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Gen_tract_profiles_single.m
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Gen_tract_profiles_single.m
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function [] = Gen_tract_profiles_single(info, tract_name, std_parameter, measure)
%% measure = 'FA', 'MD','AD' or 'RD'
%% INPUT: two crossing tracts (AFQ)
%% Number Name Full Name
% 1 Thal_Rad_L Left Thalamic Radiation
% 2 Thal_Rad_R Right Thalamic Radiation
% 3 CST_L Left Corticospinal
% 4 CST_R Right Corticospinal
% 5 Cing_L Left Cingulum Cingulate
% 6 Cing_R Right Cingulum Cingulate
% 7 Hipp_L Left Cingulum Hippocampus
% 8 Hipp_R Right Cingulum Hippocampus
% 9 Call_Maj Callosum Forceps Major
% 10 Call_Min Callosum Forceps Minor
% 11 IFOF_L Left IFOF
% 12 IFOF_R Right IFOF
% 13 ILF_L Left ILF
% 14 ILF_R Right ILF
% 15 SLF_L Left SLF
% 16 SLF_R Right SLF
% 17 Unc_L Left Uncinate
% 18 Unc_R Right Uncinate
% 19 ARC_L Left Arcuate
% 20 ARC_R Right Arcuate
%% INPUT: two crossing tracts (Dan tracts)
%% Number Name Full Name
% 1 Thal_Rad_L Left Thalamic Radiation
% 2 Thal_Rad_R Right Thalamic Radiation
% 3 CST_L Left Corticospinal
% 4 CST_R Right Corticospinal
% 5 Cing_L Left Cingulum Cingulate
% 6 Cing_R Right Cingulum Cingulate
% 7 Hipp_L Left Cingulum Hippocampus
% 8 Hipp_R Right Cingulum Hippocampus
% 9 Call_Maj Callosum Forceps Major
% 10 Call_Min Callosum Forceps Minor
% 11 IFOF_L Left IFOF
% 12 IFOF_R Right IFOF
% 13 Unc_L Left Uncinate
% 14 Unc_R Right Uncinate
% 15 ARC_L Left Arcuate
% 16 ARC_R Right Arcuate
% 17 VOF_L Left VOF
% 18 VOF_R Right VOF
% 19 pARC_L Left pArc
% 20 pARC_R Right pArc
% 21 TPC_L Left TPC
% 22 TPC_R Right TPC
% 23 MdLF-SPL_L Left MdLF-SPL
% 24 MdLF-SPL_R Right MdLF-SPL
% 25 MdLF-Ang_L Left MdLF-Ang
% 26 MdLF-Ang_R Right MdLF-Ang
% 27 Meyer_L Left Meyer
% 28 Meyer_R Right Meyer
% 29 Baum_L Left Baum
% 30 Baum_R Right Baum
% 31 SLF1_L Left SLF1
% 32 SLF1_R Right SLF1
% 33 SLF2_L Left SLF2
% 34 SLF2_R Right SLF2
% 35 SLF3_L Left SLF3
% 36 SLF3_R Right SLF3
% 37 ILF_L Left ILF
% 38 ILF_R Right ILF
%addpath(genpath(info.repo.mba));
dataPath = info.output.niftis;
%% load fe_structure
%FileName = deblank(ls(fullfile(dataPath,strcat('fe_*.mat'))));
%load(FileName);
load(info.input.optimal);
%% Load classification file
ClassFileName = info.input.classification_path;
load(ClassFileName);
%classification.index = class.index;
%classification.names = class.names;
%% Insert classification into fe structure
%ind_tracts = find(classification.index);
%classification.index = classification.index(ind_tracts);
fe = feSet(fe,'tracts_info',classification); % include tract indices in fe structure
% Obtain tract numbers
if info.segmentation_type == 'AFQ'
tract = Get_tract_number(tract_name);
else
tract = Get_tract_number_Dan(tract_name);
end
% Set parameters
%std_parameter = 3;
nameroot = 'nosub';
Nnodes = 50;
if isfield(fe.life.fit, 'weights')
ind_nnz = find(fe.life.fit.weights);
else
ind_nnz = unique(fe.life.M.Phi.subs(:,3)); % find indices of nnz fascicles
end
%% Extract fibers tract1
fgName = fe.life.M.tracts{tract}.name; fgName = strrep(fgName,' ','');
ind_tract = fe.life.M.tracts{tract}.ind;
ind_tract_nnz = intersect(ind_tract,ind_nnz);
fgTract = fe.fg.fibers(ind_tract_nnz);
fgex = MyfgCreate_img('name', fgName, 'colorRgb', [1 0 0], 'fibers', fgTract);
% clean fibers with mba
fgcx = mbaComputeFibersOutliers(fgex, std_parameter, std_parameter, 100, 'mean');
%% Compute profile tract1 using measure (FA,MD,etc) based on tract ONLY
file = deblank(ls(char(fullfile(dataPath,strcat(measure,'s'),strcat(strcat(measure,'_'),tract_name,'.nii.gz')))));
famp = niftiRead(file);
[Meas_tract, SuperFiber, ~, ~] = Compute_FA_AlongFG(fgcx, famp, [], [], Nnodes);
%% Compute tract profile using measure (FA,MD,etc)FA based on original
fileOrig = deblank(ls(char(fullfile(dataPath,strcat(measure,'s'),strcat(measure,'_original.nii.gz')))));
fampOrig = niftiRead(fileOrig);
[Meas_tract_orig, ~]= Compute_FA_AlongFG(fgcx, fampOrig, [], [], Nnodes);
%% Compute tract profile using measure (FA,MD,etc) based on prediction
filePred = deblank(ls(char(fullfile(dataPath,strcat(measure,'s'),strcat(measure,'_pred_full.nii.gz')))));
fampPred = niftiRead(filePred);
[Meas_tract_pred, ~]= Compute_FA_AlongFG(fgcx, fampPred, [], [], Nnodes);
%% Plot tract profile
Gen_profile_plot_single(Meas_tract,'r',Meas_tract_orig,'k', Meas_tract_pred,'y',tract_name, 10, Nnodes, measure)
saveas(gcf, strcat('./results/figures/',measure,'/',measure,'_profile_',tract_name,'.fig'));
saveas(gcf, strcat('./results/figures/',measure,'/',measure,'_profile_',tract_name,'.pdf'));
saveas(gcf, strcat('./results/figures/',measure,'/',measure,'_profile_',tract_name,'.png'));
profiles_data.tract_name = tract_name;
profiles_data.tract = Meas_tract;
profiles_data.tract_orig = Meas_tract_orig;
profiles_data.tract_pred = Meas_tract_pred;
save(strcat('./results/',measure,'_',tract_name,'.mat'), 'profiles_data')
end
function [] = Gen_profile_plot_single(FA_tract, clr1, FA_tract_orig, clrorig, FA_pred, clrp,tract_name, s, Nnodes, measure)
N = size(FA_tract,1);
figure
hold on
if ~isempty(FA_tract)
h1 = shadedErrorBar(1:Nnodes,nanmean(FA_tract,1),s*nanstd(FA_tract)/sqrt(N),'lineprops',clr1);
end
if ~isempty(FA_tract_orig)
h3 = shadedErrorBar(1:Nnodes,nanmean(FA_tract_orig,1),s*nanstd(FA_tract_orig)/sqrt(N),'lineprops',clrorig);
end
if ~isempty(FA_pred)
h4 = shadedErrorBar(1:Nnodes,nanmean(FA_pred,1),s*nanstd(FA_pred)/sqrt(N),'lineprops',clrp);
end
%
%legend([h1.mainLine, h2.mainLine],tract_name1,strcat(tract_name1,'+',tract_name2))
if ~isempty(FA_tract)&&isempty(FA_tract_orig)
legend([h1.mainLine],tract_name)
elseif isempty(FA_tract)
legend([h3.mainLine, h3.mainLine],'Pred full','Orig')
else
legend([h1.mainLine, h3.mainLine, h4.mainLine],tract_name,'Original','Pred Full')
end
%plot([Node_cross Node_cross],[0,0.8],'-k','DisplayName','crossing')
set(gca, 'tickdir','out', 'ticklen',[0.025 0.025], ...
'box','off','XTick', [0 round(Nnodes)/2 Nnodes], 'FontSize', 12);
xlim(gca,[1 Nnodes]);
if strcmp(measure,'FA')
ylim(gca,[0 0.8]);
yticks([0 0.2 0.4 0.6 0.8]);
else
sup_lim = 1.1*max(nanmean(FA_tract)); % set lim y axis as +10% of maximum value on profile for tract1
ylim(gca,[0 sup_lim]);
end
title_str = tract_name;
newStr = strrep(title_str,'_','-');
title(newStr, 'FontSize', 14)
xlabel('Nodes Along Tract', 'FontSize', 14);
ylabel(strcat(measure,' Value'), 'FontSize', 14);
hold off;
end