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s_ms_maps_hcp.m
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s_ms_maps_hcp.m
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function s_ms_maps_hcp(trackingType)
%
% Load FE structeres obtained by preprocessing connectomesconstrained within a
% region of interest and within the cortex and makes some basic plot of
% statistics in the connectomes
%
% See also:
% Get the base directory for the data
subjects = {'105115','110411','111312','113619','115320','117122','118730'};
if notDefined('saveDir'), savedir = fullfile('/marcovaldo/frk/Dropbox/','pestilli_etal_revision',mfilename);end
if notDefined('trackingType'), trackingType = 'lmax10';end
saveDirM = fullfile(savedir,'average_hcp_1p25mm');
doFD = true;
figVisible = 'on';
doMAPS = false;
recompute = false;
if recompute
for isbj = 1:length(subjects)
% Directory where to load the results
if isbj <= 4
datapath = '/marcovaldo/frk/2t1/HCP/';
else
datapath = '/marcovaldo/frk/2t2/HCP/';
end
% High-resolution Anatomy
t1File = fullfile(datapath,subjects{isbj},'anatomy','/T1w_acpc_dc_restore_1p25.nii.gz');
t1 = niftiRead(t1File);
saveDir = fullfile(savedir,subjects{isbj});
% File to load
connectomesPath = fullfile(datapath,subjects{isbj},'connectomes');
feFileToLoad = dir(fullfile(connectomesPath,sprintf('*%s*cerebellum*.mat',trackingType)));
fname = feFileToLoad.name(1:end-4);
feFileToLoad = fullfile(connectomesPath,fname);
fprintf('[%s] Loading: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
load(feFileToLoad);
fprintf('[%s] Extracting info: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
coords = feGet(fe,'roi coords');
xform = feGet(fe,'xform img 2 acpc');
mapsize = feGet(fe, 'map size');
if isempty(fe.fg)
fe.path.savedir = fullfile('/marcovaldo/',fe.path.savedir(strfind(fe.path.savedir,'home')+length('home'):end));
fiberPath = fullfile(fileparts(fe.path.savedir),'fibers');
fibers = dir(fullfile(fiberPath,sprintf('*%s*.pdb',trackingType)));
fg = fgRead(fullfile(fiberPath,fibers.name));
else
fg = feGet(fe,'fibers acpc');
end
w = feGet(fe,'fiber weights');
fgOpt = fgExtract(fg,w > 0,'keep');
if isempty(fe.rep)
if ~isempty(strfind(fe.path.dwifilerep,'home'))
fe.path.dwifilerep = fullfile('/marcovaldo/',fe.path.dwifilerep(strfind(fe.path.dwifilerep,'home')+length('home'):end));
end
fe = feConnectomeSetDwi(fe,fe.path.dwifilerep,true);
end
rmseM = feGetRep(fe, 'vox rmse');
slice = 2;%[-80:4:-2 2:4:80];
clear fe
if doFD
fprintf('[%s] Computing CANDIDATE fiber density: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
% Get the fiber density
% fd = feGet(fe,'fiber density');
fdImg = dtiComputeFiberDensityNoGUI(fg, xform, mapsize);
fprintf('[%s] Computing OPTIMAZED fiber density: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
fdOImg = dtiComputeFiberDensityNoGUI(fgOpt, xform, mapsize);
fprintf('[%s] Computing WEIGHTED fiber density: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
fdWImg = dtiComputeFiberDensityNoGUI(fgOpt, xform, mapsize,[],[],[],[],w);
end
if doMAPS
fprintf('[%s] Making maps: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
% Make a plot of the maps
for is = 1:length(slice)
% RMSE off the model
map='hot';maxRmse = 90;
figName = sprintf('RMSE_ModelCoronal_%s_slice%i',fname,slice(is));
fh = figure('name',figName,'visible',figVisible,'color','w');
rmseImg = feReplaceImageValues(nan(mapsize),rmseM,coords+1);
%rmseImg(rmseImg < 1)=nan;
rmseImg(rmseImg > maxRmse) = maxRmse;
ni = niftiCreate('data',rmseImg, 'fname', figName, ...
'qto_xyz',xform, ...
'fname','FDM', ...
'data_type',class(rmseImg));
sh = mbaDisplayOverlay(t1, ni, [0 slice(is) 0], [], map);
saveMapCoronal(fh,figName,saveDir,nanmean(rmseImg(:)),nanmedian(rmseImg(:)),nanstd(rmseImg(:)),maxRmse,map)
figName = sprintf('RMSE_ModelSagital_%s_slice%i',fname,slice(is));
sh = mbaDisplayOverlay(t1, ni, [slice(is) 0 0], [], map);
saveMapSagital(fh,figName,saveDir,nanmean(rmseImg(:)),nanmedian(rmseImg(:)),nanstd(rmseImg(:)),maxRmse,map)
% Ratio rmse
map = 'jet';maxRRmse = 2;minRRmse = 0.125;
figName = sprintf('Ratio_rmseCoronal_%s_slice%i',fname,slice(is));
fh = figure('name',figName,'visible',figVisible,'color','w');
rrmseImg = feReplaceImageValues(nan(mapsize),rmseR,coords);
%rrmseImg(rrmseImg > 0.73)=nan;
rrmseImg(rrmseImg > maxRRmse)=maxRRmse;
ni = niftiCreate('data',rrmseImg, 'fname', figName, ...
'qto_xyz',xform, ...
'fname','FDM', ...
'data_type',class(rrmseImg));
sh = mbaDisplayOverlay(t1, ni, [0 slice(is) 0], [], map);
saveMapCoronal(fh,figName,saveDir,nanmean(rrmseImg(:)),nanmedian(rrmseImg(:)),nanstd(rrmseImg(:)),maxRRmse,map)
figName = sprintf('Ratio_rmseSagital_%s_slice%i',fname,slice(is));
sh = mbaDisplayOverlay(t1, ni, [slice(is) 0 0], [], map);
saveMapSagital(fh,figName,saveDir,nanmean(rrmseImg(:)),nanmedian(rrmseImg(:)),nanstd(rrmseImg(:)),maxRRmse,map)
% Fiber density maps:
% This will be used to normalize the rage of the fiber density across plots
minfd = 2; % Min fiber density
maxfd = 256; % Max fiber density
% Optimized connectome
map = 'jet';
figName = sprintf('FibDensMapCoronalCan_%s_%s_slice%i',fname,'candidate',slice(is));
fh = figure('name',figName,'visible',figVisible,'color','w');
% Candidate connectome
fdImg(fdImg==0) = nan;
fdImg(isnan(rrmseImg))=nan;
fdImg(fdImg > maxfd)=maxfd;
fdImg(1,1) = maxfd;
fdImg(1,2) = minfd;
ni = niftiCreate('data',(fdImg) , 'fname', figName, ...
'qto_xyz',xform, ...
'fname','RRMSE', ...
'data_type',class(fdImg));
sh = mbaDisplayOverlay(t1, ni, [0 slice(is) 0], [],map);
saveMapCoronal(fh,figName,saveDir,nanmean(fdImg(:)),nanmedian(fdImg(:)),nanstd(fdImg(:)),maxfd ,map)
figName = sprintf('FibDensMapSagitalCan_%s_%s_slice%i',fname,'candidate',slice(is));
sh = mbaDisplayOverlay(t1, ni, [slice(is) 0 0], [],map);
saveMapSagital(fh,figName,saveDir,nanmean(fdImg(:)),nanmedian(fdImg(:)),nanstd(fdImg(:)),maxfd ,map)
% Optimized connectome
figName = sprintf('FibDensMapCoronalOpt_%s_%s_slice%i',fname,'optimized',slice(is));
fh = figure('name',figName,'visible',figVisible,'color','w');
fdOImg(1,1) = maxfd;
fdOImg(1,2) = minfd;
fdOImg(fdOImg==0) = nan;
fdOImg(fdOImg > maxfd)=maxfd;
fdOImg(isnan(rrmseImg))=nan;
ni = niftiCreate('data',(fdOImg), ...
'qto_xyz',xform, ...
'fname','FDM', ...
'data_type',class(fdOImg));
sh = mbaDisplayOverlay(t1, ni, [0 slice(is) 0], [],map);
saveMapCoronal(fh,figName,saveDir,nanmean(fdOImg(:)),nanmedian(fdOImg(:)),nanstd(fdOImg(:)),maxfd ,map)
figName = sprintf('FibDensMapSagitalOpt_%s_%s_slice%i',fname,'optimized',slice(is));
sh = mbaDisplayOverlay(t1, ni, [slice(is) 0 0], [],map);
saveMapSagital(fh,figName,saveDir,nanmean(fdOImg(:)),nanmedian(fdOImg(:)),nanstd(fdOImg(:)),maxfd ,map)
% Weight density (sum of weights)
map = 'hsv';
figName = sprintf('WeightMapCoronal_%s_%s_slice%i',fname,'optimized',slice(is));
fh = figure('name',figName,'visible',figVisible,'color','w');
fdWImg(isnan(rrmseImg))=nan;
ni = niftiCreate('data',fdWImg, 'fname', figName, ...
'qto_xyz',xform, ...
'fname','FDM', ...
'data_type',class(fdWImg));
sh = mbaDisplayOverlay(t1, ni, [0 slice(is) 0], [],map);
saveMapCoronal(fh,figName,saveDir,nanmean(fdWImg(:)),nanmedian(fdWImg(:)),nanstd(fdWImg(:)),max(fdWImg(:)) ,map)
figName = sprintf('WeightMapSagital_%s_%s_slice%i',fname,'optimized',slice(is));
sh = mbaDisplayOverlay(t1, ni, [slice(is) 0 0], [],map);
saveMapSagital(fh,figName,saveDir,nanmean(fdWImg(:)),nanmedian(fdWImg(:)),nanstd(fdWImg(:)),max(fdWImg(:)) ,map)
% RMSE of the data
figName = sprintf('RMSE_DataCoronal_%s_slice%i',fname,slice(is));
fh = figure('name',figName,'visible',figVisible,'color','w');
rmseImg = feReplaceImageValues(nan(mapsize),rmseD,coords);
rmseImg(isnan(rrmseImg))=nan;
rmseImg(rmseImg>maxRmse) = maxRmse;
ni = niftiCreate('data',rmseImg, 'fname', figName, ...
'qto_xyz',xform, ...
'fname','FDM', ...
'data_type',class(rmseImg));
sh = mbaDisplayOverlay(t1, ni, [0 slice(is) 0], [], map);
saveMapCoronal(fh,figName,saveDir,nanmean(rmseImg(:)),nanmedian(rmseImg(:)),nanstd(rmseImg(:)),maxRmse,map)
figName = sprintf('RMSE_DataSagital_%s_slice%i',fname,slice(is));
sh = mbaDisplayOverlay(t1, ni, [slice(is) 0 0], [], map);
saveMapSagital(fh,figName,saveDir,nanmean(rmseImg(:)),nanmedian(rmseImg(:)),nanstd(rmseImg(:)),maxRmse,map)
close all
drawnow
end
end
% Histogram plots
figName = sprintf('FibDensHistCandVSOpt_%s',fname);
fh = figure('name',figName,'visible',figVisible,'color','w');
%x = [1 2.^[1 2 3 4 5 6 7 8 9 10]];
x = [1:4:1024];
fdOImg(fdOImg == 0) = nan;
fdImg(fdImg == 0) = nan;
[yFD(isbj,:),xFD(isbj,:)] = hist(fdImg(:),x);
yFD(isbj,:) = 100*yFD(isbj,:)./sum(yFD(isbj,:));
[yoFD(isbj,:),xoFD(isbj,:)]= hist(fdOImg(:),x);
yoFD(isbj,:) = 100*yoFD(isbj,:)./sum(yoFD(isbj,:));
semilogx(xFD(isbj,:),yFD(isbj,:),'k-',xoFD(isbj,:),yoFD(isbj,:),'r-','linewidth',2)
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('Fascicles per voxel','FontSize',16,'FontAngle','oblique')
legend(gca,{'Candidated','Optimized'},'box','off')
set(gca,'fontsize',16, ...
'ylim', [0 20], ...
'ytick',[0 10 20], ...
'xlim', [0.5 1024],'xtick',[1 2.^[1 2 3 4 5 6 7 8 9 10]],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDir, figName),1)
figName = sprintf('RMSE_HistDataVSOpt_%s',fname);
fh = figure('name',figName,'visible',figVisible,'color','w');
x = 0:10:800;
[yoRMSE(isbj,:),xoRMSE(isbj,:)]= hist(rmseM(:),x);
yoRMSE(isbj,:) = 100*yoRMSE(isbj,:)./sum(yoRMSE(isbj,:));
plot(xoRMSE(isbj,:),yoRMSE(isbj,:),'r-','linewidth',2)
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('RMSE (raw scanner units)','FontSize',16,'FontAngle','oblique')
legend(gca,{'Model'},'box','off')
set(gca,'fontsize',16, ...
'ylim', [0 14], ...
'ytick',[0 7 14], ...
'xlim', [0 800],'xtick',[0 400 800],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDir, figName),1)
end
% Average histograms
% Save the results to file, it takes along time to load all these FE strctures...
m.density.candidatey = mean(yFD,1);
m.density.candidateSte= std(yFD,[],1)./sqrt(size(yFD,1));
m.density.optimaly = mean(yoFD,1);
m.density.optimalSte = std(yoFD,[],1)./sqrt(size(yoFD,1));
m.density.x = xFD(isbj,:);
m.density.units = {'x=Fascicles per voxel','y=percent voxels'};
m.density.yFD = yFD;
m.density.yoFD = yoFD;
% rmse data vs. model
m.rmse.model = mean(yoRMSE,1);
m.rmse.modelSte= std(yoRMSE,[],1)./sqrt(size(yoRMSE,1));
m.rmse.x = xoRMSE(isbj,:);
m.rmse.units = {'x=rmse (raw scanner units)','y=percent voxels'};
m.rmse.yoRMSE=yoRMSE;
mkdir(saveDirM)
save(fullfile(saveDirM,'mean_histograms_cerebellum.mat'),'m','fname')
else
load(fullfile(saveDirM,'mean_histograms_cerebellum.mat'),'m','fname')
if notDefined('fname')
datapath = '/marcovaldo/frk/2t1/HCP/';
connectomesPath = fullfile(datapath,subjects{1},'connectomes');
feFileToLoad = dir(fullfile(connectomesPath,sprintf('*%s*cerebellum*.mat',trackingType)));
fname = feFileToLoad.name(1:end-4);
end
end
% Histogram plots
figName = sprintf('FibDensHistCandVSOpt_%s',fname);
fh = figure('name',figName,'visible',figVisible,'color','w');
semilogx(m.density.x,m.density.candidatey,'k-',m.density.x,m.density.optimaly,'r-','linewidth',2)
hold on
semilogx([m.density.x;m.density.x], [m.density.candidatey-m.density.candidateSte; ...
m.density.candidatey+m.density.candidateSte],'k-', ...
[m.density.x;m.density.x], [m.density.optimaly-m.density.optimalSte; ...
m.density.optimaly+m.density.optimalSte],'r-','linewidth',2)
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('Fascicles per voxel','FontSize',16,'FontAngle','oblique')
legend(gca,{'Candidated','Optimized'},'box','off')
set(gca,'fontsize',16, ...
'ylim', [0 20], ...
'ytick',[0 10 20], ...
'xlim', [0.5 1024],'xtick',[1 2.^[1 2 3 4 5 6 7 8 9 10]],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDirM, figName),1)
figName = sprintf('FibDensHistCandVSOpt_PATCH_%s',fname);
fh = figure('name',figName,'visible',figVisible,'color','w');
yC = [0 m.density.candidatey-m.density.candidateSte 0,0 m.density.candidatey+m.density.candidateSte 0];
yO = [0 m.density.optimaly-m.density.optimalSte 0,0 m.density.optimaly+m.density.optimalSte 0];
patch([1 m.density.x 1024,1 m.density.x 1024], yC,'k','facecolor','k','edgecolor','k');
hold on
patch([1 m.density.x 1024,1 m.density.x 1024], yO,'r','facecolor','r','edgecolor','r');
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('Fascicles per voxel','FontSize',16,'FontAngle','oblique')
legend(gca,{'Candidated','Optimized'},'box','off')
set(gca,'fontsize',16, ...
'xscale', 'log', ...
'ylim', [0 20], ...
'ytick',[0 10 20], ...
'xlim', [0.5 1024],'xtick',[1 2.^[1 2 3 4 5 6 7 8 9 10]],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDirM, figName),1)
figName = sprintf('RMSE_mean_HistDataVSOpt_%s',fname);
fh = figure('name',figName,'visible',figVisible,'color','w');
plot(m.rmse.x,m.rmse.model,'r-','linewidth',2)
hold on
plot([m.rmse.x;m.rmse.x],[m.rmse.model-m.rmse.modelSte;m.rmse.model+m.rmse.modelSte],'r-','linewidth',2)
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('RMSE (raw scanner units)','FontSize',16,'FontAngle','oblique')
legend(gca,{'Model'},'box','off')
set(gca,'fontsize',16, ...
'ylim', [0 14], ...
'ytick',[0 7 14], ...
'xlim', [0 700],'xtick',[0 350 700],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDirM, figName),1)
end % Main function
%---------------------------------%
function saveMapSagital(fh,figName,saveDir,M,m,SD,maxfd,map)
% This helper function saves two figures for each map and eps with onlythe
% axis and a jpg with only the brain slice.
% The two can then be combined in illustrator.
%
% First we save only the slice as jpeg.
set(gca,'fontsize',16,'ytick',[-80 -40 0 40 80], ...
'ztick',[-40 0 40 80], ...
'xlim',[-80 80],'ylim',[-110 100],'zlim',[-60 80],'tickdir','out','ticklength',[0.025 0])
axis off
saveFig(fh,fullfile(saveDir,figName),'tiff')
saveFig(fh,fullfile(saveDir,figName),'png')
% Then we save the slice with the axis as
% eps. This will only generate the axis
% that can be then combined in illustrator.
axis on
grid off
title(sprintf('mean %2.2f | median %2.2f | SD %2.2f', ...
M,m,SD),'fontsize',16,'FontAngle','oblique')
zlabel('Z (mm)','fontsize',16,'FontAngle','oblique')
xlabel('X (mm)','fontsize',16,'FontAngle','oblique')
cmap = colormap(eval(sprintf('%s(255)',map)));
colorbar('ytick',linspace(0,1,5),'yticklabel', ...
{1, num2str(ceil(maxfd/8)), num2str(ceil(maxfd/4)), ...
num2str(ceil(maxfd/2)), num2str(ceil(maxfd))}, ...
'tickdir','out','ticklength',[0.025 0],'fontsize',16)
saveFig(fh,fullfile(saveDir,figName),1)
end
%---------------------------------%
function saveMapCoronal(fh,figName,saveDir,M,m,SD,maxfd,map)
% This helper function saves two figures for each map and eps with onlythe
% axis and a jpg with only the brain slice.
% The two can then be combined in illustrator.
%
% First we save only the slice as jpeg.
set(gca,'fontsize',16,'ztick',[-20 -10 0 10 20], ...
'xtick',[0 10 20 30 40 50], ...
'xlim',[-5 70],'zlim',[-30 40],'tickdir','out','ticklength',[0.025 0])
axis off
saveFig(fh,fullfile(saveDir, figName),'tiff')
saveFig(fh,fullfile(saveDir, figName),'png')
% Then we save the slice with the axis as
% eps. This will only generate the axis
% that can be then combined in illustrator.
axis on
grid off
title(sprintf('mean %2.2f | median %2.2f | SD %2.2f', ...
M,m,SD),'fontsize',16,'FontAngle','oblique')
zlabel('Z (mm)','fontsize',16,'FontAngle','oblique')
xlabel('X (mm)','fontsize',16,'FontAngle','oblique')
cmap = colormap(eval(sprintf('%s(255)',map)));
colorbar('ytick',linspace(0,1,5),'yticklabel', ...
{1, num2str(ceil(maxfd/8)), num2str(ceil(maxfd/4)), ...
num2str(ceil(maxfd/2)), num2str(ceil(maxfd))}, ...
'tickdir','out','ticklength',[0.025 0],'fontsize',16)
saveFig(fh,fullfile(saveDir, figName),1)
end
%-------------------------------%
function saveFig(h,figName,eps)
if ~exist( fileparts(figName), 'dir'), mkdir(fileparts(figName));end
fprintf('[%s] saving figure... \n%s\n',mfilename,figName);
switch eps
case {0,'jpeg'}
eval(sprintf('print(%s, ''-djpeg90'', ''-opengl'', ''%s'')', num2str(h),[figName,'.jpg']));
case {1,'eps'}
eval(sprintf('print(%s, ''-cmyk'', ''-painters'',''-depsc2'',''-tiff'',''-r500'' , ''-noui'', ''%s'')', num2str(h),[figName,'.eps']));
case 'png'
eval(sprintf('print(%s, ''-dpng'',''-r500'', ''%s'')', num2str(h),[figName,'.png']));
case 'tiff'
eval(sprintf('print(%s, ''-dtiff'',''-r500'', ''%s'')', num2str(h),[figName,'.tif']));
case 'bmp'
eval(sprintf('print(%s, ''-dbmp256'',''-r500'', ''%s'')', num2str(h),[figName,'.bmp']));
otherwise
end
end