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dfs_mua_analysis_allcombination.m
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dfs_mua_analysis_allcombination.m
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%% dfs_mua_analysis_allcombination.m
% -> EDFigure45C_EDFigure7DE_mua_mod_score.mat
% -> dfa_ahead_behind_distance_muareldist.m
% -> dfa_ahead_behind_distance_muareldist_lintrack.m
%% Brief Description
% Forelimb plants are collected during different parts of the track
% A corresponding plant triggered average is calculated
% These plant times are then shuffled n times (5000) and an equivalent
% Number of plants as are in that epoch are collected m times (1000) - this
% is my shuffled distribution per epoch
% A modulation score is then computed per epoch for the actual plant times
% and for the shuffles
% modulation score: can be just peak-trough for mua
% mod_score_stdev: is calculated as the summed square deviations from the mean
% stats on this data will look like a comparsion to the shuffled
% distributions.
clear all;
%% initialization parameters
mod_score_window=0.04; % corresponds to 80ms around the plant time
t_pre=0.07; t_post=0.07; % the usal window around which we compute one cycle of MUA fluctuations ~125 ms is a theta cycle
OffsetMin=-0.07; OffsetMax=0.07; % for shuffle analysis
number_of_shuffles=5000; % per epoch
savefigs=0; %saves the mua trace per wtrack portion for all aniamls
shuffles=0; n_samps=1000; %does the shuffles and saves them for each portion of the track in results_dir
resdir='';
%% OUTBOUND MUA
results_dir=''; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
for a=[1,2,3,4,5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_outbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
forelimb_inbound_mua = f(a).output{1,1}(i).forelimb_inbound_mua;
forelimb_outbound_mua = f(a).output{1,1}(i).forelimb_outbound_mua;
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_outbound_mua(:,2)=forelimb_outbound_mua;
forelimb_inbound_mua(:,2)=forelimb_inbound_mua;
limb_select_mua=forelimb_outbound_mua; number_steps=[number_steps; size(forelimb_outbound_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_mua_shuff(:,2)=forelimb_outbound_mua_shuff;
limb_select_mua_shuff=forelimb_outbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch_pershuff{a,i,n_shuff}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all mod_score_window ...
lags_mua mydata_mua_nulldist mod_score_perepoch_pershuff mod_score_perepoch mod_score_perepoch_rescale_pershuff_stdev ...
mod_score_perepoch_rescale_stdev
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
% degree of theta modulation Frank et al 2001
% theta_mod=maximum value -minimum value
% saveworkspace
if shuffles==1
cd(results_dir); save('')
end
%% %% INBOUND MUA
clearvars -except f t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles mod_score_window
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
for a=[1,2,3,4,5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_inbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
forelimb_inbound_mua = f(a).output{1,1}(i).forelimb_inbound_mua;
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_inbound_mua(:,2)=forelimb_inbound_mua;
limb_select_mua=forelimb_inbound_mua; number_steps=[number_steps; size(forelimb_inbound_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_mua_shuff(:,2)=forelimb_inbound_mua_shuff;
limb_select_mua_shuff=forelimb_inbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch_pershuff{a,i,n_shuff}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all mod_score_window ...
lags_mua mydata_mua_nulldist mod_score_perepoch_pershuff mod_score_perepoch mod_score_perepoch_rescale_pershuff_stdev ...
mod_score_perepoch_rescale_stdev
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
% degree of theta modulation Frank et al 2001
% theta_mod=maximum value -minimum value
% saveworkspace
if shuffles==1
cd(results_dir); save('')
end
%% outbound outer arms mua
clearvars -except t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles mod_score_window
results_dir=''; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
f1=f; clear f;
results_filename=[''];
load([results_filename,'.mat']);
f2=f; clear f;
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
f=f1;
for a=[1,2,3,4,5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_outbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_outbound_mua_outer1 = f1(a).output{1,1}(i).forelimb_outbound_mua;
forelimb_outbound_mua_outer2 = f2(a).output{1,1}(i).forelimb_outbound_mua;
forelimb_outbound_mua=[forelimb_outbound_mua_outer1;forelimb_outbound_mua_outer2];
forelimb_outbound_mua(:,2)=forelimb_outbound_mua;
limb_select_mua=forelimb_outbound_mua;
number_steps=[number_steps; size(limb_select_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_mua_shuff(:,2)=forelimb_outbound_mua_shuff;
limb_select_mua_shuff=forelimb_outbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch_pershuff{a,i,n_shuff}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all mod_score_window ...
lags_mua mydata_mua_nulldist mod_score_perepoch_pershuff mod_score_perepoch mod_score_perepoch_rescale_pershuff_stdev ...
mod_score_perepoch_rescale_stdev
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
if shuffles==1
cd(results_dir); cd(results_dir); save('')
end
%% INBOUND outer arms mua
clearvars -except t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles mod_score_window
results_dir=''; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
f1=f; clear f;
results_filename=[''];
load([results_filename,'.mat']);
f2=f; clear f;
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
f=f1;
for a=[1,2,3,4,5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_outbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_inbound_mua_outer1 = f1(a).output{1,1}(i).forelimb_inbound_mua;
forelimb_inbound_mua_outer2 = f2(a).output{1,1}(i).forelimb_inbound_mua;
forelimb_inbound_mua=[forelimb_inbound_mua_outer1;forelimb_inbound_mua_outer2];
forelimb_inbound_mua(:,2)=forelimb_inbound_mua;
limb_select_mua=forelimb_inbound_mua;
number_steps=[number_steps; size(limb_select_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_mua_shuff(:,2)=forelimb_inbound_mua_shuff;
limb_select_mua_shuff=forelimb_inbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch_pershuff{a,i,n_shuff}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all mod_score_window ...
lags_mua mydata_mua_nulldist mod_score_perepoch_pershuff mod_score_perepoch mod_score_perepoch_rescale_pershuff_stdev ...
mod_score_perepoch_rescale_stdev
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
if shuffles==1
cd(results_dir); cd(results_dir); save('')
end
%% INBOUND TJUNC mua
clearvars -except t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles mod_score_window
results_dir=''; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
f1=f; clear f;
results_filename=[''];
load([results_filename,'.mat']);
f2=f; clear f;
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
f=f1;
for a=[1,2,3,4,5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_outbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_inbound_mua_outer1 = f1(a).output{1,1}(i).forelimb_inbound_mua;
forelimb_inbound_mua_outer2 = f2(a).output{1,1}(i).forelimb_inbound_mua;
forelimb_inbound_mua=[forelimb_inbound_mua_outer1;forelimb_inbound_mua_outer2];
forelimb_inbound_mua(:,2)=forelimb_inbound_mua;
limb_select_mua=forelimb_inbound_mua;
number_steps=[number_steps; size(limb_select_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_mua_shuff(:,2)=forelimb_inbound_mua_shuff;
limb_select_mua_shuff=forelimb_inbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch_pershuff{a,i,n_shuff}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all mod_score_window ...
lags_mua mydata_mua_nulldist mod_score_perepoch_pershuff mod_score_perepoch mod_score_perepoch_rescale_pershuff_stdev ...
mod_score_perepoch_rescale_stdev
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
if shuffles==1
cd(results_dir); cd(results_dir); save('')
end
%% OUTBOUND TJUNC mua
clearvars -except animals f f1 f2 t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles mod_score_window
f=f1;
for a=[1,2,3,4,5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_outbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_outbound_mua_outer1 = f1(a).output{1,1}(i).forelimb_outbound_mua;
forelimb_outbound_mua_outer2 = f2(a).output{1,1}(i).forelimb_outbound_mua;
forelimb_outbound_mua=[forelimb_outbound_mua_outer1;forelimb_outbound_mua_outer2];
forelimb_outbound_mua(:,2)=forelimb_outbound_mua;
limb_select_mua=forelimb_outbound_mua;
number_steps=[number_steps; size(limb_select_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_mua_shuff(:,2)=forelimb_outbound_mua_shuff;
limb_select_mua_shuff=forelimb_outbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_nulldist_rescale{a,i,n_shuff}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch_pershuff{a,i,n_shuff}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all mod_score_window ...
lags_mua mydata_mua_nulldist mod_score_perepoch_pershuff mod_score_perepoch mod_score_perepoch_rescale_pershuff_stdev ...
mod_score_perepoch_rescale_stdev
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
if shuffles==1
cd(results_dir); cd(results_dir); save('')
end
%% initialization parameters
mod_score_window=0.04; % corresponds to 80ms around the plant time
t_pre=0.07; t_post=0.07; % the usal window around which we compute one cycle of MUA fluctuations ~125 ms is a theta cycle
OffsetMin=-0.07; OffsetMax=0.07; % for shuffle analysis
number_of_shuffles=5000; % per epoch
savefigs=1; %saves the mua trace per wtrack portion for all aniamls
shuffles=1; n_samps=1000; %does the shuffles and saves them for each portion of the track in results_dir
resdir='';
% LINEARTRACK
results_dir=''; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
animals={'Jaq', 'Roqui', 'Lotus'};
for a=[1,2,3]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=2:1:size(ind_select) % first epoch is exculded
counter=[counter; size(i,1)];
if ~isnan(f(a).output{1,1}(i).forelimb_outbound_mua)
animal=animals{1,a};
base_dir='';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
%keyboard
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
forelimb_inbound_mua_rl = f(a).output{1,1}(i).forelimb_inbound_mua;
forelimb_outbound_mua_lr = f(a).output{1,1}(i).forelimb_outbound_mua;
cdat_mua=imcont('data', mua', 'timestamp', muatimes');
forelimb_outbound_mua=sort([forelimb_inbound_mua_rl;forelimb_outbound_mua_lr]);
forelimb_outbound_mua(:,2)=forelimb_outbound_mua;
limb_select_mua=forelimb_outbound_mua; number_steps=[number_steps; size(forelimb_outbound_mua,1)];
trig_plot_mua=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua, 't_pre',t_pre, 't_post',t_post);
lags_mua=trig_plot_mua.lags;
% stdev mod score for all data
mydata_mua{a,i}=trig_plot_mua.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_mua{a,i}(:)-mean(mydata_mua{a,i})));
% stdev mod score for all data rescaled
mydata_mua_rescale{a,i}=(mydata_mua{a,i})./(max(mydata_mua{a,i}));
mod_score_perepoch_rescale_stdev{a,i}= sum(abs(mydata_mua_rescale{a,i}(:)-mean(mydata_mua_rescale{a,i})));
% peak-trough mod score rescaled
[pks_val,~]=findpeaks(mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
[trs_val,~]=findpeaks(-mydata_mua_rescale{a,i}, lags_mua,'MinPeakDistance', mod_score_window);
mod_score_perepoch{a,i}=max(pks_val)-min(abs(trs_val)); clear trs_val pks_val
% keyboard
% plot(mydata_mua{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_mua.peritrig_data,1)
% if sum(isnan(trig_plot_mua.peritrig_data(n_rows,:)))<2
% mydata_mua_nonnan{n_rows}=trig_plot_mua.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_mua_nonnan(:))), 'r'); hold on;
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_mua_shuff=limb_select_mua(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_mua_shuff(:,2)=forelimb_outbound_mua_shuff;
limb_select_mua_shuff=forelimb_outbound_mua_shuff;
trig_plot_mua_shuff=periseg('cdat', contchans(cdat_mua, 'chans', 1), 'segs', limb_select_mua_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_mua_shuff{a,i,i_shuff}=trig_plot_mua_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_mua_shuff trig_plot_mua_shuff;
end
mydata_mua_shuff_all=cell2mat(squeeze(((mydata_mua_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_mua_shuff_all,1),size(limb_select_mua(:,1),1));
mydata_mua_nulldist{a,i,n_shuff}=nanmean(mydata_mua_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));
% calculate also for the rescaled version
mydata_mua_nulldist_rescale{a,i,n_shuff}=(nanmean(mydata_mua_shuff_all(randIdcs,:))./(max(nanmean(mydata_mua_shuff_all(randIdcs,:)))));
mod_score_perepoch_rescale_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_mua_nulldist{a,i,n_shuff}(:)-mean(mydata_mua_nulldist{a,i,n_shuff})));