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s_ms_directions_150.m
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function s_ms_directions_150(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
datapath = '/marcovaldo/frk/2t2/predator/';
subjects = {'FP_150dirs_b1000_2000_4000_2iso'};
if notDefined('saveDir'), savedir = fullfile('/marcovaldo/frk/Dropbox','pestilli_etal_revision',mfilename);end
if notDefined('trackingType'), trackingType = 'lmax10';end
if notDefined('numDirs'), numDirs = [150:-5:10];end
addpath(genpath('/marcovaldo/frk/git/boot_dwi'))
% Bins for the fiber density estimates
xBins = [1 2 4 8 16 32 64 128 256 512 1024 2048];
x = 1:length(xBins);
% Bins for the sum of weights estimates
wxBins = [.9./(2.^[10:-1:1]) ];
wx = 1:length(wxBins);
doFD = 1;
figVisible = 'off';
probIndex = 1; %for 2000bval, Deterministic index 2: for 200 bval
for isbj = 1:length(subjects)
% High-resolution Anatomy
saveDir = fullfile(savedir,subjects{isbj});
% File to load
connectomesPath = fullfile(datapath,subjects{isbj},'connectomes');
feFileToLoad = dir(fullfile(connectomesPath,sprintf('*%s*.mat',trackingType)));
fname = feFileToLoad(probIndex).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)
fprintf('[%s] Extracting info: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
for iNumDirs = 1:length(numDirs)
xform = feGet(fe,'xform img 2 acpc');
mapsize = feGet(fe, 'map size');
nBvecs = feGet(fe,'nbvecs');
nBvals = fe.life.imagedim(4) - nBvecs;
nVoxels= feGet(fe,'nvoxels');
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');
rmseD = feGetRep(fe, 'vox rmse data');
rmseR = feGetRep(fe, 'vox rmse ratio');
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)));
fe = feSet(fe,'fg from acpc',fgRead(fullfile(fiberPath,fibers.name)));
end
w = feGet(fe,'fiber weights');
% Compute the total number of fibers retained at each direction number.
m.optimized.nfibers(iNumDirs,isbj) = sum(w > 0);
m.optimized.ndirs(iNumDirs,isbj) = numDirs(iNumDirs);
numFibers_total(iNumDirs,isbj) = length(w);
numFibers_good(iNumDirs,isbj) = sum(w > 0);
fgOpt = fgExtract(feGet(fe,'fibers acpc'),w > 0,'keep');
m.nfibers.y(iNumDirs,isbj) = numFibers_good(iNumDirs,isbj);
m.rmse.dataMean(iNumDirs,isbj) = mean(rmseD);
m.rmse.modelMean(iNumDirs,isbj) = mean(rmseM);
m.rmse.dataMedian(iNumDirs,isbj) = median(rmseD);
m.rmse.modelMedian(iNumDirs,isbj) = median(rmseM);
m.rrmse.mean(iNumDirs,isbj) = mean(rmseR);
m.rrmse.median(iNumDirs,isbj) = median(rmseR);
%theseIndices = randsample(1:nBvecs,numDirs(iNumDirs));
if numDirs(iNumDirs) < size(fe.life.bvecs,1)
[~, theseIndices] = bd_subsample(fe.life.bvecs',numDirs(iNumDirs));
else
theseIndices = 1:numDirs(iNumDirs);
end
% Update the FE structure fields that depend on the bvecs
fe.life.bvecs = fe.life.bvecs(theseIndices,:);
%if ~all(all(fe.life.bvecs==sample_bvecs));keyboard,end
fe.life.bvals = fe.life.bvals(theseIndices);
fe.life.bvecsindices = fe.life.bvecsindices(theseIndices);
fe.life.diffusion_signal_img = fe.life.diffusion_signal_img(:,theseIndices);
fe.life.imagedim(end) = numDirs(iNumDirs) + nBvals;
fe.rep.bvecs = fe.rep.bvecs(theseIndices,:);
fe.rep.bvals = fe.rep.bvals(theseIndices);
fe.rep.bvecsindices = fe.rep.bvecsindices(theseIndices);
fe.rep.diffusion_signal_img = fe.rep.diffusion_signal_img(:,theseIndices);
fe.rep.diffusion_S0_img = fe.rep.diffusion_S0_img(theseIndices);
fe.rep.imagedim(end) = numDirs(iNumDirs) + nBvals;
directionsIndices = false(nBvecs,1);
directionsIndices(theseIndices) = true;
allIndices = repmat(directionsIndices,nVoxels,1);
fe.life.Mfiber = fe.life.Mfiber(allIndices,:);
fe.life.dSig = fe.life.dSig(allIndices);
fe = feSet(fe,'fit',feFitModel(fe.life.Mfiber,fe.life.dSig','bbnnls'));
if doFD
fprintf('[%s] Computing fiber density: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
% Get the fiber density
% fd = feGet(fe,'fiber density');
fdImg = dtiComputeFiberDensityNoGUI(feGet(fe,'fibers acpc'), xform, mapsize);
fdOImg = dtiComputeFiberDensityNoGUI(fgOpt, xform, mapsize);
end
fprintf('[%s] Making histrograms: \n%s\n ======================================== \n\n',mfilename,feFileToLoad)
% Histogram plots
fdImg(fdImg==0) = nan;
fdOImg(fdOImg==0) = nan;
m.density.candidate_mean(iNumDirs,isbj) = nanmean( fdImg(:));
m.density.candidate_median(iNumDirs,isbj) = nanmedian( fdImg(:));
m.density.optimal_mean(iNumDirs,isbj) = nanmean( fdOImg(:));
m.density.candidate_median(iNumDirs,isbj) = nanmedian(fdOImg(:));
x = [1 2.^[1 2 3 4 5 6 7 8 9 10]];
x = [2:2:512];
[yFD(iNumDirs,isbj,:), xFD(iNumDirs,isbj,:)] = hist(fdImg(:),x);
yFD(iNumDirs,isbj,:) = 100*yFD(iNumDirs,isbj,:)./sum(yFD(iNumDirs,isbj,:));
[yoFD(iNumDirs,isbj,:),xoFD(iNumDirs,isbj,:)]= hist(fdOImg(:),x);
yoFD(iNumDirs,isbj,:) = 100*yoFD(iNumDirs,isbj,:)./sum(yoFD(iNumDirs,isbj,:));
x = 0:10:400;
[yRMSE(iNumDirs,isbj,:),xRMSE(iNumDirs,isbj,:)] = hist(rmseD(:),x);
yRMSE(iNumDirs,isbj,:) = 100*yRMSE(iNumDirs,isbj,:)./sum(yRMSE(iNumDirs,isbj,:));
[yoRMSE(iNumDirs,isbj,:),xoRMSE(iNumDirs,isbj,:)]= hist(rmseM(:),x);
yoRMSE(iNumDirs,isbj,:) = 100*yoRMSE(iNumDirs,isbj,:)./sum(yoRMSE(iNumDirs,isbj,:));
x = logspace(-.3,.3,32);
[yRrmse(iNumDirs,isbj,:),xRrmse(iNumDirs,isbj,:)] = hist(rmseR(:),x);
yRrmse(iNumDirs,isbj,:) = 100*(yRrmse(iNumDirs,isbj,:)./sum(yRrmse(iNumDirs,isbj,:)));
clear fdOImg fdImg
end
end
m.nfibers.x = numDirs;
% Average histograms
saveDir = fullfile(savedir,'average_150_2mm');
% Save the results to file, it takes along time to load all these FE strctures...
%m.density.candidatey = squeeze(mean(yFD,2));
%m.density.candidateSte= squeeze(std(yFD,[],2)./sqrt(size(yFD,2)));
%m.density.optimaly = squeeze(mean(yoFD,2));
%m.density.optimalSte = squeeze(std(yoFD,[],2)./sqrt(size(yoFD,2)));
%m.density.x = squeeze(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.data = squeeze(mean(yRMSE,2));
m.rmse.dataSte = squeeze(std(yRMSE,[],2)./sqrt(size(yRMSE,2)));
m.rmse.model = squeeze(mean(yoRMSE,2));
m.rmse.modelSte= squeeze(std(yoRMSE,[],2)./sqrt(size(yoRMSE,2)));
m.rmse.x = squeeze(xRMSE(:,isbj,:));
m.rmse.units = {'x=rmse (raw scanner units)','y=percent voxels'};
m.rmse.yRMSE=yRMSE;
m.rmse.yoRMSE=yoRMSE;
% rmse data vs. model
m.rrmse.y = squeeze(mean(yRrmse,2));
m.rrmse.ste = squeeze(std(yRrmse,[],2)./sqrt(size(yRrmse,2)));
m.rrmse.x = squeeze(xRrmse(:,isbj,:));
m.rrmse.units = {'x=Rrmse (a.u.)','y=percent voxels'};
m.rrmse.yRrmse=yRrmse;
mkdir(saveDir)
save(fullfile(saveDir,'mean_histograms_steps10.mat'),'m','numFibers_total','numFibers_good')
% Histogram plots
figName = sprintf('FibDensHistCandVSOpt_NDirs%i_%i_%i_%i_%i_%s',numDirs, fname(1:39));
colors = {[.9 .3 .3],[.9 .45 .35],[.9 .55 .5],[.9 .6 .6],[.9 .8 .8]};
fh = figure('name',figName,'visible',figVisible,'color','w');
for iNDirs = 1:size(m.density.x,1)
semilogx(m.density.x(iNDirs, :),m.density.candidatey(iNDirs, :),'k-','linewidth',2);
hold on
semilogx([m.density.x(iNDirs, :);m.density.x(iNDirs, :)], [m.density.candidatey(iNDirs, :)-m.density.candidateSte(iNDirs, :); ...
m.density.candidatey(iNDirs, :)+m.density.candidateSte(iNDirs, :)],'k-');
semilogx(m.density.x(iNDirs, :),m.density.optimaly(iNDirs, :),'r-','linewidth',2, 'color',colors{iNDirs})
semilogx([m.density.x(iNDirs, :);m.density.x(iNDirs, :)],[m.density.optimaly(iNDirs, :)-m.density.optimalSte(iNDirs, :); ...
m.density.optimaly(iNDirs, :)+m.density.optimalSte(iNDirs, :)],'-','linewidth',2,'color',colors{iNDirs})
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 30], ...
'ytick',[0 15 30], ...
'xlim', [0.5 2^10],'xtick',[0 m.density.x(iNDirs, :)],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDir, figName),1)
end
figName = sprintf('RMSE_mean_HistDataVSOpt_NDirs%i_%i_%i_%i_%i_%s',numDirs, fname(1:39));
fh = figure('name',figName,'visible',figVisible,'color','w');
for iNDirs = 1:size(m.rmse.x,1)
plot(m.rmse.x(iNDirs, :),m.rmse.data(iNDirs, :),'k-','linewidth',2,'color',[.15 .15 .15].*iNDirs)
hold on
plot(m.rmse.x(iNDirs, :),m.rmse.model(iNDirs, :),'r-','linewidth',2, 'color',colors{iNDirs})
plot([m.rmse.x(iNDirs, :);m.rmse.x(iNDirs, :)], ...
[m.rmse.data(iNDirs, :)-m.rmse.dataSte(iNDirs, :);m.rmse.data(iNDirs, :)+m.rmse.dataSte(iNDirs, :)],'k-','linewidth',2,'color',[.1 .1 .1].*iNDirs);
plot([m.rmse.x(iNDirs, :);m.rmse.x(iNDirs, :)],[m.rmse.model(iNDirs, :)-m.rmse.modelSte(iNDirs, :);m.rmse.model(iNDirs, :)+m.rmse.modelSte(iNDirs, :)],'r-','linewidth',2, 'color',colors{iNDirs})
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('RMSE (raw scanner units)','FontSize',16,'FontAngle','oblique')
legend(gca,{'Data','Model'},'box','off')
set(gca,'fontsize',16, ...
'ylim', [0 30], ...
'ytick',[0 15 30], ...
'xlim', [0 100],'xtick',[0 50 100],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDir, figName),1)
end
for iNDirs = 1:size(m.rrmse.x,1)
figName = sprintf('RRMSE_ratio_HistDataVSOpt_NDirs%i_%s',numDirs(iNDirs), fname(1:39));
fh = figure('name',figName,'visible',figVisible,'color','w');
xpatch = [.5 m.rrmse.x(iNDirs, m.rrmse.x(iNDirs, :)<=1) 1];
ypatch = m.rrmse.y(iNDirs, m.rrmse.x(iNDirs, :)<=1);
proportionModelBetter = sum(ypatch);
ypatch = [0 ypatch ypatch(end)];
patch([xpatch,fliplr(xpatch)],[ypatch,zeros(size(ypatch)),],[.8 .8 .8])
hold on
plot([1 1],[0 14],'k--')
plot(m.rrmse.x(iNDirs, :),m.rrmse.y(iNDirs, :),'k-','linewidth',2)
plot([m.rrmse.x(iNDirs, :);m.rrmse.x(iNDirs, :)],[m.rrmse.y(iNDirs, :)-m.rrmse.ste(iNDirs, :);m.rrmse.y(iNDirs, :)+m.rrmse.ste(iNDirs, :)],'r-','linewidth',2)
ylabel('Percent voxels','FontSize',16,'FontAngle','oblique')
xlabel('R_{rmse}','FontSize',16,'FontAngle','oblique');
title(sprintf('R < 1 in %2.0f%% of voxels',round(proportionModelBetter)),'FontSize',16,'FontAngle','oblique')
set(gca,'fontsize',16, ...
'ylim', [0 14], ...
'ytick',[0 7 14], 'xscale', 'log', ...
'xlim', [.5 2],'xtick',[.5 1 2],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDir, figName),1)
end
figName = sprintf('NFibers_hist_NDirs%i_%i_%i_%i_%i_%s',numDirs, fname(1:39));
fh = figure('name',figName,'visible',figVisible,'color','w');
y = m.nfibers.y./500000;
x = m.nfibers.x;
semilogx(x,y,'ko-')
ylabel('Proportion supported fascicles','FontSize',16,'FontAngle','oblique')
xlabel('Number of diffusion directions','FontSize',16,'FontAngle','oblique')
set(gca,'fontsize',16, ...
'xlim', [6 100],'xtick',fliplr(x),...
'ylim', [0.1 .2],'ytick',[0.1 0.15 .2],...
'box','off','tickdir','out','ticklength',[0.025 0])
saveFig(fh,fullfile(saveDir, 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