Choosing a skull clearing technique for chronic mesoscopic optical imaging in awake mice.

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MATLAB · 1,905 lines · 58 KB · no license

function varargout = rsfcGUI(varargin)
% RSFCGUI MATLAB code for rsfcGUI.fig
% RSFCGUI, by itself, creates a new RSFCGUI or raises the existing
% singleton*.
%
% H = RSFCGUI returns the handle to a new RSFCGUI or the handle to
% the existing singleton*.
%
% RSFCGUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in RSFCGUI.M with the given input arguments.
%
% RSFCGUI('Property','Value',...) creates a new RSFCGUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before rsfcGUI_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to rsfcGUI_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help rsfcGUI
% Last Modified by GUIDE v2.5 12-Mar-2020 00:52:04
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @rsfcGUI_OpeningFcn, ...
'gui_OutputFcn', @rsfcGUI_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before rsfcGUI is made visible.
function rsfcGUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to rsfcGUI (see VARARGIN)
% Choose default command line output for rsfcGUI
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes rsfcGUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = rsfcGUI_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbuttonTestMovie.
function pushbuttonTestMovie_Callback(hObject, eventdata, handles)
% hObject handle to pushbuttonTestMovie (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
% cross-correlate for different seeds
[nY,nX,nLam,nT] = size(rsfc.I);
brainIdx = find(rsfc.brain_mask == 1);
yAll = reshape(rsfc.I(:,:,1,:),[nY*nX,nT]);
y = yAll(brainIdx,:);
idx = find(isnan(mean(y,1)) == 0)
y1 = y(:,idx);
rsfc.tRS1 = rsfc.tRS(idx);
nT1 = length(idx);
size(y)
size(y1)
y1 = interp1(rsfc.tRS1',y1',rsfc.tRS')';
yMean = mean(y1,1);
a = pinv(yMean*yMean')*yMean*y1';
ynew = y1'-yMean'*a;
h = 3;
Chb1 = zeros(nY*nX,nT);
size(Chb1)
size(ynew')
Chb1(brainIdx,:) = ynew';
Chb1 = reshape(Chb1, [nY, nX, nT]);
pROI = [60 40; 60 80; 74 34];
y2 = zeros(nT,size(pROI,1));
for ii=1:size(pROI,1)
y2(:,ii) = squeeze(mean(mean(Chb1(pROI(ii,2)+[-h:h], pROI(ii,1)+[-h:h],:),1),2));
end
iLam = 1;
% bandpass filter
wn(2) = 0.08*2/rsfc.fs;
wn(1) = 0.009*2/rsfc.fs;
[fb,fa] = butter(5,wn,'bandpass');
y = reshape(Chb1,[nY*nX nT]);
% y = reshape(I(:,:,iLam,:),[nY*nX nT]);
y = y(brainIdx,:);
% lstInc = find(~isnan(mean(y,1)));
% y = y(:,lstInc);
y = filtfilt(fb,fa,y')';
xLst = [20:2:75];
for iX = 1:length(xLst)
pSeed = [xLst(iX) 80];
ySeed = squeeze(mean(mean(Chb1(pSeed(2)+[-h:h],pSeed(1)+[-h:h],:),1),2));
% ySeed = squeeze(mean(mean(I(pSeed(2)+[-h:h],pSeed(1)+[-h:h],iLam,lstInc),1),2));
ySeed = filtfilt(fb,fa,ySeed);
yCC = (y*ySeed) ./ (std(y,[],2)*std(ySeed)) / length(ySeed);
Chb1CC = zeros(nY,nX);
Chb1CC(brainIdx) = yCC;
axes(handles.axes1)
imagesc(Chb1CC,[-1 1])
hold on
plot(pSeed(1),pSeed(2),'o')
hold off
pause(0.5)
end
% --------------------------------------------------------------------
function menuLoad_Callback(hObject, eventdata, handles)
% hObject handle to menuLoad (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% load your needed variables into rsfc.XXX rsfc.YYY etc
% clear -global all
global rsfc
[filename, pathname] = uigetfile('*_I.mat');
hwait = waitbar(0,'Loading...');
load([pathname filename]);
rsfc.I = I;
rsfc.Iunfilt = Iunfilt;
rsfc.I0 = I0;
rsfc.tRS = tRS;
rsfc.I0_unsampled = I0_unsampled;
rsfc.sampling_rate = size(rsfc.I0_unsampled,1)/size(rsfc.I0,1);
rsfc.atlas = imread('miceatlas.png');
waitbar(0.66,hwait)
axes(handles.axes1)
colormap('jet');
if (exist('rsfc_brainMask.mat','file') ~= 0) && (get(handles.radiobutton_brainmaskON,'Value') == get(handles.radiobutton_brainmaskON,'Max'))
mask = load('rsfc_brainMask.mat');
rsfc.brain_mask = mask.brain_mask;
rsfc.Xi = mask.Xi;
rsfc.Yi = mask.Yi;
img = rsfc.I0(:,:,1).*uint16(rsfc.brain_mask);
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
else
img = rsfc.I0(:,:,1);
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
end
axes(handles.axes4)
imagesc(rsfc.atlas);
close(hwait);
clear A I mask;
% --- Executes on mouse press over axes background.
function axes1_ButtonDownFcn(hObject, eventdata, handles)
% hObject handle to axes1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
keyboard
% --- Executes on button press in pushbuttonSelectSeed.
function pushbuttonSelectSeed_Callback(hObject, eventdata, handles)
% hObject handle to pushbuttonSelectSeed (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global count;
global pts;
global str
global Chb1CC
global ct
ct = 0;
str = {};
cstring = 'rgbcmyk';
[nY,nX,nLam,nT] = size(rsfc.I);
nT = length(rsfc.tRS);
brainIdx = find(rsfc.brain_mask == 1);
hpf = str2num(get(handles.editHPF,'string'));
lpf = str2num(get(handles.editLPF,'string'));
wn(2) =lpf*2/rsfc.fs;
wn(1) = hpf*2/rsfc.fs;
if wn(1)>0 && wn(2)>0
[fb,fa] = butter(5,wn,'bandpass');
elseif wn(2) > 0
[fb,fa] = butter(5,wn(2),'low');
elseif wn(1) > 0
[fb,fa] = butter(5,wn(2),'high');
end
pSeed = ginput(1);
% If the seed in selected in the baseline image. (Baseline image was unsampled and other images were downsampled.)
if get(handles.togglebuttonbaselineimage,'value') == 1
pSeed = pSeed/rsfc.sampling_rate;
end
pSeed = round(pSeed)
if count == 0
pts = pSeed;
else
pts = [pts; pSeed];
end
y = rsfc.y;
h = str2double(get(handles.seedsize,'string'));
ySeed = squeeze(mean(mean(y(pSeed(2)+[-floor(h/2):floor(h/2)],pSeed(1)+[-floor(h/2):floor(h/2)],:),1),2));
% ySeed = squeeze(mean(mean(I(pSeed(2)+[-h:h],pSeed(1)+[-h:h],iLam,lstInc),1),2));
if wn(1)>0 || wn(2)>0
ySeed = filtfilt(fb,fa,ySeed);
end
ySeedunfilt = squeeze(mean(mean(rsfc.Iunfilt(pSeed(2)+[-floor(h/2):floor(h/2)],pSeed(1)+[-floor(h/2):floor(h/2)],:),1),2));
ySeedunGR = squeeze(mean(mean(rsfc.yunGR(pSeed(2)+[-floor(h/2):floor(h/2)],pSeed(1)+[-floor(h/2):floor(h/2)],:),1),2));
y = reshape(y,[nY*nX nT]);
y = y(brainIdx,:);
% yCC = (y*ySeed) ./ (std(y,[],2)*std(ySeed)) / length(ySeed);
yCC = corr(y.',ySeed);
Chb1CC = zeros(nY,nX);
Chb1CC(brainIdx) = yCC;
seedTC = mean(abs(yCC));
set(handles.text4,'String',num2str(seedTC))
axes(handles.axes1)
h = imagesc(Chb1CC,[-1 1]),colorbar;
colormap('jet');
set(h, 'ButtonDownFcn', {@mouseclick, handles});
for u = 1:size(pts,1)
text(pts(u,1),pts(u,2),['O seed' num2str(u)], 'FontSize', 13);
end
for u = 1:length(rsfc.refX)
text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
end
% Dispaly time course of seed
axes(handles.axes2)
hold on
if get(handles.radiobutton_globalsignal,'Value') == get(handles.radiobutton_globalsignal,'Max')
h = plot(rsfc.tRS,ySeedunfilt, cstring(mod(count,7)+1));
else
h = plot(rsfc.tRS,ySeed, cstring(mod(count,7)+1));
end
for u = 1: count+1
str{u} = ['seed' num2str(u)];
end
legend(str);
hold off
count = count+1;
% Display power spectrum of filtered and unfiltered data
L = length(ySeed);
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
ySeedf = fft(ySeed,NFFT)/L;
f = rsfc.fs/2*linspace(0,1,NFFT/2+1);
axes(handles.axes3)
semilogx(f,2*abs(ySeedf(1:NFFT/2+1)),'b','LineWidth',2);
hold on
xlim([0.005 1])
% f = log(f);
ySeedunfiltf = fft(ySeedunfilt,NFFT)/L;
semilogx(f,2*abs(ySeedunfiltf(1:NFFT/2+1)),'r','LineWidth',2);
xlim([0.005 1])
hold on
ySeedunGRf = fft(ySeedunGR,NFFT)/L;
semilogx(f,2*abs(ySeedunGRf(1:NFFT/2+1)),'k','LineWidth',2);
xlim([0.005 1])
hold off
filtpower = rsfc.df*sum(abs(ySeedf).^2);
unfiltpower = rsfc.df*sum(abs(ySeedunfiltf).^2);
filterunGRpower = rsfc.df*sum(abs(ySeedunGRf).^2);
set(handles.text_filteredpower,'String',num2str(filtpower));
set(handles.text_unfilteredpower,'String',num2str(unfiltpower));
set(handles.text21,'String',num2str(filterunGRpower));
% plotyy(f,2*abs(ySeedf(1:NFFT/2+1)),f,2*abs(ySeedunfilt(1:NFFT/2+1)));
% figure;
% colormap('gray');
% imagesc(rsfc.I0_unsampled',[0 800]);
% for u = 1:size(pts,1)
% text(4*pts(u,2),4*pts(u,1),['O seed'], 'FontSize', 13);
% end
%
if (get(handles.radiobutton_savetsps,'Value') == get(handles.radiobutton_savetsps,'Max'))
figure(2);
subplot(5,1,1)
plot(rsfc.tRS,ySeedunfilt);
xlabel('time');
ylabel('magnitude')
title('Unfiltered Signal');
subplot(5,1,2)
plot(rsfc.tRS,rsfc.yglobal);
xlabel('time');
ylabel('magnitude')
title('Global Unfiltered Signal');
subplot(5,1,3)
plot(rsfc.tRS,ySeed)
xlabel('time');
ylabel('magnitude');
title('Filtered Signal');
subplot(5,1,4)
semilogx(f,2*abs(ySeedf(1:NFFT/2+1)),'b','LineWidth',2);
hold on
xlim([0.005 1]);
semilogx(f,2*abs(ySeedunfiltf(1:NFFT/2+1)),'r','LineWidth',2);
title('Frequency Spectrum');
xlabel('frequency');
ylabel('power');
subplot(5,1,5)
semilogx(f,2*abs(rsfc.meanyallf(1:NFFT/2+1)),'b','LineWidth',2);
xlim([0.005 1]);
title('Golabl Frequency Spectrum');
xlabel('frequency');
ylabel('power');
figure(101);
hold on;
h = plot(rsfc.tRS,ySeed, cstring(mod(count,7)+1));
xlabel('time(sec)');
ylabel('magnitude');
Ts = rsfc.tRS;
save('plotdata.mat','ySeed','Ts');
for u = 1: count+1
str{u} = ['seed' num2str(u)];
end
legend(str);
hold off;
nameplot = inputdlg('Please enter the name to save the plot');
ts = rsfc.tRS;
save([nameplot{1} '.mat'],'ySeed','ts');
end
if (get(handles.radiobutton_SaveImage,'Value') == get(handles.radiobutton_SaveImage,'Max'))
figure;
colormap('jet');
imagesc(Chb1CC,[-1 1]),colorbar;
for u = 1:size(pts,1)
text(pts(u,1),pts(u,2),['O seed' num2str(u)], 'FontSize', 18);
% text(pts(u,2),pts(u,1),['O seed'], 'FontSize', 13);
end
name = inputdlg('Please enter the name to save the image');
save([name{1} '.mat'],'Chb1CC');
end
set(handles.togglebuttonbaselineimage,'value',0);
% --- Executes on button press in pushbuttonPreProcess.
function pushbuttonPreProcess_Callback(hObject, eventdata, handles)
% hObject handle to pushbuttonPreProcess (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global count
count = 0;
hpf = str2num(get(handles.editHPF,'string'));
lpf = str2num(get(handles.editLPF,'string'));
hwait = waitbar(0,'Pre-processing...');
[nY,nX,nLam,nT] = size(rsfc.I);
% nY1 = size(rsfc.I0,1);
% sampling_rate = nY1/nY;
% brain_mask = rsfc.brain_mask(1:sampling_rate:end,1:sampling_rate:end);
% brainIdx = find(brain_mask == 1);
brainIdx = find(rsfc.brain_mask == 1);
% Selecting the data matrix
yAll = reshape(rsfc.I(:,:,1,:),[nY*nX,nT]);
y1 = yAll(brainIdx,:);
tRS = rsfc.tRS;
% idx = find(isnan(mean(y,1)) == 0 & mean(y,1) < 220);
% idx = find(isnan(mean(y,1)) == 0) ;
% y1 = y(:,idx);
% tRS1 = rsfc.tRS(idx);
nT
% Removing data with artifacts
if isfield(rsfc,'excludeData')
disp('here');
if (isempty(rsfc.excludeData) == 0)
for u = 1:size(rsfc.excludeData,1)
u
idx1 = length(find(tRS<=rsfc.excludeData(u,1)));
idx2 = length(find(tRS<=rsfc.excludeData(u,2)));
if u == 1
idxs = idx1:idx2;
else
idxs = [idxs idx1:idx2];
end
end
y1(:,idxs) = [];
tRS(idxs) = [];
rsfc.I(:,:,:,idxs) = [];
rsfc.excludeData = [];
rsfc.Iunfilt(:,:,:,idxs) = [];
axes(handles.axes2)
cla reset
end
end
% nT1 = length(tRS1);
rsfc.tRS = tRS;
% y11 = y1;
% y1 = interp1(rsfc.tRS1',y1',rsfc.tRS')';
nT = length(tRS);
size(y1)
nY
nX
nT
rsfc.yunGR = zeros(nY*nX,nT);
rsfc.yunGR(brainIdx,:) = y1;
rsfc.yunGR = reshape(rsfc.yunGR,[nY nX nT]);
% size(y1)
% figure; plot(rsfc.tRS1,y11(5000,:)); figure; plot(rsfc.tRS,y1(5000,:));
% normalize data...
% Just testing this out... is it a good thing to do?
% we are trying it out so that pixels with large amplitude from spectular
% reflections don't dominate the mean
% y1 = y1./(mean(y1,2)*ones(1,size(y1,2)));
% subtract global mean
% if 0
% check matlab regression also
if get(handles.radiobutton_noGSR,'Value')
ynew = y1';
elseif get(handles.radiobutton_GSR,'Value')
yMean = mean(y1,1);
a = pinv(yMean*yMean')*yMean*y1';
size(a)
ynew = y1'-yMean'*a;
else
infarctIdx = find(rsfc.infarct_mask == 1);
yAll = reshape(rsfc.I(:,:,1,:),[nY*nX,nT]);
yinfarct = yAll(infarctIdx,:);
yinfarctMean = mean(yinfarct,1);
% a = pinv(yMean*yMean')*yMean*yinfarct';
% yinfarctnew = yinfarct'-yMean'*a;
noninfarctIdx = setdiff(brainIdx,infarctIdx);
ynoninfarct = yAll(noninfarctIdx,:);
ynoninfarctMean = mean(ynoninfarct,1);
% a = pinv(yMean*yMean')*yMean*ynoninfarct';
% ynoninfarctnew = ynoninfarct'-yMean'*a;
yMean = [ynoninfarctMean; yinfarctMean];
a = pinv(yMean*yMean')*yMean*y1';
disp('size of a is')
size(a)
ynew = y1'-yMean'*a;
% yAll(infarctIdx,:) = yinfarctnew';
% yAll(noninfarctIdx,:) = ynoninfarctnew';
%
% ynew = yAll(brainIdx,:);
% ynew = ynew';
% noninfarctIdx = setdiff(brainIdx,infarctIdx);
% % Selecting the data matrix
% yAll = reshape(rsfc.I(:,:,1,:),[nY*nX,nT]);
% yinfarct = yAll(infarctIdx,:);
% ynoninfarct = yAll(noninfarctIdx,:);
% yinfarctMean = mean(yinfarct,1);
% ynoninfarctMean = mean(ynoninfarct,1);
% yMean = yinfarctMean.*ynoninfarctMean;
% a = pinv(yMean*yMean')*yMean*y1';
% ynew = y1'-yMean'*a;
end
size(ynew)
% else
% ynew = y1';
% end
% Chb1 = zeros(nY*nX,nT1);
% Chb1(brainIdx,:) = ynew';
% Chb1 = reshape(Chb1, [nY, nX, nT1]);
Chb1 = zeros(nY*nX,nT);
size(Chb1)
Chb1(brainIdx,:) = ynew';
Chb1 = reshape(Chb1, [nY, nX, nT]);
% bandpass filter
rsfc.fs = 1/(rsfc.tRS(2)-rsfc.tRS(1));
wn(2) = lpf*2/rsfc.fs;
wn(1) = hpf*2/rsfc.fs;
if wn(1)>0 && wn(2)>0
[fb,fa] = butter(5,wn,'bandpass');
elseif wn(2) > 0
[fb,fa] = butter(5,wn(2),'low');
elseif wn(1) > 0
[fb,fa] = butter(5,wn(2),'high');
end
y = reshape(Chb1,[nY*nX nT]);
y = y(brainIdx,:);
if wn(1) > 0 || wn(2)>0
y = filtfilt(fb,fa,y')';
end
yy = zeros(nY*nX,nT);
yy(brainIdx,:) = y;
% yy = reshape(yy,[nY nX nT1]);
yy = reshape(yy,[nY nX nT]);
% nMin = 8;
% dt = 0.5;
% tRS = min(rsfc.tRS)+dt:dt:(nMin*60-dt);
% nT = length(tRS);
% yyy = zeros(nY,nX,nT);
% for iT = 1:nT
% iT
% lstIdx = find(rsfc.tRS>(tRS(iT)-dt) & rsfc.tRS<=(tRS(iT)+dt));
% yyy(:,:,1,iT) = mean(yy(:,:,lstIdx),3);
% end
% rsfc.tRS = tRS;
rsfc.y = yy;
y = rsfc.y;
[nY,nX,nT] = size(y);
y = reshape(y,[nY*nX nT]);
axes(handles.axes1)
colormap(jet);
h = imagesc(rsfc.I0.*(rsfc.brain_mask));
set(h, 'ButtonDownFcn', {@mouseclick, handles});
brainIdx = find(rsfc.brain_mask == 1);
y = y(brainIdx,:);
hpf = str2num(get(handles.editHPF,'string'));
lpf = str2num(get(handles.editLPF,'string'));
wn(2) =lpf*2/rsfc.fs;
wn(1) = hpf*2/rsfc.fs;
if wn(1)>0 && wn(2)>0
[fb,fa] = butter(5,wn,'bandpass');
elseif wn(2) > 0
[fb,fa] = butter(5,wn(2),'low');
elseif wn(1) > 0
[fb,fa] = butter(5,wn(2),'high');
end
I = rsfc.y;
t = rsfc.tRS;
save('Processeddata','I','t');
% create N*N correlation matrix (N = number of pixels in the brain)
% yCC = (y*y') ./ (std(y,[],2)*std(y,[],2)') / size(y,1);
yCC = corr(y.',y.');
load('atlas.mat');
rsfc.areaM = 8/(sqrt((xi(2)-xi(1))^2+(yi(2)-yi(1))^2));
yCCavg = mean(abs(yCC),1);
CI = mean(yCCavg);
set(handles.text_avgconnectivityglobal,'String',num2str(CI));
cutoff = str2num(get(handles.edit_cutoffCI,'string'));
idxs = find(yCCavg >= cutoff);
set(handles.text_globalArea,'String',num2str(length(idxs)*rsfc.areaM));
BOC = zeros(nY,nX);
BOC(brainIdx) = yCCavg;
axes(handles.axes5)
colormap(jet);
imagesc(BOC); colorbar;
rsfc.GCM = BOC;
rsfc.Sign_img = ((repmat((1:nY)',1,nX)-xi(1)).*(yi(2)-yi(1)))-((repmat(1:nX,nY,1)-yi(1)).*(xi(2)-xi(1)));
Oneside_idxs = find(rsfc.GCM >= cutoff & rsfc.Sign_img > 0 & rsfc.brain_mask == 1);
Otherside_idxs = find(rsfc.GCM >= cutoff & rsfc.Sign_img < 0 & rsfc.brain_mask == 1);
Oneside_idxs_CI = find( rsfc.Sign_img > 0 & rsfc.brain_mask == 1);
Otherside_idxs_CI = find( rsfc.Sign_img < 0 & rsfc.brain_mask == 1);
Oneside_CI = mean(abs(rsfc.GCM(Oneside_idxs_CI)));
Otherside_CI = mean(abs(rsfc.GCM(Otherside_idxs_CI)));
set(handles.text_ConnectivityIndex_1,'String',num2str(Oneside_CI ));
set(handles.text_ConnectivityIndex_2,'String',num2str(Otherside_CI));
set(handles.text_area_1,'String',num2str(length(Oneside_idxs)*rsfc.areaM));
set(handles.text_area_2,'String',num2str(length(Otherside_idxs)*rsfc.areaM));
% calculate gobal time series signal
rsfc.yglobal = squeeze(mean(mean(rsfc.Iunfilt,1),2));
% calculate global frequency signal
L = length(rsfc.yglobal);
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
yall = reshape(rsfc.Iunfilt,[nY*nX nT]);
yall = yall(brainIdx,:);
yallf = fft(yall',NFFT)/L;
rsfc.meanyallf = mean(abs(yallf),2);
rsfc.f = rsfc.fs/2*linspace(0,1,NFFT/2+1);
rsfc.df = (rsfc.f(2)-rsfc.f(1))/2;
globalpower = rsfc.df*sum(abs(rsfc.meanyallf).^2);
set(handles.text_globalpower,'String',num2str(globalpower));
% Left Right Spatial Correlation
axes(handles.axes6)
imagesc(rsfc.I0); colormap('gray');
if exist('BLpts.mat','file')
load('BLpts.mat')
else
h = msgbox(' Please select 2 points bregma and lambda in axes 6');
uiwait(h);
[y,x] = ginput(2);
save('BLpts.mat','y','x');
end
a = y(2)-y(1);
b = x(1)-x(2);
c = (y(1)-y(2))*x(1)+(x(2)-x(1))*y(1);
rsfc.yLRC = zeros(nY,nX);
for u = 1:nY
for v = 1:nX
if rsfc.brain_mask(u,v) == 1
xs = round(2*(b^2*u-a*b*v-a*c)/(a^2+b^2)-u);
ys = round(2*(a^2*v-a*b*u-b*c)/(a^2+b^2)-v);
if xs <1
xs = 1;
end
if xs > nY
xs = nY;
end
if ys <1
ys = 1;
end
if ys > nX
ys = nX;
end
h = str2double(get(handles.seedsize,'string'));
x1_min = min(max(xs-floor(h/2),1),nY);
x1_max = min(max(xs+floor(h/2),1),nY);
y1_min = min(max(ys-floor(h/2),1),nY);
y1_max = min(max(ys+floor(h/2),1),nY);
x2_min = min(max(u-floor(h/2),1),nY);
x2_max = min(max(u+floor(h/2),1),nY);
y2_min = min(max(v-floor(h/2),1),nY);
y2_max = min(max(v+floor(h/2),1),nY);
% CR = corr(squeeze(rsfc.y(xs,ys,:)),squeeze(rsfc.y(u,v,:)));
CR = corr(squeeze(mean(mean(rsfc.y(x1_min:x1_max,y1_min:y1_max,:),1),2)),squeeze(mean(mean(rsfc.y(x2_min:x2_max,y2_min:y2_max,:),1),2)));
rsfc.yLRC(xs,ys) = CR;
rsfc.yLRC(u,v) = CR;
end
end
end
imagesc(rsfc.yLRC,[-1 1]); colormap('jet'); colorbar;
LRC = rsfc.yLRC;
save('LRC.mat','LRC');
close(hwait);
% --- Executes on button press in togglebuttonbaselineimage.
function togglebuttonbaselineimage_Callback(hObject, eventdata, handles)
% hObject handle to togglebuttonbaselineimage (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of togglebuttonbaselineimage
global rsfc
global pts
global Chb1CC
axes(handles.axes1);
if get(hObject,'Value') == 1
colormap('gray');
% img = rsfc.I0(:,:,1).*rsfc.brain_mask;
img = rsfc.I0_unsampled(:,:,1);
clims = [min(img(:)) max(img(:))];
imagesc(img,[rsfc.MinS rsfc.MaxS]); colorbar;
hold on
for u = 1:size(pts,1)
text(rsfc.sampling_rate*pts(u,1),rsfc.sampling_rate*pts(u,2),['O seed' num2str(u)], 'FontSize', 13);
end
for u = 1:length(rsfc.refX)
text(rsfc.sampling_rate*rsfc.refX(u),rsfc.sampling_rate*rsfc.refY(u),'X', 'FontSize', 13);
end
hold off
else
colormap('jet');
h = imagesc(Chb1CC,[-1 1]); colorbar;
hold on
for u = 1:size(pts,1)
text(pts(u,1),pts(u,2),['O seed' num2str(u)], 'FontSize', 13);
end
for u = 1:length(rsfc.refX)
text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
end
hold off
set(h, 'ButtonDownFcn', {@mouseclick, handles});
end
% --- Executes on button press in togglebutton2.
function togglebutton2_Callback(hObject, eventdata, handles)
% hObject handle to togglebutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of togglebutton2
global count
global pts
global rsfc
count = 0;
pts = [0 0];
axes(handles.axes2)
cla reset
axes(handles.axes3);
cla reset
axes(handles.axes1);
cla reset
colormap('jet');
% img = rsfc.I0(:,:,1).*rsfc.brain_mask;
img = rsfc.I0(:,:,1);
clims = [min(img(:)) max(img(:))];
h = imagesc(img,clims);
for u = 1:length(rsfc.refX)
text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
end
set(h, 'ButtonDownFcn', {@mouseclick, handles});
set(handles.togglebuttonbaselineimage,'value',0);
% --- Executes on button press in pushbuttonbrainmask.
function pushbuttonbrainmask_Callback(hObject, eventdata, handles)
% hObject handle to pushbuttonbrainmask (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
final_brain_mask = false(size(rsfc.I0));
while(1)
axes(handles.axes1)
MinMinS = min(rsfc.I0(:)); MaxMaxS = max(rsfc.I0(:));
while(1)
imagesc(rsfc.I0(:,:,1),[ MinMinS MaxMaxS]); colorbar;
colormap('gray');
button = questdlg('Are you happy with the image contrast?','Repeat selection or not...','No');
if strcmp(button,'No')
answer= inputdlg({'Enter minimum intensity level of survey scan image'},'Enter minimum intensity level:',1,{num2str(MinMinS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MinMinS = str2num(answer1.number);
answer= inputdlg({'Enter maximum intensity level of survey scan image'},'Enter maximum intensity level:',1,{num2str(MaxMaxS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MaxMaxS = str2num(answer1.number);
else
break;
end
end
rsfc.MaxS = MaxMaxS;
rsfc.MinS = MinMinS;
h = msgbox(' Please select the brain region');
uiwait(h)
% axes(handles.axes6)
% imagesc(rsfc.yLRC,[-1 1]);
[brain_mask,Xi,Yi] = roipoly;
hold on;
plot(Xi,Yi,'color','k');
hold off;
button = questdlg('Are you statisfied with ROI?');
if strcmp(button, 'Yes')
final_brain_mask(brain_mask==1) = 1;
button = questdlg('Do you want to select another ROI?');
if ~strcmp(button, 'Yes')
break;
end
end
end
if (get(handles.radiobutton4,'Value') == get(handles.radiobutton4,'Max'))
figure; imshow(rsfc.I0,[MinMinS MaxMaxS]); colormap('gray');
end
brain_mask = final_brain_mask;
save('rsfc_brainMask.mat','brain_mask','Xi','Yi');
rsfc.brain_mask = final_brain_mask;
rsfc.Xi = Xi;
rsfc.Yi = Yi;
% --- Executes on button press in pushbutton_slecttime.
function pushbutton_slecttime_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_slecttime (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global tidx
global h
global ct
if ct > 0
delete(h);
end
ct = 1;
axes(handles.axes2)
[X,~] =ginput(1);
tidx = length(find(rsfc.tRS < X))+1;
% tp = rsfc.tRS(idx);
axes(handles.axes1)
colormap('jet');
if (get(handles.radiobutton_brainmaskON,'Value') == get(handles.radiobutton_brainmaskON,'Max'))
img = rsfc.I(:,:,1,tidx).*rsfc.brain_mask;
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
else
img = rsfc.I0(:,:,1);
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
end
axes(handles.axes2)
hold on
h = text(rsfc.tRS(tidx),0,'X','FontSize',15);
hold off
% --- Executes on button press in pushbutton_forward.
function pushbutton_forward_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_forward (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global tidx
global h
delete(h)
tidx = tidx+1;
axes(handles.axes1)
colormap('jet');
if (get(handles.radiobutton_brainmaskON,'Value') == get(handles.radiobutton_brainmaskON,'Max'))
img = rsfc.I(:,:,1,tidx).*rsfc.brain_mask;
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
else
img = rsfc.I(:,:,1,tidx);
colormap('gray');
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
end
axes(handles.axes2)
hold on
h = text(rsfc.tRS(tidx),0,'X','FontSize',13);
hold off
% --- Executes on button press in pushbutton_backward.
function pushbutton_backward_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_backward (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global tidx
global h
delete(h)
tidx = tidx-1;
axes(handles.axes1)
colormap('jet');
if (get(handles.radiobutton_brainmaskON,'Value') == get(handles.radiobutton_brainmaskON,'Max'))
img = rsfc.I(:,:,1,tidx).*rsfc.brain_mask;
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
else
img = rsfc.I(:,:,1,tidx);
colormap('gray');
clims = [min(img(:)) max(img(:))];
imagesc(img,clims);
end
axes(handles.axes2)
hold on
h = text(rsfc.tRS(tidx),0,'X','FontSize',13);
hold off
function editHPF_Callback(hObject, eventdata, handles)
% hObject handle to editHPF (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of editHPF as text
% str2double(get(hObject,'String')) returns contents of editHPF as a double
function editLPF_Callback(hObject, eventdata, handles)
% hObject handle to editLPF (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of editLPF as text
% str2double(get(hObject,'String')) returns contents of editLPF as a double
% --- Executes on button press in pushbutton.
function pushbutton_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global obj_count
k = waitforbuttonpress;
if k==0
pt1 = get(gca,'CurrentPoint'); % button down detected
finalRect = rbbox; % return figure units
pt2 = get(gca,'CurrentPoint');
%Checking boundary conditions
if pt1(1,1) < rsfc.tRS(1)
pt1(1,1) = rsfc.tRS(1);
end
if pt1(1,1) > rsfc.tRS(end)
pt1(1,1) = rsfc.tRS(end);
end
if pt2(1,1) < rsfc.tRS(1)
pt2(1,1) = rsfc.tRS(1);
end
if pt2(1,1) > rsfc.tRS(end)
pt2(1,1) = rsfc.tRS(end);
end
%Arrange pts in increasing order
if pt1(1,1) > pt2(1,1)
temp = pt1(1,1);
pt1(1,1) = pt2(1,1);
pt2(1,1) = temp;
end
if isfield(rsfc,'excludeData')
rsfc.excludeData(end+1,:) = [pt1(1,1) pt2(1,1)];
else
rsfc.excludeData(1,:) = [pt1(1,1) pt2(1,1)];
end
axes(handles.axes2)
yy = ylim();
hp = patch([pt1(1,1) pt2(1,1) pt2(1,1) pt1(1,1)],[yy(1) yy(1) yy(2) yy(2)],'m','facealpha',0.3,'edgecolor','k');
idx = size(rsfc.excludeData,1);
rsfc.excludeData_handle(idx) = hp;
obj_count(idx) = idx;
set(hp,'ButtonDownFcn', sprintf('rsfc_excludeData_PatchCallback(%d)',idx) );
end
% --- Executes on button press in radiobutton_brainmaskON.
function radiobutton_brainmaskON_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton_brainmaskON (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton_brainmaskON
% --- Executes on button press in pushbutton_iterrativeparcellation.
function pushbutton_iterrativeparcellation_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_iterrativeparcellation (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
y = rsfc.y;
[nY,nX,nT] = size(y);
y = reshape(y,[nY*nX nT]);
brainIdx = find(rsfc.brain_mask == 1);
y = y(brainIdx,:);
hpf = str2num(get(handles.editHPF,'string'));
lpf = str2num(get(handles.editLPF,'string'));
wn(2) =lpf*2/rsfc.fs;
wn(1) = hpf*2/rsfc.fs;
if wn(1)>0 && wn(2)>0
[fb,fa] = butter(5,wn,'bandpass');
elseif wn(2) > 0
[fb,fa] = butter(5,wn(2),'low');
elseif wn(1) > 0
[fb,fa] = butter(5,wn(2),'high');
end
% create N*N correlation matrix (N = number of pixels in the brain)
% yCC = (y*y') ./ (std(y,[],2)*std(y,[],2)') / size(y,1);
yCC = corr(y.',y.');
% yCCavg = mean(abs(yCC),1);
% BOC = zeros(nY,nX);
% BOC(brainIdx) = yCCavg;
% figure; imagesc(BOC); colorbar;
% Singular Value Decomposition
% [U,~,V] = svd(yCC);
axes(handles.axes1)
while(1)
% intial assumption of parcels based on first ten modes of connectivity matrix
yatlas = zeros(1,length(brainIdx));
l = length(yatlas);
button = questdlg('Would you like to calculate another parcellation map ?');
if strcmp(button,'Yes')
button2 = questdlg('Would you like to open saved seeds for parcellation');
if strcmp(button2,'Yes')
[filename,pathname] = uigetfile;
load([pathname filename]);
load('atlas.mat');
ya = [328 328]; xa = [129 459];
fixedPoints = [[xi(1) yi(1)]; [xi(2) yi(2)]];
movingPoints = [[xa(1) ya(1)]; [xa(2) ya(2)]];
tform = fitgeotrans(movingPoints,fixedPoints,'nonreflectivesimilarity');
[xp,yp] = transformPointsForward(tform, xp, yp);
yp = round(yp);
xp = round(xp);
else
answer = char(inputdlg('Please enter number of parcellations'));
psize = str2num(answer);
% for u = 1:l
% [~,id] = max(abs(U(u,1:psize)));
% yatlas(u) = U(u,id)*id/abs(U(u,id));
% end
% yintial_atlas = yatlas;
% idx = find(yatlas < 0);
% yatlas(idx) = yatlas(idx) + (2*psize+1);
% For seed based parcellation
imagesc(rsfc.I0);
colormap('gray');
h = msgbox('Please select the seeds for parcellation');
uiwait(h);
[yp,xp] = ginput(psize);
yp = round(yp);
xp = round(xp);
end
p_timetraces = zeros(length(xp),nT);
for u = 1:size(p_timetraces,1)
p_timetraces(u,:) = rsfc.y(xp(u),yp(u),:);
end
yR = corr(y.',p_timetraces.');
for w = 1:l
[~,id] = max(yR(w,:));
yatlas(w) = id;
end
brain_atlas = zeros(nY,nX);
brain_atlas(brainIdx) = yatlas;
figure; imagesc(brain_atlas); colorbar;
% iterative parcellation (clustering algorithm)
% p_timetraces = zeros(2*psize,nT);
count = 0;
u = 0;
while(1)
u = u+1;
% for v = 1:10
% idx1 = find(yatlas == -v);
% idx2 = find(yatlas == v);
% p_timetraces(v,:) = mean(y(idx1,:),1);
% p_timetraces(v+10,:) = mean(y(idx2,:),1);
% end
for v = 1:size(p_timetraces,1)
% idx1 = find(yatlas == -v);
% idx2 = find(yatlas == v);
idx = find(yatlas == v);
p_timetraces(v,:) = mean(y(idx,:),1);
% p_timetraces(v+10,:) = mean(y(idx2,:),1);
if idx < 50
if count == 0
lst = v;
count = count+1;
else
lst = [lst idx];
count = count+1;
end
end
end
if count > 0
p_timetraces(lst,:) = [];
lst = [];
count = 0;
end
if wn(1)>0 || wn(2)>0
p_timetraces = filtfilt(fb,fa,p_timetraces')';
end
% yR = (y*p_timetraces')./ (std(y,[],2)*std(p_timetraces,[],2)') / size(y,1);
yR = corr(y.',p_timetraces.');
for w = 1:l
[~,id] = max(yR(w,:));
yatlas(w) = id;
end
if u > 1
if isequal(yatlas,yatlasCond)
break;
end
end
yatlasCond = yatlas;
% idx1 = find(yatlas < 11);
% yatlas(idx1) = -1*yatlas(idx1);
% idx2 = find(yatlas > 10);
% yatlas(idx2) = yatlas(idx2)-10;
end
no_of_iterations = u
brain_atlas = zeros(nY,nX);
brain_atlas(brainIdx) = yatlas;
figure; imagesc(brain_atlas); colorbar;
save('brain_atlas.mat','brain_atlas');
% pR = corr(p_timetraces.',p_timetraces.');
% C = linkage(pR);
% figure; dendrogram(C);
else
break;
end
end
% U = U(:,1:10);
% Rmatrix = (y*U')./(std(y,[],2)'*std(U,[],2))/ size(y,1);
% for u = 1:l
% [min,id] = unique(Rmatrix(1:10,u));
% yatlas(u) = min*id/abs(min);
% end
function seedsize_Callback(hObject, eventdata, handles)
% hObject handle to seedsize (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of seedsize as text
% str2double(get(hObject,'String')) returns contents of seedsize as a double
% --- Executes during object creation, after setting all properties.
function seedsize_CreateFcn(hObject, eventdata, handles)
% hObject handle to seedsize (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton_avgfreqspectrum.
function pushbutton_avgfreqspectrum_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_avgfreqspectrum (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
y = rsfc.y;
[nY,nX,nT] = size(y);
y = reshape(y,[nY*nX nT]);
brainIdx = find(rsfc.brain_mask == 1);
y = y(brainIdx,:);
hpf = str2num(get(handles.editHPF,'string'));
lpf = str2num(get(handles.editLPF,'string'));
wn(2) =lpf*2/rsfc.fs;
wn(1) = hpf*2/rsfc.fs;
if wn(1)>0 && wn(2)>0
[fb,fa] = butter(5,wn,'bandpass');
elseif wn(2) > 0
[fb,fa] = butter(5,wn(2),'low');
elseif wn(1) > 0
[fb,fa] = butter(5,wn(2),'high');
end
% create N*N correlation matrix (N = number of pixels in the brain)
% yCC = (y*y') ./ (std(y,[],2)*std(y,[],2)') / size(y,1);
yCC = corr(y.',y.');
yCCavg = mean(abs(yCC),1);
BOC = zeros(nY,nX);
BOC(brainIdx) = yCCavg;
figure; imagesc(BOC,[0 0.5]); colormap('jet'); colorbar;
% y = rsfc.y;
% fs = 11;
% [nY,nX,nT] = size(y);
% y = reshape(y,[nY*nX nT]);
% brainIdx = find(rsfc.brain_mask == 1);
% y = y(brainIdx,:);
% L = size(y,1);
% NFFT = 2^nextpow2(L); % Next power of 2 from length of y
% yf = fft(y',NFFT)'/L;
% f = fs/2*linspace(0,1,NFFT/2+1);
% yfavg = mean(yf,1);
% figure;
% semilogx(f,2*abs(yfavg(1:NFFT/2+1)),'b','LineWidth',2);
% --- Executes on button press in radiobutton_SaveImage.
function radiobutton_SaveImage_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton_SaveImage (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton_SaveImage
% --- Executes on button press in pushbutton11.
function pushbutton11_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton11 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
y = rsfc.y;
[AVIfileOut, AVIpathOut] = uiputfile( '*.avi','Create AVI movie name...');
AVIfileName = [AVIpathOut AVIfileOut];
writerObj = VideoWriter(AVIfileName,'Uncompressed AVI');
writerObj.FrameRate = 40;
open(writerObj);
l = size(y,3);
cmap = colormap(jet(256));
% cmap = cmap(1:51,:);
ymin = min(y(:));
ymax = max(y(:));
y = (y-ymin)/(ymax-ymin);
MinMinS = min(y(:));
MaxMaxS = max(y(:));
while(1)
figure(1); imagesc(y(:,:,1),[MinMinS MaxMaxS]);
colormap(jet);
colorbar;
button = questdlg('Are you happy with the image contrast?','Repeat selection or not...','No');
if strcmp(button,'No')
answer= inputdlg({'Enter minimum intensity level of survey scan image'},'Enter minimum intensity level:',1,{num2str(MinMinS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MinMinS = str2num(answer1.number);
answer= inputdlg({'Enter maximum intensity level of survey scan image'},'Enter maximum intensity level:',1,{num2str(MaxMaxS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MaxMaxS = str2num(answer1.number);
else
break;
end
end
for u = 1:l
figure(111)
imagesc(squeeze(y(:,:,u)),[MinMinS MaxMaxS]);
text(10,10,[num2str(int16(rsfc.tRS(u))) 'Secs']);
colormap(jet);
F = getframe(gcf);
writeVideo(writerObj,F);
%mov = addframe(mov,F);
end;
%mov = close(mov);
close(writerObj);
% --- Executes on button press in pushbutton_registeratlas.
function pushbutton_registeratlas_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_registeratlas (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
h = msgbox('Please select two points in the image');
uiwait(h);
% reading the points Bregma and Lambda from image.
MinMinS = min(rsfc.I0(:)); MaxMaxS = max(rsfc.I0(:));
axes(handles.axes1)
while(1)
imagesc(rsfc.I0(:,:,1),[ MinMinS MaxMaxS]); colorbar;
colormap('gray');
button = questdlg('Are you happy with the image contrast?','Repeat selection or not...','No');
if strcmp(button,'No')
answer= inputdlg({'Enter minimum intensity level of survey scan image'},'Enter minimum intensity level:',1,{num2str(MinMinS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MinMinS = str2num(answer1.number);
answer= inputdlg({'Enter maximum intensity level of survey scan image'},'Enter maximum intensity level:',1,{num2str(MaxMaxS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MaxMaxS = str2num(answer1.number);
else
break;
end
end
rsfc.MaxS = MaxMaxS;
rsfc.MinS = MinMinS;
[y,x] = ginput(2)
yi = y;
xi = x;
save('atlas.mat','yi','xi');
% Bregma and Lambda locations from atlas.
ya = [328 328]; xa = [129 459];
% Since trying to register atlas to the data, atlas points are moving
% points and image points are fixed points
fixedPoints = [[x(1) y(1)]; [x(2) y(2)]];
movingPoints = [[xa(1) ya(1)]; [xa(2) ya(2)]];
[m1,n1] = size(rsfc.I0)
[m2,n2,~] = size(rsfc.atlas);
tform = fitgeotrans(movingPoints,fixedPoints,'nonreflectivesimilarity');
count = 0;
% img3(:,:,1) = imwarp(rsfc.atlas(:,:,1),tform);
% img3(:,:,2) = imwarp(rsfc.atlas(:,:,2),tform);
% img3(:,:,3) = imwarp(rsfc.atlas(:,:,3),tform);
u = repmat((1:m2)',1,n2);
v = repmat(1:n2,m2,1);
[ut,vt] = transformPointsForward(tform, u(:), v(:));
img3 = rsfc.atlas(1:m1,1:n1,:)*0;
L = length(u(:));
for xx = 1:L
if ut(xx) < 1 || ut(xx) > m1 || vt(xx) < 1 || vt(xx) > n1
else
img3(round(ut(xx)),round(vt(xx)),:) = rsfc.atlas(u(xx),v(xx),:);
end
end
% for xx = 1:
% img3(round(ut),round(vt),:) = rsfc.atlas(u,v,:);
% figure;
% imagesc(img3)
% for u = 1:m2
% % u
% for v = 1:n2
% [tempx,tempy] = transformPointsForward(t,v,u);
% % [tempx,tempy] = tforminv(t,u,v);
% if tempx < 1 || tempx > n1 || tempy < 1 || tempy > m1
% else
% count = count+1;
% img3(round(tempy),round(tempx),:) = rsfc.atlas(u,v,:);
% % img3(u,v,:) = img1(round(tempy),round(tempx),:);
% end
% end
% end
% count
% u
% v
axes(handles.axes4)
imagesc(img3)
% axis([0,m1,0,n1]);
% --- Executes during object creation, after setting all properties.
function text4_CreateFcn(hObject, eventdata, handles)
% hObject handle to text4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% --- Executes on button press in pushbutton_selectROI.
function pushbutton_selectROI_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_selectROI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
GCM = rsfc.GCM;
while(1)
axes(handles.axes5)
imagesc(rsfc.GCM,[0 max(GCM(:))]); colorbar;
for u = 1:length(rsfc.refX)
text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
end
[mask,Xi,Yi] = roipoly;
axes(handles.axes5)
hold on;
plot(Xi,Yi,'color','k');
button = questdlg('Are you statisfied with ROI');
if strcmp(button, 'Yes');
break;
end
end
cutoff = str2num(get(handles.edit_cutoffCI,'string'));
idxs1 = find(mask==1);
idxs2 = find(mask==1 & rsfc.GCM >= cutoff);
set(handles.text_areaofROI,'String',num2str(length(idxs2)));
if(isempty(idxs1)==0)
set(handles.text_avgconnectivity,'String',num2str(mean(GCM(idxs1))));
else
set(handles.text_avgconnectivity,'String','0');
end
axes(handles.axes5)
imagesc(rsfc.GCM,[cutoff max(GCM(:))]); colorbar;
plot(Xi,Yi,'color','k');
for u = 1:length(rsfc.refX)
text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
end
rsfc.Smask = mask;
rsfc.SXi = Xi;
rsfc.SYi = Yi;
function edit_cutoffCI_Callback(hObject, eventdata, handles)
% hObject handle to edit_cutoffCI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit_cutoffCI as text
% str2double(get(hObject,'String')) returns contents of edit_cutoffCI as a double
global rsfc
cutoff = str2num(get(handles.edit_cutoffCI,'string'));
idxs = find(rsfc.brain_mask==1 & rsfc.GCM >= cutoff);
set(handles.text_globalArea,'String',num2str(length(idxs)*rsfc.areaM));
Oneside_idxs = find(rsfc.GCM >= cutoff & rsfc.Sign_img > 0 & rsfc.brain_mask == 1);
Otherside_idxs = find(rsfc.GCM >= cutoff & rsfc.Sign_img < 0 & rsfc.brain_mask == 1);
% Oneside_CI = mean(abs(rsfc.GCM(Oneside_idxs)));
% Otherside_CI = mean(abs(rsfc.GCM(Otherside_idxs)));
% set(handles.text_ConnectivityIndex_1,'String',num2str(Oneside_CI ));
% set(handles.text_ConnectivityIndex_2,'String',num2str(Otherside_CI));
set(handles.text_area_1,'String',num2str(length(Oneside_idxs)*rsfc.areaM));
set(handles.text_area_2,'String',num2str(length(Otherside_idxs)*rsfc.areaM));
if isfield(rsfc,'Smask')
cutoff = str2num(get(handles.edit_cutoffCI,'string'));
idxs2 = find(rsfc.Smask==1 & rsfc.GCM >= cutoff);
set(handles.text_areaofROI,'String',num2str(length(idxs2)*rsfc.areaM));
end
axes(handles.axes5)
if cutoff >= max(rsfc.GCM(:))
maxI = cutoff+0.1;
else
maxI = max(rsfc.GCM(:));
end
imagesc(rsfc.GCM,[cutoff maxI]); colorbar;
if isfield(rsfc,'Smask')
plot(rsfc.SXi,rsfc.SYi,'color','k');
end
for u = 1:length(rsfc.refX)
text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
end
% --- Executes during object creation, after setting all properties.
function edit_cutoffCI_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit_cutoffCI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit_minI_Callback(hObject, eventdata, handles)
% hObject handle to edit_minI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit_minI as text
% str2double(get(hObject,'String')) returns contents of edit_minI as a double
% --- Executes during object creation, after setting all properties.
function edit_minI_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit_minI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit_MaxI_Callback(hObject, eventdata, handles)
% hObject handle to edit_MaxI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit_MaxI as text
% str2double(get(hObject,'String')) returns contents of edit_MaxI as a double
% --- Executes during object creation, after setting all properties.
function edit_MaxI_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit_MaxI (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function mouseclick(hObject, eventdata, handles)
global rsfc
parent = (get(hObject, 'Parent')) ;
pts = round(get(parent, 'CurrentPoint'));
%
cutoff = str2num(get(handles.edit_cutoffCI,'string'));
if cutoff >= max(rsfc.GCM(:))
maxI = cutoff+0.1;
else
maxI = max(rsfc.GCM(:));
end
axes(handles.axes5)
imagesc(rsfc.GCM,[cutoff maxI]); colorbar;
y = [pts(1,2) pts(1,2)];
x = xlim();
line(x,y,'LineWidth',2,'color','r');
y = ylim();
x = [pts(1,1), pts(1,1)];
line(x,y,'LineWidth',2,'color','r');
% --- Executes on button press in pushbutton_SelectReferencePoints.
function pushbutton_SelectReferencePoints_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_SelectReferencePoints (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
axes(handles.axes1)
imagesc(rsfc.I0);
[X,Y] = ginput(3);
rsfc.refX = X;
rsfc.refY = Y;
for u = 1:length(X)
text(X(u),Y(u),'X', 'FontSize', 13);
end
% axes(handles.axes5)
% imagesc(rsfc.GCM);
% for u = 1:length(rsfc.refX)
% text(rsfc.refX(u),rsfc.refY(u),'X', 'FontSize', 13);
% end
% --- Executes on button press in radiobutton_globalsignal.
function radiobutton_globalsignal_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton_globalsignal (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton_globalsignal
% --- Executes on button press in radiobutton4.
function radiobutton4_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton4
% --- Executes on button press in radiobutton_savetsps.
function radiobutton_savetsps_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton_savetsps (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton_savetsps
% --- Executes on button press in pushbutton_atlasseeds.
function pushbutton_atlasseeds_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_atlasseeds (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
% --- Executes on button press in radiobutton_globaltimesignal.
function radiobutton_globaltimesignal_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton_globaltimesignal (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton_globaltimesignal
global rsfc
if (get(handles.radiobutton_globaltimesignal,'Value') == get(handles.radiobutton_brainmaskON,'Max'))
axes(handles.axes2)
hold on
plot(rsfc.tRS,rsfc.yglobal);
end
% --- Executes on button press in radiobutton_globalfreqsignal.
function radiobutton_globalfreqsignal_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton_globalfreqsignal (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton_globalfreqsignal
global rsfc
if (get(handles.radiobutton_globalfreqsignal,'Value') == get(handles.radiobutton_brainmaskON,'Max'))
L = length(rsfc.yglobal);
NFFT = 2^nextpow2(L);
axes(handles.axes3)
hold on
semilogx(rsfc.f,2*abs(rsfc.meanyallf(1:NFFT/2+1)),'k','LineWidth',2);
xlim([0.005 1])
end
% --- Executes on button press in pushbutton_autoanalysis.
function pushbutton_autoanalysis_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_autoanalysis (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
y = rsfc.y;
[nY, nX, nT] = size(y);
brainIdx = find(rsfc.brain_mask == 1);
h1 = msgbox('Please select the seeds file');
uiwait(h1);
[filename,pathname] = uigetfile;
h2 = msgbox('please select the folder for output files');
uiwait(h2);
pathnameout = uigetdir;
load([pathname filename]);
load('atlas.mat');
ya = [328 328]; xa = [129 459];
fixedPoints = [[xi(1) yi(1)]; [xi(2) yi(2)]];
movingPoints = [[xa(1) ya(1)]; [xa(2) ya(2)]];
tform = fitgeotrans(movingPoints,fixedPoints,'nonreflectivesimilarity');
[xp,yp] = transformPointsForward(tform, xp, yp);
yp = round(yp);
xp = round(xp);
h = str2num(get(handles.seedsize,'string'));%Seed dimension will be h*2+1 x h*2+1
seedTC = zeros(length(xp),1);
filtandGRpower = zeros(length(xp),1);
unfiltpower= zeros(length(xp),1);
filteredpower= zeros(length(xp),1);
y1 = reshape(y,[nY*nX nT]);
y1 = y1(brainIdx,:);
for u=1:length(xp)
ySeed = squeeze(mean(mean(y(xp(u)+[-floor(h/2):floor(h/2)],yp(u)+[-floor(h/2):floor(h/2)],:),1),2));
ySeedunfilt = squeeze(mean(mean(rsfc.Iunfilt(xp(u)+[-floor(h/2):floor(h/2)],yp(u)+[-floor(h/2):floor(h/2)],:),1),2));
ySeedunGR = squeeze(mean(mean(rsfc.yunGR(xp(u)+[-floor(h/2):floor(h/2)],yp(u)+[-floor(h/2):floor(h/2)],:),1),2));
% yCC = (y*ySeed) ./ (std(y,[],2)*std(ySeed)) / length(ySeed);
yCC = corr(y1.',ySeed);
Chb1CC = zeros(nY,nX);
Chb1CC(brainIdx) = yCC;
f1 = figure(101); imagesc(Chb1CC,[-1 1]); colormap('jet'); text(yp(u),xp(u),'X');
saveas(f1, [pathnameout '/Seed' num2str(u) '.tif']);
close(f1);
seedTC(u) = mean(abs(yCC));
L = length(ySeed);
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
ySeedf = fft(ySeed,NFFT)/L;
f = rsfc.fs/2*linspace(0,1,NFFT/2+1);
ySeedunfiltf = fft(ySeedunfilt,NFFT)/L;
ySeedunGRf = fft(ySeedunGR,NFFT)/L;
filtandGRpower(u) = rsfc.df*sum(abs(ySeedf).^2);
unfiltpower(u) = rsfc.df*sum(abs(ySeedunfiltf).^2);
filteredpower(u) = rsfc.df*sum(abs(ySeedunGRf).^2);
f2 = figure(202);
subplot(5,1,1)
plot(rsfc.tRS,ySeedunfilt);
xlabel('time');
ylabel('magnitude')
title('Unfiltered Signal');
subplot(5,1,2)
plot(rsfc.tRS,rsfc.yglobal);
xlabel('time');
ylabel('magnitude')
title('Global Unfiltered Signal');
subplot(5,1,3)
plot(rsfc.tRS,ySeed)
xlabel('time');
ylabel('magnitude');
title('Filtered Signal');
subplot(5,1,4)
semilogx(f,2*abs(ySeedf(1:NFFT/2+1)),'b','LineWidth',2);
hold on
xlim([0.005 1]);
semilogx(f,2*abs(ySeedunfiltf(1:NFFT/2+1)),'r','LineWidth',2);
title('Frequency Spectrum');
xlabel('frequency');
ylabel('power');
subplot(5,1,5)
semilogx(f,2*abs(rsfc.meanyallf(1:NFFT/2+1)),'b','LineWidth',2);
xlim([0.005 1]);
title('Golabl Frequency Spectrum');
xlabel('frequency');
ylabel('power');
saveas(f2, [pathnameout '/Seed' num2str(u) 'plot.tif']);
close(f2);
end
T = table(seedTC, filtandGRpower, unfiltpower, filteredpower);
writetable(T,[pathnameout '/Outputdata.csv']);
function edit_filterunGR_Callback(hObject, eventdata, handles)
% hObject handle to edit_filterunGR (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit_filterunGR as text
% str2double(get(hObject,'String')) returns contents of edit_filterunGR as a double
% --- Executes during object creation, after setting all properties.
function edit_filterunGR_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit_filterunGR (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton_selectInfarctRegion.
function pushbutton_selectInfarctRegion_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_selectInfarctRegion (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global rsfc
global rsfc
while(1)
axes(handles.axes1)
MinMinS = min(rsfc.I0(:)); MaxMaxS = max(rsfc.I0(:));
while(1)
imagesc(rsfc.I0(:,:,1),[ MinMinS MaxMaxS]); colorbar;
colormap('gray');
button = questdlg('Are you happy with the image contrast?','Repeat selection or not...','No');
if strcmp(button,'No')
answer= inputdlg({'Enter minimum intensity level of survey scan image'},'Enter minimum intensity level:',1,{num2str(MinMinS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MinMinS = str2num(answer1.number);
answer= inputdlg({'Enter maximum intensity level of survey scan image'},'Enter maximum intensity level:',1,{num2str(MaxMaxS)}); % {'0'});
answer1 = cell2struct(answer, 'number', 1);
MaxMaxS = str2num(answer1.number);
else
break;
end
end
rsfc.MaxS = MaxMaxS;
rsfc.MinS = MinMinS;
h = msgbox(' Please select the brain region');
uiwait(h)
% axes(handles.axes6)
% imagesc(rsfc.yLRC,[-1 1]);
[brain_mask,Xi,Yi] = roipoly;
hold on;
plot(Xi,Yi,'color','k');
hold off;
button = questdlg('Are you statisfied with ROI?');
if strcmp(button, 'Yes')
break;
end
end
if (get(handles.radiobutton4,'Value') == get(handles.radiobutton4,'Max'))
figure; imshow(rsfc.I0,[MinMinS MaxMaxS]); colormap('gray');
end
% save('rsfc_brainMask.mat','brain_mask','Xi','Yi');
rsfc.infarct_mask = brain_mask;
% rsfc.Xi = Xi;
% rsfc.Yi = Yi;

rsfcGUI.m at commit 80811b6, no license · at the source

Overview

Authors: Bengisu Solgun¹, Buket Dönmez-Demir¹, Aslıhan Bahadır-Varol¹, Hülya Karataş, Şefik Evren Erdener¹

ORCID iDs: Bengisu Solgun, Buket Dönmez-Demir, Şefik Evren Erdener

Hacettepe University, Institute of Neurological Sciences and Psychiatry, Ankara, Turkey

Institutions: Hacettepe University (Türkiye)

Journal: Neurophotonics, volume 13, issue 2, article 025006

Dates: received 21 July 2025; accepted 26 February 2026; published online 24 March 2026; in print April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1117/1.nph.13.2.025006 · PMID 41884118 · PMCID PMC13012183 · OpenAlex W7140201567

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: mouse (organism)

Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, fMRI & imaging

Keywords: mouse skull, optical clearing, optical transparent window, cortical spreading depression, laser speckle contrast imaging, intrinsic optical signal imaging

Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex

Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Significance: Transcranial imaging windows are crucial for in vivo imaging in rodents. Although conventional methods such as open-skull and thinned-skull windows have been widely utilized, minimally invasive skull clearing methods have recently been developed and have yet to be comprehensively evaluated for chronic mesoscopic imaging in awake mice.

Aim: We aimed to methodically compare and modify current topical skull optical clearing methods in search of an optimal technique for chronic awake widefield imaging.

Approach: We used widefield intrinsic optical-signal imaging and laser speckle contrast imaging to investigate the applicability of these methods.

Results: Application of a UV-curable optical adhesive, NOA61 both as a monotreatment and in combination with EDTA, proved suitable for long-term awake mesoscopic imaging, providing long-term global transparency. The previously described Triple-S method led to inflammatory-like reaction and/or opacification of the window over subsequent days. Cyanoacrylate or methyl-methacrylate (Metabond-equivalent) treatments, on the other hand, led to drying artifacts, occluding parts of the window. Therefore, these latter methods made awake bihemispheric imaging impractical. EDTA-NOA61 combination was then tested for documenting interhemispheric connectivity changes following optogenetic triggering of cortical spreading depolarizations.

Conclusions: We discussed the advantages and drawbacks of previous skull clearing methods and identified suitable methods for awake mesoscopic chronic imaging. These findings highlight the importance of minimally invasive skull optical clearing methods for widefield use in awake mice.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

BUNPC/rsfcIOS

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 80811b6453fb2a4ce3940541af046fb5a8ee9118, 9 June 2021

Languages: MATLAB (2)

Size: 7 files, 2 scripts

Software Heritage: not archived

Found in: “Code and Data Availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Signal Processing Toolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

3 files

rsfcGUI.m, MATLAB, 1,905 lines
rsfc_excludeData_PatchCallback.m, MATLAB, 15 lines
README.md, Text, 24 lines

At the source: github.com/BUNPC/rsfcIOS

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

doi:10.6084/m9.figshare.c.7933739, at figshare; found in “Code and Data Availability”

Code and Data Availability

The data sets generated and analyzed during the current study are available on FigShare repository (https://doi.org/10.6084/m9.figshare.c.7933739). For functional connectivity analysis, publicly available MATLAB code by Boston University Neurophotonics Center was used (https://github.com/BUNPC/rsfcIOS).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 71 references.

Cite

This paper

Solgun, B., Dönmez-Demir, B., Bahadır-Varol, A., Karataş, H., & Erdener, Ş. E. (2026). Choosing a skull clearing technique for chronic mesoscopic optical imaging in awake mice. Neurophotonics, 13(2), 025006. https://doi.org/10.1117/1.nph.13.2.025006

BibTeX

@article{solgun2026choosing, author = {Solgun, Bengisu and Dönmez-Demir, Buket and Bahadır-Varol, Aslıhan and Karataş, Hülya and Erdener, Şefik Evren}, title = {{Choosing a skull clearing technique for chronic mesoscopic optical imaging in awake mice}}, journal = {Neurophotonics}, year = {2026}, month = mar, volume = {13}, number = {2}, pages = {025006}, publisher = {Society of Photo-Optical Instrumentation Engineers}, issn = {2329-423X}, doi = {10.1117/1.nph.13.2.025006}, url = {https://doi.org/10.1117/1.nph.13.2.025006}, pmid = {41884118}, pmcid = {PMC13012183} }

RIS

TY - JOUR AU - Solgun, Bengisu AU - Dönmez-Demir, Buket AU - Bahadır-Varol, Aslıhan AU - Karataş, Hülya AU - Erdener, Şefik Evren TI - Choosing a skull clearing technique for chronic mesoscopic optical imaging in awake mice T2 - Neurophotonics J2 - Neurophotonics PY - 2026 DA - 2026/03/24 VL - 13 IS - 2 SP - 025006 SN - 2329-423X PB - Society of Photo-Optical Instrumentation Engineers DO - 10.1117/1.nph.13.2.025006 UR - https://doi.org/10.1117/1.nph.13.2.025006 LA - en ER -

CSL-JSON

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