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s_fe_mt_ips_tract_virtual_lesion_figure.m
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s_fe_mt_ips_tract_virtual_lesion_figure.m
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function s_fe_mt_ips_tract_virtual_lesion_figure(hemisphere)
%
% This script shows the path neighborhood for the tract connecting MT+ (Zilles t al ROI from
% freesurfer) and the Superior parietal Cortex (Aparc Freesurfer segementation).
% The following are the steps we perform:
% - It loads a whole-brain tractography solution.
% - It loads the MTand Parietal ROI.
% - It finds the tract connecting the two ROIs.
% - It builds a connectome model in the ROI defined by the tract
% - It performs a virtual lesion on the tract connecting MT and parietal.
%
% Copyright by Franco Pestilli Stanford University, 2014
% Handle parallel computing
if matlabpool('size') == 0
c = parcluster;
c.NumWorkers = 12;
matlabpool(c);
end
% Get the base directory for the data
datapath = '/marcovaldo/frk/2t1/predator/';
subjects = {...
'FP_96dirs_b2000_1p5iso', ...
% 'MP_96dirs_b2000_1p5iso', ...
% 'KK_96dirs_b2000_1p5iso', ...
% 'JW_96dirs_b2000_1p5iso', ...
% 'HT_96dirs_b2000_1p5iso', ...
% 'KW_96dirs_b2000_1p5iso', ...
};
if notDefined('saveDir'), savedir = fullfile('/marcovaldo/frk/Dropbox','pestilli_etal_revision',mfilename);end
if notDefined('trackingType'), trackingType = 'lmax10';end
if notDefined('hemisphere'), hemisphere = {'left','right'};end
if notDefined('plotAnatomy'), plotAnatomy = 1;end
anatomyPath = '/dev/data/anatomy/';
parietalRoiName = 'lh_MT_label_smooth3mm.nii.gz';
mtRoiName = 'rh_MT_label_smooth3mm.nii.gz';
parietalRoiName = 'lh_superiorparietal_label_smooth3mm.nii.gz';
mtRoiName = 'rh_superiorparietal_label_smooth3mm.nii.gz';
% Logical operation to perform ith each ROI
roi_operations = {'and','and'};
for iSbj = 1:length(subjects)
% Load the FE structure
saveDir = fullfile(savedir,subjects{iSbj});
fibergroupPath = fullfile(datapath,subjects{iSbj},'fibers');
fgFileToLoad = dir(fullfile(fibergroupPath,sprintf('*%s*.pdb',trackingType)));
fname = fgFileToLoad(1).name;
fgFileToLoad = fullfile(fibergroupPath,fname);
fgFileToLoad1 = dir(fullfile(fibergroupPath,sprintf('*%s*.pdb','lmax8')));
fname1 = fgFileToLoad1(1).name;
fgFileToLoad1 = fullfile(fibergroupPath,fname1);
fgFileToLoad2 = dir(fullfile(fibergroupPath,sprintf('*%s*.pdb','lmax2')));
fname2 = fgFileToLoad2(1).name;
fgFileToLoad2 = fullfile(fibergroupPath,fname2);
for ih = 1:length(hemisphere)
fprintf('[%s] Loading: \n%s\n ======================================== \n\n',mfilename,fgFileToLoad)
fg = fgRead(fgFileToLoad);
%fg = fgMerge(fg,fgRead(fgFileToLoad1));
%fg = fgMerge(fg,fgRead(fgFileToLoad2));
% Set all the variables that depend on the hemisphere
switch hemisphere{ih}
case {'left'}
parietalRoiName = 'lh_superiorparietal_label_smooth3mm.nii.gz';
mtRoiName = 'lh_MT_label_smooth3mm.nii.gz';
axisLims = [-67 -18 -110 -40 -18 80];
vw = [-75,30];
slices = {[-18 0 0],[0 -40 0],[0 0 -14 ]};
lght = 'left';
SLaxLims = [-55 2 -120 120 -20 40 ];
histcolor{1} = [0.4 0.4 0.4];
histcolor{2} = [.6 0.4 0.4];
case {'right'}
parietalRoiName = 'rh_superiorparietal_label_smooth3mm.nii.gz';
mtRoiName = 'rh_MT_label_smooth3mm.nii.gz';
axisLims = [18 67 -110 -40 -18 80];
vw = [75,30];
slices = {[18 0 0],[0 -40 0],[0 0 -14 ]};
lght = 'right';
SLaxLims = [-2 55 -120 120 -20 40 ];
histcolor{1} = [0 0 0];
histcolor{2} = [.8 0.4 0.4];
otherwise
keyboard
end
% Load the ROIs
FS_SUBJECT = matchSubject2FSSUBJ(subjects{iSbj});
roiDir = fullfile(anatomyPath,FS_SUBJECT,'label');
mtFileName = fullfile(roiDir,mtRoiName);
parietalFileName = fullfile(roiDir,parietalRoiName);
% Find the fascicles in the connectome that touch both MT+ and parietal.
mt = dtiImportRoiFromNifti(mtFileName,[parietalFileName(1:end-7),'_ROI.mat']);
parietal = dtiImportRoiFromNifti(parietalFileName,[parietalFileName(1:end-7),'_ROI.mat']);
tic, fprintf('\n[%s] Segmenting tract from connectome... \n',mfilename)
[mt2parietalTract, keepFascicles] = feSegmentFascicleFromConnectome(fg, {mt,parietal}, {'endpoints','endpoints'}, 'mt_parietal');
% Clean the fibers by length, fibers that too long are likely to go far
% frontal and not just touch MT+ and parietal.
[~, keep] = mbaComputeFibersOutliers(mt2parietalTract,3,3);
fprintf('\n[%s] Found a tract with %i fibers... \n',mfilename,sum(keep))
mt2parietalTract = fgExtract(mt2parietalTract,find(keep),'keep');
toc
% Find the Coordinates of the mt-parietal tract
tic, fprintf('\n[%s] Create ROI from MT-Parietal tract... \n',mfilename)
tractRoi = dtiCreateRoiFromFibers(mt2parietalTract);toc
tic, fprintf('\n[%s] Removing fibers not going throught the tractROI... \n',mfilename)
[fg,~, ~, ~] = dtiIntersectFibersWithRoi([],'and',2,tractRoi,fg);
fg = feClipFibersToVolume(fg,tractRoi.coords,1);toc
% Build LiFE model only in this volume, fit, cull
dwiPath = fullfile(datapath,subjects{iSbj},'diffusion_data');
dwiFiles = dir(fullfile(dwiPath,sprintf('run*.gz')));
dwiFile = fullfile(dwiPath,dwiFiles(1).name);
dwiFileRepeat = fullfile(dwiPath,dwiFiles(2).name);
t1File = fullfile(datapath,subjects{iSbj},'anatomy','t1.nii.gz');
% Directory where to save the fe structures
saveDirC = fullfile(datapath,subjects{iSbj},'connectomes');
feFileName = [parietal.name, '_', fname(1:40), '.mat'];
fe = feConnectomeInit(dwiFile,fg,feFileName,saveDirC,dwiFileRepeat,t1File);
M = feGet(fe,'mfiber');
dSig = feGet(fe,'dsigdemeaned');
fit = feFitModel(M,dSig,'bbnnls');
fe = feSet(fe,'fit',fit);clear fit
fg = feGet(fe,'fibers acpc');
[mt2parietalTract, keepFascicles] = feSegmentFascicleFromConnectome(fg, {mt,parietal}, {'endpoints','endpoints'}, 'mt_parietal');
% Perform a virtual lesion: MT+ and parietal.
display.tract = true;display.distributions = true;
[SE(iSbj,ih), fh] = feVirtualLesion(fe,keepFascicles,display);
figName = sprintf('virtual_lesion_rmse_distributions_%s_%s',trackingType,hemisphere{ih});
saveFig(fh(1),fullfile(saveDir,figName),'eps')
figName = sprintf('virtual_lesion_mean_rmse_distributions_%s_%s',trackingType,hemisphere{ih});
saveFig(fh(2),fullfile(saveDir,figName),'eps')
close all
if plotAnatomy
% Load the T1 file for display
t1 = niftiRead(t1File);
% Show te new fiber group
figName = sprintf('MT_IPS_connection_anatomy_%s',hemisphere{ih});
figureHandle = mrvNewGraphWin(figName);
h = mbaDisplayBrainSlice(t1, slices{1});
hold on
h = mbaDisplayBrainSlice(t1, slices{2});
h = mbaDisplayBrainSlice(t1, slices{3});
tractColor = [.5 .89 .4];
[figureHandle, lightHandle] = mbaDisplayConnectome(mbaFiberSplitLoops(mt2parietalTract.fibers),figureHandle,tractColor,'uniform');
delete(lightHandle);
view(vw(1),vw(2)); axis(axisLims);
camlight(lght);
set(gcf,'Position',[0.0148 0.0148 .35 .87])
drawnow
saveFig(figureHandle,fullfile(saveDir,figName),'jpg')
end
end
end
tic, fprintf('\n[%s] Saving results of virtual lesion... \n',mfilename)
save(fullfile(savedir,'strength_of_evidence.mat'),'SE'); toc
end % Main function
%%%%%%%%%%%%%%%%%%%%%%%
function FS_SUBJECT = matchSubject2FSSUBJ(subject)
switch subject
case {'FP_96dirs_b2000_1p5iso'}
FS_SUBJECT = 'pestilli_test';
case {'KW_96dirs_b2000_1p5iso'}
FS_SUBJECT = 'weiner';
case {'MP_96dirs_b2000_1p5iso'}
FS_SUBJECT = 'lmperry';
case {'HT_96dirs_b2000_1p5iso'}
FS_SUBJECT = 'takemura';
case {'JW_96dirs_b2000_1p5iso'}
FS_SUBJECT = 'winawer';
case {'KK_96dirs_b2000_1p5iso'}
FS_SUBJECT = 'knk';
otherwise
keyboard
end
end
function [fh,sh] = makeBrainMap(fe,t1,slice,axLims,figName,saveDir)
% Make a map of the RMSE WITH and WITHOUT the fascicle:
coords = feGet(fe,'roi coords') + 1;
xform = feGet(fe,'xform img 2 acpc');
% Cross-validate RMSE
rmse = feGetRep(fe, 'vox rmse');
img = feReplaceImageValues(nan(feGet(fe,'map size')),rmse,coords);
maxr = 50;
% Make anifti file from the rmse
ni = niftiCreate('data',mbaNormalize(img,[0,1]), ...
'qto_xyz',xform, ...
'fname','rmse', ...
'data_type',class(img));
% Open a figure
fh = mrvNewGraphWin(figName);
% Show the anatomy with the overlay
sh = mbaDisplayOverlay(t1, ni, slice, [], 'hot');
axis(axLims)
saveMap(fh,figName,saveDir,nanmean(img(:)),nanmedian(img(:)),nanstd(img(:)),maxr)
end
%---------------------------------%
function saveMap(fh,figName,saveDir,M,m,SD,maxfd)
% 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 0 20 40], ...
'xtick',[-50 -25 0 25 50], ...
'tickdir','out','ticklength',[0.025 0])
axis off
saveFig(fh,fullfile(saveDir,'maps',figName),'tiff')
saveFig(fh,fullfile(saveDir,'maps',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)
zlabel('Z (mm)','fontsize',16)
xlabel('X (mm)','fontsize',16)
cmap = colormap(hot(255));
colorbar('ytick',linspace(0,1,5),'yticklabel', ...
{linspace(0,1,5)*50}, ...
'tickdir','out','ticklength',[0.025 0],'fontsize',16)
saveFig(fh,fullfile(saveDir,'maps',figName),1)
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function saveFig(h,figName,type)
% MAke sure the folder to save the figure exists
[p,f,e] = fileparts(figName);
[success,message] = mkdir(p);
if ~isempty(message), disp(sprintf('%s.',message));end
% Find out which type of figure and geenerate the proper printing command.
switch type
case {0,'jpeg','jpg'}
printCommand = (sprintf('print(%s, ''-djpeg90'',''-r500'' , ''-noui'', ''-opengl'', ''%s'')', num2str(h),figName));
case {1,'eps'}
printCommand = (sprintf('print(%s, ''-cmyk'', ''-depsc2'',''-tiff'',''-r500'' , ''-noui'', ''%s'')', num2str(h),figName));
case 'png'
printCommand = (sprintf('print(%s, ''-dpng'',''-r500'', ''%s'')', num2str(h),figName));
case 'tiff'
printCommand = (sprintf('print(%s, ''-dtiff'',''-r500'', ''%s'')', num2str(h),figName));
case 'bmp'
printCommand = (sprintf('print(%s, ''-dbmp256'',''-r500'', ''%s'')', num2str(h),figName));
otherwise
keyboard
end
% do the printing here:
fprintf('[%s] saving figure... \n%s\n',mfilename,figName);
eval(printCommand);
end