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sc1_sc2_neocortical.m
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function varargout=sc1_sc2_neocortical(what,varargin)
% Analysis of cortical data
% This is modernized from the analysis in sc1_sc2_imana, in that it uses
% the new FS_LR template
% baseDir = '/Volumes/MotorControl/data/super_cerebellum_new';
baseDir = 'Z:\data\super_cerebellum_new';
wbDir = fullfile(baseDir,'sc1','surfaceWB');
fsDir = fullfile(baseDir,'sc1','surfaceFreesurfer');
atlasDir = '~/Data/Atlas_templates/standard_mesh';
anatomicalDir = fullfile(baseDir,'sc1','anatomicals');
regDir = fullfile(baseDir,'sc1','RegionOfInterest');
glmDir = 'GLM_firstlevel_4';
studyDir = {'sc1','sc2'};
Hem = {'L','R'};
hemname = {'CortexLeft','CortexRight'};
fshem = {'lh','rh'};
subj_name = {'s01','s02','s03','s04','s05','s06','s07','s08','s09','s10','s11',...
's12','s13','s14','s15','s16','s17','s18','s19','s20','s21','s22','s23','s24',...
's25','s26','s27','s28','s29','s30','s31'};
returnSubjs=[2,3,4,6,8,9,10,12,14,15,17,18,19,20,21,22,24,25,26,27,28,29,30,31];
switch(what)
case 'SURF:recon_all' % STEP 2.2: Calls recon_all
% STUDY 1 ONLY
% Calls recon-all, which performs, all of the
% FreeSurfer cortical reconstruction process
% example: sc1_imana('surf_freesurfer',1)
sn=varargin{1}; % subjNum
for i=sn
freesurfer_reconall(fullfile(baseDir,'sc1','surfaceFreesurfer'),subj_name{i},fullfile(anatomicalDir,subj_name{i},['anatomical.nii']));
end
case 'SURF:wb_resample'
% This reslice from the individual surface into the the fs_lr standard mesh - This replaces
% calls to freesurfer_registerXhem, freesurfer_mapicosahedron_xhem, & caret_importfreesurfer.
% It requires connectome wb to be installed.
sn=returnSubjs;
vararginoptions(varargin,{'sn'});
for i=sn
fprintf('reslicing %d\n',i);
surf_resliceFS2WB(subj_name{i},fsDir,wbDir,'resolution','32k');
end;
case 'SURF:map_beta' % STEP 11.5: Map con / ResMS (.nii) onto surface (.gifti)
sn = returnSubjs; % subjNum
study = [1 2];
glm = 4; % glmNum
hemis = [1 2];
resolution = '32k';
D=dload(fullfile(baseDir,'sc1_sc2_taskConds_GLM.txt'));
vararginoptions(varargin,{'sn','study','glm','what','hemis','resolution'});
for s=sn
for h=hemis
surfDir = fullfile(wbDir, subj_name{s});
white=fullfile(surfDir,sprintf('%s.%s.white.%s.surf.gii',subj_name{s},Hem{h},resolution));
pial=fullfile(surfDir,sprintf('%s.%s.pial.%s.surf.gii',subj_name{s},Hem{h},resolution));
C1=gifti(white);
C2=gifti(pial);
for st = study
glmDir =fullfile(baseDir,studyDir{st},sprintf('GLM_firstlevel_%d',glm),subj_name{s});
T=load(fullfile(glmDir,'SPM_info.mat'));
filenames={};
for i=1:length(T.run);
filenames{i} = fullfile(glmDir,sprintf('beta_%4.4d.nii',i));
end;
filenames{i+1} = fullfile(glmDir,'ResMS.nii');
outfile = fullfile(surfDir,sprintf('%s.%s.%s.beta.%s.func.gii',subj_name{s},Hem{h},studyDir{st},resolution));
G=surf_vol2surf(C1.vertices,C2.vertices,filenames,'column_names',filenames,'anatomicalStruct',hemname{h});
save(G,outfile);
fprintf('mapped %s %s %s \n',subj_name{s},Hem{h},studyDir{st});
end;
end;
end
case 'SURF:map_con' % 1. Step: Map con / ResMS (.nii) onto surface (.gifti)
sn = returnSubjs; % subjNum
study = [1 2];
glm = 4; % glmNum
hemis = [1 2];
resolution = '32k';
D=dload(fullfile(baseDir,'sc1_sc2_taskConds_GLM.txt'));
vararginoptions(varargin,{'sn','study','glm','what','hemis','resolution'});
for s=sn
for h=hemis
surfDir = fullfile(wbDir, subj_name{s});
white=fullfile(surfDir,sprintf('%s.%s.white.%s.surf.gii',subj_name{s},Hem{h},resolution));
pial=fullfile(surfDir,sprintf('%s.%s.pial.%s.surf.gii',subj_name{s},Hem{h},resolution));
C1=gifti(white);
C2=gifti(pial);
for st = study
glmDir =fullfile(baseDir,studyDir{st},sprintf('GLM_firstlevel_%d',glm),subj_name{s});
T=getrow(D,D.StudyNum==st);
filenames={};
for i=1:length(T.condNames);
filenames{i} = fullfile(glmDir,sprintf('con_%s-rest.nii',T.condNames{i}));
end;
filenames{i+1} = fullfile(glmDir,'ResMS.nii');
T.condNames{i+1}= 'ResMS';
outfile = fullfile(surfDir,sprintf('%s.%s.%s.con.%s.func.gii',subj_name{s},Hem{h},studyDir{st},resolution));
G=surf_vol2surf(C1.vertices,C2.vertices,filenames,'column_names',T.condNames,'anatomicalStruct',hemname{h});
save(G,outfile);
fprintf('mapped %s %s %s \n',subj_name{s},Hem{h},studyDir{st});
end;
end;
end
case 'SURF:map_con_sess' % Map contrasts and sessions to surface, including ResMS (.nii) prewhitening
sn = returnSubjs; % subjNum
study = [1 2];
glm = 4; % glmNum
hemis = [1 2];
resolution = '32k';
D=dload(fullfile(baseDir,'sc1_sc2_taskConds_GLM.txt'));
vararginoptions(varargin,{'sn','study','glm','what','hemis','resolution'});
for s=sn
for h=hemis
surfDir = fullfile(wbDir, subj_name{s});
white=fullfile(surfDir,sprintf('%s.%s.white.%s.surf.gii',subj_name{s},Hem{h},resolution));
pial=fullfile(surfDir,sprintf('%s.%s.pial.%s.surf.gii',subj_name{s},Hem{h},resolution));
C1=gifti(white);
C2=gifti(pial);
for st = study
T=getrow(D,D.StudyNum==st);
glmDir =fullfile(baseDir,studyDir{st},sprintf('GLM_firstlevel_%d',glm),subj_name{s});
S= load(fullfile(glmDir,'SPM_info.mat'));
for se=[1 2]
fprintf('SN:%s Hem:%s ST:%s SE:%d:',subj_name{s},Hem{h},studyDir{st},se);
filename=fullfile(glmDir,'ResMS.nii');
ResMS=surf_vol2surf(C1.vertices,C2.vertices,filename);
numCond =max(S.cond);
Data=zeros(size(ResMS.cdata,1),numCond+1);
for i=1:numCond
betaNum = find(S.cond==i & S.sess==se);
for j=1:length(betaNum)
filenames{j}=fullfile(glmDir,sprintf('beta_%04d.nii',betaNum(j)));
end
Beta=surf_vol2surf(C1.vertices,C2.vertices,filenames);
Data(:,i)=mean(Beta.cdata,2)./sqrt(ResMS.cdata);
fprintf('%d ',i);
end;
fprintf('\n');
Data(:,numCond+1)=0;
Data=bsxfun(@minus,Data,mean(Data,2));
G=surf_makeFuncGifti(Data,'anatomicalStruct',hemname{h},'columnNames',[T.condNames(2:end);'Rest']);
outfile = fullfile(surfDir,sprintf('%s.%s.%s.wcon.sess%d.%s.func.gii',subj_name{s},Hem{h},studyDir{st},se,resolution));
save(G,outfile);
end;
end;
end
end
case 'SURF:combine_con' % Does univariate noise normalization (combined) and then combines the contrast files by the common baseline
sn = returnSubjs; % subjNum
glm = 4; % glmNum
hemis = [1 2];
resolution = '32k';
T=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
vararginoptions(varargin,{'sn','glm','what','hemis','resolution'});
for s=sn
surfDir = fullfile(wbDir, subj_name{s});
for h=hemis
for st=[1 2]
G{st}=gifti(fullfile(surfDir,sprintf('%s.%s.%s.con.%s.func.gii',subj_name{s},Hem{h},studyDir{st},resolution)));
N=size(G{st}.cdata,1);
wcon{st} = [G{st}.cdata(:,2:end-1) zeros(N,1)]; % Throw out instruction and add rest
Ts = getrow(T,T.StudyNum==st); % Get info of the contrast for this experiment
wcon{st}=bsxfun(@minus,wcon{st},mean(wcon{st}(:,Ts.overlap==1),2)); % REmovemean of the shared conditions
end;
resMS=(G{1}.cdata(:,end)+G{1}.cdata(:,end))/2; % Pool the residual mean square variances across experiments
W = [wcon{1} wcon{2}]; % Concatinate the data
W = bsxfun(@rdivide,W,sqrt(resMS)); % Common noise normalization by sqrt of resMS
outfile = fullfile(surfDir,sprintf('%s.%s.wcon.%s.func.gii',subj_name{s},Hem{h},resolution));
Go=surf_makeFuncGifti(W,'columnNames',T.condNames,'anatomicalStruct',hemname{h});
save(Go,outfile);
fprintf('combined %s %s \n',subj_name{s},Hem{h});
end;
end;
case 'SURF:groupFiles'
hemis=[1 2];
sn = returnSubjs;
cd(fullfile(wbDir,'group32k'));
for h=hemis
inputFiles = {};
for s=1:length(sn)
inputFiles{s} = fullfile(wbDir, subj_name{sn(s)},sprintf('%s.%s.wcon.32k.func.gii',subj_name{sn(s)},Hem{h}));
columnName{s} = subj_name{sn(s)};
end;
groupfile=sprintf('group.wcon.%s.func.gii',Hem{h});
outfilenamePattern=sprintf('wcon.%%s.%s.func.gii',Hem{h});
surf_groupGiftis(inputFiles,'groupsummary',groupfile,'outcolnames',subj_name(sn),'outfilenamePattern',outfilenamePattern);
end;
case 'SURF:groupSmooth'
kernel =3;
cd(fullfile(wbDir,'group32k'));
for h=1:2
fname = sprintf('group.wcon.%s.func.gii',Hem{h});
oname = sprintf('group.swcon.%s.func.gii',Hem{h});
A=gifti(fname);
A.cdata(isnan(sum(A.cdata,2)),:)=0;
save(A,'temp.func.gii');
com = sprintf('wb_command -metric-smoothing fs_LR.32k.%s.midthickness.surf.gii temp.func.gii %d group.swcon.%s.func.gii -fix-zeros',Hem{h},kernel,Hem{h});
system(com);
end;
delete('temp.func.gii');
case 'SURF:resample32k' % Resample functional data from group164 to group32
hemis=[1 2];
sn = [2,3,4];
sourceDir =fullfile(wbDir,'group164k');
targetDir =fullfile(wbDir,'group32k');
T=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
for h=hemis
% wb_command -metric-resample group164K/group.wcon.L.func.gii group164K/fs_LR.164k.L.sphere.surf.gii group32K/fs_LR.32k.L.sphere.surf.gii BARYCENTRIC group32K/group.wcon.L.func.gii
% wb_command -metric-resample group164K/group.wcon.R.func.gii group164K/fs_LR.164k.R.sphere.surf.gii group32K/fs_LR.32k.R.sphere.surf.gii BARYCENTRIC group32K/group.wcon.R.func.gii
end;
case 'SURF:voxel2vertex' % Tansforms voxels-based data (from regions_cortex.mat) to vertices
% Data should be P(voxel) x K (conditions / values)
sn = [];
hem = [];
numVert = 32492;
vararginoptions(varargin,{'data','hem','sn'});
load(fullfile(rootDir,'sc1','RegionOfInterest','data',subj_name{sn},sprintf('regions_cortex.mat'))); % 'regions' are defined in 'ROI_define'
% Figure out mapping from Nodes to voxels in region
N = length(R{hem}.linvoxidxs);
MAP = nan(size(R{hem}.location2linvoxindxs)); % Make a structure of vertices (without medial wall to indices in ROI structure)
for i=1:N
MAP(R{hem}.location2linvoxindxs==R{hem}.linvoxidxs(i))=i;
end;
vData = nan(numVert,size(data,2)); % Make outout data structure
for i=1:length(R{hem}.location)
vData(R{hem}.location(i),:)=nanmean(data(MAP(i,:),:),1);
end;
varargout = {vData};
case 'SURF:getAllData' % returns all cortical data - zero centered
A=gifti(fullfile(wbDir,'group32k','Icosahedron-362.32k.L.label.gii')); % Determine medial wall
indx = find(A.cdata>0);
Data=[];
for h=1:2
C=gifti(fullfile(wbDir,'group32k',sprintf('group.wcon.%s.func.gii',Hem{h})));
D.indx=indx;
D.data = C.cdata(indx,:);
D.hem = ones(size(D.data,1),1)*h;
D.parcel = A.cdata(indx,1);
Data=addstruct(Data,D);
end;
Data.data = bsxfun(@minus,Data.data ,mean(Data.data,2));
Data = getrow(Data,~isnan(sum(Data.data,2)));
varargout={Data};
case 'PARCEL:annot2labelgii' % Make an annotation file (freesurfer) into a Gifti
filename = varargin{1};
for i=1:2
name = [fshem{i} '.' filename '.annot'];
[v,label,colorT]=read_annotation(name);
values=colorT.table(:,5);
newlabel = zeros(size(label));
for j=1:length(values)
newlabel(label==values(j))=j-1; % New label values starting from 0
end;
RGB = colorT.table(:,1:3)/256; % Scaled between 0 and 1
G=surf_makeLabelGifti(newlabel,'anatomicalStruct',hemname{i},'labelNames',colorT.struct_names,'labelRGBA',[RGB ones(size(RGB,1),1)]);
save(G,[fshem{i} '.' filename '.label.gii']);
end;
case 'PARCEL:fsaverage2FSLR' % Transform anything from fsaverage to fs_LR
infilename = varargin{1}; % '~/Data/Atlas_templates/CorticalParcellations/Yeo2011/Yeo_JNeurophysiol11_FreeSurfer/fsaverage/label/lh.Yeo2011_17Networks_N1000.label.gii'
outfilename= varargin{2}; % '~/Data/Atlas_templates/FS_LR_164/Yeo_JNeurophysiol11_17Networks.164k.L.label.gii';
hem = varargin{3};
surf = varargin{4}; % '164k','32k'
inSphere = fullfile(atlasDir,['fs_' Hem{hem}],['fsaverage.' Hem{hem} '.sphere.164k_fs_' Hem{hem} '.surf.gii']);
outSphere = fullfile(atlasDir,'resample_fsaverage',['fs_LR-deformed_to-fsaverage.' Hem{hem} '.sphere.' surf '_fs_LR.surf.gii']);
% Convert surface to Gifti
system(['wb_command -label-resample ' infilename ' ' inSphere ' ' outSphere ' ADAP_BARY_AREA ' outfilename ' -area-surfs ' inSphere ' ' outSphere]);
case 'PARCEL:Yeo2015' % Make the probabilistic model from Yeo 2015 into a parcellation
hem=[1 2];
for h=hem
infilename = fullfile(wbDir,'group32k',sprintf('Yeo_CerCor2015_12Comp.32k.%s.func.gii',Hem{h}));
outfilename = fullfile(wbDir,'group32k',sprintf('Yeo_CerCor2015_12Comp.32k.%s.label.gii',Hem{h}));
A=gifti(infilename);
[~,data]= max(A.cdata,[],2);
data(sum(A.cdata,2)==0,1)=0;
G = surf_makeLabelGifti(data,'anatomicalStruct',hemname{h},'labelRGBA',[colorcube(13) ones(13,1)]);
save(G,outfilename);
end;
case 'DCBC:computeDistances' % Compute individual Dijkstra distances between vertices
sn=returnSubjs;
hem = [1 2];
resolution = '32k';
maxradius = 40;
vararginoptions(varargin,{'sn','hem','resolution','maxradius'});
for s=sn
for h=hem
surfDir = fullfile(wbDir, subj_name{s});
white=fullfile(surfDir,sprintf('%s.%s.white.%s.surf.gii',subj_name{s},Hem{h},resolution));
pial=fullfile(surfDir,sprintf('%s.%s.pial.%s.surf.gii',subj_name{s},Hem{h},resolution));
C1=gifti(white);
C2=gifti(pial);
vertices = double((C1.vertices' + C2.vertices' )/2); % Midgray surface
faces = double(C1.faces');
numVert=size(vertices,2);
n2f=surfing_nodeidxs2faceidxs(faces);
D=inf([numVert numVert],'single');
for i=1:numVert;
[subV, subF, subIndx,vidxs, fidxs]=surfing_subsurface(vertices, faces, i, maxradius, n2f); % construct correct sub surface
D(vidxs,i)=single(surfing_dijkstradist(subV,subF,subIndx,maxradius));
if (mod(i,100)==0)
fprintf('.');
end
end;
fprintf('\n');
save(fullfile(surfDir,sprintf('distances.%s.mat',Hem{h})),'D','-v7.3');
end;
end;
case 'DCBC:avrgdistances' % Average individual distances
sn=[2,3,4,6,8,9,10,12,14];
hem = [1 2];
resolution = '32k';
vararginoptions(varargin,{'sn','hem','resolution','maxradius'});
avrgD=single(zeros(32492,32492));
N=length(sn);
for h=hem
for s=1:length(sn)
fprintf('.');
surfDir = fullfile(wbDir, subj_name{sn(s)});
load(fullfile(surfDir,sprintf('distances.%s.mat',Hem{h})));
D(isinf(D))=45;
w=single((s-1)/N);
avrgD = w.*avrgD+(1-w)*D;
end;
fprintf('\n');
avrgDs=sparse(double(avrgD));
save(fullfile(wbDir,'group32k',sprintf('distances_sp.%s.mat',Hem{h})),'avgrDs');
end;
case 'DCBC:sphericalDist' % Quick fix: Compute distances on the Sphere
hem = [1 2];
resolution = '32k';
A=gifti(fullfile(wbDir,'group32k','fs_LR.32k.L.sphere.surf.gii'));
C = double(A.vertices');
Dist = surfing_eucldist(C,C);
Dist(Dist>50)=0;
Dist=sparse(Dist);
save(fullfile(wbDir,'group32k',sprintf('distanceSp_sp.mat')),'Dist');
case 'Eval:DCBC' % Get the DCBC evaluation
sn=returnSubjs;
hem = [1 2];
resolution = '32k';
taskSet = [1 2];
condType = 'unique'; % Evaluate on all or only unique conditions?
bins = [0:5:40]; % Spatial bins in mm
parcel = []; % N*2 matrix for both hemispheres
RR=[];
distFile = 'distAvrg_sp';
icoRes = 2562;
vararginoptions(varargin,{'sn','hem','bins','parcel','condType','taskSet','resolution','distFile','icoRes'});
D=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
numBins = numel(bins)-1;
for h=hem
load(fullfile(wbDir,'group32k',distFile));
% Now find the pairs that we care about to safe memory
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CHANGE: Exclude only medial walll!
% The node indices of medial wall are defined by taking the union of seven existing parcellations, including
% 'Glasser','Yeo17','Yeo7','Power2011','Yeo2015','Desikan', and 'Dextrieux', which stored as external
% .mat files "medialWallIndex_%hem.mat" for both hems.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mw = load(fullfile(wbDir, sprintf('group%s', resolution), sprintf('medialWallIndex_%s.mat', Hem{h})));
labels = gifti(fullfile(wbDir, sprintf('group%s', resolution), sprintf('Icosahedron-%d.%s.%s.label.gii',icoRes,resolution,Hem{h})));
% Here, we use it to find the union with the label-0 of Icosahedron-(icoRes) as our final medial wall
% vertIdx is the indices that we want (without medial wall)
vertIdx = setdiff(1:size(avrgDs), union(mw.mwIdx, find(labels.cdata(:,1)==0)));
avrgDs = avrgDs(vertIdx,vertIdx);
par = parcel(vertIdx,h);
% END CHANGE
[row,col,avrgD]=find(avrgDs);
inSp = sub2ind(size(avrgDs),row,col);
sameReg=(bsxfun(@ne,par',par)+1);
sameReg=sameReg(inSp);
clear avrgDs par;
for ts = taskSet;
D1=getrow(D,D.StudyNum==ts);
switch condType,
case 'unique'
% if funcMap - only evaluate unique tasks in sc1 or sc2
condIdx=D1.condNum(D1.overlap==0); % get index for unique tasks
case 'all'
condIdx=D1.condNum;
end
for s=sn
% CHANGE: This should be re-written to start from the wcon data
% Start
A=gifti(fullfile(wbDir,subj_name{s},sprintf('%s.%s.%s.con.%s.func.gii',subj_name{s},Hem{h},studyDir{ts},resolution)));
Data = [A.cdata(:,2:end-1) zeros(size(A.cdata,1),1)]; % bRemove intrstuction and add rest
Data = bsxfun(@rdivide,Data,sqrt(A.cdata(:,end))); % Noise normalize
% End CHANGE
Data = Data(vertIdx,condIdx); % Take the right subset
Data = bsxfun(@minus,Data,mean(Data,2));
Data = single(Data');
[K,P]=size(Data);
clear A;
SD = sqrt(sum(Data.^2)/K);
VAR = (SD'*SD);
COV = Data'*Data/K;
fprintf('%d',s);
for bw=[1 2]
for i=1:numBins
fprintf('.');
in = i+(bw-1)*numBins;
inBin = avrgD>bins(i) & avrgD<=bins(i+1) & sameReg==bw;
R.SN(in,1) = s;
R.hem(in,1) = h;
R.studyNum(in,1) = ts;
R.N(in,1) = sum(inBin(:));
R.avrDist(in,1) = mean(avrgD(inBin));
R.bwParcel(in,1)= bw-1;
R.bin(in,1) = i;
R.distmin(in,1) = bins(i);
R.distmax(in,1) = bins(i+1);
R.meanVAR(in,1) = full(nanmean(VAR(inSp(inBin))));
R.meanCOV(in,1) = full(nanmean(COV(inSp(inBin))));
end;
end;
clear VAR COV;
R.corr=R.meanCOV./sqrt(R.meanVAR);
fprintf('\n');
RR = addstruct(RR,R);
end;
end;
end;
varargout={RR};
case 'EVAL:doEval' % Recipe for producing the DCBC evaluation results
% for h=1:2
% A=gifti(sprintf('Yeo_JNeurophysiol11_7Networks.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Yeo7_Sphere_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Yeo_JNeurophysiol11_17Networks.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Yeo17_Sphere_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Glasser_2016.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% parcel(isnan(parcel))=0;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Glasser_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Icosahedron-42.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Icosahedron42_Sphere_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Icosahedron-162.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Icosahedron162_Sphere_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Icosahedron-362.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Icosahedron362_Sphere_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Power2011.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Power2011_Sphere_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Desikan.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Desikan_all.mat','-struct','T');
%
% for h=1:2
% A=gifti(sprintf('Dextrieux.32k.%s.label.gii',Hem{h}));
% parcel(:,h)=A.cdata;
% end;
% T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
% save('Eval_Dextrieux_all.mat','-struct','T');
for h=1:2
A=gifti(sprintf('Yeo_CerCor2015_12Comp.32k.%s.label.gii',Hem{h}));
parcel(:,h)=A.cdata;
end;
T=sc1_sc2_neocortical('Eval:DCBC','hem',[1 2],'parcel',parcel,'condType','all','distFile','distSphere_sp');
save('Eval_Yeo2015_Sphere_all.mat','-struct','T');
case 'EVAL:plotSingle' % Plots a single evaluation
toPlot = 'Power2011';
condType='all';
CAT.linecolor={'k','r'};
CAT.linestyle={'-','-'};
CAT.linewidth=2;
CAT.markertype='none';
CAT.errorcolor={'k','r'};
vararginoptions(varargin,{'toPlot','condType'});
T=load(['Eval_' toPlot '_Sphere_' condType '.mat']);
D=tapply(T,{'bin','SN','distmin','distmax','bwParcel'},{'corr'},{'avrDist'});
D.binC = (D.distmin+D.distmax)/2;
lineplot(D.binC,D.corr,'split',D.bwParcel,'CAT',CAT);
set(gca,'XLim',[0 40],'YLim',[-0.01 0.165],'XTick',[5:5:35]);
drawline(0,'dir','horz');
set(gcf,'PaperPosition',[2 2 3 3.7]);
wysiwyg;
keyboard;
case 'EVAL:plotEval' % Comparision plot of different parcellations
g1 = [0.5 0.5 0.5]; % Gray 1
toPlot={'Glasser','Yeo17','Yeo7','Power2011','Yeo2015','Desikan','Dextrieux','Icosahedron362'};
CAT.linecolor={'r','b','b','b','g','k','k',g1};
CAT.linestyle={'-','-',':','--','-','-',':','-'};
CAT.linewidth=2;
CAT.markertype='none';
CAT.errorcolor={'r','b','b','b','g','k','k',g1};
condType='all';
vararginoptions(varargin,{'toPlot','condType'});
T=[];
for i=1:length(toPlot)
D=load(sprintf('Eval_%s_Sphere_%s.mat',toPlot{i},condType));
TT=tapply(D,{'bin','SN','distmin','distmax'},{'corr','mean','name','corrB','subset',D.bwParcel==0},...
{'corr','mean','name','corrW','subset',D.bwParcel==1});
TT.parcel=ones(length(TT.SN),1)*i;
T=addstruct(T,TT);
end;
T.DCBC=T.corrB-T.corrW;
T.binC = (T.distmin+T.distmax)/2;
lineplot(T.binC,T.DCBC,'CAT',CAT,'split',T.parcel,'leg',toPlot);
set(gca,'XLim',[0 40],'YLim',[-0.01 0.06],'XTick',[5:5:35]);
set(gcf,'PaperPosition',[2 2 3.3 3]);
wysiwyg;
case 'ROI:defineROI'
sn = returnSubjs;
vararginoptions(varargin,{'sn'});
for s=sn
R=[];
idx=1;
mask = fullfile(baseDir,'sc1','GLM_firstlevel_4',subj_name{s},'mask.nii'); % mask from original GLM
surfDir = fullfile(wbDir,'group32k');
subjDir = fullfile(wbDir,subj_name{s});
for h=1:2
G = gifti(fullfile(surfDir,'Icosahedron-42.32k.L.label.gii'));
R{h}.type = 'surf_nodes'; % workbench version
R{h}.location = find(G.cdata(:,1)>0);
R{h}.white = fullfile(subjDir,sprintf('%s.%s.white.32k.surf.gii',subj_name{s},Hem{h}));
R{h}.pial = fullfile(subjDir,sprintf('%s.%s.pial.32k.surf.gii',subj_name{s},Hem{h}));
R{h}.linedef = [5,0,1];
R{h}.image = mask;
R{h}.name = sprintf('cortex_%s.%s',subj_name{s},Hem{h});
end
R = region_calcregions(R);
save(fullfile(regDir,'data',subj_name{s},'regions_cortex.mat'),'R');
fprintf('\nHemisphereic region has been defined for %s \n',subj_name{s});
end;
case 'ROI:betas' % extract betas for the ROI/study at hand. Results should be indentical to the
% ROI:betas case in sc1_sc2_imana, but for speed reasons we do it
% here directly from the beta_*.nii files, rather than going back
% to the time series.
sn=returnSubjs; % subjNum
study=1; % studyNum
glm='4'; % glmNum
roi='cortex'; % 'cortical_lobes','whole_brain','yeo','desikan','cerebellum','yeo_cerebellum'
vararginoptions(varargin,{'sn','study','glm','roi'});
for s=sn
glmDir = sprintf('GLM_firstlevel_%s',glm);
glmDirSubj=fullfile(baseDir,studyDir{study},glmDir,subj_name{s});
T=load(fullfile(glmDirSubj,'SPM_info.mat'));
% load data
load(fullfile(regDir,'data',subj_name{s},sprintf('regions_%s.mat',roi))); % 'regions' are defined in 'ROI_define'
% Get the raw data files
fname={};
for i=1:length(T.SN)
fname{i}=fullfile(glmDirSubj,sprintf('beta_%4.4d.nii',i));
end;
fname{end+1}=fullfile(glmDirSubj,'ResMS.nii');
V=spm_vol(char(fname));
D = region_getdata(V,R); % Data is N x P
for r=1:numel(R), % R is the output 'regions' structure from 'ROI_define'
% Get betas (univariately prewhitened)
B{r}.resMS = D{r}(end,:);
B{r}.betasNW = D{r}(1:end-1,:); % no noise normalisation
B{r}.betasUW = bsxfun(@rdivide,B{r}.betasNW,sqrt(B{r}.resMS)); % univariate noise normalisation
B{r}.region = r;
B{r}.regName = R{r}.name;
B{r}.SN = s;
end
% Save output for each subject
outfile = sprintf('betas_%s.mat',roi);
save(fullfile(baseDir,studyDir{study},'RegionOfInterest',sprintf('glm%s',glm),subj_name{s},outfile),'B');
fprintf('betas computed and saved for %s for study%d \n',subj_name{s},study);
end
case 'CLUSTER:spectral'
K = 10; % Number of clusters
normalisation = 3;
vararginoptions(varargin,{'K'});
D=sc1_sc2_neocortical('SURF:getAllData');
T=tapply(D,{'hem','parcel'},{'data'}); % Condense
A=bsxfun(@rdivide,T.data,sqrt(sum(T.data.^2,2))); % Normalize
Ang = 1-A*A';
W=exp(-(Ang.^2)*3); % Gaussian affinity matrix
cl = SpectralClustering(W,K,normalisation);
[~,T.cl]=max(cl,[],2);
for i=1:K
Centroids(i,:)=mean(T.data(T.cl==i,:),1); % Get the centroids
end;
nodes=bsxfun(@rdivide,D.data,sqrt(sum(D.data.^2,2)));
NodeAng=1-nodes*Centroids';
[~,D.cl]=min(NodeAng,[],2);
for h=1:2
Data = zeros(32492,1);
Data(D.indx(D.hem==h))=D.cl(D.hem==h);
GC=surf_makeLabelGifti(Data,'anatomicalStruct',hemname{h},'labelRGBA',[zeros(1,4);[colorcube(K) ones(K,1)]]);
save(GC,fullfile(wbDir,'group32k',sprintf('specCluster.%d.%s.label.gii',K,Hem{h})));
end;
con = [10 15 21];
subplot(1,2,1);
sc1_sc2_neocortical('CLUSTER:visualize',D.data,D.cl,Centroids,'threshold',0.9,'con',con);
subplot(1,2,2);
sc1_sc2_neocortical('CLUSTER:visualize',T.data,T.cl,Centroids,'threshold',0,'con',con);
keyboard;
case 'CLUSTER:visualize'
X=varargin{1};
cl=varargin{2};
centroids=varargin{3};
threshold=0.7; % Length threshold
color_dots=1;
plot_lines=1;
K=max(cl);
color=colorcube(K+1);
con=[ 50 49 40]; % unidrnd(N,1,3); % [4 8 24]; %
vararginoptions(varargin(4:end),{'threshold','plot_lines','color_dots','con'});
L = sqrt(sum(X(:,:).^2,2));
th = quantile(L,threshold);
D=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
Y=X(:,con);
for i=1:K
if (color_dots==1)
scatterplot3(X(:,con(1)),X(:,con(2)),X(:,con(3)),'markercolor',color(i,:),'markerfill',color(i,:),'subset',cl==i & L>th);
elseif(color_dots==2)
scatterplot3(X(:,con(1)),X(:,con(2)),X(:,con(3)),'markercolor',[0.6 0.6 0.6],'markerfill',[0.6 0.6 0.6],'subset',cl==i & L>th);
end;
hold on;
if (plot_lines)
a=quiver3(0,0,0,centroids(i,con(1)),centroids(i,con(2)),centroids(i,con(3)),0);
set(a,'LineWidth',3,'Color',color(i,:));
end;
end;
hold off;
l=abs(minmax(minmax(X(:,con))));
xlabel(D.condNames{con(1)});
ylabel(D.condNames{con(2)});
zlabel(D.condNames{con(3)});
set(gca,'XLim',[-l l],'YLim',[-l l],'ZLim',[-l l]);
axis equal;
set(gcf,'PaperPosition',[2 2 8 6]);
wysiwyg;
case 'GRAD:compute' % Computes local gradient
cd(fullfile(wbDir,'group32k'));
normalize=0; % Normalize each location before computing the gradient?
vararginoptions(varargin,{'normalize'})
for h=1:2
sname = sprintf('fs_LR.32k.%s.midthickness.surf.gii',Hem{h});
fname = sprintf('group.swcon.%s.func.gii',Hem{h});
if (normalize)
A = gifti(fname);
X = A.cdata;
X = bsxfun(@rdivide,X,sqrt(sum(X.^2,2)));
B = surf_makeFuncGifti(X,'anatomicalStruct',hemname{h});
fname = sprintf('group.nwcon.%s.func.gii',Hem{h});
save(B,fname);
end
oname = sprintf('group.grad.%s.func.gii',Hem{h});
vname = sprintf('group.vec.%s.func.gii',Hem{h});
com = sprintf('wb_command -metric-gradient %s %s %s -vectors %s',sname,fname,oname,vname);
system(com);
end;
case 'GRAD:compare'
cd(fullfile(wbDir,'group32k'));
for h=1:2
fname = sprintf('group.vec.%s.func.gii',Hem{h});
oname = sprintf('group.mgrad.%s.func.gii',Hem{h});
A=gifti(fname);
[N,Q]=size(A.cdata);
V=reshape(A.cdata,N,3,Q/3);
A=sqrt(sum(V.^2,2));
A=mean(A,3);
V=permute(V,[3 2 1]);
for i=1:size(V,3)
B(i,1:2)=sqrt(eigs(double(V(:,:,i)'*V(:,:,i)),2));
if (mod(i,1000)==0)
fprintf('.');
end
end;
fprintf('\n');
K=surf_makeFuncGifti([A B(:,1) B(:,1)-B(:,2) B(:,1)./sum(B,2)],'columnName',{'avrgGrad','sqEig1','sqEigD','sqEigN'},'anatomicalStruct',hemname{h});
save(K,oname);
end;
end;