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make_permFidelity_for_eachROI.m
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make_permFidelity_for_eachROI.m
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% 201028 jyh
% make a file for each ROI that contains permuted color selectivity values of all subjects and all cond.
% should fed into a R code later
clear all;
%% Experiment info. & directory
exp='v3';
% ROIs = {'V2_3dg_fmasked_q.001','V3_3dg_fmasked_q.001', 'V4v_fmasked_q.001' }; %, 'V2_3dg_fmasked_q.001','V3_3dg_fmasked_q.001', 'V4v_fmasked_q.001'};
ROIs = {'LGN_hk'}; %, 'V2_3dg_fmasked_q.001','V3_3dg_fmasked_q.001', 'V4v_fmasked_q.001'};
% ROIs = {'V1_3dg_fmasked_q.001'};
nChan = 8; % num. of channels
nColor = 8; % num. of colors used in the experiment
nAttCond = 2; % attention, inattention
nCond = nColor*nAttCond; % num. of condition (8 colors X in vs. out)
nColCond = 2; % cardinal, intercardinal
nLMSCond = 2; % L-M, S
carInx = ([1:(nColor/2)]-1)*2+1; %1, 3, 5, 7
intInx = ([1:(nColor/2)])*2; %2,4,6,8
LMInx = [1,5];
SInx = [3,7];
nBin = 1;
baseDir = sprintf('/Volumes/Duri/data/Color%s', exp);
addpath('/Users/auna/Script/');
fmDir = 'Img_data/forwardmodel'; %
tunDir = fullfile(fmDir, 'sc_dt_hp_am'); %where tuning value txt is located
permDir = fullfile(fmDir, 'perm_sc_dt_hp_am');
statDir = fullfile(baseDir, 'perm_fidelity');
statFileName = 'fidelity_2TRlag_neutralModel.csv';
nPerm = 1000;
%% Subject info.
% %v3
SN = {};
SN{end+1} = '01'; % PSY
SN{end+1} = '03'; % KIS
SN{end+1} = '04'; % JBH
SN{end+1} = '05'; % HJH %%% check alignment for sub4,5 - 6 deg mask 181210
SN{end+1} = '06'; % KB
SN{end+1} = '07'; % SHY
SN{end+1} = '08'; % LSY
SN{end+1} = '09'; % HJH2
SN{end+1} = '10'; % CES
SN{end+1} = '11'; % YYH
SN{end+1} = '12'; % LHB
SN{end+1} = '13'; % JHY
SN{end+1} = '14'; % KYJ
%% Parameters for plots
lineWidth = 3;
lineColors = {[1 0.6 0.784],...
[1 0 0.6],...
[0.855 0.702 1],...
[0.18 0.40 0.73],...
[0 1 1],...
[0 0.498 0],...
[0.4 0.8 0],...
[0.878 0.537 0.098]};
Hnames= {'All colors', 'Cardinal colors only'};
CInames = {'Cardinal', 'Int.cardinal'};
LMSnames = {'L-M', 'S'};
Attnames = {'In', 'Out'};
car_LMnames = {'L-M color 1', 'L-M color 5'};
car_Snames = {'S color 3', 'S color 7'};
CIColors = {lineColors{2}, lineColors{4}; lineColors{2}, lineColors{4}};
LMSColors = {lineColors{2}, lineColors{4}; lineColors{2}, lineColors{4}};
car_LMColors = {lineColors{LMInx(1)},...
lineColors{LMInx(1)}*0.6;lineColors{LMInx(2)}, lineColors{LMInx(2)}*0.6};
car_SColors = {lineColors{SInx(1)},...
lineColors{SInx(1)}*0.6;lineColors{SInx(2)}, lineColors{SInx(2)}*0.6};
opacity = 0.3;
%% Selectivity index
Methods={'Information fidelity'};
sigtxt = {'not significant', 'significant'};
%% permutation array
perm_colSel = cell(nPerm, length(Methods), length(Hnames), 2,2);
perm_carLMS_colSel = cell(nPerm, length(Methods), length(Hnames), 2,2);
perm_ttfit = zeros(nPerm, length(Methods), length(Hnames), 2, 2, 3); %last dim: p,t, & sig value
perm_carLMS_ttfit = zeros(nPerm, length(Methods), length(Hnames), 2, 2, 3); %last dim: p,t, & sig value
perm_tval = cell(length(ROIs), length(Methods), length(Hnames), 2,2);
perm_carLMS_tval = cell(length(ROIs), length(Methods), length(Hnames), 2,2);
perm_rmfit = zeros(nPerm, length(ROIs), length(Methods), length(Hnames), 3); %main1,2,int1, %last dim: p,f, & sig value
perm_carLMS_rmfit = zeros(nPerm, length(ROIs), length(Methods), length(Hnames),3);
perm_fval = cell(length(ROIs), length(Methods), length(Hnames)); %main1, main2, int
perm_carLMS_fval = cell(length(ROIs), length(Methods), length(Hnames), 3);
perm_ANOVA = cell(length(Methods), length(Hnames));
multcomp_test = zeros(nPerm, length(ROIs), length(Methods), length(Hnames), 4);
carLMS_multcomp_test = zeros(nPerm, length(ROIs), length(Methods), length(Hnames), 4);
factorNames = {'C vs. I ', 'Attention'; 'L-M vs. S', 'Attention'};
carLMS_factorNames = {'L-M1 vs. L-M5', 'Attention'; 'S3 vs. S7', 'Attention'};
%% real_data array
real_tval = cell(length(ROIs), length(Methods), length(Hnames), 2,2);
real_carLMS_tval = cell(length(ROIs), length(Methods), length(Hnames), 2,2);
rmfit = zeros(length(ROIs), length(Methods), length(Hnames),3);
carLMS_rmfit = zeros(length(ROIs), length(Methods), length(Hnames),3);
ANOVA = cell(length(Methods), length(Hnames));
multcomp_test = zeros(length(ROIs), length(Methods), length(Hnames), 4);
carLMS_multcomp_test = zeros(length(ROIs), length(Methods), length(Hnames), 4);
%% load perm data
for xROI = 1:length(ROIs)
fprintf('.....ROI: %s permutation start.....\n\n', ROIs{xROI})
for xPerm = 1:nPerm
if mod(xPerm,100) == 0; fprintf('ROI: %s testing no.%d\n', ROIs{xROI}, xPerm); end
for xSN = 1:length(SN)
fileName = sprintf('perm%d_%s_tuning_%s2TR_used4TRs_neutralModel_shift.txt', xPerm, SN{xSN}, ROIs{xROI});
TT{xSN} = load(fullfile(baseDir, SN{xSN}, permDir, [ROIs{xROI} '_2TRlag_neutralModel'], fileName));
% zscore across channel
for rr = 1:nCond % cc: row, color x attention
zTT{xSN}(rr,1:nChan) = zscore(TT{xSN}(rr, :));
end
end
tTT = [];
for xSN = 1:length(SN)
for rr = 1:nCond
tTT(xSN, 1:nChan, rr) = zTT{xSN}(rr,:);
end
end
cTT = [];
cTT(:,1:nChan,1,1) = mean(tTT(:,:,carInx), 3); %cardinal, in
cTT(:,1:nChan,2,1) = mean(tTT(:,:,intInx), 3); %intercardinal, in
cTT(:,1:nChan,1,2) = mean(tTT(:,:,carInx+nColor), 3); %cardinal, out
cTT(:,1:nChan,2,2) = mean(tTT(:,:,intInx+nColor), 3); %intercardinal, out
car_cTT = [];
car_cTT(:,1:nChan,1,1) = mean(tTT(:,:,LMInx), 3); %L-M color, in
car_cTT(:,1:nChan,2,1) = mean(tTT(:,:,SInx), 3); %S color, in
car_cTT(:,1:nChan,1,2) = mean(tTT(:,:,LMInx+nColor), 3); %L-M color, out
car_cTT(:,1:nChan,2,2) = mean(tTT(:,:,SInx+nColor), 3); %S color, out
carLM_cTT = [];
carLM_cTT(:,1:nChan,1,1) = mean(tTT(:,:,LMInx(1)), 3); %L-M color1, in
carLM_cTT(:,1:nChan,2,1) = mean(tTT(:,:,LMInx(2)), 3); %L-M color2, in
carLM_cTT(:,1:nChan,1,2) = mean(tTT(:,:,LMInx(1)+nColor), 3); %L-M color1, out
carLM_cTT(:,1:nChan,2,2) = mean(tTT(:,:,LMInx(2)+nColor), 3); %L-M color2, out
carS_cTT = [];
carS_cTT(:,1:nChan,1,1) = mean(tTT(:,:,SInx(1)), 3); %L-M color1, in
carS_cTT(:,1:nChan,2,1) = mean(tTT(:,:,SInx(2)), 3); %L-M color2, in
carS_cTT(:,1:nChan,1,2) = mean(tTT(:,:,SInx(1)+nColor), 3); %L-M color1, out
carS_cTT(:,1:nChan,2,2) = mean(tTT(:,:,SInx(2)+nColor), 3); %L-M color2, out
% Add one more channel to the end to make it odd number
% Add the last channel value as channel 9
pTT = []; car_pTT = []; carLM_pTT = []; carS_pTT = [];
pTT(:,1+1:nChan+1,:,:) = cTT(:,1:nChan,:,:);
pTT(:,1,:,:) = pTT(:,nChan+1,:,:);
car_pTT(:,1+1:nChan+1,:,:) = car_cTT(:,1:nChan,:,:);
car_pTT(:,1,:,:) = car_pTT(:,nChan+1,:,:);
carLM_pTT(:,1+1:nChan+1,:,:) = carLM_cTT(:,1:nChan,:,:);
carLM_pTT(:,1,:,:) = carLM_pTT(:,nChan+1,:,:);
carS_pTT(:,1+1:nChan+1,:,:) = carS_cTT(:,1:nChan,:,:);
carS_pTT(:,1,:,:) = carS_pTT(:,nChan+1,:,:);
%% information fiedelity
chanCenter = ceil(nChan/2); % for shifted data, center is 4
oriUnit = 2*pi/(nChan);
oriRad = 0:oriUnit:2*pi;
% oriRad = oriRad-oriRad(chanCenter);
my_function = cos(abs(oriRad)-pi);
% Cardinal vs. intercardinal
for xCol = 1:nColCond
for xAtt = 1:nAttCond
xTT = pTT(:,:,xCol,xAtt);
nnChan = size(xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = xTT(:,i) .* my_function(i);
end
perm_colSel{xPerm,1,1,xCol,xAtt} = mean(e,2);
perm_colSel_SEM{xPerm,1,1,xCol,xAtt} = std(perm_colSel{xPerm,1,1,xCol,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
%SEM
% L-M vs. S
for xLMS = 1:nLMSCond
for xAtt = 1:nAttCond
car_xTT = car_pTT(:,:,xLMS,xAtt);
nnChan = size(car_xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = car_xTT(:,i) .* my_function(i);
end
perm_colSel{xPerm,1,2,xLMS,xAtt} = mean(e,2);
perm_colSel_SEM{xPerm,1,2,xLMS,xAtt} = std(perm_colSel{xPerm,1,2,xLMS,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
% L-M 1 vs. L-M 2
for xLM = 1:nLMSCond
for xAtt = 1:nAttCond
carLM_xTT = carLM_pTT(:,:,xLM,xAtt);
nnChan = size(carLM_xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = carLM_xTT(:,i) .* my_function(i);
end
perm_carLMS_colSel{xPerm,1,1,xLM,xAtt} = mean(e,2);
perm_carLMS_colSel_SEM{xPerm,1,1,xLM,xAtt} = std(perm_carLMS_colSel{xPerm,1,1,xLM,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
% S 1 vs. S 2
for xS = 1:nLMSCond
for xAtt = 1:nAttCond
carS_xTT = carS_pTT(:,:,xS,xAtt);
nnChan = size(carS_xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = carS_xTT(:,i) .* my_function(i);
end
perm_carLMS_colSel{xPerm,1,2,xS,xAtt} = mean(e,2);
perm_carLMS_colSel_SEM{xPerm,1,2,xS,xAtt} = std(perm_carLMS_colSel{xPerm,1,2,xS,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
end % perm
%% load real data
TT = cell(length(SN), 1); zTT = cell(length(SN), 1);
for xSN = 1:length(SN)
% if isitLGN == 1
% if xSN == 2 || xSN == 6 || xSN ==9 || xSN == 10 || xSN == 1
% ROIs{xROI} = 'LGN_hk3_p.05';
% else
% ROIs{xROI} = 'LGN_hk2_p.05';
% end
% end
% tunTxt = sprintf('_tuning_%s_2TR_used4TRs_shift', ROIs{xROI});
tunTxt = sprintf('_tuning_%s2TR_used4TRs_neutralModel_shift', ROIs{xROI});
fileName = sprintf('%s%s.txt', SN{xSN}, tunTxt);
TT{xSN} = load(fullfile(baseDir, SN{xSN}, tunDir, fileName));
% zscore across channel
for rr = 1:nCond % cc: row, color x attention
zTT{xSN}(rr,1:nChan) = zscore(TT{xSN}(rr, :));
end
end
tTT = []; % zcTT(sub, channel, cond(color*attention)
for xSN = 1:length(SN)
for rr = 1:nCond
tTT(xSN, 1:nChan, rr) = zTT{xSN}(rr,:);
end
end
%% Tuning for each cond.
% cTT(sub, channel, color, cond)
cTT = [];
cTT(:,1:nChan,1,1) = mean(tTT(:,:,carInx), 3); %cardinal, in
cTT(:,1:nChan,2,1) = mean(tTT(:,:,intInx), 3); %intercardinal, in
cTT(:,1:nChan,1,2) = mean(tTT(:,:,carInx+nColor), 3); %cardinal, out
cTT(:,1:nChan,2,2) = mean(tTT(:,:,intInx+nColor), 3); %intercardinal, out
car_cTT = [];
car_cTT(:,1:nChan,1,1) = mean(tTT(:,:,LMInx), 3); %L-M color, in
car_cTT(:,1:nChan,2,1) = mean(tTT(:,:,SInx), 3); %S color, in
car_cTT(:,1:nChan,1,2) = mean(tTT(:,:,LMInx+nColor), 3); %L-M color, out
car_cTT(:,1:nChan,2,2) = mean(tTT(:,:,SInx+nColor), 3); %S color, out
carLM_cTT = [];
carLM_cTT(:,1:nChan,1,1) = mean(tTT(:,:,LMInx(1)), 3); %L-M color1, in
carLM_cTT(:,1:nChan,2,1) = mean(tTT(:,:,LMInx(2)), 3); %L-M color2, in
carLM_cTT(:,1:nChan,1,2) = mean(tTT(:,:,LMInx(1)+nColor), 3); %L-M color1, out
carLM_cTT(:,1:nChan,2,2) = mean(tTT(:,:,LMInx(2)+nColor), 3); %L-M color2, out
carS_cTT = [];
carS_cTT(:,1:nChan,1,1) = mean(tTT(:,:,SInx(1)), 3); %L-M color1, in
carS_cTT(:,1:nChan,2,1) = mean(tTT(:,:,SInx(2)), 3); %L-M color2, in
carS_cTT(:,1:nChan,1,2) = mean(tTT(:,:,SInx(1)+nColor), 3); %L-M color1, out
carS_cTT(:,1:nChan,2,2) = mean(tTT(:,:,SInx(2)+nColor), 3); %L-M color2, out
%SEM
cSEM_CI = []; cSEM_LMS = [];
cSEM_CI(1, 1:nChan,:,:) = std(cTT,0,1) ./ (sqrt(length(SN)-1));
cSEM_LMS(1, 1:nChan,:,:) = std(car_cTT,0,1) ./ (sqrt(length(SN)-1));
cSEM_carLM(1, 1:nChan,:,:) = std(carLM_cTT,0,1) ./ (sqrt(length(SN)-1));
cSEM_carS(1, 1:nChan,:,:) = std(carS_cTT,0,1) ./ (sqrt(length(SN)-1));
%% PLOT %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% when shifted, tuning functions are centered on channel 4
% channel 8 value is added before channel 1, so total of 9 channels exist.
%% plot each subj's tuning
% Add one more channel to the end to make it odd number
% Add the last channel value as channel 9
pTT = []; car_pTT = []; carLM_pTT = []; carS_pTT = [];
pTT(:,1+1:nChan+1,:,:) = cTT(:,1:nChan,:,:);
pTT(:,1,:,:) = pTT(:,nChan+1,:,:);
car_pTT(:,1+1:nChan+1,:,:) = car_cTT(:,1:nChan,:,:);
car_pTT(:,1,:,:) = car_pTT(:,nChan+1,:,:);
carLM_pTT(:,1+1:nChan+1,:,:) = carLM_cTT(:,1:nChan,:,:);
carLM_pTT(:,1,:,:) = carLM_pTT(:,nChan+1,:,:);
carS_pTT(:,1+1:nChan+1,:,:) = carS_cTT(:,1:nChan,:,:);
carS_pTT(:,1,:,:) = carS_pTT(:,nChan+1,:,:);
colSel = cell(length(Methods), length(Hnames), 2,2);
colSel_SEM = cell(length(Methods), length(Hnames), 2,2);
carLMS_colSel = cell(length(Methods), length(Hnames), 2,2);
carLMS_colSel_SEM = cell(length(Methods), length(Hnames), 2,2);
% Cardinal vs. intercardinal
for xCol = 1:nColCond
for xAtt = 1:nAttCond
xTT = pTT(:,:,xCol,xAtt);
nnChan = size(xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = xTT(:,i) .* my_function(i);
end
colSel{1,1,xCol,xAtt} = mean(e,2);
colSel_SEM{1,1,xCol,xAtt} = std(colSel{1,1,xCol,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
%SEM
% L-M vs. S
for xLMS = 1:nLMSCond
for xAtt = 1:nAttCond
car_xTT = car_pTT(:,:,xLMS,xAtt);
nnChan = size(car_xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = car_xTT(:,i) .* my_function(i);
end
colSel{1,2,xLMS,xAtt} = mean(e,2);
colSel_SEM{1,2,xLMS,xAtt} = std(colSel{1,2,xLMS,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
% L-M 1 vs. L-M 2
for xLM = 1:nLMSCond
for xAtt = 1:nAttCond
carLM_xTT = carLM_pTT(:,:,xLM,xAtt);
nnChan = size(carLM_xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = carLM_xTT(:,i) .* my_function(i);
end
carLMS_colSel{1,1,xLM,xAtt} = mean(e,2);
carLMS_colSel_SEM{1,1,xLM,xAtt} = std(carLMS_colSel{1,1,xLM,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
% S 1 vs. S 2
for xS = 1:nLMSCond
for xAtt = 1:nAttCond
carS_xTT = carS_pTT(:,:,xS,xAtt);
nnChan = size(carS_xTT,2);
c_center = ceil(nnChan/2);
e = zeros(length(SN), nnChan);
for i = 1:nnChan
e(:,i) = carS_xTT(:,i) .* my_function(i);
end
carLMS_colSel{1,2,xS,xAtt} = mean(e,2);
carLMS_colSel_SEM{1,2,xS,xAtt} = std(carLMS_colSel{1,2,xS,xAtt},0,1) ...
./ (sqrt(length(SN)-1));
end
end
%% reorganize all of the perm data for one ROI into one array
out_colSel = []; out_car_colSel = [];
out_carLM_colSel = []; out_carS_colSel =[];
for xPerm=1:nPerm
for xCol=1:2
for xAtt=1:2
%1:SN 2:perm No. 3:Col(1:car, 2:int) 4:Att(1:in, 2:out), 5:fidelity
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = perm_colSel{xPerm,1,1,xCol,xAtt};
out_colSel = [out_colSel; tmp_repSel]; %colsel of all conds for one ROI
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = perm_colSel{xPerm,1,2,xCol,xAtt};
out_car_colSel = [out_car_colSel; tmp_repSel]; %colsel of all conds for one ROI
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = perm_carLMS_colSel{xPerm,1,1,xCol,xAtt};
out_carLM_colSel = [out_carLM_colSel; tmp_repSel]; %colsel of all conds for one ROI
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = perm_carLMS_colSel{xPerm,1,2,xCol,xAtt};
out_carS_colSel = [out_carS_colSel; tmp_repSel]; %colsel of all conds for one ROI
end
end
end
%% add real data to the array as perm no. 1001
for xPerm = 1001
for xCol=1:2
for xAtt=1:2
%1:SN 2:perm No. 3:Col(1:car, 2:int) 4:Att(1:in, 2:out), 5:fidelity
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = colSel{1,1,xCol,xAtt};
out_colSel = [out_colSel; tmp_repSel]; %colsel of all conds for one ROI
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = colSel{1,2,xCol,xAtt};
out_car_colSel = [out_car_colSel; tmp_repSel]; %colsel of all conds for one ROI
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = carLMS_colSel{1,1,xCol,xAtt};
out_carLM_colSel = [out_carLM_colSel; tmp_repSel]; %colsel of all conds for one ROI
tmp_repSel = [];
tmp_repSel(:,1) = 1:length(SN);
tmp_repSel(:,2:4) = repmat([xPerm, xCol,xAtt], [length(SN),1]);
tmp_repSel(:,5) = carLMS_colSel{1,2,xCol,xAtt};
out_carS_colSel = [out_carS_colSel; tmp_repSel]; %colsel of all conds for one ROI
end
end
end
writematrix(out_colSel, fullfile(statDir, sprintf('%s_perm_%s', ROIs{xROI}, statFileName)));
writematrix(out_car_colSel, fullfile(statDir, sprintf('%s_perm_car_%s', ROIs{xROI}, statFileName)));
writematrix(out_carLM_colSel, fullfile(statDir, sprintf('%s_perm_carLM_%s', ROIs{xROI}, statFileName)));
writematrix(out_carS_colSel, fullfile(statDir, sprintf('%s_perm_carS_%s', ROIs{xROI}, statFileName)));
end