trno,1)=1;
end
end
else
%S-
sm_trno=sm_trno+1;
this_Sm(sm_trno,1)=0;
for ii_lick=1:no_licks(trial_no)
if (lick_times(trial_no,ii_lick)>=time_licks(ii))&(lick_times(trial_no,ii_lick)<time_licks(ii+1))
this_Sm(sm_trno,1)=1;
end
end
end
end
% end
end
if ii==1
no_sptrial_start=caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_sptrials+1;
no_smtrial_start=caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_smtrials+1;
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_sptrials=...
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_sptrials+sp_trno;
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_smtrials=...
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_smtrials+sm_trno;
no_sptrial_end=caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_sptrials;
no_smtrial_end=caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).no_smtrials;
end
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).splicks(ii,no_sptrial_start:no_sptrial_end)=this_Sp;
caimanhandles.per_mouse(caimanhandles.caimandr_choices.mouse(filNum)).hM4D(caimanhandles.caimandr_choices.hM4D(filNum)).smlicks(ii,no_smtrial_start:no_smtrial_end)=this_Sm;
no_pvals=no_pvals+1;
p_val_Sp_Sm(no_pvals)=1;
if (~isempty(this_Sm))&(~isempty(this_Sp))
p_val_done=0;
no_these_p_vals=0;
ii_s=1;
these_pvals=[];
while p_val_done==0
if ii_s+trials_used-1>length(this_Sp)
p_val_done=1;
else
if ii_s+trials_used-1>length(this_Sm)
p_val_done=1;
else
no_these_p_vals=no_these_p_vals+1;
these_pvals(no_these_p_vals)=ranksum(this_Sm(ii_s:ii_s+trials_used-1),this_Sp(ii_s:ii_s+trials_used-1));
ii_s=ii_s+trials_used;
end
end
end
p_val_Sp_Sm(no_pvals)=mean(these_pvals);
else
p_val_Sp_Sm(no_pvals)=1;
end
% if (~isempty(this_Sm))&(~isempty(this_Sp))
% p_val_Sp_Sm(no_pvals)=ranksum(this_Sm,this_Sp);
% else
% p_val_Sp_Sm(no_pvals)=1;
% end
%ranksum gives NaN if the values are all the same
if isnan(p_val_Sp_Sm(no_pvals))
p_val_Sp_Sm(no_pvals)=1;
end
time_p_lick(no_pvals)= (time_licks(ii)+time_licks(ii+1))/2;
fractional_lick_time_on_sp(ii)=mean(this_Sp);
fractional_lick_time_on_sm(ii)=mean(this_Sm);
end
logpmin=-20;
figNo=figNo+1;
try
close(figNo)
catch
end
figure(figNo)
plot(time_p_lick,log10(p_val_Sp_Sm),'-k','LineWidth',2)
hold on
plot([time_p_lick(1) time_p_lick(end)],[log10(0.05) log10(0.05)],'-r','LineWidth',1)
%Odor on markers
plot([0 0],[logpmin 0.5],'-k')
odorhl=plot([0 mean(delta_odor)],[logpmin+0.5 logpmin+0.5],'-k','LineWidth',5);
plot([mean(delta_odor) mean(delta_odor)],[logpmin 0.5],'-k')
%Reinforcement markers
plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)],[logpmin 0.5],'-r')
reinfhl=plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[logpmin+0.5 logpmin+0.5],'-r','LineWidth',5);
plot([mean(delta_odor_on_reinf_on)+mean(delta_reinf) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[logpmin 0.5],'-r')
switch no_trial_windows
case 1
title(['log(p value) for the difference in licks Sp vs Sm for percent correct <65']);
case 2
title(['log(p value) for the difference in licks Sp vs Sm for percent correct >=65&<80']);
case 3
title(['log(p value) for the difference in licks Sp vs Sm for percent correct >80']);
end
xlabel('Time (sec)')
ylabel('log10(p value)')
ylim([logpmin 0.5])
xlim([-10 20])
%Save the logp data
caimanhandles.per_file(filNum).trial_window(no_trial_windows).log10p_val_Sp_Sm=log10(p_val_Sp_Sm);
caimanhandles.per_file(filNum).trial_window(no_trial_windows).time_p_lick=time_p_lick;
%Find the decision time for odor detection
ii_zero=find(time_p_lick>0,1,'first');
decision_ii=find(p_val_Sp_Sm(time_p_lick>0)<0.05,1,'first');
if ~isempty(decision_ii)
decision_time=time_p_lick(decision_ii-1+ii_zero);
else
decision_time=time_p_lick(end);
end
%Note, if the decision time is longer than 10 seconds it is assigned 10 +
%gaussian noise (so that they do not overlap)
if decision_time>10
decision_time=10+randn;
end
caimanhandles.per_file(filNum).trial_window(no_trial_windows).tdecision_time=decision_time;
%Find the decision time for water detection (this will only work for <65%)
dt_reinf_on=mean(delta_odor_on_reinf_on);
ii_zero_reinf=find(time_p_lick>dt_reinf_on,1,'first');
decision_ii_reinf=find(p_val_Sp_Sm(time_p_lick>dt_reinf_on)<0.05,1,'first');
if ~isempty(decision_ii_reinf)
decision_time_reinf=time_p_lick(decision_ii_reinf-1+ii_zero_reinf);
else
decision_time_reinf=time_p_lick(end);
end
decision_time_reinf=decision_time_reinf-dt_reinf_on;
if decision_time_reinf>10
decision_time_reinf=10+randn;
end
%These if statements are here to exclude NaNs and zeros
if (sum(isnan(Splick_freq))==0)&(sum(isnan(Smlick_freq))==0)&(length(this_Sp)>10)&(length(this_Sm)>10)
no_files_included=no_files_included+1;
per_file_lick_log10_p_val(no_files_included,1:length(log10(p_val_Sp_Sm)))=log10(p_val_Sp_Sm);
per_file_lick_freq_sp(no_files_included,1:length(Splick_freq_no_conv))=Splick_freq_no_conv;
per_file_lick_freq_sm(no_files_included,1:length(Smlick_freq_no_conv))=Smlick_freq_no_conv;
per_file_hM4D(no_files_included)=caimanhandles.caimandr_choices.hM4D(filNum);
per_file_mouse(no_files_included)=caimanhandles.caimandr_choices.mouse(filNum);
per_file_noSp(no_files_included)=length(this_Sp);
per_file_noSm(no_files_included)=length(this_Sm);
per_file_trial_window(no_files_included)=no_trial_windows;
per_file_decision_time(no_files_included)=decision_time;
per_file_decision_time_reinf(no_files_included)=decision_time_reinf;
per_file_fractional_lick_time_on_sp(no_files_included,1:length(fractional_lick_time_on_sp))=fractional_lick_time_on_sp;
per_file_fractional_lick_time_on_sm(no_files_included,1:length(fractional_lick_time_on_sm))=fractional_lick_time_on_sm;
end
catch
pffft=1;
end
%If there were a subset of trials with light on for optogenetics
%show the p values for light on vs off
if (sum(all_opto_on_per_trial==1)>0)&(sum(all_opto_on_per_trial==0)>0)
%S+ light on vs off
figNo=figNo+1;
try
close(figNo)
catch
end
figure(figNo)
try
no_pvals=0;
p_vals=[];
time_p_lick=[];
for ii=1:length(time_licks)-1
sp_trno_on=0;
sp_trno_off=0;
this_Sp_on=[];
this_Sp_off=[];
for trial_no=1:num_odor_trials
% if sum((lick_times(trial_no,1:no_licks(trial_no))>=t_odor_on)&(lick_times(trial_no,1:no_licks(trial_no))<=t_odor_off))<lick_threshold
if dFF_trial_mask(trial_no)==1
if strcmp(all_lda_events{trial_no},'S+')
if all_opto_on_per_trial(trial_no)==1
%S+ light on
sp_trno_on=sp_trno_on+1;
this_Sp_on(sp_trno_on,1)=0;
for ii_lick=1:no_licks(trial_no)
if (lick_times(trial_no,ii_lick)>=time_licks(ii))&(lick_times(trial_no,ii_lick)<time_licks(ii+1))
this_Sp_on(sp_trno_on,1)=1;
end
end
else
%S+ light off
sp_trno_off=sp_trno_off+1;
this_Sp_off(sp_trno_off,1)=0;
for ii_lick=1:no_licks(trial_no)
if (lick_times(trial_no,ii_lick)>=time_licks(ii))&(lick_times(trial_no,ii_lick)<time_licks(ii+1))
this_Sp_off(sp_trno_off,1)=1;
end
end
end
end
end
% end
end
no_pvals=no_pvals+1;
if (~isempty(this_Sp_on))&(~isempty(this_Sp_off))
p_vals(no_pvals)=ranksum(this_Sp_on,this_Sp_off);
else
p_vals(no_pvals)=1;
end
%ranksum gives NaN if the values are all the same
if isnan(p_vals(no_pvals))
p_vals(no_pvals)=1;
end
time_p_lick(no_pvals)= (time_licks(ii)+time_licks(ii+1))/2;
end
logpmin=-20;
subplot(2,1,1)
hold on
plot(time_p_lick,log10(p_vals),'-k','LineWidth',2)
plot([time_p_lick(1) time_p_lick(end)],[log10(0.05) log10(0.05)],'-r','LineWidth',1)
%Odor on markers
plot([0 0],[logpmin 0.5],'-k')
odorhl=plot([0 mean(delta_odor)],[logpmin+0.5 logpmin+0.5],'-k','LineWidth',5);
plot([mean(delta_odor) mean(delta_odor)],[logpmin 0.5],'-k')
%Reinforcement markers
plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)],[logpmin 0.5],'-r')
reinfhl=plot([mean(delta_odor_on_reinf_on) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[logpmin+0.5 logpmin+0.5],'-r','LineWidth',5);
plot([mean(delta_odor_on_reinf_on)+mean(delta_reinf) mean(delta_odor_on_reinf_on)+mean(delta_reinf)],[logpmin 0.5],'-r')
switch no_trial_windows
case 1
title(['log(p value) for lick difference S+ light on vs light off for %correct <65']);
case 2
title(['log(p value) for lick difference S+ light on vs light off for %correct >=65&<80']);
case 3
title(['log(p value) for lick difference S+ light on vs light off for %correct >80']);
end
xlabel('Time (sec)')