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spDist_plotHRFs_ERA_pRFvoxSelection_btwnRFstats.m
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spDist_plotHRFs_ERA_pRFvoxSelection_btwnRFstats.m
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% spDist_pilot_scanner_plotHRFs_ERA.m
%
% loads trialData files & plots HRFs for distractor/no-distractor trials
% (Note: different # of trials...)
%
% like fidelity, compares matched trial epochs
%
% stats: 3-way shuffled ANOVA, then 2-way shuffled ANOVAs per ROI
%
% TODO: use 'plot' instead of 'text' for significance markers
%
function spDist_plotHRFs_ERA_pRFvoxSelection_btwnRFstats(subj,sess,ROIs,alignment)
task_dir = 'spDist';
root = spDist_loadRoot;
rng(spDist_randSeed);
if nargin < 1 || isempty(subj)
subj = {'AY','CC','EK','KD','MR','SF','XL'};
end
if nargin < 2 || isempty(sess)
sess_template = {'spDist1','spDist2'};
sess = cell(length(subj),1); for ss = 1:length(subj); sess{ss} = sess_template; end
clear sess_template
end
if nargin < 3 || isempty(ROIs)
% all ROIs
ROIs = {'V1V2V3','V3AB','hV4','LO1','IPS0IPS1','IPS2IPS3','sPCS'};
end
if nargin < 4 || isempty(alignment)
alignment= 'targ_ang_all';
end
if alignment == 'dist_ang_all';
do_distalign_plot =1;
else
do_distalign_plot =0;
end
% do stats? (takes ~10 mins)
do_stats = 0;
% inspect # voxels?
make_vox_plot = 1;
% if so, save them?
save_stats = 0;
func_suffix = 'surf';
ve_thresh = 0.1;
pol_diff_thresh_in = 15;
pol_diff_thresh_out = 165;
min_ecc = 2;
max_ecc = 15;
vox_inn =nan(length(subj),2,length(ROIs),360);
vox_outn =nan(length(subj),2,length(ROIs),360);
% 'overall' delay-period (maximizes difference between distractor
% present/absent
% (because we use less than/equal to for tpt selection...)
% (this is based on tr - we want to INCLUDE TRs that span this range)
delay_range = [9 15]; % TR beginning at 7, ending at 15, to match IEM training
%delay_range = [7 15];
% these are based on *time*, not TR...
% as in Fig. 1, 4, 5 - 3 epochs for comparing before/during/after distractor
delay_tpt_range = [3.75 5.25; 8.25 9.75; 10.5 12]; %for stats - up for discussion?
% >= n,1 and < n,2
epoch_str = {'PRE','DIST','POST'};
t_markers = [0 4.5 12]; % beginning of delay, beginning of distractor, beginning of response
%% load data
startidx = 1;
for ss = 1:length(subj)
for sess_idx = 1:length(sess{ss})
for vv = 1:length(ROIs)
fn = sprintf('%s/%s_trialData/%s_%s_%s_%s_trialData.mat',root,task_dir,subj{ss},sess{ss}{sess_idx},ROIs{vv},func_suffix);
fprintf('loading %s...\n',fn);
data = load(fn);
if vv == 1 && ss == 1 && sess_idx == 1
% initialize variables...
nblank = length(ROIs)*numel(sess)*size(data.dt_all,1);
all_hrfs_in = nan(nblank,size(data.dt_all,3));
all_hrfs_out = nan(nblank,size(data.dt_all,3));
all_vox_in = nan(nblank,1);
all_vox_out = nan(nblank,1);
all_vox_in_ecc = nan(nblank,1);
all_vox_out_ecc = nan(nblank,1);
all_vox_in_sigma = nan(nblank,1);
all_vox_out_sigma = nan(nblank,1);
all_conds = nan(nblank,size(data.c_all,2));
all_subj = nan(nblank,1);
all_ROIs = nan(nblank,1);
all_sess = nan(nblank,1);
TR = data.TR;
which_TRs = data.which_TRs;
end
thisidx = startidx:(startidx+size(data.dt_all,1)-1);
%%% additions for selecting based on ecc, pol ang diff %%%
tmp_x = data.rf.x0; %this is in unit?
tmp_y =data.rf.y0.*-1; % because vista gives -y as upper vf
tmp_pol = atan2d(tmp_y,tmp_x);
tmp_ecc = data.rf.ecc;
for pp = 1:length(data.targ_ang_all)
clear align_loc
align_loc = data.(sprintf('%s',alignment));
tmprel = align_loc(pp) - tmp_pol; % align_loc will either be targ or dist
pol_diff(1,:)= abs(mod((tmprel+180), 360)-180); %dont care about sign here, just need angular difference magnitude
%vox selection criteria using pol ang sep, max, min ecc
which_vox_in = data.rf.ve>=ve_thresh & pol_diff(1,:) <= pol_diff_thresh_in & tmp_ecc > min_ecc & tmp_ecc < max_ecc;
which_vox_out = data.rf.ve>=ve_thresh & pol_diff(1,:) >= pol_diff_thresh_out & tmp_ecc > min_ecc & tmp_ecc < max_ecc;
vox_inn(ss,sess_idx,vv,pp) = sum(which_vox_in);
vox_outn(ss,sess_idx,vv,pp) = sum(which_vox_out);
idx = thisidx(pp);
all_hrfs_in(idx,:) = squeeze(nanmean(data.dt_allz(pp,which_vox_in,:),2))';
all_hrfs_out(idx,:) = squeeze(nanmean(data.dt_allz(pp,which_vox_out,:),2))';
all_vox_in(idx,:) = sum(which_vox_in);
all_vox_out(idx,:) = sum(which_vox_out);
all_vox_in_ecc(idx,:) = mean(data.rf.ecc(which_vox_in));
all_vox_out_ecc(idx,:) = mean(data.rf.ecc(which_vox_out));
all_vox_in_sigma(idx,:) = mean(data.rf.sigma(which_vox_in));
all_vox_out_sigma(idx,:) = mean(data.rf.sigma(which_vox_out));
all_vox_out(idx,:) = sum(which_vox_out);
clear pol_diff tmp_rel idx
end
all_conds(thisidx,:) = data.c_all;
all_subj(thisidx) = ss;
all_ROIs(thisidx) = vv;
all_sess(thisidx) = sess_idx;
startidx = thisidx(end)+1;
clear data tmp_y tmp_x tmp_pol tmp_ecc
end
end
end
%% remove baseline
baseline_TRs = which_TRs < 0;
all_hrfs_in = all_hrfs_in - mean(all_hrfs_in(:,baseline_TRs),2);
all_hrfs_out = all_hrfs_out - mean(all_hrfs_out(:,baseline_TRs),2);
%% plot mean HRF activations throughout the delay-period. Sort by RF condition and distractor absent (mag) v distractor present (blue)
%TOP: RFin
%MIDDLE: RFout
%BOTTOM: RFin-out
all_HRFs{1} = all_hrfs_in;
all_HRFs{2} = all_hrfs_out;
%all_HRFs{3} = all_hrfs_in - all_hrfs_out;
all_HRFs{3} = ((all_hrfs_in + 100) - (all_hrfs_out +100)) ./ ((all_hrfs_in + 100) + (all_hrfs_out + 100));
cond_colors = spDist_condColors;
cu = unique(all_conds(:,1));
all_mean_hrf = cell(length(all_HRFs),length(cu),1);
axhrf = nan(length(all_HRFs),length(ROIs));
mh = nan(length(ROIs),length(t_markers));
% start with just subplots of timeseries (w/ errorbars?)
figure;
for hh = 1:length(all_HRFs)
for vv = 1:length(ROIs)
axhrf(hh,vv) = subplot(length(all_HRFs),length(ROIs),(hh-1)*length(ROIs)+vv); hold on;
% draw 'baseline'
plot([which_TRs(1) which_TRs(end)]*TR,[0 0],'k-','LineWidth',0.75);
% draw event markers
mh(vv,:) = plot(t_markers.*[1;1],[0 .1],'-','Color',[0.7 0.7 0.7],'LineWidth',0.75);
for cc = 1:length(cu)
thisd = nan(length(subj),size(all_HRFs{1},2));
for ss = 1:length(subj)
thisidx = all_subj == ss & all_ROIs == vv & all_conds(:,1)==cu(cc);
thisd(ss,:) = nanmean(all_HRFs{hh}(thisidx,:),1); %using nanmean here
all_mean_hrf{hh,cc}(vv,:,ss) = nanmean(all_HRFs{hh}(thisidx,:),1);
clear thisidx;
end
% plot, like for reconstructions, such that middle of each
% datapoint is at middle of TR
if hh ==1
plot(which_TRs*TR + TR/2,nanmean(thisd,1),'-','LineWidth',1.0,'Color',cond_colors(cc,:));
elseif hh ==2
plot(which_TRs*TR + TR/2,nanmean(thisd,1),'--','LineWidth',1.0,'Color',cond_colors(cc,:)); % dashed for RFout
elseif hh ==3
plot(which_TRs*TR + TR/2,nanmean(thisd,1),':','LineWidth',1.0,'Color',cond_colors(cc,:));
end
btwn_fill = [nanmean(thisd,1)+1.*nanstd(thisd,[],1)/sqrt(length(subj)) fliplr( nanmean(thisd,1)-1.*nanstd(thisd,[],1)/sqrt(length(subj)) )]; % geh test
fill([which_TRs*TR + TR/2 fliplr(which_TRs*TR + TR/2)],btwn_fill,cond_colors(cc,:),'linestyle','none','facealpha',0.3);
clear thisd;
end
title(ROIs{vv});
if vv==1 && hh ==1
ylabel({'Voxels in RF'; 'BOLD Z-score'});
xticks([0 4.5 12])
set(gca,'XTickLabel',[0 4.5 12])
xlabel('Time (s)');
yticks([-.2:.2:1.4])
elseif vv==1 && hh ==2
ylabel({'Voxels out of RF ';'BOLD Z-score'});
set(gca,'xTickLabel',[]);
elseif vv==1 && hh ==3
ylabel({'Voxels in RF -out of RF';'BOLD Z-score'});
xticks([0 4.5 12])
set(gca,'XTickLabel',[0 4.5 12])
xlabel('Time (s)');
else
yticks([-.2:.2:1.4])
xticks([0 4.5 12])
set(gca,'YTickLabel',[]);
set(gca,'xTickLabel',[]);
end
end
myy = cell2mat(get(axhrf(hh,:),'YLim'));
set(axhrf(hh,:),'YLim',[min(myy(:,1)) max(myy(:,2))],'XLim',[which_TRs(1) which_TRs(end)]*TR,'TickDir','out');
set(mh,'YData',[min(myy(:,1)) max(myy(:,2))]);
if length(ROIs)==7
set(gcf,'Position',[ 157 697 2041 594])
else
set(gcf,'Position',[ 157 697 2541 594])
end
%match_ylim(get(gcf,'Children'));
end
%% plot # voxels per trial, subject, ROI, RF-in & RF-out
if make_vox_plot ==1
subj_col =lines(7);
myd = {};
myd{1} = vox_inn;
myd{2} = vox_outn;
dplot = nan(360,1);
d_store=[];
figure(4)
for ss=1:length(subj)
for vv =1:length(ROIs)
dtmpi = squeeze(myd{1}(ss,:,vv,:))';
di = dtmpi(~isnan(dtmpi)); % get rid of nans which exist bc of preallocation for runs length(max_runs)
%di = di(di~=0);
dtmpo = squeeze(myd{2}(ss,:,vv,:))';
do = dtmpo(~isnan(dtmpo));
%do = do(do~=0);
figure(4)
subplot(length(subj),length(ROIs),(ss-1)*length(ROIs)+vv);
hold on;
plot(di(:),'r-')
plot(do(:),'b--')
% imagesc([di(:) do(:)])
d_store =[d_store; mean(di) mean(do) ss vv ]
voxtxt = sprintf('\n%s %s %.3f in, %.3f out', ss, vv, mean(di),mean(do));
fprintf('%s\n',voxtxt);
%xlim([0 3])
clear dtmpi dtmpo
if ss==1
title(ROIs{vv})
elseif ss==1 && vv==1
ylabel('# voxels per trial')
xlabel(sprintf('%s',subj{ss}))
else
end
end
end
end
store_mu_in = nan(length(subj),length(ROIs)) ;
store_mu_out = nan(length(subj),length(ROIs));
for vv=1:length(ROIs)
for ss = 1:length(subj)
idx = d_store(:,4)==vv & d_store(:,3)==ss;
store_mu_in(ss,vv) = d_store(idx,1);
store_mu_out(ss,vv) = d_store(idx,2);
end
end
if alignment == 'targ_ang_all';
subjvoxplot = figure('Name','subjvoxplot');
store_vox =[];
store_vox_ecc =[];
store_vox_sigma =[];
for vv = 1:length(ROIs)
for ss =1:length(subj)
thisidx = all_subj ==ss & all_ROIs == vv ; % condition is irrelevant here
vin(vv,ss) = mean(all_vox_in(thisidx,:));
vout(vv,ss) = mean(all_vox_out(thisidx,:));
vin_ecc(vv,ss) = nanmean(all_vox_in_ecc(thisidx,:));
vout_ecc(vv,ss) = nanmean(all_vox_out_ecc(thisidx,:));
vin_sigma(vv,ss) = nanmean(all_vox_in_sigma(thisidx,:));
vout_sigma(vv,ss) = nanmean(all_vox_out_sigma(thisidx,:));
figure(subjvoxplot)
subplot(length(subj),length(ROIs),(ss-1)*length(ROIs)+vv);
plot(all_vox_in(thisidx,:),'r-')
hold on;
plot(all_vox_out(thisidx,:),'b-')
if vv==1
ylabel(sprintf('%s',subj{ss}))
xlabel('Trial')
else
end
if ss==1
title(ROIs{vv})
else
end
end
store_vox =[store_vox; mean(vin(vv,:)) mean(vout(vv,:)) vv];
store_vox_ecc =[store_vox_ecc; mean(vin_ecc(vv,:)) mean(vout_ecc(vv,:)) vv];
store_vox_sigma =[store_vox_sigma; mean(vin_sigma(vv,:)) mean(vout_sigma(vv,:)) vv];
end
elseif alignment == 'dist_ang_all';
subjvoxplot = figure('Name','subjvoxplot');
store_vox =[];
store_vox =[];
store_vox_ecc =[];
store_vox_sigma=[];
for cc = 2 % need to isolate distractor only trials here, otherwise MGS will incorrectly populate
for vv = 1:length(ROIs)
for ss =1:length(subj)
thisidx = all_subj ==ss & all_ROIs == vv & all_conds(:,1)==cu(cc);
vin(cc,vv,ss) = mean(all_vox_in(thisidx,:));
vout(cc,vv,ss) = mean(all_vox_out(thisidx,:));
vin_ecc(vv,ss) = nanmean(all_vox_in_ecc(thisidx,:));
vout_ecc(vv,ss) = nanmean(all_vox_out_ecc(thisidx,:));
vin_sigma(vv,ss) = nanmean(all_vox_in_sigma(thisidx,:));
vout_sigma(vv,ss) = nanmean(all_vox_out_sigma(thisidx,:));
figure(subjvoxplot)
subplot(length(subj),length(ROIs),(ss-1)*length(ROIs)+vv);
plot(all_vox_in(thisidx,:),'r-')
hold on;
plot(all_vox_out(thisidx,:),'b-')
if vv==1
ylabel(sprintf('%s',subj{ss}))
xlabel('Trial')
else
end
if ss==1
title(ROIs{vv})
else
end
end
store_vox = [store_vox; mean(vin(cc,vv,:)) mean(vout(cc,vv,:)) vv];
store_vox_ecc =[store_vox_ecc; mean(vin_ecc(vv,:)) mean(vout_ecc(vv,:)) vv];
store_vox_sigma =[store_vox_sigma; mean(vin_sigma(vv,:)) mean(vout_sigma(vv,:)) vv];
end
end
end
%% plot within dist condition & between RF condition
HRF_str ={'RFin','RFout','RFin-out'};
%HRF_str ={'RFin','RFout','RFin-out','RF mod'};
% store something that's ROI x time x subj for each condition
cu = unique(all_conds(:,1));
axhrf = nan(length(all_HRFs),length(ROIs));
mh = nan(length(ROIs),length(t_markers));
condstr = {'Dist Absent','Dist Present'};
figure('name','Figure2A');
for cc = 1:length(cu)
for vv = 1:length(ROIs)
axhrf(cc,vv) = subplot(length(cu),length(ROIs),(cc-1)*length(ROIs)+vv); hold on;
% draw 'baseline'
plot([which_TRs(1) which_TRs(end)]*TR,[0 0],'k-','LineWidth',0.75);
% draw event markers
mh(vv,:) = plot(t_markers.*[1;1],[0 .1],'-','Color',[0.7 0.7 0.7],'LineWidth',0.75);
for hh = 1:2
thisd = nan(length(subj),size(all_HRFs{1},2));
for ss = 1:length(subj)
thisidx = all_subj == ss & all_ROIs == vv & all_conds(:,1)==cu(cc);% & floor(all_conds_task(:,1)/10)==which_conds(cc);
thisd(ss,:) = nanmean(all_HRFs{hh}(thisidx,:),1); %using nanmean here
clear thisidx;
end
% plot, like for reconstructions, such that middle of each
% datapoint is at middle of TR
if hh ==1
lh(hh,cc) = plot(which_TRs*TR + TR/2,nanmean(thisd,1),'-','LineWidth',1.0,'Color',cond_colors(cc,:));
else
lh(hh,cc) = plot(which_TRs*TR + TR/2,nanmean(thisd,1),'--','LineWidth',1.0,'Color',cond_colors(cc,:));
end
btwn_fill = [nanmean(thisd,1)+1.*nanstd(thisd,[],1)/sqrt(length(subj)) fliplr( nanmean(thisd,1)-1.*nanstd(thisd,[],1)/sqrt(length(subj)) )]; % geh test
fill([which_TRs*TR + TR/2 fliplr(which_TRs*TR + TR/2)],btwn_fill,cond_colors(cc,:),'linestyle','none','facealpha',0.3);
clear thisd;
end
title(ROIs{vv});
if vv==1 && hh ==2
ylabel({'Voxels in vs out RF'; 'BOLD Z-score'});
xlabel('Time (s)');
xticks([0 4.5 12])
set(gca,'XTickLabel',[0 4.5 12])
else
xticks([0 4.5 12])
set(gca,'XTickLabel',[0 4.5 12])
end
%
% if vv==length(ROIs) && hh==2
% legend(lh,sprintf('%s RFin',condstr{cc}),sprintf('%s RFout',condstr{cc}),'location','NorthEast');
% else
% end
end
myy = cell2mat(get(axhrf(cc,:),'YLim'));
set(axhrf(cc,:),'YLim',[min(myy(:,1)) max(myy(:,2))],'XLim',[which_TRs(1) which_TRs(end)]*TR,'TickDir','out');
set(mh,'YData',[min(myy(:,1)) max(myy(:,2))]);
if length(ROIs)==7
set(gcf,'Position',[ 429 451 2041 471])
else
set(gcf,'Position',[ 429 451 2541 471])
end
%legend(lh,sprintf('%s RFin',condstr{cc}),sprintf('%s RFout',condstr{cc}),'location','NorthEast');
%match_ylim(get(gcf,'Children'));
end
%% in 'dist_ang_all' alignment case, do the following plot
if do_distalign_plot==1
all_HRFs{1} = all_hrfs_in;
all_HRFs{2} = all_hrfs_out;
all_HRFs{3} = all_hrfs_in - all_hrfs_out;
cond_colors = spDist_condColors;
HRF_str ={'RFin','RFout','RFin-out'};
% store something that's ROI x time x subj for each condition
cu = unique(all_conds(:,1));
axhrf = nan(length(all_HRFs),length(ROIs));
mh = nan(length(ROIs),length(t_markers));
condstr = {'Dist Absent','Dist Present'};
figure;
for cc = 2 %need only one condition here
for vv = 1:length(ROIs)
axhrf(cc,vv) = subplot(1,length(ROIs),vv); hold on;
% draw 'baseline'
plot([which_TRs(1) which_TRs(end)]*TR,[0 0],'k-','LineWidth',0.75);
% draw event markers
mh(vv,:) = plot(t_markers.*[1;1],[0 .1],'-','Color',[0.7 0.7 0.7],'LineWidth',0.75);
for hh = 1:2 % only care about RFin, RFout here, therefore dont loop over length(all_mean_HRF)
thisd = nan(length(subj),size(all_HRFs{1},2));
for ss = 1:length(subj)
thisidx = all_subj == ss & all_ROIs == vv & all_conds(:,1)==cu(cc);% & floor(all_conds_task(:,1)/10)==which_conds(cc);
thisd(ss,:) = nanmean(all_HRFs{hh}(thisidx,:),1); %using nanmean here
clear thisidx;
end
% plot, like for reconstructions, such that middle of each
% datapoint is at middle of TR
if hh ==1
lh(hh,cc) = plot(which_TRs*TR + TR/2,nanmean(thisd,1),'-','LineWidth',1.0,'Color',cond_colors(cc,:));
else
lh(hh,cc) = plot(which_TRs*TR + TR/2,nanmean(thisd,1),'--','LineWidth',1.0,'Color',cond_colors(cc,:));
end
btwn_fill = [nanmean(thisd,1)+1.*nanstd(thisd,[],1)/sqrt(length(subj)) fliplr( nanmean(thisd,1)-1.*nanstd(thisd,[],1)/sqrt(length(subj)) )];
fill([which_TRs*TR + TR/2 fliplr(which_TRs*TR + TR/2)],btwn_fill,cond_colors(cc,:),'linestyle','none','facealpha',0.3);
clear thisd btwn_fill fill
end
title(ROIs{vv});
if vv==1 && hh ==2
ylabel({'Voxels in vs out RF'; 'BOLD Z-score'});
xlabel('Time (s)');
xticks([0 4.5 12])
set(gca,'XTickLabel',[0 4.5 12])
else
xticks([0 4.5 12])
set(gca,'XTickLabel',[0 4.5 12])
end
end
myy = cell2mat(get(axhrf(cc,:),'YLim'));
set(axhrf(cc,:),'YLim',[min(myy(:,1)) max(myy(:,2))],'XLim',[which_TRs(1) which_TRs(end)]*TR,'TickDir','out');
set(mh,'YData',[min(myy(:,1)) max(myy(:,2))]);
if length(ROIs)==7
set(gcf,'Position',[ 429 451 2041 223])
else
set(gcf,'Position',[ -71 407 2541 223])
end
end
else
end
%% bar graph of mean delay period activity
% for hh =1:length(all_HRFs)
% figure;
% sgtitle(HRF_str{hh})
% plot([0 length(ROIs)+1],[0 0],'k--');
%
% offsets = linspace(-0.15,0.15,length(all_mean_hrf));
%
% for cc = 1:length(cu)
% % NOTE: here, we use lte rather than lt...
% this_TR_range = which_TRs>=delay_tpt_range(1,1)&which_TRs<=delay_tpt_range(2,2);
%
% all_mean_delay = squeeze(mean(all_mean_hrf{hh,cc}(:,this_TR_range,:),2)); % ROI x subj
% hold on;
% for vv = 1:length(ROIs)
% thise = std(all_mean_delay(vv,:),[],2)/sqrt(length(subj));
% thism = mean(all_mean_delay(vv,:),2);
%
% plot(vv*[1 1]+offsets(cc),thism+thise*[-1 1],'-','LineWidth',1.5,'Color',cond_colors(cc,:));
% plot(vv+offsets(cc),thism,'o','Color',cond_colors(cc,:),'MarkerFaceColor','w','MarkerSize',8,'LineWidth',1.5);
% end
%
%
% end
%
%
% set(gca,'XTick',1:length(ROIs),'XTickLabel',ROIs,'XTickLabelRotation',-45,'FontSize',14,'TickDir','out','Box','off');
% ylabel({'BOLD Z-score';'Mean delay period activation'});
% end
%% plot, mean delay period activity RF in vs RF out, within distractor condition
RFmodfig = figure('Name','RFmod');
HRF_str ={'RFin','RFout','RfModIdx'};
store_mean =nan(2,2,length(ROIs));
for cc =1:length(cu)
figure;
sgtitle(condstr{cc})
plot([0 length(ROIs)+1],[0 0],'k--');
for hh = 1:length(all_mean_hrf)
offsets = linspace(-0.15,0.15,length(all_mean_hrf));
% NOTE: here, we use lte rather than lt...
% this_TR_range = which_TRs >=delay_tpt_range(1,1) & which_TRs<delay_tpt_range(2,2);
this_TR_range = (which_TRs*TR)>=delay_tpt_range(1,1) & (which_TRs*TR)<delay_tpt_range(3,2);
all_mean_delay = squeeze(mean(all_mean_hrf{hh,cc}(:,this_TR_range,:),2)); % ROI x subj
for vv = 1:length(ROIs)
thise = std(all_mean_delay(vv,:),[],2)/sqrt(length(subj));
thism = mean(all_mean_delay(vv,:),2);
if hh==1
plot(vv*[1 1]+offsets(cc),thism+thise*[-1 1],'-','LineWidth',1.5,'Color',cond_colors(cc,:));
hold on;
plot(vv+offsets(cc),thism,'o','Color',cond_colors(cc,:),'MarkerFaceColor',cond_colors(cc,:),'MarkerSize',8,'LineWidth',1.5);
elseif hh==2
plot(vv*[1 1]+offsets(cc),thism+thise*[-1 1],'-','LineWidth',1.5,'Color',cond_colors(cc,:));
plot(vv+offsets(cc),thism,'o','Color',cond_colors(cc,:),'MarkerFaceColor','w','MarkerSize',8,'LineWidth',1.5);
elseif hh==3
set(0, 'CurrentFigure', RFmodfig)
hold on;
plot(vv*[1 1]+offsets(cc),thism+thise*[-1 1],'-','LineWidth',1.5,'Color',cond_colors(cc,:));
plot(vv+offsets(cc),thism,'o','Color',cond_colors(cc,:),'MarkerFaceColor','k','MarkerSize',8,'LineWidth',1.5);
end
store_mean(cc,hh,vv) = thism;
end
end
set(gca,'XTick',1:length(ROIs),'XTickLabel',ROIs,'XTickLabelRotation',-45,'FontSize',14,'TickDir','out','Box','off');
ylabel({'BOLD Z-score';'Mean delay period activation'});
end
% create a container for all labels ( ROI x DIST COND x RF COND x SUBJ)
% corresponding data using all TRs in our window - from 3.25 to 12
data_allt = nan(length(subj)*length(ROIs)*size(delay_tpt_range,1)*length(all_mean_hrf),1);
labels_allt = nan(length(subj)*length(ROIs)*size(delay_tpt_range,1)*length(all_mean_hrf),4); % subj, ROI, epochs, RFs
idx = 1;
for hh = 1:length(all_mean_hrf) % RF-in X RF-out X RF-in minus RF-out
for ss = 1:length(subj)
for vv = 1:length(ROIs)
for cc = 1:length(cu)
this_TR_range = (which_TRs*TR) >= delay_tpt_range(1,1) & (which_TRs*TR) < delay_tpt_range(3,2);
data_allt(idx) = mean(all_mean_hrf{hh,cc}(vv,this_TR_range,ss));
labels_allt(idx,:) = [vv cc hh ss]; % ROI COND RF EPOCH SUBJ
idx = idx+1;
end
end
end
end
% REGRESSION ON RFIN, DISTRACTOR ABSENT (OR PRESENT) %SUBJECTS!!!
oneway_delayROI_realF = nan(2,1);
pval_roi= nan(2,1);
fitresult = cell( 3, 1 );
which_RF = 1;
for cc =1 %:length(cu)
clear thisidx
thisidx = labels_allt(:,2)==cc & labels_allt(:,3)==which_RF; % change this for 1 or 3 depending - are we using mod?
myd = data_allt(thisidx);
myrois = labels_allt(thisidx,1);
[oneway_delayROI_realF(cc),pval_roi(cc)] = RMAOV1_gh([data_allt(thisidx) labels_allt(thisidx,[1 4])]);
RMAOV1([data_allt(thisidx) labels_allt(thisidx,[1 4])]) % use OG version to print results to command line
% Initialize arrays to store fits and goodness-of-fit.
% Fit: 'distabsent_RFin'.
[xData, yData] = prepareCurveData( myrois, myd );
% Set up fittype and options. %USE THE
% ft = fittype( {'(sin(x-pi))', '((x-10)^2)', '1'}, 'independent', 'x', 'dependent', 'y', 'coefficients', {'a', 'b', 'c'} );
ft = fittype( 'poly1' );
% Fit model to data.
gof = struct( 'sse', cell( 3, 1 ), ...
'rsquare', [], 'dfe', [], 'adjrsquare', [], 'rmse', [] );
[fitresult{cc}, gof(1)] = fit( xData, yData, ft );
gof_store(cc) = gof(1).rsquare;
% Plot fit with data.
figure( 'Name', condstr{cc} );
h = plot( fitresult{cc}, xData, yData );
legend( h, 'amplitude ', condstr{cc}, 'Location', 'NorthEast' );
% Label axes
xlabel ROIs
ylabel Amplitude
grid on
title(sprintf('R-square = %0.2f \n ',gof_store(cc)))
clear myd myrois gof
end
% SAMPLE MEANS
% REGRESSION ON RFIN, DISTRACTOR ABSENT (OR PRESENT) ON INDIVIDUAL SUBJECTS
oneway_delayROIsubj_realF = nan(length(cu),length(subj));
pval_roisubj = nan(length(cu),length(subj));
betas = nan(length(cu),length(subj));
rsq_store = nan(length(cu),length(subj));
fitresult = cell(2,7);
which_RF = 1;
for cc =1 %:length(cu)
for ss =1:length(subj)
clear thisidx myd myrois xData yData
thisidx = labels_allt(:,2)==cc & labels_allt(:,3)==which_RF & labels_allt(:,4)==ss;
myd = data_allt(thisidx);
myrois = labels_allt(thisidx,1);
% Initialize arrays to store fits and goodness-of-fit.
gof = struct( 'sse', cell( 3, 1 ), ...
'rsquare', [], 'dfe', [], 'adjrsquare', [], 'rmse', [] );
% Fit: 'distabsent_RFin'.
[xData, yData] = prepareCurveData( myrois, myd );
% Set up fittype and options. %USE THE
ft = fittype( 'poly1' );
% Fit model to data.
[fitresult{cc,ss}, gof(1)] = fit( xData, yData, ft);
betas(cc,ss) = fitresult{cc,ss}.p1;
rsq_store(cc,ss) = gof(1).rsquare;
% Plot fit with data.
figure( 'Name',sprintf('subj %s %s',subj{ss},condstr{cc}) );
h = plot( fitresult{cc,ss}, xData, yData);
legend( h, 'amplitude ', condstr{cc}, 'Location', 'NorthEast' );
% Label axes
xlabel ROIs
ylabel Amplitude
grid on
title(sprintf('R-square = %0.2f \n ',rsq_store(cc,ss)))
clear thisiidx xData yData
end
[~,p_store(cc),~,stats] = ttest(betas(cc,:));
realT(cc) = stats.tstat;
end
% shuffle roi labels for regression, within subj
iter=1000;
fitresult_shuf = cell( 2,length(subj),iter);
betas_shuf = nan(length(subj),iter);
labels_shuf = labels_allt;
which_RF = 1; % on which RF condition do we want to perform regression?
for cc =1 % only performing this for distractor absent, built to add distractor present if needs change
tic;
for ii = 1:iter
for ss = 1:length(subj)
% cond, epoch, subj % ROI RF COND EPOCH SUBJ
subjidx = labels_allt(:,2)==cc & labels_allt(:,3)==which_RF & labels_allt(:,4)==ss;
thisidx = find(subjidx);
shufidx = randperm(length(thisidx))';
labels_shuf(subjidx,1) = labels_allt(thisidx(shufidx),1); %shuffle ROI labels for this subj
myd = data_allt(subjidx); % maintain original data indices
myrois = labels_shuf(subjidx,1); % shuffle only this ss labels
gof = struct( 'sse', cell( 3, 1 ), ...
'rsquare', [], 'dfe', [], 'adjrsquare', [], 'rmse', [] );
% Fit: 'distabsent_RFin'.
[xData, yData] = prepareCurveData( myrois, myd );
% Set up fittype and options. %USE THE
ft = fittype( 'poly1' );
% Fit model to data.
[fitresult_shuf{cc,ss,ii}, gof(1)] = fit( xData, yData, ft);
betas_shuf(ss,ii) = fitresult_shuf{cc,ss,ii}.p1;
rsq_store_shuf(ss,ii) = gof(1).rsquare;
clear xData yData gof my myrois subjidx thisidx shufidx ft
end
[~,p_store,~,stats] =ttest(betas_shuf(:,ii));
shufT(ii) = stats.tstat;
end
toc
end
figure; hist(shufT,50)
hold on; plot([realT realT], [ylim],'r','linewidth',2)
exact_P = 2 * min(mean(shufT(:) > realT), mean(shufT(:) < realT))
delay_2way_realF = nan(2,3);
% IV1 = ROI, IV2 = RF
for cc =1 %:length(cu)
clear thisidx
thisidx = labels_allt(:,2)==cc & labels_allt(:,3)~=3;
[delay_2way_realF(cc,:)] = RMAOV2_gh([data_allt(thisidx) labels_allt(thisidx,[1 3 4])]);
RMAOV2([data_allt(thisidx) labels_allt(thisidx,[1 3 4])])
end
%%%
% do shuffled 2-way ANOVA for each ROI
iter=1000;
delay_2way_shufF = nan(iter,3);
fprintf('Shuffling - 2 way ANOVAs\n');
% get data for this ROI
thisidx = labels_allt(:,2)==1 & labels_allt(:,3)~=3;
thisdata = data_allt(thisidx);
thislabels = labels_allt(thisidx,[1 3 4]); % ROI RF SUBJ
tic;
for ii = 1:iter
data_shuf = nan(size(thisdata));
for ss = 1:length(subj)
subjidx = thislabels(:,3)==ss;
thisidx = find(subjidx);
shufidx = randperm(length(thisidx));
data_shuf(subjidx) = thisdata(thisidx(shufidx));
end
delay_2way_shufF(ii,:) = RMAOV2_gh([data_shuf thislabels]); %shuffling data, not labels ...
end
toc;
% calculate p-values for 2-way ANOVA
delay_2way_labels = {'ROI','RF','ROI x RF'};
delay_2way_pval = nan(1,3);
for ee =1:3
delay_2way_pval(ee) = mean( delay_2way_realF(1,ee) <= delay_2way_shufF(:,ee)'); % 3 pvalues: COND, EPOCH, COND x EPOCH
end
% FDR thresholds
% fdr_thresh = nan(3,size(epoch_2way_pval,3)); % # of RF conds x # main effects x # ROIs
% fdr_mask = nan(3,size(epoch_2way_pval,3));
%
% for ii = 1:size(epoch_2way_pval,2) % this should be the # of effects, ie 3, correct across ROI
% [fdr_thresh(ii,:), fdr_mask(ii,:)] = fdr(squeeze(epoch_2way_pval(ii,:)),0.05);
% end
%
%
%%%
% ONE-WAY ANOVA PER ROI, WITH RF AS FACTOR - DO THIS
delay_1way_realF = nan(2,length(ROIs));
delay_1way_realP = nan(2,length(ROIs));
for cc =1:length(cu)
for vv = 1:length(ROIs)
clear thisidx
thisidx = labels_allt(:,1)==vv & labels_allt(:,2)==cc & labels_allt(:,3)~=3;
[ delay_1way_realF(cc,vv), delay_1way_realP(cc,vv)] = RMAOV1_gh([data_allt(thisidx) labels_allt(thisidx,[3 4])]);
end
figure;
plot( delay_1way_realF(cc,:),'ko-') % take a look at F-stats
title(condstr{cc})
set(gca,'XTick',1:length(ROIs),'XTickLabel',ROIs,'XTickLabelRotation',-45,'FontSize',14,'TickDir','out','Box','off');
ylabel({'F-values, one-way ANOVA, '})
end
% do shuffled 1-way ANOVA for each ROI
delay_1way_shufF = nan(length(ROIs),iter);
fprintf('Shuffling subj-wise RF labels- 1 way ANOVAs\n');
for vv = 1:length(ROIs)
fprintf('ROI %s\n',ROIs{vv});
clear thisidx thisdata thislabels
% get data for this ROI
thisidx = labels_allt(:,1)==vv & labels_allt(:,2)==1 & labels_allt(:,3)~=3; % for this ROI, no distractor, not RFmod
thisdata = data_allt(thisidx);
thislabels = labels_allt(thisidx,[3 4]); % RF SUBJ
tic;
for ii = 1:iter
data_shuf = nan(size(thisdata));
for ss = 1:length(subj)
subjidx = thislabels(:,2)==ss;
thisidx = find(subjidx);
shufidx = randperm(length(thisidx));
data_shuf(subjidx) = thisdata(thisidx(shufidx));
clear subjidx thisidx shufidx
end
delay_1way_shufF(vv,ii) = RMAOV1_gh([data_shuf thislabels]);
end
toc;