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Copy pathdrgCaImAnBatchPerSessionEventsPerTriallickvsdFFOptFlow.m
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drgCaImAnBatchPerSessionEventsPerTriallickvsdFFOptFlow.m
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function drgCaImAnBatchPerSessionEventsPerTriallickvsdFFOptFlow(choiceBatchPathName,choiceFileName)
%This code does a movement and lick derivative analysis per trial
% Needs a choices file such as drgCaImAnChoicesDiego20180910_mmPVG04_Cerebellum
% Needs the output files from drgCaImAn_batch_dropc.m
warning('off')
close all
clear all
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=[];
min_trials=20;
tic
if nargin==0
[choiceFileName,choiceBatchPathName] = uigetfile({'drgCaImAnChoices*.m'},'Select the .m file with all the choices for analysis');
end
fprintf(1, ['\ndrgCaImAnBatchPerSessionEventsPerTriallickvsdFFOptFlow run for ' choiceFileName '\n\n']);
addpath(choiceBatchPathName)
eval(['handles=' choiceFileName(1:end-2) ';'])
handles.choiceFileName=choiceFileName;
handles.choiceBatchPathName=choiceBatchPathName;
caimanhandles=handles;
%Read the files and calculate the dF/F in each window
num_odor_trials=0;
epochs_per_trial=[];
num_odor_trials_dFF=0;
all_lda_events=[];
all_lda_input_timecourse=[];
no_timepoints=2000000;
all_lda_events_miss_FA=[];
lick_times=[];
no_licks=[];
dLickTraces=[];
%Variables for optical flow
skip_ii=19;
baseline_ii=200;
for filNum=1:caimanhandles.caimandr_choices.no_files
%Read the CalmAn_batch file
if iscell(caimanhandles.caimandr_choices.PathName)==0
load([caimanhandles.caimandr_choices.PathName caimanhandles.caimandr_choices.FileName{filNum}])
else
load([caimanhandles.caimandr_choices.PathName{filNum} caimanhandles.caimandr_choices.FileName{filNum}])
end
%Read the Optical flow file
if iscell(caimanhandles.caimandr_choices.PathName)==0
load([caimanhandles.caimandr_choices.PathName caimanhandles.caimandr_choices.OpFileName{filNum}])
else
load([caimanhandles.caimandr_choices.PathName{filNum} caimanhandles.caimandr_choices.OpFileName{filNum}])
end
%Align the optical flow to the start of imaging
baseline_end_ii=100*frame_rate;
mag_meanlaser99=prctile(mag_meanlaser(1:baseline_end_ii),99);
mag_meanlaser1=prctile(mag_meanlaser(1:baseline_end_ii),1);
mean_laser_start=mean(mag_meanlaser(skip_ii+1:baseline_ii));
if exist('first_image_ii')==0
first_image_ii=find(mag_meanlaser(skip_ii+1:end)>0.5*(mag_meanlaser99-mag_meanlaser1)+mean_laser_start,1)+skip_ii;
end
timeOF=([1:length(mag_meantail)]/(frame_rate))-(first_image_ii/(frame_rate));
first_num_odor_trials(filNum)=num_odor_trials+1;
for trNo=1:no_odor_trials
%Save epoch
num_odor_trials=num_odor_trials+1;
%Save lda
all_lda_events{num_odor_trials}=lda_event{trNo};
szlit=size(lda_input_timecourse);
all_lda_input_timecourse(1:length(time_to_eventLDA),1:szlit(2),num_odor_trials)=lda_input_timecourse(:,:,trNo);
all_lda_no_comp(num_odor_trials)=szlit(2);
all_lda_fileNo(num_odor_trials)=filNum;
no_timepoints=min([no_timepoints length(time_to_eventLDA)]);
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;
%Was dF/F calculated?
if sum(which_trial_Hit==trNo)>0
%Calculate norm dFF
ref_win=(time_to_event>=caimanhandles.caimandr_choices.ref_win(1))&(time_to_event<=caimanhandles.caimandr_choices.ref_win(2));
ref_dFF=[];
ref_dFF=mean(Hit_traces(which_trial_Hit==trNo,ref_win),2);
num_odor_trials_dFF=num_odor_trials_dFF+1;
this_num_odor_trial(num_odor_trials_dFF)=num_odor_trials;
szwins=size(caimanhandles.caimandr_choices.wins);
%Save the licks for this trial
this_Hitii_lick=which_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,:);
for winNo=1:szwins(1)
%Calculate dFF
win=(time_to_event>=caimanhandles.caimandr_choices.wins(winNo,1))&(time_to_event<=caimanhandles.caimandr_choices.wins(winNo,2));
win_dFF=[];
win_dFF=mean(Hit_traces(which_trial_Hit==trNo,win),2);
szhit=size(Hit_traces(which_trial_Hit==trNo,win));
% norm_dFF=win_dFF./ref_dFF;
all_win_dFF(winNo,num_odor_trials_dFF,1:length(win_dFF))=win_dFF;
no_traces_win_dFF(winNo,num_odor_trials_dFF)=length(win_dFF);
mean_win_dFF(winNo,num_odor_trials_dFF)=mean(win_dFF);
SD_win_dFF(winNo,num_odor_trials_dFF)=std(win_dFF);
CI_win_dFF(winNo,num_odor_trials_dFF,:) = bootci(1000, @mean, win_dFF);
%Calculate lick frequency for this window
lick_freq(winNo,num_odor_trials_dFF)=sum( (these_Hitii_lick_times>=caimanhandles.caimandr_choices.wins(winNo,1))&...
(these_Hitii_lick_times<=caimanhandles.caimandr_choices.wins(winNo,2)))/(caimanhandles.caimandr_choices.wins(winNo,2)-...
caimanhandles.caimandr_choices.wins(winNo,1));
end
epochs_per_trial_dFF(num_odor_trials_dFF)=1;
trial_dFF(num_odor_trials_dFF)=num_odor_trials;
%Calculate the average snip for this trial
Hitii=handles_out.Hit_trial_no(trNo);
no_time_points=length(handles_out.componentNo(1).trialNo(Hitii).hit_traces);
num_traces=handles_out.trialNo(Hitii).trace_numHit;
these_traces=zeros(num_traces,no_time_points);
for trace_num=1:handles_out.trialNo(Hitii).trace_numHit
these_traces(trace_num,:)=handles_out.componentNo(trace_num).trialNo(Hitii).hit_traces;
end
mean_snip_dFF(num_odor_trials_dFF,1:no_time_points)=mean(these_traces,1);
CI_snip_dFF(num_odor_trials_dFF,1:2,1:no_time_points)=bootci(1000, @mean, these_traces);
time(num_odor_trials_dFF).time_to_event=handles_out.time_to_eventHit;
%Get the movement
snip_maskOF=(timeOF>=epoch_time(trNo)-dt_before)...
&(timeOF<=epoch_time(trNo)+dt_after);
velocity(num_odor_trials_dFF,1:sum(snip_maskOF))=mag_meantail(snip_maskOF);
time_OFsnip(num_odor_trials_dFF,1:sum(snip_maskOF))=([1:sum(snip_maskOF)]*(1/frame_rate))-dt_before;
no_timepointsOF(num_odor_trials_dFF)=sum(snip_maskOF);
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;
%Was dF/F calculated?
if sum(which_trial_Miss==trNo)>0
%Calculate norm dFF
ref_win=(time_to_event>=caimanhandles.caimandr_choices.ref_win(1))&(time_to_event<=caimanhandles.caimandr_choices.ref_win(2));
ref_dFF=[];
ref_dFF=mean(Miss_traces(which_trial_Miss==trNo,ref_win),2);
num_odor_trials_dFF=num_odor_trials_dFF+1;
this_num_odor_trial(num_odor_trials_dFF)=num_odor_trials;
szwins=size(caimanhandles.caimandr_choices.wins);
%Save lick times
this_Missii_lick=which_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,:);
for winNo=1:szwins(1)
%Calculate dFF
win=(time_to_event>=caimanhandles.caimandr_choices.wins(winNo,1))&(time_to_event<=caimanhandles.caimandr_choices.wins(winNo,2));
win_dFF=[];
win_dFF=mean(Miss_traces(which_trial_Miss==trNo,win),2);
% norm_dFF=win_dFF./ref_dFF;
all_win_dFF(winNo,num_odor_trials_dFF,1:length(win_dFF))=win_dFF;
no_traces_win_dFF(winNo,num_odor_trials_dFF)=length(win_dFF);
SD_win_dFF(winNo,num_odor_trials_dFF)=std(win_dFF);
mean_win_dFF(winNo,num_odor_trials_dFF)=mean(win_dFF);
CI_win_dFF(winNo,num_odor_trials_dFF,:) = bootci(1000, @mean, win_dFF);
%Calculate lick frequency for this window
lick_freq(winNo,num_odor_trials_dFF)=sum( (these_Missii_lick_times>=caimanhandles.caimandr_choices.wins(winNo,1))&...
(these_Missii_lick_times<=caimanhandles.caimandr_choices.wins(winNo,2)))/(caimanhandles.caimandr_choices.wins(winNo,2)-...
caimanhandles.caimandr_choices.wins(winNo,1));
end
epochs_per_trial_dFF(num_odor_trials_dFF)=2;
trial_dFF(num_odor_trials_dFF)=num_odor_trials;
%Calculate the average snip for this trial
Missii=handles_out.Miss_trial_no(trNo);
no_time_points=length(handles_out.componentNo(1).trialNo(Missii).miss_traces);
num_traces=handles_out.trialNo(Missii).trace_numMiss;
these_traces=zeros(num_traces,no_time_points);
for trace_num=1:handles_out.trialNo(Missii).trace_numMiss
these_traces(trace_num,:)=handles_out.componentNo(trace_num).trialNo(Missii).miss_traces;
end
mean_snip_dFF(num_odor_trials_dFF,1:no_time_points)=mean(these_traces,1);
CI_snip_dFF(num_odor_trials_dFF,1:2,1:no_time_points)=bootci(1000, @mean, these_traces);
time(num_odor_trials_dFF).time_to_event=handles_out.time_to_eventMiss;
%Get the movement
snip_maskOF=(timeOF>=epoch_time(trNo)-dt_before)...
&(timeOF<=epoch_time(trNo)+dt_after);
velocity(num_odor_trials_dFF,1:sum(snip_maskOF))=mag_meantail(snip_maskOF);
time_OFsnip(num_odor_trials_dFF,1:sum(snip_maskOF))=([1:sum(snip_maskOF)]*(1/frame_rate))-dt_before;
no_timepointsOF(num_odor_trials_dFF)=sum(snip_maskOF);
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;
%Was dF/F calculated?
if sum(which_trial_FA==trNo)>0
%Calculate norm dFF
ref_win=(time_to_event>=caimanhandles.caimandr_choices.ref_win(1))&(time_to_event<=caimanhandles.caimandr_choices.ref_win(2));
ref_dFF=[];
ref_dFF=mean(FA_traces(which_trial_FA==trNo,ref_win),2);
num_odor_trials_dFF=num_odor_trials_dFF+1;
this_num_odor_trial(num_odor_trials_dFF)=num_odor_trials;
szwins=size(caimanhandles.caimandr_choices.wins);
%Save lick times
this_FAii_lick=which_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,:);
for winNo=1:szwins(1)
%Calculate dFFF
win=(time_to_event>=caimanhandles.caimandr_choices.wins(winNo,1))&(time_to_event<=caimanhandles.caimandr_choices.wins(winNo,2));
win_dFF=[];
win_dFF=mean(FA_traces(which_trial_FA==trNo,win),2);
% norm_dFF=win_dFF./ref_dFF;
all_win_dFF(winNo,num_odor_trials_dFF,1:length(win_dFF))=win_dFF;
no_traces_win_dFF(winNo,num_odor_trials_dFF)=length(win_dFF);
mean_win_dFF(winNo,num_odor_trials_dFF)=mean(win_dFF);
SD_win_dFF(winNo,num_odor_trials_dFF)=std(win_dFF);
CI_win_dFF(winNo,num_odor_trials_dFF,:) = bootci(1000, @mean, win_dFF);
%Calculate lick frequency for this window
lick_freq(winNo,num_odor_trials_dFF)=sum( (these_FAii_lick_times>=caimanhandles.caimandr_choices.wins(winNo,1))&...
(these_FAii_lick_times<=caimanhandles.caimandr_choices.wins(winNo,2)))/(caimanhandles.caimandr_choices.wins(winNo,2)-...
caimanhandles.caimandr_choices.wins(winNo,1));
end
epochs_per_trial_dFF(num_odor_trials_dFF)=3;
trial_dFF(num_odor_trials_dFF)=num_odor_trials;
%Calculate the average snip for this trial
FAii=handles_out.FA_trial_no(trNo);
no_time_points=length(handles_out.componentNo(1).trialNo(FAii).FA_traces);
num_traces=handles_out.trialNo(FAii).trace_numFA;
these_traces=zeros(num_traces,no_time_points);
for trace_num=1:handles_out.trialNo(FAii).trace_numFA
these_traces(trace_num,:)=handles_out.componentNo(trace_num).trialNo(FAii).FA_traces;
end
mean_snip_dFF(num_odor_trials_dFF,1:no_time_points)=mean(these_traces,1);
CI_snip_dFF(num_odor_trials_dFF,1:2,1:no_time_points)=bootci(1000, @mean, these_traces);
time(num_odor_trials_dFF).time_to_event=handles_out.time_to_eventFA;
%Get the movement
snip_maskOF=(timeOF>=epoch_time(trNo)-dt_before)...
&(timeOF<=epoch_time(trNo)+dt_after);
velocity(num_odor_trials_dFF,1:sum(snip_maskOF))=mag_meantail(snip_maskOF);
time_OFsnip(num_odor_trials_dFF,1:sum(snip_maskOF))=([1:sum(snip_maskOF)]*(1/frame_rate))-dt_before;
no_timepointsOF(num_odor_trials_dFF)=sum(snip_maskOF);
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;
%Was dF/F calculated?
if sum(which_trial_CR==trNo)>0
%Calculate norm dFF
ref_win=(time_to_event>=caimanhandles.caimandr_choices.ref_win(1))&(time_to_event<=caimanhandles.caimandr_choices.ref_win(2));
ref_dFF=[];
ref_dFF=mean(CR_traces(which_trial_CR==trNo,ref_win),2);
num_odor_trials_dFF=num_odor_trials_dFF+1;
this_num_odor_trial(num_odor_trials_dFF)=num_odor_trials;
szwins=size(caimanhandles.caimandr_choices.wins);
%Save lick times
this_CRii_lick=which_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,:);
for winNo=1:szwins(1)
%Calculate dFF
win=(time_to_event>=caimanhandles.caimandr_choices.wins(winNo,1))&(time_to_event<=caimanhandles.caimandr_choices.wins(winNo,2));
win_dFF=[];
win_dFF=mean(CR_traces(which_trial_CR==trNo,win),2);
% norm_dFF=win_dFF./ref_dFF;
all_win_dFF(winNo,num_odor_trials_dFF,1:length(win_dFF))=win_dFF;
no_traces_win_dFF(winNo,num_odor_trials_dFF)=length(win_dFF);
SD_win_dFF(winNo,num_odor_trials_dFF)=std(win_dFF);
mean_win_dFF(winNo,num_odor_trials_dFF)=mean(win_dFF);
CI_win_dFF(winNo,num_odor_trials_dFF,:) = bootci(1000, @mean, win_dFF);
%Calculate lick frequency for this window
lick_freq(winNo,num_odor_trials_dFF)=sum( (these_CRii_lick_times>=caimanhandles.caimandr_choices.wins(winNo,1))&...
(these_CRii_lick_times<=caimanhandles.caimandr_choices.wins(winNo,2)))/(caimanhandles.caimandr_choices.wins(winNo,2)-...
caimanhandles.caimandr_choices.wins(winNo,1));
end
%Calculate the average snip for this trial
CRii=handles_out.CR_trial_no(trNo);
no_time_points=length(handles_out.componentNo(1).trialNo(CRii).CR_traces);
num_traces=handles_out.trialNo(CRii).trace_numCR;
these_traces=zeros(num_traces,no_time_points);
for trace_num=1:handles_out.trialNo(CRii).trace_numCR
these_traces(trace_num,:)=handles_out.componentNo(trace_num).trialNo(CRii).CR_traces;
end
mean_snip_dFF(num_odor_trials_dFF,1:no_time_points)=mean(these_traces,1);
CI_snip_dFF(num_odor_trials_dFF,1:2,1:no_time_points)=bootci(1000, @mean, these_traces);
time(num_odor_trials_dFF).time_to_event=handles_out.time_to_eventCR;
epochs_per_trial_dFF(num_odor_trials_dFF)=4;
trial_dFF(num_odor_trials_dFF)=num_odor_trials;
%Get the movement
snip_maskOF=(timeOF>=epoch_time(trNo)-dt_before)...
&(timeOF<=epoch_time(trNo)+dt_after);
velocity(num_odor_trials_dFF,1:sum(snip_maskOF))=mag_meantail(snip_maskOF);
time_OFsnip(num_odor_trials_dFF,1:sum(snip_maskOF))=([1:sum(snip_maskOF)]*(1/frame_rate))-dt_before;
no_timepointsOF(num_odor_trials_dFF)=sum(snip_maskOF);
end
end
if num_odor_trials_dFF==45
pffft=1;
end
end
noROIs(filNum)=szhit(1);
end
%Trim the time course for LDA
all_lda_input_timecourse=all_lda_input_timecourse(1:no_timepoints,:,:);
time_to_eventLDA=time_to_eventLDA(1,1:no_timepoints);
%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(2*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
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])
if ~isfield(caimanhandles.caimandr_choices,'start_reversal')
caimanhandles.caimandr_choices.start_reversal=200;
end
%For reversals plot violin plot of percent
if caimanhandles.caimandr_choices.start_reversal<caimanhandles.caimandr_choices.no_files
%Keep track of the percent correct
%Plot percent correct vs trial
figNo=figNo+1;
try
close(figNo)
catch
end
hFig1 = figure(figNo);
set(hFig1, 'units','normalized','position',[.25 .65 .5 .25])
hold on
%Parameters for violin plot
edges=0:5:100;
rand_offset=0.8;
%Trials before reversal
x_val=1;
handles_out2.pctPerWin(1).pct=perCorr(1:first_num_odor_trials(caimanhandles.caimandr_choices.start_reversal)-1);
bar(x_val,mean(handles_out2.pctPerWin(1).pct),'FaceColor',[0.7 0.7 0.7])
[handles_out2.pctPerWin(1).pct_mean, handles_out2.pctPerWin(1).pct_CI]=drgViolinPoint(handles_out2.pctPerWin(1).pct,edges,x_val,rand_offset,'k',2);
%Trials after reversal
x_val=2;
handles_out2.pctPerWin(2).pct=perCorr(first_num_odor_trials(caimanhandles.caimandr_choices.start_reversal)+15:first_num_odor_trials(caimanhandles.caimandr_choices.start_reversal)+65);
bar(x_val,mean(handles_out2.pctPerWin(2).pct),'FaceColor',[0.7 0.7 0.7])
[handles_out2.pctPerWin(2).pct_mean, handles_out2.pctPerWin(2).pct_CI]=drgViolinPoint(handles_out2.pctPerWin(2).pct,edges,x_val,rand_offset,'k',2);
%Trials at end
x_val=3;
handles_out2.pctPerWin(3).pct=perCorr(end-100:end);
bar(x_val,mean(handles_out2.pctPerWin(3).pct),'FaceColor',[0.7 0.7 0.7])
[handles_out2.pctPerWin(3).pct_mean, handles_out2.pctPerWin(3).pct_CI]=drgViolinPoint(handles_out2.pctPerWin(3).pct,edges,x_val,rand_offset,'k',2);
%Draw lines between points
plot([1 2 3],[mean(handles_out2.pctPerWin(1).pct) mean(handles_out2.pctPerWin(2).pct) mean(handles_out2.pctPerWin(3).pct)],'-k')
ylim([0 120])
xlim([0.3 3.7])
ylabel('Percent correct')
xticks([1 2 3 5 6 7])
xticklabels({'Before','After','End'})
window_labels{1}='Before';
window_labels{2}='After';
window_labels{3}='End';
fprintf(1, ['\n\nranksum p values for percent correct windows\n\n']);
no_pvals=0;
for ii=1:3
for jj=ii+1:3
no_pvals=no_pvals+1;
if (adtest(handles_out2.pctPerWin(ii).pct)==1)||(adtest(handles_out2.pctPerWin(ii).pct)==1)
p_vals_corr(no_pvals)=ranksum(handles_out2.pctPerWin(ii).pct,handles_out2.pctPerWin(jj).pct);
fprintf(1, ['p values ranksum for ' window_labels{ii} ' vs. ' window_labels{jj} ' =%d\n'],p_vals_corr(no_pvals));
else
[h p_vals_corr(no_pvals)]=ttest2(handles_out2.pctPerWin(ii).pct,handles_out2.pctPerWin(jj).pct);
fprintf(1, ['p values t test for ' window_labels{ii} ' vs. ' window_labels{jj} ' =%d\n'],p_vals_corr(no_pvals));
end
end
end
pFDRcorr=drsFDRpval(p_vals_corr);
fprintf(1, ['pFDR for significant difference percent correct = %d\n\n'],pFDRcorr);
end
%plot number of ROIs
figNo=figNo+1;
try
close figNo
catch
end
hFig = figure(figNo);
set(hFig, 'units','normalized','position',[.25 .1 .7 .7])
hold on
plot([1:caimanhandles.caimandr_choices.no_files],noROIs,'-ok')
ylim([0 1.2*max(noROIs)])
title('Number of ROIs per file')
xlabel('File no')
ylabel('No ROIs')
%Plot norm dFF for each window
if caimanhandles.caimandr_choices.start_reversal>0
if caimanhandles.caimandr_choices.start_reversal<caimanhandles.caimandr_choices.no_files
tr_reversal=first_num_odor_trials(caimanhandles.caimandr_choices.start_reversal);
else
tr_reversal=200000;
end
else
tr_reversal=0;
end
for winNo=1:szwins(1)
figNo=figNo+1;
try
close figNo
catch
end
hFig = figure(figNo);
set(hFig, 'units','normalized','position',[.25 .05+0.3*(winNo-1) .5 .25])
hold on
for dFF_trNo=1:num_odor_trials_dFF
%Plot odor 1 (S+ forward)
subplot(2,1,1)
hold on
if dFF_trNo<tr_reversal
%If Hit or Miss
if (epochs_per_trial_dFF(dFF_trNo)==1)||(epochs_per_trial_dFF(dFF_trNo)==2)
%Confidence interval
this_CI=zeros(1,2);
this_CI(1,1:2)=CI_win_dFF(winNo,dFF_trNo,:);
plot([trial_dFF(dFF_trNo) trial_dFF(dFF_trNo)],this_CI,'-k')
if epochs_per_trial_dFF(dFF_trNo)==1
%Hit
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'or')
end
if epochs_per_trial_dFF(dFF_trNo)==4
%CR
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'ob')
end
if epochs_per_trial_dFF(dFF_trNo)==2
%Miss
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'oc')
end
if epochs_per_trial_dFF(dFF_trNo)==3
%FA
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'om')
end
end
else
%If CR or FA
if (epochs_per_trial_dFF(dFF_trNo)==3)||(epochs_per_trial_dFF(dFF_trNo)==4)
%Confidence interval
this_CI=zeros(1,2);
this_CI(1,1:2)=CI_win_dFF(winNo,dFF_trNo,:);
plot([trial_dFF(dFF_trNo) trial_dFF(dFF_trNo)],this_CI,'-k')
if epochs_per_trial_dFF(dFF_trNo)==1
%Hit
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'or')
end
if epochs_per_trial_dFF(dFF_trNo)==4
%CR
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'ob')
end
if epochs_per_trial_dFF(dFF_trNo)==2
%Miss
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'oc')
end
if epochs_per_trial_dFF(dFF_trNo)==3
%FA
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'om')
end
end
end
%Plot odor 2 (S- forward)
subplot(2,1,2)
hold on
if dFF_trNo>=tr_reversal
%If Hit or Miss
if (epochs_per_trial_dFF(dFF_trNo)==1)||(epochs_per_trial_dFF(dFF_trNo)==2)
%Confidence interval
this_CI=zeros(1,2);
this_CI(1,1:2)=CI_win_dFF(winNo,dFF_trNo,:);
plot([trial_dFF(dFF_trNo) trial_dFF(dFF_trNo)],this_CI,'-k')
if epochs_per_trial_dFF(dFF_trNo)==1
%Hit
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'or')
end
if epochs_per_trial_dFF(dFF_trNo)==4
%CR
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'ob')
end
if epochs_per_trial_dFF(dFF_trNo)==2
%Miss
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'oc')
end
if epochs_per_trial_dFF(dFF_trNo)==3
%FA
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'om')
end
end
else
%If CR or FA
if (epochs_per_trial_dFF(dFF_trNo)==3)||(epochs_per_trial_dFF(dFF_trNo)==4)
%Confidence interval
this_CI=zeros(1,2);
this_CI(1,1:2)=CI_win_dFF(winNo,dFF_trNo,:);
plot([trial_dFF(dFF_trNo) trial_dFF(dFF_trNo)],this_CI,'-k')
if epochs_per_trial_dFF(dFF_trNo)==1
%Hit
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'or')
end
if epochs_per_trial_dFF(dFF_trNo)==4
%CR
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'ob')
end
if epochs_per_trial_dFF(dFF_trNo)==2
%Miss
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'oc')
end
if epochs_per_trial_dFF(dFF_trNo)==3
%FA
plot(trial_dFF(dFF_trNo), mean_win_dFF(winNo,dFF_trNo),'om')
end
end
end
end
for spno=1:2
subplot(2,1,spno)
if isfield(caimanhandles.caimandr_choices,'start_reversal')
filNum=caimanhandles.caimandr_choices.start_reversal;
if (filNum>0)&(filNum<caimanhandles.caimandr_choices.no_files)
plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[prctile(mean_win_dFF(:),1)-0.1*(prctile(mean_win_dFF(:),99)-prctile(mean_win_dFF(:),1)) 1.2*prctile(mean_win_dFF(:),99)-0.1*(prctile(mean_win_dFF(:),99)+prctile(mean_win_dFF(:),1))],'-k','LineWidth',4)
text(first_num_odor_trials(filNum)+2,prctile(mean_win_dFF(:),1)+0.9*(prctile(mean_win_dFF(:),99)-prctile(mean_win_dFF(:),1)),'Reversal','Color','k','FontSize',18)
end
end
if isfield(caimanhandles.caimandr_choices,'start_gogo')
filNum=caimanhandles.caimandr_choices.start_gogo;
if (filNum>0)&(filNum<caimanhandles.caimandr_choices.no_files)
plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[prctile(mean_win_dFF(:),1)-0.1*(prctile(mean_win_dFF(:),99)-prctile(mean_win_dFF(:),1)) 1.2*prctile(mean_win_dFF(:),99)-0.1*(prctile(mean_win_dFF(:),99)+prctile(mean_win_dFF(:),1))],'-k','LineWidth',4)
text(first_num_odor_trials(filNum)+2,prctile(mean_win_dFF(:),1)+0.9*(prctile(mean_win_dFF(:),99)-prctile(mean_win_dFF(:),1)),'Go-go','Color','k','FontSize',18)
end
end
if isfield(caimanhandles.caimandr_choices,'start_session')
if length(caimanhandles.caimandr_choices.start_session)>=2
for sessionNo=2:length(caimanhandles.caimandr_choices.start_session)
filNum=caimanhandles.caimandr_choices.start_session(sessionNo);
plot([first_num_odor_trials(filNum) first_num_odor_trials(filNum)],[prctile(mean_win_dFF(:),1)-0.1*(prctile(mean_win_dFF(:),99)-prctile(mean_win_dFF(:),1)) 1.2*prctile(mean_win_dFF(:),99)-0.1*(prctile(mean_win_dFF(:),99)+prctile(mean_win_dFF(:),1))],'-k')
% text(first_num_odor_trials(filNum)+2,80,'Reversal','Color','k','FontSize',18)
end
end
end
plot([1 num_odor_trials_dFF],[0 0], '-k')
if spno==1
title(['Odor 1 (S+ forward)'])
else
title(['Odor 2 (S- forward)'])
end
xlabel('Trial number')
ylabel('Normalized dF/F')
ylim([prctile(mean_win_dFF(:),1)-0.1*(prctile(mean_win_dFF(:),99)-prctile(mean_win_dFF(:),1)) 1.2*prctile(mean_win_dFF(:),99)-0.1*(prctile(mean_win_dFF(:),99)+prctile(mean_win_dFF(:),1))])
end
suptitle(['Normalized dF/F for window No ' num2str(winNo) ' Hit(red) Miss(cyan) FA(magenta) CR(blue)'])
end
%Do a linear discriminant analysis
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}='dF/F LDA analysis for trials with percent correct <65';
supertitle_description{2}='dF/F LDA analysis for trials with percent correct >=65&<80';
supertitle_description{3}='dF/F LDA analysis for trials with percent correct >=80';
else
%Forward and reverse
total_trial_windows=2;
supertitle_description{1}='dF/F LDA analysis for trials before reversal';
supertitle_description{2}='dF/F LDA analysis for trials after reversal at end of the session';
end
firstFig=figNo+1;
maxPC1=-2000;
maxPC2=-2000;
minPC1=20000;
minPC2=20000;
trial_window_description{1}='percent correct <65';
trial_window_description{2}='percent correct >=65&<80';
trial_window_description{3}='percent correct >=80';
lick_threshold=20; %This is the threshold to exclude the runs where Ming was adding water manually
t_odor_on=0;
t_odor_off=4;
for no_trial_windows=1:total_trial_windows
handles_par(no_trial_windows).time_to_eventLDA=time_to_eventLDA;
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;80 100.1];
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 (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_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)>=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;
%Calculate dimensionality in the different windows
for winNo=1:szwins(1)
%Calculate dFF
win=(time_to_event>=caimanhandles.caimandr_choices.wins(winNo,1))&(time_to_event<=caimanhandles.caimandr_choices.wins(winNo,2));
szalit=size(all_lda_input_timecourse);
no_comps=szalit(2);
%Get the data
%Columns: cells, Rows: dF/F
Signal=zeros(sum(win)*sum(dFF_trial_mask),no_comps);
no_datapoints=0;
for trNo=1:length(dFF_trial_mask)
if dFF_trial_mask(trNo)
Signal(no_datapoints+1:no_datapoints+sum(win),:)=all_lda_input_timecourse(win,:,trNo);
no_datapoints=no_datapoints+sum(win);
end
end
handles_outs.pcorwwin(no_trial_windows).Dim_out(winNo) = nansum(eig(cov(Signal)))^2/nansum(eig(cov(Signal)).^2);
switch no_trial_windows
case 1
fprintf(1, ['Dimensionality for window %d and percent correct <65 = %d\n'], winNo, handles_outs.pcorwwin(no_trial_windows).Dim_out(winNo));
case 2
fprintf(1, ['Dimensionality for window %d and percent correct >=65&<80 = %d\n'], winNo, handles_outs.pcorwwin(no_trial_windows).Dim_out(winNo));
case 3
fprintf(1, ['Dimensionality for window %d and percent correct >80 = %d\n'], winNo, handles_outs.pcorwwin(no_trial_windows).Dim_out(winNo));
end
end
dt_lick=time_to_eventLDA(2)-time_to_eventLDA(1);
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/(time_to_eventLDA(2)-time_to_eventLDA(1)));
%First 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;
%Plot 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));
%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);
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