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sc1_sc2_functionalAtlas.m
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function varargout=sc1_sc2_functionalAtlas(what,varargin)
% Directories
baseDir = '/Users/maedbhking/Documents/Cerebellum_Cognition';
% baseDir = '/Users/maedbhking/Remote/Documents2/Cerebellum_Cognition';
baseDir = '/Volumes/MotorControl/data/super_cerebellum_new';
% baseDir = '/Users/jdiedrichsen/Data/super_cerebellum_new';
atlasDir='/Users/maedbhking/Documents/Atlas_templates/';
studyDir{1} =fullfile(baseDir,'sc1');
studyDir{2} =fullfile(baseDir,'sc2');
studyStr = {'SC1','SC2','SC12'};
behavDir ='/data';
suitDir ='/suit';
caretDir ='/surfaceCaret';
regDir ='/RegionOfInterest/';
encodeDir ='/encoding';
funcRunNum = [51,66]; % first and last behavioural run numbers (16 runs per subject)
run = {'01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16'};
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];
hem={'lh','rh'};
hemName={'LeftHem','RightHem'};
switch what
case 'PARTICIPANT:info'
% load in excel file with participant info
D=dload(fullfile(baseDir,'sc1_sc2_taskDesign.txt'));
% how many women/men ?
idx=D.include==1;
fprintf('there are %d women and %d men \n',sum(char(D.Sex(idx))=='F'),sum(char(D.Sex(idx))=='M'));
% average age across men and women
numdays = datenum(D.BEHA1(idx),'dd-mm-yy') - datenum(D.DOB(idx),'dd-mm-yy');
ages=round(numdays/365);
fprintf('the average age is %2.2f (sd=%2.2f) \n',mean(ages),std(ages))
% average time between sessions
numdays = datenum(D.BEHB1(idx),'dd-mm-yy') - datenum(D.BEHA1(idx),'dd-mm-yy');
fprintf('the average amount of time between task sets is %2.2f days (sd=%2.2f) \n',mean(numdays),std(numdays));
% SCANNING
numdays = datenum(D.SCANA1(idx),'dd-mm-yy') - datenum(D.BEHA3(idx),'dd-mm-yy');
fprintf('the average amount of time from behav 3 to scan 1 in setA is %2.2f days (sd=%2.2f) \n',mean(numdays),std(numdays));
numdays = datenum(D.SCANB1(idx),'dd-mm-yy') - datenum(D.BEHB3(idx),'dd-mm-yy');
fprintf('the average amount of time from behav 3 to scan 1 in setB is %2.2f days (sd=%2.2f) \n',mean(numdays),std(numdays));
numdays = datenum(D.SCANA2(idx),'dd-mm-yy') - datenum(D.SCANA1(idx),'dd-mm-yy');
fprintf('the average amount of time from scan 1 to scan 2 in setA is %2.2f days (sd=%2.2f) \n',mean(numdays),std(numdays));
numdays = datenum(D.SCANB2(idx),'dd-mm-yy') - datenum(D.SCANB1(idx),'dd-mm-yy');
fprintf('the average amount of time from scan 1 to scan 2 in setB is %2.2f days (sd=%2.2f) \n',mean(numdays),std(numdays));
% BEHAVIOUR
numdays = datenum(D.BEHA3(idx),'dd-mm-yy') - datenum(D.BEHA1(idx),'dd-mm-yy');
fprintf('the average amount of time across behav sessions in setA is %2.2f days (sd=%2.2f) \n',mean(numdays+1),std(numdays+1));
numdays = datenum(D.BEHB3(idx),'dd-mm-yy') - datenum(D.BEHB1(idx),'dd-mm-yy');
fprintf('the average amount of time across behav sessions in setB is %2.2f days (sd=%2.2f) \n',mean(numdays+1)+1,std(numdays+1));
case 'BEHAVIOURAL:get_data'
sess=varargin{1}; % 'behavioural' or 'scanning'
sn=returnSubjs;
tasks={'stroop','nBack','visualSearch','GoNoGo','nBackPic','affective','emotional','ToM','arithmetic','intervalTiming',...
'CPRO','prediction','spatialMap','mentalRotation','emotionProcess','respAlt','visualSearch2','nBackPic2','ToM2'};
study=[1;1;1;1;1;1;1;1;1;1;2;2;2;2;2;2;2;2;2];
T=[];
for s=sn,
for t=1:length(tasks),
D = dload(fullfile(studyDir{study(t)},behavDir,subj_name{s},sprintf('sc%d_%s_%s.dat',study(t),subj_name{s},tasks{t})));
switch sess,
case 'training'
A = getrow(D,D.runNum>=1 & D.runNum<=51);
case 'scanning'
A = getrow(D,D.runNum>=funcRunNum(1) & D.runNum<=funcRunNum(2));
end
S.taskName=A.taskName;
S.numCorr=A.numCorr;
S.rt=A.rt;
S.SN=repmat(s,length(A.taskName),1);
S.runNum=A.runNum;
S.respMade=A.respMade;
T=addstruct(T,S);
end
fprintf('subj%d done \n',s)
end
% save out results
save(fullfile(studyDir{2},behavDir,sprintf('%sLearning.mat',sess)),'T');
case 'PLOT:behavioural'
sess=varargin{1}; % 'behavioural' or 'scanning'
type=varargin{2}; % plot 'subject' or 'run'
vararginoptions({varargin{3:end}},{'CAT'}); % option if doing individual map analysis
% load in data
load(fullfile(studyDir{2},behavDir,sprintf('%sLearning.mat',sess)),'T')
switch type,
case 'subject'
lineplot([T.SN], T.numCorr,'subset', T.respMade>0,'CAT',CAT);
xlabel('Subject')
ylabel('Percent correct')
case 'run'
lineplot([T.runNum], T.numCorr,'subset', T.respMade>0,'CAT',CAT);
hold on
CAT.errorcolor={'r'};
CAT.linecolor={'r'};
lineplot([T.runNum], T.numCorr,'subset', T.respMade>0 & strcmp(T.taskName,'spatialMap'),'CAT',CAT);
hold on
CAT.errorcolor={'g'};
CAT.linecolor={'g'};
lineplot([T.runNum], T.numCorr,'subset', T.respMade>0 & strcmp(T.taskName,'emotional'),'CAT',CAT);
xlabel('Run')
ylabel('Percent correct')
end
case 'ACTIVITY:make_model' % make X matrix (feature models)
study=varargin{1}; % 1, 2, or [1,2]
model=varargin{2}; % 'full' or 'excludeMotor'
F=dload(fullfile(baseDir,'motorFeats.txt')); % load in motor features
% sort out which study we're taking (or both) ?
if length(study)>1,
Fs=F;
else
Fs=getrow(F,F.studyNum==study);
end
numConds=length(Fs.studyNum);
% rest
if length(study)>1,
rest=[29,61];
else
rest=numConds;
end
featNames=Fs.condNames;
% make feature model
switch model,
case 'full'
x=[eye(numConds) Fs.lHand./Fs.duration Fs.rHand./Fs.duration Fs.saccades./Fs.duration];
featNames{numConds+1}='lHand';
featNames{numConds+2}='rHand';
featNames{numConds+3}='saccades';
case 'excludeMotor'
x=[eye(numConds)];
end
X.x=x;
X.idx=[1:size(x,1)]';
% normalise features
X.x = bsxfun(@minus,X.x,nanmean(X.x));
X.x = bsxfun(@rdivide,X.x,sqrt(nansum(X.x.^2))); % Normalize to unit length vectors
X=X.x;
varargout={X,featNames,numConds,F};
case 'ACTIVITY:patterns'
study=varargin{1}; % [1,2]
lambda=.01;
model='full';
vararginoptions({varargin{2:end}},{'lambda','model'});
% load in activity patterns
[data,volIndx,V]=sc1_sc2_functionalAtlas('EVAL:get_data',returnSubjs,study,'eval');
% get feature model
[X,featNames,numConds]=sc1_sc2_functionalAtlas('ACTIVITY:make_model',study,model); % load in model (including motor features)
% regress out motor features
for s=1:length(returnSubjs),
B(:,s,:)=(X'*X+eye(size(X,2))*lambda)\(X'*data(:,:,s));
fprintf('ridge regress done for subj%d done \n',returnSubjs(s))
% evaluate prediction for each subj
SST = nansum(data(:,:,s).*data(:,:,s));
u=permute(B(:,s,:),[1 3 2]);
Ypred(:,:,s)=X*u;
res =data(:,:,s)-Ypred(:,:,s);
SSR = nansum(res.^2);
R2(s) = 1-nansum(SSR)/nansum(SST);
end;
clear data
% subtract baseline
baseline=nanmean(B,1);
B=bsxfun(@minus,B,baseline);
varargout={B,featNames,numConds,volIndx,V,R2,Ypred};
case 'ACTIVITY:writeOut_GROUP'
writeOut=varargin{1}; % 'paper' (average taskConds - SUIT space - metric file)
% or 'website' (all taskConds - MNI and SUIT space - nifti file)
study=[1,2];
websiteDir_SUIT=fullfile(baseDir,'website_maps','SUIT_group_contrasts');dircheck(websiteDir_SUIT) % where website contrasts are being saved for SUIT
websiteDir_MNI=fullfile(baseDir,'website_maps','MNI_group_contrasts');dircheck(websiteDir_MNI) % where website contrasts are being saved for MNI
suitDir=fullfile(studyDir{2},caretDir,'suit_flat','glm4'); % where figure contrasts are being saved
% get activity patterns
[B,featNames,numConds,volIndx,V]=sc1_sc2_functionalAtlas('ACTIVITY:patterns',study);
% set up volume info
Yy=zeros(length(featNames),length(returnSubjs),V.dim(1)*V.dim(2)*V.dim(3));
C{1}.dim=V.dim;
C{1}.mat=V.mat;
% make volume
Yy(:,:,volIndx)=B;
Yy=permute(Yy,[2 1 3]);
indices=nanmean(Yy,1);
indices=reshape(indices,[size(indices,2),size(indices,3)]);
% vol data
indices=reshape(indices,[size(indices,1) V.dim(1),V.dim(2),V.dim(3)]);
for i=1:size(indices,1),
data=reshape(indices(i,:,:,:),[C{1}.dim]);
C{i}.dat=data;
end
switch writeOut,
case 'paper'
% load in task structure file for paper
F=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
% map vol 2 surf
S=caret_suit_map2surf(C,'space','SUIT','stats','nanmean','column_names',featNames); % MK created caret_suit_map2surf to allow for output to be used as input to caret_save
% average shared tasks
condNumUni=[F.condNumUni;62;63;64];
X1=indicatorMatrix('identity_p',condNumUni);
uniqueTasks=S.data*X1; % try pinv here ?
% get new condNames (unique only)
condNames=[F.condNames(F.StudyNum==1);F.condNames(F.StudyNum==2 & F.overlap==0)];
condNames{length(condNames)+1}='lHand';
condNames{length(condNames)+1}='rHand';
condNames{length(condNames)+1}='saccades';
S.data=uniqueTasks;
S.column_name=condNames';
S.num_cols=size(S.column_name,2);
S.column_color_mapping=S.column_color_mapping(1:S.num_cols,:);
outName='unCorr_avrgTaskConds'; % average of certain tasks
% save out metric files for paper
caret_save(fullfile(suitDir,sprintf('%s.metric',outName)),S);
case 'website'
% load in task structure file for website
F=dload(fullfile(baseDir,'website_taskNames.txt'));
exampleVol=fullfile(studyDir{2},'suit','glm4','s02','wdbeta_0001.nii');% must be better way of doing this
X=spm_vol(exampleVol);
% loop over task conditions
for c=1:length(C),
X.fname=fullfile(websiteDir_SUIT,sprintf('%s.nii',F.condNames{c}));
X.private.dat.fname=fullfile(websiteDir_SUIT,sprintf('%s.nii',F.condNames{c}));
% normalise to SUIT and MNI
spm_write_vol(X,C{c}.dat);
cd(websiteDir_SUIT)
% normalise to MNI
suit_mni2suit(sprintf('%s.nii',F.condNames{c}),'def','suit2mni');
fprintf('save out vol in MNI %s \n',F.condNames{c})
movefile(sprintf('Ws2m_%s.nii',F.condNames{c}),fullfile(websiteDir_MNI,sprintf('%s.nii',F.condNames{c})))
delete(fullfile(websiteDir_SUIT,sprintf('Ws2m_%s.nii',F.condNames{c})));
end
end
case 'ACTIVITY:writeOut_INDIV'
writeOut=varargin{1}; % 'paper' (average taskConds - SUIT space - metric file)
% or 'website' (all taskConds - MNI and SUIT space - nifti file)
study=[1,2];
% get activity patterns
[B,featNames,numConds,volIndx,V]=sc1_sc2_functionalAtlas('ACTIVITY:patterns',study);
for s=1:length(returnSubjs),
websiteDir_SUIT=fullfile(baseDir,'website_maps','SUIT_individual_contrasts',subj_name{returnSubjs(s)});dircheck(websiteDir_SUIT) % where website contrasts are being saved for SUIT
websiteDir_MNI=fullfile(baseDir,'website_maps','MNI_individual_contrasts',subj_name{returnSubjs(s)});dircheck(websiteDir_MNI) % where website contrasts are being saved for MNI
suitDir=fullfile(studyDir{2},caretDir,sprintf('x%s',subj_name{returnSubjs(s)}),'cerebellum');dircheck(suitDir) % where figure contrasts are being saved
if exist(fullfile(studyDir{2},caretDir,sprintf('x%s',subj_name{returnSubjs(s)}),'cerebellum',sprintf('%s_unCorr_avrgTaskConds.metric',subj_name{returnSubjs(s)})));
delete(fullfile(studyDir{2},caretDir,sprintf('x%s',subj_name{returnSubjs(s)}),'cerebellum',sprintf('%s_unCorr_avrgTaskConds.metric',subj_name{returnSubjs(s)}))); % average of certain task
end
% set up volume info
Yy=zeros(length(featNames),1,V.dim(1)*V.dim(2)*V.dim(3));
C{1}.dim=V.dim;
C{1}.mat=V.mat;
% make volume
Yy(:,:,volIndx)=B(:,s,:);
Yy=permute(Yy,[2 1 3]);
indices=reshape(Yy,[size(Yy,2),size(Yy,3)]);
% vol data
indices=reshape(indices,[size(indices,1) V.dim(1),V.dim(2),V.dim(3)]);
for i=1:size(indices,1),
data=reshape(indices(i,:,:,:),[C{1}.dim]);
C{i}.dat=data;
end
switch writeOut,
case 'paper'
% load in task structure file for paper
F=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
% map vol 2 surf
S=caret_suit_map2surf(C,'space','SUIT','stats','nanmean','column_names',featNames); % MK created caret_suit_map2surf to allow for output to be used as input to caret_save
% average shared tasks
condNumUni=[F.condNumUni;62;63;64];
X1=indicatorMatrix('identity_p',condNumUni);
uniqueTasks=S.data*X1; % try pinv here ?
% get new condNames (unique only)
condNames=[F.condNames(F.StudyNum==1);F.condNames(F.StudyNum==2 & F.overlap==0)];
condNames{length(condNames)+1}='lHand';
condNames{length(condNames)+1}='rHand';
condNames{length(condNames)+1}='saccades';
S.data=uniqueTasks;
S.column_name=condNames';
S.num_cols=size(S.column_name,2);
S.column_color_mapping=S.column_color_mapping(1:S.num_cols,:);
outName='unCorr_avrgTaskConds'; % average of certain tasks
% save out metric files for paper
caret_save(fullfile(suitDir,sprintf('%s.metric',outName)),S);
case 'website'
% load in task structure file for website
F=dload(fullfile(baseDir,'website_taskNames.txt'));
exampleVol=fullfile(studyDir{2},'suit','glm4','s02','wdbeta_0001.nii');% must be better way of doing this
X=spm_vol(exampleVol);
% loop over task conditions
for c=1:length(C),
X.fname=fullfile(websiteDir_SUIT,sprintf('%s.nii',F.condNames{c}));
X.private.dat.fname=fullfile(websiteDir_SUIT,sprintf('%s.nii',F.condNames{c}));
% normalise to SUIT and MNI
spm_write_vol(X,C{c}.dat);
cd(websiteDir_SUIT)
% normalise to MNI
suit_mni2suit(sprintf('%s.nii',F.condNames{c}),'def','suit2mni');
fprintf('save out vol in MNI %s \n',F.condNames{c})
movefile(sprintf('Ws2m_%s.nii',F.condNames{c}),fullfile(websiteDir_MNI,sprintf('%s.nii',F.condNames{c})))
delete(fullfile(websiteDir_SUIT,sprintf('Ws2m_%s.nii',F.condNames{c})));
end
end
clear Yy C
end
case 'ACTIVITY:checkRidge'
lambda=varargin{1};
study=[1,2];
[data]=sc1_sc2_functionalAtlas('EVAL:get_data',returnSubjs,study,'eval');
[~,~,~,~,~,R2_full,Ypred_full]=sc1_sc2_functionalAtlas('ACTIVITY:patterns',study,'lambda',lambda,'model','full');
[~,~,~,~,~,R2_noMotor,Ypred_noMotor]=sc1_sc2_functionalAtlas('ACTIVITY:patterns',study,'lambda',lambda,'model','excludeMotor');
% plot pred against data for full and noMotor models
subplot(2,1,1)
Ypred=permute(Ypred_full,[1 3 2]); data=permute(data,[1 3 2]);
plot(nanmean(Ypred,3),nanmean(data,3))
title('full model')
text(1,1,sprintf('R^2=%2.3',nanmean(R2_noMotor)));
subplot(2,1,2)
Ypred=permute(Ypred_noMotor,[1 3 2]); data=permute(data,[1 3 2]);
plot(nanmean(Ypred_noMotor,3),nanmean(data,3))
title('no motor')
xlabel('Ypred');ylabel('data');text(1,1,sprintf('R^2=%2.3',nanmean(R2_noMotor)));
fprintf('lambda=%2.2f:average prediction of full model across subjects is %2.4f \n',lambda,nanmean(R2_full));
fprintf('lambda=%2.2f: average prediction of noMotor model across subjects is %2.4f \n',lambda,nanmean(R2_noMotor));
%
case 'ACTIVITY:checkRest_indivSubjects'
taskConds=[28,31]; % no instruct, no rest
indx=1;
for study=1:2,
for s=1:length(returnSubjs),
% load in SPM
SPM_info=load(fullfile(studyDir{study},'GLM_firstlevel_4',subj_name{returnSubjs(s)},'SPM_info.mat'));
% load in betas (resliced into suit space)
betaDir=dir(fullfile(studyDir{study},'suit','glm4',subj_name{returnSubjs(s)},'*wdbeta*'));
% get task conditions
for c=1:taskConds(study),
idx=find(SPM_info.cond==c);
Vi=cellstr(char(betaDir(idx).name));
taskN=SPM_info.TN{SPM_info.cond==c};
cd(fullfile(studyDir{study},'suit','glm4',subj_name{returnSubjs(s)}));
% make contrast
spm_imcalc(Vi,sprintf('wdcon_%s.nii',taskN),'(i1+i2+i3+i4+i5+i6+i7+i8+i9+i10+i11+i12+i13+i14+i15+i16)/16');
% store contrast name
conName{c}=sprintf('wdcon_%s.nii',taskN);
end
% calculate average across task conditions (must be better
% way than manual input)
if study==1,
spm_imcalc(conName','wdcon_taskAvrg.nii','(i1+i2+i3+i4+i5+i6+i7+i8+i9+i10+i11+i12+i13+i14+i15+i16+i17+i18+i19+i20+i21+i22+i23+i24+i25+i26+i27+i28)/28');
elseif study==2,
spm_imcalc(conName','wdcon_taskAvrg.nii','(i1+i2+i3+i4+i5+i6+i7+i8+i9+i10+i11+i12+i13+i14+i15+i16+i17+i18+i19+i20+i21+i22+i23+i24+i25+i26+i27+i28+i29+i30+i31)/31');
end
% calculate rest
spm_imcalc('wdcon_taskAvrg.nii','wdcon_rest.nii','i1*-1');
% map 2 surf
data(indx,:)=suit_map2surf('wdcon_rest.nii');
featNames{indx}=sprintf('sc%d-%s-rest',study,subj_name{returnSubjs(s)});
indx=indx+1;
end
end
% map vol 2 surf
S=caret_struct('metric','data',data','column_name',featNames);
% save out rest metric file (both sc1 and sc2, all subjs)
caret_save(fullfile(studyDir{2},caretDir,'suit_flat','glm4','rest_indivSubjs.metric'),S)
case 'ACTIVITY:checkRest_avrg_taskSet'
indx=1;
for study=1:2,
for s=1:length(returnSubjs),
conName{s}=fullfile(studyDir{study},'suit','glm4',subj_name{returnSubjs(s)},'wdcon_rest.nii');
end
cd(fullfile(studyDir{study},'suit','glm4',subj_name{returnSubjs(s)}))
% average rest across subjects
spm_imcalc(conName','wdcon_rest_avrg.nii','(i1+i2+i3+i4+i5+i6+i7+i8+i9+i10+i11+i12+i13+i14+i15+i16+i17+i18+i19+i20+i21+i22+i23+i24)/24');
% map 2 surf
data(indx,:)=suit_map2surf('wdcon_rest_avrg.nii');
delete(fullfile(studyDir{study},'suit','glm4',subj_name{returnSubjs(s)},'wdcon_rest_avrg.nii'));
featNames{indx}=sprintf('sc%d-rest',study);
indx=indx+1;
end
% map vol 2 surf
S=caret_struct('metric','data',data','column_name',featNames);
% save out rest metric file (both sc1 and sc2, all subjs)
caret_save(fullfile(studyDir{2},caretDir,'suit_flat','glm4','rest_avrg.metric'),S)
case 'ACTIVITY:reliability_shared'
glm=varargin{1};
type=varargin{2}; % 'cerebellum' or 'cortex' 'basalGanglia'
D=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
load(fullfile(studyDir{2},encodeDir,sprintf('glm%d',glm),sprintf('allVox_sc1_sc2_sess_%s.mat',type)));
Y=Yy;clear Yy;
numSubj=length(Y);
S=[];
for subj=1:numSubj,
R=[];
idx=1;
for study=1:2,
D1=getrow(D,D.StudyNum==study);
sharedConds=D1.condNumUni.*D1.overlap;
if study==1,
condNames=D1.condNames(find(sharedConds));
end
% sharedConds=sharedConds(randperm(numel(sharedConds{2}))); % Shuffle
cN=condNum{study}-1; % Important: In the allVox file, instruction is still included!
pN=partNum{study}; % Partition Numner
sN=(pN>8)+1; % Sessions Number
for se=1:2,
X1=indicatorMatrix('identity_p',cN.*(sN==se)); % This one is the matrix that related trials-> condition numbers
X2=indicatorMatrix('identity_p',sharedConds); % THis goes from condNum to shared condNumUni
Yf(:,:,idx,subj)=pinv(X1*X2)*Y{subj}{study};
Yf(:,:,idx,subj)=bsxfun(@minus,Yf(:,:,idx,subj),nanmean(Yf(:,:,idx,subj)));
idx=idx+1;
end;
end;
for c=1:size(Yf,1),
CORR(c,:,:,subj)=interSubj_corr(Yf(c,:,:,subj));
T.SN = returnSubjs(subj);
T.within1 = CORR(c,1,2,subj);
T.within2 = CORR(c,3,4,subj);
T.across = nanmean(nanmean(CORR(c,1:2,3:4,subj)));
T.condNum = c;
T.condNames={condNames{c}};
R=addstruct(R,T);
clear T
end;
S=addstruct(S,R);
clear R
end;
save(fullfile(studyDir{2},regDir,'glm4','patternReliability.mat'),'S','CORR')
case 'ACTIVITY:reliability_overall'
glm=varargin{1};
type=varargin{2}; % 'cerebellum' or 'cortex' 'basalGanglia'
study=varargin{3}; % studyNum = 1 or 2
D=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
load(fullfile(studyDir{2},encodeDir,sprintf('glm%d',glm),sprintf('allVox_sc1_sc2_sess_%s.mat',type)));
Y=Yy;clear Yy;
numSubj=length(Y);
S=[];
for subj=1:numSubj,
R=[];
idx=1;
D1=getrow(D,D.StudyNum==study);
% sharedConds=D1.condNumUni.*D1.overlap;
overallConds=D1.condNumUni.*D1.StudyNum;
if study==1,
condNames=D1.condNames(find(overallConds));
end
% sharedConds=sharedConds(randperm(numel(sharedConds{2}))); % Shuffle
cN=condNum{study}-1; % Important: In the allVox file, instruction is still included!
pN=partNum{study}; % Partition Numner
sN=(pN>8)+1; % Sessions Number
for se=1:2,
X1=indicatorMatrix('identity_p',cN.*(sN==se)); % This one is the matrix that related trials-> condition numbers
X2=indicatorMatrix('identity_p',overallConds); % THis goes from condNum to shared condNumUni
Yf(:,:,idx,subj)=pinv(X1*X2)*Y{subj}{study};
Yf(:,:,idx,subj)=bsxfun(@minus,Yf(:,:,idx,subj),nanmean(Yf(:,:,idx,subj)));
idx=idx+1;
end;
CORRMatrix=corr(Yf(:,:,1,subj),Yf(:,:,2,subj));
CORR(:,subj)=diag(CORRMatrix);
% for c=1:size(Yf,1),
% CORR(c,:,:,subj)=interSubj_corr_voxel(Yf(c,:,:,subj));
% T.SN = returnSubjs(subj);
% T.within1 = CORR(c,1,2,subj);
% T.within2 = CORR(c,3,4,subj);
% T.across = nanmean(nanmean(CORR(c,1:2,3:4,subj)));
% T.condNum = c;
% T.condNames={condNames{c}};
% R=addstruct(R,T);
% clear T
% end;
fprintf('subj%d done',returnSubjs(subj));
end;
save(fullfile(studyDir{study},regDir,'glm4','patternReliability_voxel.mat'),'CORR')
case 'ACTIVITY:modelThresh' % DEPRECIATED CASE
threshold=0.2;
[B,featNames,V,volIndx]=sc1_sc2_functionalAtlas('ACTIVITY:patterns','group',[1,2],'averageConds');
[RVox]=sc1_sc2_functionalAtlas('PREDICTIONS:datasets','R','run');
% load in task structure file
F=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
subjs=size(B,2);
numConds=size(B,1);
P=size(B,3);
% get thresholded R on group
RVox_group=nanmean(RVox,1);
volIndx_thresh=find(RVox_group>threshold);
B_thresh=zeros(numConds,subjs,P);
for s=1:subjs,
B_thresh(:,s,volIndx_thresh)=B(:,s,volIndx_thresh);
end
% make volume
Yy=zeros(numConds,subjs,V.dim(1)*V.dim(2)*V.dim(3));
for s=1:subjs,
Yy(:,s,volIndx)=B_thresh(:,s,:);
end
Yy=permute(Yy,[2 1 3]);
% set up volume info
C{1}.dim=V.dim;
C{1}.mat=V.mat;
indices=nanmean(Yy,1);
indices=reshape(indices,[size(indices,2),size(indices,3)]);
% map vol2surf
indices=reshape(indices,[size(indices,1) V.dim(1),V.dim(2),V.dim(3)]);
for i=1:size(indices,1),
data=reshape(indices(i,:,:,:),[C{1}.dim]);
C{i}.dat=data;
end
S=caret_suit_map2surf(C,'space','SUIT','stats','nanmean','column_names',featNames); % MK created caret_suit_map2surf to allow for output to be used as input to caret_save
% get averageConds
condNumUni=[F.condNumUni;62;63;64];
X1=indicatorMatrix('identity_p',condNumUni);
uniqueTasks=S.data*X1; % try pinv here ?
% get new condNames (unique only)
condNames=[F.condNames(F.StudyNum==1);F.condNames(F.StudyNum==2 & F.overlap==0)];
condNames{length(condNames)+1}='lHand';
condNames{length(condNames)+1}='rHand';
condNames{length(condNames)+1}='saccades';
S.data=uniqueTasks;
S.column_name=condNames';
S.num_cols=size(S.column_name,2);
S.column_color_mapping=S.column_color_mapping(1:S.num_cols,:);
% save out metric
outDir=fullfile(studyDir{2},caretDir,'suit_flat','glm4');
outName='unCorr_avrgTaskConds_thresh';
caret_save(fullfile(outDir,sprintf('%s.metric',outName)),S);
case 'PLOT:reliabilityA'
% load relability
load(fullfile(studyDir{2},regDir,'glm4','patternReliability_cerebellum.mat'));
% figure();lineplot(S.condNum,[S.within1,S.within2,S.across],'leg',{'within1','within2','across'})
A=tapply(S,{'SN'},{'across'},{'within1'},{'within2'});
% within & between-dataset reliability
myboxplot([],[A.within1 A.within2 A.across],'style_twoblock','plotall',1);
drawline(0,'dir','horz');
ttest(sqrt(A.within1.*A.within2),A.across,2,'paired');
x1=nanmean(A.within1);x2=nanmean(A.within2);x3=nanmean(A.across);
SEM1=std(A.within1)/sqrt(length(returnSubjs));SEM2=std(A.within2)/sqrt(length(returnSubjs));SEM3=std(A.across)/sqrt(length(returnSubjs));
fprintf('average corr for set A is %2.3f; CI:%2.3f-%2.3f \n average corr for set B is %2.3f; CI:%2.3f-%2.3f and average corr across sets A and B is %2.3f; CI:%2.3f-%2.3f \n',...
x1,x1-(1.96*SEM1),x1+(1.96*SEM1),x2,...
x2-(1.96*SEM2),x2+(1.96*SEM2),...
x3,x3-(1.96*SEM3),x3+(1.96*SEM3));
case 'PLOT:reliability_voxel'
for sess=1:2,
load(fullfile(studyDir{sess},regDir,'glm4','patternReliability_voxel.mat'))
data(:,sess)=nanmean(CORR,2);
clear CORR
end
% get average across task sets
data_average=nanmean(data,2);
data_average=data_average';
[~,~,~,idx]=sc1_sc2_functionalAtlas('PREDICTIONS:datasets','R','run');
V=spm_vol(fullfile(studyDir{1},suitDir,'anatomicals','cerebellarGreySUIT.nii'));
C{1}.dim=V.dim;
C{1}.mat=V.mat;
Yy=zeros(size(data_average,1),V.dim(1)*V.dim(2)*V.dim(3));
% make vol
Yy(:,idx)=data_average;
% get avrg across subjs
indices=nanmean(Yy,1);
% map vol2surf
data=reshape(indices,[V.dim(1),V.dim(2),V.dim(3)]);
C{1}.dat=data;
M=caret_suit_map2surf(C,'space','SUIT','stats','nanmean'); % MK created caret_suit_map2surf to allow for output to be used as input to caret_save
% save out metric
caret_save(fullfile(studyDir{2},caretDir,'suit_flat','glm4','voxel_reliability.metric'),M);
case 'PREDICTIONS:taskModel' % DEPRECIATED. ACTIVITY:make_model needs to be modified for this function to work.
sn=varargin{1}; % returnSubjs
study=varargin{2}; % 1 or 2
partition=varargin{3}; % session or run
lambdas=[.01:.1:.5];
subjs=length(sn);
l=1;
% load X
[Xx,~,numConds]=sc1_sc2_functionalAtlas('ACTIVITY:make_model',study,'yes');
% loop over subjects
Ys=[];
for s=1:subjs,
encodeSubjDir = fullfile(studyDir{study},encodeDir,'glm4',subj_name{sn(s)}); % set directory
% load Y (per subj)
load(fullfile(encodeSubjDir,'Y_info_glm4_grey_nan.mat'));
Yp=getrow(Y,Y.cond~=0);
switch partition,
case 'run'
part=Yp.run;
block=run;
case 'session'
part=Yp.sess;
block=[1,2]; % session
end
% normalise (either by run or by session)
N = (numConds)*numel(run);
B = indicatorMatrix('identity',part);
R = eye(N)-B*pinv(B);
X = R*Xx; % Subtract block mean (from X)
X=bsxfun(@rdivide,X,sqrt(sum(X.*X)/(size(X,1)-numel(block))));
Yact = R*Yp.data; % Subtract block mean (from Y)
Yact=bsxfun(@rdivide,Yact,sqrt(sum(Yact.*Yact)/(size(Yact,1)-numel(block))));
% run encoding model (with ridge regress)
[M.R2_vox,M.R_vox]=encode_crossval(Yact,X,part,'ridgeFixed','lambda',lambdas(l));
fprintf('subj%d:model done ...\n',sn(s))
M.SN=sn(s);
M.idx=Yp.nonZeroInd(1,:);
Ys=addstruct(Ys,M);
clear Yp
end
% save out results
save(fullfile(studyDir{study},encodeDir,'glm4',sprintf('encode_taskModel_cerebellum_%s.mat',partition)),'Ys','-v7.3');
case 'PREDICTIONS:datasets' % get predictions across datasets
stat=varargin{1}; % 'R' or 'R2' ?
partition=varargin{2}; % cv across 'run' or 'session' ?
F=dload(fullfile(baseDir,'motorFeats.txt')); % load in motor features
YN=[];
for i=1:2,
numCond=length(F.condNum(F.studyNum==i));
load(fullfile(studyDir{i},encodeDir,'glm4',sprintf('encode_taskModel_%s.mat',partition)))
Ys.study=repmat(i,size(Ys.SN,1),1);
YN=addstruct(YN,Ys);
end
X=indicatorMatrix('identity_p',YN.SN);
% which stat: R or R2 ?
switch stat,
case 'R'
data=YN.R_vox;
case 'R2'
data=YN.R2_vox;
end
% get average of models across datasets
RVox=pinv(X)*data;
RVox_sc1=nanmean(nanmean(data(YN.study==1,:)));
RVox_sc2=nanmean(nanmean(data(YN.study==2,:)));
varargout={RVox,RVox_sc1,RVox_sc2,YN.idx(1,:)};
case 'PREDICTIONS:vol2surf' % visualise predictions for motor feats
stat=varargin{1}; % 'R' or 'R2' ?
partition=varargin{2}; % cv across 'run' or 'session' ?
[RVox,RVox_sc1,RVox_sc2,idx]=sc1_sc2_functionalAtlas('PREDICTIONS:datasets',stat,partition);
% what is within task set reliability ?
fprintf('set A reliability is %2.3f \n',RVox_sc1);
fprintf('set B reliability is %2.3f \n',RVox_sc2);
SN=length(returnSubjs);
V=spm_vol(fullfile(studyDir{1},suitDir,'anatomicals','cerebellarGreySUIT.nii'));
C{1}.dim=V.dim;
C{1}.mat=V.mat;
Yy=zeros(size(RVox,1),V.dim(1)*V.dim(2)*V.dim(3));
% make vol
Yy(:,idx)=RVox;
% get avrg across subjs
indices=nanmean(Yy,1);
% map vol2surf
data=reshape(indices,[V.dim(1),V.dim(2),V.dim(3)]);
C{1}.dat=data;
M=caret_suit_map2surf(C,'space','SUIT','stats','nanmean'); % MK created caret_suit_map2surf to allow for output to be used as input to caret_save
% save out metric
caret_save(fullfile(studyDir{2},caretDir,'suit_flat','glm4','taskModel.metric'),M);
case 'RELIABILITY:get_spatialFreq'
study=varargin{1};
frequencyBands = [0 0.5 1 1.5 2 inf];
load(fullfile(studyDir{study},'encoding','glm4','cereb_avrgDataStruct.mat'));
RR=[];
sn=unique(T.SN);
for s = 1:length(sn)
fprintf('subject %d\n',sn(s));
for se=1:2
S=getrow(T,T.SN == sn(s) & T.sess==se);
for c=1:max(T.cond)
X=zeros(V.dim);
X(volIndx)=S.data(c,:);
X(isnan(X))=0;
% Y=mva_frequency3D(X,frequencyBands,'Voxelsize',[2 2 2],'plotSlice',15);
Y=mva_frequency3D(X,frequencyBands,'Voxelsize',[2 2 2]);
R=getrow(S,c);
for f=1:size(Y,4);
YY=Y(:,:,:,f);
R.data=YY(volIndx);
R.freq = f;
R.freqLow = frequencyBands(f);
RR=addstruct(RR,R);
end;
end;
end;
end;
save(fullfile(studyDir{study},'encoding','glm4','cereb_avrgDataStruct_freq.mat'),'-struct','RR');
case 'RELIABILITY:spatialFreqCorr'
study = [1 2]; % %experiment
glm = 'glm4';
vararginoptions(varargin,{'study','glm'});
C=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
A=[];
for e=study
T=load(fullfile(studyDir{e},'encoding',glm,'cereb_avrgDataStruct_freq.mat'));
D=load(fullfile(studyDir{e},'encoding',glm,'cereb_avrgDataStruct.mat'));
D.T.freq = ones(length(D.T.SN),1)*0;
D.T.freqLow = -1*ones(length(D.T.SN),1);
T=addstruct(T,D.T);
T.study = ones(length(T.SN),1)*e;
% Recenter Data and combine
commonCond = C.condNum(C.StudyNum==e & C.overlap==1);
for sn = unique(T.SN)'
for se = unique(T.sess)'
for f = unique(T.freq)'
i = find(T.SN==sn & T.sess==se & T.freq==f);
j = find(T.SN==sn & T.sess==se & T.freq==f & ismember(T.cond,commonCond));
T.data(i,:)=bsxfun(@minus,T.data(i,:),nanmean(T.data(j,:)));
end;
end;
end;
A=addstruct(A,T);
end;
% before - code below was computing corr on study 2 only (structure
% was T instead of A)
D=[];
sn=unique(A.SN);
numSubj = length(sn);
SS=[];
for st=1:2, % loop over studies
RR=[];
for f=unique(A.freq)', % loop over frequencies
for s = 1:numSubj % loop over subjects
for se=1:2 % loop over sessions
temp = A.data(A.study==st & A.SN==sn(s) & A.sess==se & A.freq==f,:);
% temp = bsxfun(@minus,temp,mean(temp));
D(:,:,s+(se-1)*length(sn))=temp;
end;
end;
C=intersubj_corr(D);
R.sess = [ones(1,numSubj) ones(1,numSubj)*2]';
R.subj = [1:numSubj 1:numSubj]';
R.subj = sn(R.subj);
R.freq = f*ones(numSubj*2,1);
R.study= st*ones(numSubj*2,1);
SameSess = bsxfun(@eq,R.sess',R.sess);
SameSubj = bsxfun(@eq,R.subj',R.subj);
for i=1:numSubj*2;
R.withinSubj(i,:)=C(i,SameSubj(i,:) & ~SameSess(i,:));
R.betweenSubj(i,:)=mean(C(i,~SameSubj(i,:)));
R.totSS(i,1) = nansum(nansum(D(:,:,i).^2));
end;
RR=addstruct(RR,R);
end;
clear temp D R
SS=addstruct(SS,RR);
end
save(fullfile(studyDir{2},'encoding','glm4','cereb_spatialCorr_freq.mat'),'-struct','SS');
varargout={SS};
case 'PLOT:spatialFreqCorr'
CAT=varargin{1};
% load in spatialCorrFreq struct
T=load(fullfile(studyDir{2},'encoding','glm4','cereb_spatialCorr_freq.mat'));
xlabels={'overall','0-0.5','0.5-1','1-1.5','1.5-2','>2'};
T=tapply(T,{'subj','freq'},{'withinSubj'},{'betweenSubj'},{'totSS'},'subset',ismember(T.subj,returnSubjs));
T.freqK = T.freq>0;
[ss,sn]=pivottable(T.subj,[],T.totSS,'mean','subset',T.freq==0);
a(sn,1)=ss;
T.relSS=T.totSS./a(T.subj);
lineplot([T.freqK T.freq],[T.relSS],'CAT',CAT);
set(gca,'XTickLabel',xlabels,'YLim',[0 0.35]);
ylabel('Relative Power');
xlabel('Cycles/cm');
title('Relative amount of Variance per Frequency band');
case 'PLOT:interSubjCorr'
CAT=varargin{1};
% load in spatialCorrFreq struct
T=load(fullfile(studyDir{2},'encoding','glm4','cereb_spatialCorr_freq.mat'));
T=tapply(T,{'subj','freq'},{'withinSubj'},{'betweenSubj'},{'totSS'},'subset',ismember(T.subj,returnSubjs));
T.freqK = T.freq>0;
lineplot([T.freqK T.freq],[T.withinSubj T.betweenSubj],'CAT',CAT,'leg','auto');
case 'REPRESENTATION:get_distances'
type=varargin{1}; % 'cerebellum'
removeMotor=varargin{2}; % 'hands','saccades','all','none'
taskType=varargin{3}; % 'unique' or 'all' task conditions ?
load(fullfile(studyDir{2},regDir,'glm4',sprintf('G_hat_sc1_sc2_%s.mat',type)))
subjs=size(G_hat,3);
% load in condName info
T=dload(fullfile(baseDir,'sc1_sc2_taskConds.txt'));
% load feature matrix
F=dload(fullfile(baseDir,'motorFeats.txt')); % load in motor features
% get unique tasks
switch taskType,
case 'unique'
X1=indicatorMatrix('identity_p',T.condNumUni);
for s=1:subjs,
G(:,:,s)=X1'*(G_hat(:,:,s)*X1);
end
condNames=[T.condNames(T.StudyNum==1);T.condNames(T.StudyNum==2 & T.overlap==0)];
case 'all'
G=G_hat;
condNames=T.condNames;
end
numDist=size(G,1);
switch removeMotor,
case 'all'
X = [F.lHand./F.duration F.rHand./F.duration F.saccades./F.duration];
case 'none'
X = [];
end
% get unique taskConds
if strcmp(taskType,'unique'),
X = pivottablerow(T.condNumUni,X,'mean(x,1)');
end
X = [X eye(numDist)];
X = bsxfun(@minus,X,mean(X));
X = bsxfun(@rdivide,X,sqrt(sum(X.^2))); % Normalize to unit length vectors
% Get RDM
for s=1:subjs,
H=eye(numDist)-ones(numDist)/numDist; % centering matrix
G(:,:,s)=H*G(:,:,s)*H'; % subtract out mean pattern
IPM=rsa_vectorizeIPM(G(:,:,s));
con = indicatorMatrix('allpairs',[1:numDist]);
N = rsa_squareIPM(IPM);
D = rsa.rdm.squareRDM(diag(con*N*con'));
fullRDM(:,:,s) = D;
end
varargout={fullRDM,condNames,X,taskType};
case 'REPRESENTATION:reliability'
glm=varargin{1};
type=varargin{2}; % 'cerebellum'
% example 'sc1_sc2_imana('CHECK:DIST',4,'cerebellum')
load(fullfile(regDir,sprintf('glm%d',glm),sprintf('G_hat_sc1_sc2_sess_%s.mat',type)));
D=dload('sc1_sc2_taskConds.txt');
D1=getrow(D,D.StudyNum==1);
D2=getrow(D,D.StudyNum==2);
% Look at the shared conditions only
i1 = find(D1.overlap==1);
i2 = find(D2.overlap==1);
[~,b] = sort(D2.condNumUni(i2)); % Bring the indices for sc2 into the right order.
i2=i2(b);
numCond = length(i1);
numSubj = size(G_hat_sc1,4);
numSess = 2;
C=indicatorMatrix('allpairs',[1:numCond]);
for i=1:numSubj