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conn_displayroi.m
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function hfig=conn_displayroi(option,varargin)
% internal function ROI-to-ROI results display
%
global CONN_x CONN_gui;
if ~nargin, option='init'; end
hfig=[];
if ~ischar(option), % gui-callback
if ishandle(option)&&isequal(get(option,'type'),'figure'), hfig=option;
else hfig=gcbf;
end
if isempty(hfig), hfig=gcf; end
option=varargin{2};
varargin=varargin(3:end);
margin=nargin-2;
else
margin=nargin;
if isempty(regexpi(option,'^init')), hfig=gcf; end
end
init=false;
initxy=false;
DOSCALEF=true;
switch(lower(option)),
case {'init','initfile','initspm'}
init=true;
ncon=1;
if isempty(CONN_gui)||~isfield(CONN_gui,'font_offset'), conn_font_init; end
if margin>1,
data.source=0; %if numel(varargin)>=2, data.source=varargin{2}; else data.source=[]; end
data.thres=1;
data.side=3;
data.initfile='';
if strcmpi(option,'initfile')
if conn_fileutils('isdir',varargin{1})
if conn_existfile(fullfile(varargin{1},'ROI.mat')), varargin{1}=fullfile(varargin{1},'ROI.mat');
elseif conn_existfile(fullfile(varargin{1},'SPM.mat')), varargin{1}=fullfile(varargin{1},'SPM.mat');
else error('unable to find ROI.mat or SPM.mat file in results directory %s',varargin{1});
end
end
[fpath,fname,fext]=fileparts(varargin{1});
if strcmp([fname,fext],'SPM.mat'), option='initspm'; end
end
%h=conn_msgbox('computing ROI-level results, please wait...','conn_displayroi');
if strcmpi(option,'initfile'),
data.initfile=varargin{1};
results=conn_loadmatfile(varargin{1});
results=results.ROI;
filepath=fileparts(varargin{1});
if isempty(data.source), data.source=0; end
if numel(varargin)>=2, ncon=varargin{2}; end
if numel(varargin)>=3, data.thres=varargin{3}; end
if numel(varargin)>=4, data.side=varargin{4}; end
elseif strcmpi(option,'initspm'),
data.initfile=varargin{1};
filepath=fileparts(varargin{1});
if isempty(filepath), filepath=pwd; end
if isempty(data.source), data.source=0; end
SPM=struct; conn_loadmatfile(varargin{1},'SPM');
SPM_h=permute(SPM.xX_multivariate.h,[3,4,1,2]);
if size(SPM_h,3)>1||size(SPM_h,4)>1, SPM_h=sqrt(sum(abs(SPM_h(:,:,:)).^2,3)); end
SPM_F=permute(SPM.xX_multivariate.F,[3,4,1,2]);
if isfield(SPM.xX_multivariate,'p'), SPM_p=SPM.xX_multivariate.p;
else
SPM_p=nan(size(SPM_F));
idxvalid=~(isnan(SPM_F)|SPM_F==0);
switch(SPM.xX_multivariate.statsname)
case 'T', SPM_p(idxvalid)=spm_Tcdf(-SPM_F(idxvalid),SPM.xX_multivariate.dof);
case 'F', SPM_p(idxvalid)=1-spm_Fcdf(SPM_F(idxvalid),SPM.xX_multivariate.dof(1),SPM.xX_multivariate.dof(2));
case 'X', SPM_p(idxvalid)=1-spm_Xcdf(SPM_F(idxvalid),SPM.xX_multivariate.dof);
end
end
SPM_F(SPM_F==0)=nan;
nrois=size(SPM_F,1);
[fpath,fname,fext]=fileparts(SPM.xY.VY(1).fname);
if isempty(fpath), fpath=filepath; end
info=[];
try, info=conn_jsonread(fullfile(fpath,[fname,fext])); end
if isempty(info)
info.names=arrayfun(@(n)sprintf('ROI#%04d',n),1:nrois,'uni',0);
info.coords=repmat({[0 0 0]},nrois,1);
end
if ~iscell(info.coords), info.coords=num2cell(info.coords,2); end % [nroisx3]
%z=permute(randn([nrois,nrois,size(SPM.xX_multivariate.Zfiles)]),[3,2,4,1]);
vol=conn_fileutils('spm_vol',char(SPM.xX_multivariate.Zfiles));
z=conn_fileutils('spm_read_vols',vol);
z=permute(reshape(z,[nrois,nrois,size(SPM.xX_multivariate.Zfiles)]),[3,2,4,1]); % subjects x rois (targets) x conditions x rois (seeds)
% subjects x rois x conditions
validrois=any(all(all(~isnan(z),1),3),4); % note: eliminates ROIs that have no valid connectivity data with any other ROI
if ~all(validrois)
fprintf('warning: disregarding the following ROIs from this group-level analysis due to missing-data %s\n',sprintf('%s ',info.names{~validrois}));
z=z(:,validrois,:,validrois);
SPM_h=SPM_h(validrois,validrois,:,:);
SPM_F=SPM_F(validrois,validrois,:,:);
SPM_p=SPM_p(validrois,validrois,:,:);
info.names=info.names(validrois);
info.coords=info.coords(validrois);
end
results=struct(...
'xX', struct('isSurface',SPM.xX.isSurface,'isMtx',SPM.xX.isMtx,'SelectedSubjects',SPM.xX.SelectedSubjects,'name',{SPM.xX_multivariate.Xnames},'X',SPM.xX_multivariate.X), ...
'data',z,...
'h', SPM_h,...
'F', SPM_F,...
'p', SPM_p,...
'dof', SPM.xX_multivariate.dof,...
'statsname',SPM.xX_multivariate.statsname,...
'c', SPM.xX_multivariate.C,...
'c2', SPM.xX_multivariate.M,...
'ynames',{SPM.xX_multivariate.Ynames}, ...
'names',{info.names},...
'names2',{info.names},...
'xyz', {info.coords},...
'xyz2',{info.coords} ...
);
if numel(varargin)>=2, ncon=varargin{2}; end
if numel(varargin)>=3, data.thres=varargin{3}; end
else
[results,filepath]=conn_process(varargin{:});
end
if isempty(filepath), data.defaultfilepath=pwd;
else data.defaultfilepath=filepath;
end
if ~isempty(data.initfile)&&isempty(fileparts(data.initfile)), data.initfile=fullfile(data.defaultfilepath,data.initfile); end
%close(h);
else
data.thres=1;
data.side=3;
[filename,filepath]=conn_fileutils('uigetfile','*ROI*.mat');
if ~ischar(filename), return; end
results=conn_loadmatfile(fullfile(filepath,filename));results=results.ROI;
data.initfile=fullfile(filepath,filename);
data.source=0;
if isempty(filepath), data.defaultfilepath=pwd;
else data.defaultfilepath=filepath;
end
end
hmsginit=conn_msgbox('Initializing. Please wait...','conn_displayroi',-1);
data.roifile=fullfile(data.defaultfilepath,'ROI.mat');
if isempty(data.initfile), data.initfile=data.roifile; end
if ~isfield(results(1),'data')||numel(results)>1
if 1 % note: disregard any pre-computed mvpa stats for consistency
data.MVPAh=[];
data.MVPAF=[];
data.MVPAp=[];
data.MVPAdof=[];
data.MVPAstatsname=[];
else
data.MVPAh=cat(1,results.MVPAh);
data.MVPAF=cat(1,results.MVPAF);
data.MVPAp=cat(1,results.MVPAp);
temp={results.MVPAdof};
if any(cellfun('length',temp)>1), temp=cellfun(@(x)[ones(1,max(0,2-length(x))),x(:)'],temp,'uni',0); end
data.MVPAdof=cell2mat(temp(:));
data.MVPAstatsname=results(1).MVPAstatsname;
end
data.h=cat(1,results.h);
data.F=cat(1,results.F);
data.p=cat(1,results.p);
data.dof=cat(1,results.dof);
data.statsname=results(1).statsname;
temp=cat(4,results.y); % subjects x rois (targets) x nconditions x rois (seeds)
data.results=results(1);
data.results.data=temp;
clear temp;
else
data.MVPAh=[];
data.MVPAF=[];
data.MVPAp=[];
data.MVPAdof=[];
data.MVPAstatsname=[];
data.h=results.h;
data.F=results.F;
data.p=results.p;
data.dof=repmat(results.dof,numel(data.F),1);
data.statsname=results.statsname;
data.results=results;
end
if isempty(data.MVPAdof), data.MVPAdofstr={};
elseif ~any(any(diff(data.MVPAdof,1,1),1),2),
if size(data.MVPAdof,2)==1, data.MVPAdofstr=repmat({['(',num2str(data.MVPAdof(1)),')']},numel(data.MVPAdof),1);
else data.MVPAdofstr=repmat({['(',num2str(data.MVPAdof(1,1)),',',num2str(data.MVPAdof(1,2)),')']},size(data.MVPAdof,1),1);
end
else
if size(data.MVPAdof,2)==1, data.MVPAdofstr=arrayfun(@(dof)['(',num2str(dof(1)),')'],data.MVPAdof,'uni',0);
else data.MVPAdofstr=cellfun(@(dof)['(',num2str(dof(1)),',',num2str(dof(2)),')'],num2cell(data.MVPAdof,2),'uni',0);
end
end
if size(data.F,2)>=size(data.F,1)&&max(max(abs(data.F(:,1:size(data.F,1))-data.F(:,1:size(data.F,1))')))<1e-10, data.issymmetric=true; else data.issymmetric=false; end
if strcmp(data.statsname,'T')&&all(data.dof(:)==data.dof(1)), data.p2=nan(size(data.p));data.p2(~isnan(data.F))=spm_Tcdf(data.F(~isnan(data.F)),data.dof(1));
else data.p2=1-data.p;
end
%if size(data.dof,2)>1&&~any(diff(data.dof(:,1))), data.statsname=[data.statsname,'(',num2str(data.dof(1)),')']; end
if ~any(any(diff(data.dof,1,1),1),2),
if size(data.dof,2)==1, data.dofstr=repmat({['(',num2str(data.dof(1)),')']},numel(data.dof),1);
else data.dofstr=repmat({['(',num2str(data.dof(1,1)),',',num2str(data.dof(1,2)),')']},size(data.dof,1),1);
end
else
if size(data.dof,2)==1, data.dofstr=arrayfun(@(dof)['(',num2str(dof(1)),')'],data.dof,'uni',0);
else data.dofstr=cellfun(@(dof)['(',num2str(dof(1)),',',num2str(dof(2)),')'],num2cell(data.dof,2),'uni',0);
end
end
data.names=results(1).names;
data.names=regexprep(data.names,{'_1_1$','^rs\.','^rsREL\.','^aal\.'},'');
data.namesreduced=regexprep(data.names,{'^BA\.(\d+) \(([LR])\)\. .*','^\((-?\d+),(-?\d+),(-?\d+)\)$','^SLrois\.|^aal\.|^atlas\.|^networks\.','\s\(([LlRr])\)','([^\(\)]*[^\.])\s*\(.+\)\s*$'},{'$1$2','($1 $2 $3)','',' ${lower($1)}','$1'});
data.names2=results(1).names2;
data.names2=regexprep(data.names2,{'_1_1$','^rs\.','^rsREL\.','^aal\.'},'');
data.names2reduced=regexprep(data.names2,{'^BA\.(\d+) \(([LR])\)\. .*','^\((-?\d+),(-?\d+),(-?\d+)\)$','^SLrois\.|^aal\.|^atlas\.|^networks\.','\s\(([LlRr])\)','([^\(\)]*[^\.])\s*\(.+\)\s*$'},{'$1$2','($1 $2 $3)','',' ${lower($1)}','$1'});
data.xyz=cat(1,results(1).xyz{:});
data.xyz2=cat(1,results(1).xyz2{:});
data.displaytheserois=1:length(data.names);
data.tfceZ=[];
data.tfceZpeaks=[];
data.tfceZd=[];
data.cMVPAh=[];
data.cMVPAF=[];
data.cMVPAp=[];
data.thr=.01;
data.thrtype=1;
data.mvpathr=.05;
data.mvpathrtype=6;
data.thres_defaults={ {.05,1,.05,32}, {.01,1,.05,6}, {.05,4,0,1}, {.05,2,0,1}, {.01,1,.05,30}, {.001,1,.05,15} };
if ~isempty(data.thres),
if iscell(data.thres)
[data.thr,data.thrtype,data.mvpathr,data.mvpathrtype]=deal(data.thres{:});
data.thres=numel(data.thres_defaults)+1;
else
[data.thr,data.thrtype,data.mvpathr,data.mvpathrtype]=deal(data.thres_defaults{data.thres}{:});
data.side=3;
end
end
data.mvpasortresultsby=2;
data.mvpathrtype_all={'none',...
'cluster-level p-uncorrected (SPC TFCE score)', 'cluster-level p-FDR corrected (SPC TFCE score)', 'cluster-level p-FWE corrected (SPC TFCE score)',...
'cluster-level p-uncorrected (SPC mass/intensity)', 'cluster-level p-FDR corrected (SPC mass/intensity)', 'cluster-level p-FWE corrected (SPC mass/intensity)',...
'cluster-level p-uncorrected (SPC size)', 'cluster-level p-FDR corrected (SPC size)', 'cluster-level p-FWE corrected (SPC size)',...
'network-level p-uncorrected (NBS TFCE score)', 'network-level p-FDR corrected (NBS TFCE score)', 'network-level p-FWE corrected (NBS TFCE score)',...
'network-level p-uncorrected (NBS mass/intensity)', 'network-level p-FDR corrected (NBS mass/intensity)', 'network-level p-FWE corrected (NBS mass/intensity)',...
'network-level p-uncorrected (NBS size)', 'network-level p-FDR corrected (NBS size)', 'network-level p-FWE corrected (NBS size)',...
'ROI-level p-uncorrected (ROI TFCE score)', 'ROI-level p-FDR corrected (ROI TFCE score)', 'ROI-level p-FWE corrected (ROI TFCE score)',...
'ROI-level p-uncorrected (ROI mass/intensity)', 'ROI-level p-FDR corrected (ROI mass/intensity)', 'ROI-level p-FWE corrected (ROI mass/intensity)',...
'ROI-level p-uncorrected (ROI size)', 'ROI-level p-FDR corrected (ROI size)', 'ROI-level p-FWE corrected (ROI size)',...
'ROI-level p-uncorrected (multivariate omnibus test)', 'ROI-level p-FDR corrected (multivariate omnibus test)',...
'cluster-level p-uncorrected (multivariate omnibus test)', 'cluster-level p-FDR corrected (multivariate omnibus test)'};
data.mvpathrtype_isnonparam=ismember(1:numel(data.mvpathrtype_all),[2:28]);
data.mvpathrtype_iscluster=ismember(1:numel(data.mvpathrtype_all),[2:10,31:32]);
data.mvpathrtype_isnetwork=ismember(1:numel(data.mvpathrtype_all),11:19);
data.mvpathrtype_isroi=ismember(1:numel(data.mvpathrtype_all),[20:28,29:30]);
data.mvpathrtype_isce=ismember(1:numel(data.mvpathrtype_all),[2:4,11:13,20:22]);
data.mvpathrtype_ismass=ismember(1:numel(data.mvpathrtype_all),[5:7,14:16,23:25]);
data.mvpathrtype_issize=ismember(1:numel(data.mvpathrtype_all),[8:10,17:19,26:28]);
data.mvpathrtype_isftest=ismember(1:numel(data.mvpathrtype_all),[29:30,31:32]);
data.mvpathrtype_shown=[14:16,31:32,5:7,29:30,23:25,1]; %[15,24,6,1];
%data.mvpaside=3;
data.PERM=[];
data.iPERM=[];
data.clusters=[];
data.view=0;
data.proj=[];
data.x=[];
data.y=[];
data.z=[];
data.bgz=0;
data.maxz=[];
data.display='connectivity';
%data.displayreduced=0;
%data.displaytheserois=1:length(data.names2);
data.displayreduced=1;
data.displaylabels=1;
data.displaybrains=1;
data.display3d=0;
data.displaygui=1;
data.pausegui=1;
data.mvpaenablethr=1;
data.enablethr=1;
data.displayconnectionstats=0;
data.displayroilabelsinstats=0;
data.displayallmeasures=0;
data.visible='on';
data.plotposition={[.01,.27,.53,.60],[.01,.05,.88,.9]};
data.plotconnoptions.menubar=false;
data.plotconnoptions.LINEWIDTH=2;
data.plotconnoptions.LINESTYLEMTX=0;
data.plotconnoptions.DOFFSET=.35;
data.plotconnoptions.BSCALE=.25;
data.plotconnoptions.BTRANS=.10;
data.plotconnoptions.LTRANS=1;
data.plotconnoptions.RSCALE=.75;
data.plotconnoptions.LCOLOR=3;
data.plotconnoptions.LCOLORSCALE=1;
data.plotconnoptions.LCURVE=2;
data.plotconnoptions.LBUNDL=.5;
data.plotconnoptions.FONTSIZE=max(4,[0,1]+4+CONN_gui.font_offset);
data.plotconnoptions.FONTANGLE=0;
if 1, data.plotconnoptions.BCOLOR=.975*[1,1,1];
elseif isfield(CONN_gui,'backgroundcolor'), data.plotconnoptions.BCOLOR=CONN_gui.backgroundcolor;
else data.plotconnoptions.BCOLOR=[0.12 0.126 0.132];
end
data.plotconnoptions.NPLOTS=12;
data.plotconnoptions.Projections={[0,-1,0;0,0,1;-1,0,0],[1,0,0;0,0,1;0,1,0],[1,0,0;0,1,0;0,0,1]};
data.plotconnoptions.nprojection=1;
data.plotconnoptions.Projections_axes={{'y','z'},{'x','z'},{'x','y'}};
FSfolder=fullfile(fileparts(which('conn')),'utils','surf');
rend(1)=reducepatch(conn_surf_readsurf(fullfile(FSfolder,'lh.pial.surf')),.02,'fast');
rend(2)=reducepatch(conn_surf_readsurf(fullfile(FSfolder,'rh.pial.surf')),.02,'fast');
%[xyz,faces]=read_surf(fullfile(FSfolder,'lh.cortex.surf'));
%rend(1)=reducepatch(struct('vertices',xyz,'faces',faces+1),.02,'fast');
%[xyz,faces]=read_surf(fullfile(FSfolder,'rh.cortex.surf'));
%rend(2)=reducepatch(struct('vertices',xyz,'faces',faces+1),.02,'fast');
data.plotconnoptions.rende=struct('vertices',cat(1,rend.vertices),'faces',[rend(1).faces; size(rend(1).vertices,1)+rend(2).faces]);
data.xy2=200*[cos(2*pi*(0:numel(data.displaytheserois)-1)'/numel(data.displaytheserois)),sin(2*pi*(0:numel(data.displaytheserois)-1)'/numel(data.displaytheserois))];
data.xy2_clusters=[];
if isfield(CONN_gui,'refs')&&isfield(CONN_gui.refs,'canonical')&&isfield(CONN_gui.refs.canonical,'filename')&&~isempty(CONN_gui.refs.canonical.filename)
filename=CONN_gui.refs.canonical.filename;
else
filename=fullfile(fileparts(which('spm')),'canonical','avg152T1.nii');
end
data.ref=spm_vol(filename);
color1=data.plotconnoptions.BCOLOR;
color2=color1; %.975*[1 1 1];
color3=color2+(.5-color2)*.15; %.9*[1 1 1];
foregroundcolor=.5*.8+.2*(1-round(color1));
%if all(data.plotconnoptions.BCOLOR>.8), color2=data.plotconnoptions.BCOLOR;
%else color2=.94*[1,1,1];
%end
%color1=[.5/6,1/6,2/6];
%color2=[.5/6,1/6,2/6];
hmsg=[];%figure('units','norm','position',[.01,.1,.98,.8],'numbertitle','off','name','ROI second-level results. Initializing...','color',color1,'colormap',gray,'menubar','none','toolbar','none','interruptible','off');
h0=get(0,'screensize');
hfig=figure('visible','off','renderer','opengl','units','pixels','position',[h0(3)-.75*h0(3)+2,h0(4)-.9*h0(4)-48,.75*h0(3)-2*2,.9*h0(4)]);
%h0=get(0,'screensize'); h0=h0(1,3:4)-h0(1,1:2)+1; h0=h0/max(1,max(abs(h0))/2000);
%minheight=500;
%hfig=figure('visible','off','renderer','opengl','units','pixels','position',[0*72+1,h0(2)-max(minheight,.5*h0(1))-48,h0(1)-0*72-1,max(minheight,.5*h0(1))]);
data.hfig=hfig;
set(hfig,'units','norm','numbertitle','off','name',['ROI second-level results explorer ',data.defaultfilepath],'color',color1,'colormap',gray,'menubar','none','toolbar','none','interruptible','off','tag','conn_displayroi','keypressfcn',@conn_displayroi_keypress,'windowbuttondownfcn',@(varargin)conn_display_windowbuttonmotionfcn('down'),'windowbuttonupfcn',@(varargin)conn_display_windowbuttonmotionfcn('up'),'visible','on');
%uicontrol('style','frame','units','norm','position',[.0,.95,.5,.05],'backgroundcolor',color2,'foregroundcolor',color2);
hframe1=uicontrol('style','frame','units','norm','position',[0,0,1,.27],'backgroundcolor',color2,'foregroundcolor',color2,'parent',data.hfig);
hframe2=uicontrol('style','frame','units','norm','position',[0,.87,1,.13],'backgroundcolor',color3,'foregroundcolor',color3,'parent',data.hfig);
hframe3=0;%uicontrol('style','frame','units','norm','position',[.62,.35,.33,.45],'backgroundcolor',1*[1 1 1],'foregroundcolor',.85*[1,1,1],'parent',data.hfig);
huicontrol_cthr=uicontrol('style','popupmenu','units','norm','position',[.20,.965,.605,.03],'string',{...
'standard settings for cluster-based inferences #1: Functional Network Connectivity',...
'standard settings for cluster-based inferences #2: Spatial Pairwise Clustering',...
'standard settings for cluster-based inferences #3: Threshold Free Cluster Enhancement',...
'alternative settings for connection-based inferences: parametric univariate statistics ',...
'alternative settings for ROI-based inferences: parametric multivariate statistics',...
'alternative settings for network-based inferences: Network Based Statistics',...
'<HTML><i>customize (advanced Family-Wise Error control settings)</i></HTML>'},'tag','highlight','fontname','arial','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'fwec.option'},'value',data.thres,'tooltipstring','Select false-positive control method','backgroundcolor',.9*[1,1,1]);
huicontrol_cthr0=uicontrol('style','text','units','norm','position',[.03,.925,.17,.03],'fontsize',8+CONN_gui.font_offset,'string','connection threshold: p < ','horizontalalignment','right','fontweight','bold','foregroundcolor',1-color3,'backgroundcolor',color3,'interruptible','off','parent',data.hfig);
huicontrol_cthr1=uicontrol('style','edit','units','norm','position',[.20,.925,.10,.03],'fontsize',8+CONN_gui.font_offset,'string',num2str(data.thr),'foregroundcolor',1-color3,'backgroundcolor',color3,'interruptible','off','callback',{@conn_displayroi,'fwec.connectionlevel.value'},'tooltipstring','Connection-level threshold value (false-positive threshold value for individual connections)','parent',data.hfig);
huicontrol_cthr2=uicontrol('style','popupmenu','units','norm','position',[.325,.915,.25,.04],'fontsize',8+CONN_gui.font_offset,'string',{'p-uncorrected','p-FDR corrected','p-FDR corrected (TFCE)','p-FWE corrected (TFCE)','F/T/X stat'},'foregroundcolor',1-color3,'backgroundcolor',color3,'tooltipstring','<HTML>False-positive control type for individual connections</HTML>','interruptible','off','callback',{@conn_displayroi,'fwec.connectionlevel.type'},'value',max(1,min(5, data.thrtype)),'parent',data.hfig);
huicontrol_cthr3=uicontrol('style','popupmenu','units','norm','position',[.605,.915,.20,.04],'fontsize',8+CONN_gui.font_offset,'string',{'positive contrast (one-sided)','negative contrast (one-sided)','two-sided'},'foregroundcolor',1-color3,'backgroundcolor',color3,'tooltipstring','Analysis results directionality','interruptible','off','callback',{@conn_displayroi,'fwec.connectionlevel.side'},'value',data.side,'parent',data.hfig);
huicontrol_ccthr0=uicontrol('style','text','units','norm','position',[.03,.885,.17,.03],'fontsize',8+CONN_gui.font_offset,'string','cluster threshold: p < ','horizontalalignment','right','fontweight','bold','foregroundcolor',1-color3,'backgroundcolor',color3,'fontweight','bold','interruptible','off','parent',data.hfig);
huicontrol_ccthr1=uicontrol('style','edit','units','norm','position',[.20,.885,.10,.03],'fontsize',8+CONN_gui.font_offset,'string',num2str(data.mvpathr),'foregroundcolor',1-color3,'backgroundcolor',color3,'interruptible','off','callback',{@conn_displayroi,'fwec.clusterlevel.value'},'tooltipstring','<HTML>Cluster-level threshold value (false-positive threshold value for individual clusters/groups of connections)','parent',data.hfig);
huicontrol_ccthr2=uicontrol('style','popupmenu','units','norm','position',[.325,.875,.48,.04],'fontsize',8+CONN_gui.font_offset,'string',data.mvpathrtype_all(data.mvpathrtype_shown),'value',find(data.mvpathrtype_shown==data.mvpathrtype),'foregroundcolor',1-color3,'backgroundcolor',color3,'tooltipstring',...
['<HTML>Type of cluster- or ROI- level false-positive control',...
'<br/> <br/> - choose <i>network</i> measures for <b>non-parametric network-level inferences</b> (NBS: Network Based Statistics, Zalesky et al. 2010)<br/> Networks represent maximal subgraphs of suprathreshold-connected ROIs (groups of ROIs and suprathreshold effects/connections among them)<br/> Network size and Network mass measures both represent measures of degree/cost of these subgraphs (i.e. number and strength of suprathreshold effects/connections within each graph) <br/> Network TFCE scores represent a combined measure of network size and mass, defined as the Threshold Free Cluster Enhancement score for the chosen support section (Smith and Nichols 2009) <br/> Multiple comparison correction is implemented at the network-level (FWE/FDR across multiple networks). Network-level inferences remain valid when used in combination with arbitrary (e.g. p-uncorrected) connection thresholds<br/> e.g. <b>connection-level threshold p < 0.01 (p-uncorrected) & cluster-threshold p < 0.05 (network p-FDR corrected)</b>',...
'<br/> <br/> - choose <i>cluster</i> measures for <b>parametric cluster-level inferences</b> (FNC: Functional Network Connetivity, Jafri et al. 2008)<br/> Clusters represent groups of effects/connections within- or between- networks (networks here refers to groups of ROIs)<br/> multivariate omnibus-test represents a multivariate measure characterizing the strength of all effects/connections within a network or between two networks<br/> Multiple comparison correction is implemented at the cluster-level (FWE/FDR across multiple clusters). Cluster-level inferences remain valid when used in combination with arbitrary (e.g. p-uncorrected) connection thresholds<br/> e.g. <b>connection-level threshold p < 0.05 (p-uncorrected) & cluster-threshold p < 0.05 (cluster p-FDR corrected)</b>',...
'<br/> <br/> - choose <i>cluster</i> measures for <b>non-parametric cluster-level inferences</b> (SPC: Spatial Pairwise Clustering, Zalesky et al. 2012)<br/> Clusters represent groups of suprathreshold effects/connections<br/> Cluster size and cluster mass measures both represent measures of degree/cost of these clusters (i.e. number and strength of suprathreshold effects/connections within each cluster)<br/> Cluster TFCE scores represent a combined measure of cluster size and mass, defined as the Threshold Free Cluster Enhancement score for the chosen support section (Smith and Nichols 2009) <br/> Multiple comparison correction is implemented at the cluster-level (FWE/FDR across multiple clusters). Cluster-level inferences remain valid when used in combination with arbitrary (e.g. p-uncorrected) connection thresholds<br/> e.g. <b>connection-level threshold p < 0.01 (p-uncorrected) & cluster-threshold p < 0.05 (cluster p-FDR corrected)</b>',...
'<br/> <br/> - choose <i>ROI</i> for <b>parametric or non-parametric ROI-level inferences</b><br/> multivariate omnibus-test represents a multivariate measure characterizing the strength of all effects/connections from each ROI<br/> ROI size and ROI mass measures both represent measures of degree/cost of each ROI (i.e. number and strength of suprathreshold effects/connections from each ROI)<br/> ROI TFCE scores represent a combined measure of ROI size and mass, defined as the Threshold Free Cluster Enhancement score for the chosen support section (Smith and Nichols 2009) <br/> Multiple comparison correction is implemented at the ROI-level (FWE/FDR across multiple ROIs). ROI-level inferences are invariant to the choice of connection-level threshold<br/> e.g. <b>connection-level threshold p < 0.01 (p-uncorrected) & cluster-threshold p < 0.05 (ROI p-FDR corrected)</b>',...
'<br/> <br/> - choose <i>none</i> for <b>parametric connection-level inferences</b> <br/> This option is only appropriately corrected for multiple comparisons when used in combination with p-FDR corrected connection thresholds (but not with p-uncorrected connection thresholds)<br/> e.g. <b>connection-level threshold p < 0.05 (p-FDR corrected) & cluster-threshold none</b><br/></HTML>'],...
'interruptible','off','callback',{@conn_displayroi,'fwec.clusterlevel.type'},'value',max([1,find(data.mvpathrtype_shown==data.mvpathrtype,1)]),'parent',data.hfig);
huicontrol_cthr4=uicontrol('style','checkbox','units','norm','position',[.88,.87,.12,.03],'string','show details','tag','highlight','value',0,'fontsize',8+CONN_gui.font_offset,'horizontalalignment','right','foregroundcolor',1-color3,'backgroundcolor',color3,'callback',{@conn_displayroi,'advancedthr'},'tooltipstring','Displays advanced thresholding options');
hhelp=uicontrol('style','pushbutton','units','norm','position',[.81,.965,.02,.03],'fontsize',7+CONN_gui.font_offset,'foregroundcolor',foregroundcolor,'backgroundcolor',color2,'string','?','tag','highlight','tooltipstring','<HTML>Documentation about available methods of statistical inference</HTML>','interruptible','off','callback',@(varargin)conn('gui_help','url','http://www.conn-toolbox.org/fmri-methods/cluster-level-inferences'),'parent',data.hfig);
%huicontrol_ccthr3=uicontrol('style','popupmenu','units','norm','position',[.66,.35,.33,.04],'fontsize',8+CONN_gui.font_offset,'string',{'threshold seed ROIs (F-test)','threshold seed ROIs (NBS; by intensity)','threshold seed ROIs (NBS; by size)','threshold networks (NBS; by intensity)','threshold networks (NBS; by size)'},'foregroundcolor',1-color2,'backgroundcolor',color2,'fontweight','bold','horizontalalignment','right','value',data.mvpathrmeasure,'tooltipstring','Threshold individual seed ROIs or individual networks (subsets of connected ROIs)','interruptible','off','callback',{@conn_displayroi,'mvpathrmeasure'},'parent',data.hfig);
data.handles=[...
huicontrol_cthr,...
huicontrol_cthr1,...
huicontrol_cthr2,...
huicontrol_cthr3,...
uicontrol('style','text','units','norm','position',[.15,.875,.70,.07],'fontsize',7+CONN_gui.font_offset,'string','','foregroundcolor',.5*[1 1 1],'backgroundcolor',color3,'horizontalalignment','center','parent',data.hfig),...
huicontrol_cthr4, ... %0, ...%uicontrol('style','listbox','units','norm','position',[.57,.31,.41,.39],'fontsize',8+CONN_gui.font_offset,'string',' ','max',2,'foregroundcolor',.9-.8*color2,'backgroundcolor',color2,'tooltipstring','Selecting one or several seed ROIs limits the analyses only to the connectivity between the selected seeds and all of the ROIs in the network','interruptible','off','callback',{@conn_displayroi,'list1'},'keypressfcn',@conn_menu_search,'visible','off','parent',data.hfig),...
uicontrol('style','text','units','norm','position',[.05,.22,.90,.03],'fontsize',7+CONN_gui.font_offset,'string',sprintf('%-24s %-20s %+12s %+12s %+12s','Analysis Unit','Statistic','p-unc','p-FDR','p-FWE'),'foregroundcolor',.5*[1 1 1],'backgroundcolor',color2,'fontname','monospaced','horizontalalignment','left','parent',data.hfig),...
uicontrol('style','listbox','units','norm','position',[.05,.07,.90,.15],'fontsize',7+CONN_gui.font_offset,'string',' ','tag','highlight','fontname','monospaced','foregroundcolor',round(1-color2),'backgroundcolor',color2,'tooltipstring','Statistics for each connection, ROI, or cluster. Right-click to export table to .txt file','max',2,'interruptible','off','callback',{@conn_displayroi,'list2'},'keypressfcn',@conn_menu_search,'parent',data.hfig),...
uicontrol('style','checkbox','units','norm','position',[.05,.04,.20,.03],'fontsize',8+CONN_gui.font_offset,'string','display extended stats','tag','highlight','foregroundcolor',1-color2,'backgroundcolor',color2,'interruptible','off','callback',{@conn_displayroi,'displayconnectionstats'},'value',data.displayconnectionstats,'tooltipstring','Check to display additional and post-hoc statistics for all suprathreshold units','parent',data.hfig),... %uicontrol('style','text','units','norm','position',[.61,.50,.38,.04],'fontsize',8+CONN_gui.font_offset,'string','Define thresholds:','foregroundcolor',color2,'backgroundcolor',1-.5*color2,'fontweight','bold','horizontalalignment','left','parent',data.hfig),...
uicontrol('style','text','units','norm','position',[.64,.76,.14,.03],'string','Display&Print','horizontalalignment','center','fontweight','bold','fontname','arial','fontsize',9+CONN_gui.font_offset,'foregroundcolor',foregroundcolor,'backgroundcolor',color2,'parent',data.hfig),... %uicontrol('style','pushbutton','units','norm','position',[.84,.05,.14,.04],'fontsize',8+CONN_gui.font_offset,'string','non-parametric stats','callback',{@conn_displayroi,'enableperm'},'tooltipstring','Enables permutation-test based statistics (Cluster and ROI size/mass statistics)','parent',data.hfig),...
uicontrol('style','text','units','norm','position',[.80,.76,.14,.03],'string','Tools','horizontalalignment','center','fontweight','bold','fontname','arial','fontsize',9+CONN_gui.font_offset,'foregroundcolor',foregroundcolor,'backgroundcolor',color2,'parent',data.hfig),... %uicontrol('style','text','units','norm','position',[.61,.95,.38,.04],'fontsize',8+CONN_gui.font_offset,'string','Define connectivity matrix:','foregroundcolor',color2,'backgroundcolor',1-.5*color2,'fontweight','bold','horizontalalignment','left','parent',data.hfig),...
uicontrol('style','pushbutton','units','norm','position',[.64,.40,.29,.04],'fontsize',7+CONN_gui.font_offset,'foregroundcolor',foregroundcolor,'backgroundcolor',color2,'string',sprintf('Analysis of %d connections among %d ROIs',numel(data.names)*(numel(data.names)-1)/2*(1+~data.issymmetric),numel(data.names)),'tag','highlight','tooltipstring','<HTML>Defines subset of ROIs to include in these analyses (among all the sources selected in the first-level analysis definition)<br/>note: this choice affects all inferences</HTML>','interruptible','off','callback',{@conn_displayroi,'roi.select'},'parent',data.hfig),...
uicontrol('style','checkbox','units','norm','position',[.25,.04,.20,.03],'fontsize',8+CONN_gui.font_offset,'string','display extended roi labels','tag','highlight','foregroundcolor',1-color2,'backgroundcolor',color2,'interruptible','off','callback',{@conn_displayroi,'displayroilabelstats'},'value',data.displayroilabelsinstats,'visible','off','tooltipstring','Check to include complete labels when describing ROIs','parent',data.hfig),... %uicontrol('style','pushbutton','units','norm','position',[.61,.10,.38,.04],'fontsize',8+CONN_gui.font_offset,'string','Select all','tooltipstring','Looks at the connectivity between all ROIs in the network','interruptible','off','callback',{@conn_displayroi,'selectall'},'parent',data.hfig),...
uicontrol('style','pushbutton','units','norm','position',[.64,.36,.29,.04],'fontsize',7+CONN_gui.font_offset,'foregroundcolor',foregroundcolor,'backgroundcolor',color2,'string','ROIs sorted using hierarchical clustering','tag','highlight','tooltipstring','<HTML>Defines ROIs order and clusters<br/>note: this choice affects all cluster-based inferences (but not connection- ROI- or network-based inferences)</HTML>','interruptible','off','callback',{@conn_displayroi,'roi.order'},'parent',data.hfig),...
huicontrol_ccthr1,...
huicontrol_ccthr2,...
uicontrol('style','pushbutton','units','norm','position',[.80,.68,.13,.04],'fontsize',8+CONN_gui.font_offset,'string','Export mask','tag','highlight','foregroundcolor',foregroundcolor,'backgroundcolor',color2,'callback',{@conn_displayroi,'export_mask'},'tooltipstring','Exports list of suprathreshold connections in ROI-to-ROI connectivity matrix','parent',data.hfig),... %0,...%uicontrol('style','popupmenu','units','norm','position',[.68,.34,.15,.04],'fontsize',8+CONN_gui.font_offset,'string',{'connection-level results','seed-level results','network-level results'},'foregroundcolor',1-color2,'backgroundcolor',color2,'tooltipstring','Criteria for sorting results in statistics table','interruptible','off','callback',{@conn_displayroi,'mvpasort'},'value',data.mvpasortresultsby),...
uicontrol('style','pushbutton','units','norm','position',[.80,.72,.13,.04],'fontsize',8+CONN_gui.font_offset,'string','Import values','tag','highlight','foregroundcolor',foregroundcolor,'backgroundcolor',color2,'callback',{@conn_displayroi,'import_values'},'tooltipstring','Imports individual connectivity values for each suprathreshold connection and for each subject into CONN toolbox as second-level covariates','parent',data.hfig),... %uicontrol('style','checkbox','units','norm','position',[.61,.36,.03,.03],'fontsize',8+CONN_gui.font_offset,'foregroundcolor',1-color2,'backgroundcolor',color2,'interruptible','off','callback',{@conn_displayroi,'mvpaenablethr'},'value',data.mvpaenablethr,'tooltipstring','Enable Seed/Network threshold','parent',data.hfig)...
uicontrol('style','pushbutton','units','norm','position',[.80,.64,.13,.04],'fontsize',8+CONN_gui.font_offset,'string','Export data','tag','highlight','foregroundcolor',foregroundcolor,'backgroundcolor',color2,'callback',{@conn_displayroi,'export_data'},'tooltipstring','Exports all connectvity values (entire ROI-to-ROI matrix) for each individual subject and condition to matrix NIFTI file','parent',data.hfig),...
hframe1,...%uicontrol('style','pushbutton','units','norm','position',[0,0,.10,.025],'fontsize',8+CONN_gui.font_offset,'backgroundcolor',get(hfig,'color'),'string','display options','tooltipstring','Controls the way functional results are displayed (right-click on figure to get this same menu and remove this button)','callback','set(findobj(gcbf,''type'',''uicontextmenu'',''tag'',''conn_displayroi_plot''),''visible'',''on'')'),...
uicontrol('style','text','units','norm','position',[.05,0,.90,.03],'string','','foregroundcolor',1-color2,'backgroundcolor',color2,'parent',data.hfig),...
huicontrol_cthr0,...
huicontrol_ccthr0,...
hframe2,...
hframe3, ...
uicontrol('style','pushbutton','units','norm','position',[.71,.695,.06,.05],'string','Ring Print','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'ring_print'},'tooltipstring','<HTML><b>Ring print</b><br/>Prints current results on ROI-ring display</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.64,.530,.06,.05],'string','Glass display','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'glass_view'},'tooltipstring','<HTML><b>Glass display</b><br/>Displays current results on 3d glass-brain</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.71,.530,.06,.05],'string','Glass print','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'glass_print'},'tooltipstring','<HTML><b>Glass print</b><br/>Prints current results on 3d glass-brain</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.64,.475,.06,.05],'string','Plot effects','tag','highlight','fontname','arial','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'cluster_view'},'tooltipstring','<HTML><b>Plot effects</b><br/>Explores/displays average effect sizes within each suprathreshold cluster or connection</HTML>'),...
uicontrol('style','pushbutton','units','norm','position',[.71,.475,.06,.05],'string','Plot design','tag','highlight','fontname','arial','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'plot_design'},'tooltipstring','<HTML><b>Plot design</b><br/>Displays General Linear Model design matrix and additional details</HTML>'), ...
uicontrol('style','pushbutton','units','norm','position',[.80,.58,.13,.04],'string','Bookmark','tag','highlight','foregroundcolor',foregroundcolor,'backgroundcolor',color2,'fontname','arial','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'bookmark'},'tooltipstring','Bookmark this second-level results explorer view'),...
uicontrol('style','pushbutton','units','norm','position',[.80,.54,.13,.04],'string','Open folder','tag','highlight','foregroundcolor',foregroundcolor,'backgroundcolor',color2,'fontname','arial','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'openfolder'},'tooltipstring','Open folder containing current second-level results files'), ...
uicontrol('style','pushbutton','units','norm','position',[.80,.50,.13,.04],'string','Graphic options','tag','highlight','foregroundcolor',foregroundcolor,'backgroundcolor',color2,'fontname','arial','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'menubar'},'tooltipstring','Show/hide menubar for advanced display/graphic options'), ...
uicontrol('style','pushbutton','units','norm','position',[.64,.640,.06,.05],'string','Matrix display','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'matrix_view'},'tooltipstring','<HTML><b>Matrix display</b><br/>Displays current results on ROI-to-ROI matrix display</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.71,.640,.06,.05],'string','Matrix print','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'matrix_print'},'tooltipstring','<HTML><b>Matrix print</b><br/>Prints current results on ROI-to-ROI matrix display</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.64,.695,.06,.05],'string','Ring Display','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'ring_view'},'tooltipstring','<HTML><b>Ring display</b><br/>Displays current results on ROI-ring display</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.64,.585,.06,.05],'string','Lines display','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'lines_view'},'tooltipstring','<HTML><b>Connections display</b><br/>Displays current results on ROI-to-ROI connections display</HTML>','parent',data.hfig), ...
uicontrol('style','pushbutton','units','norm','position',[.71,.585,.06,.05],'string','Lines print','tag','highlight','fontname','arial','fontweight','bold','fontsize',8+CONN_gui.font_offset,'callback',{@conn_displayroi,'lines_print'},'tooltipstring','<HTML><b>Connections print</b><br/>Prints current results on ROI-to-ROI connections display</HTML>','parent',data.hfig) ...
];
uiwrap(hhelp);
uiwrap(data.handles([8, 12,14, 17,18,19,31,32,33]));
bp=[36 26 27 28 34 35 29 30 37 38 ];
bp_isprint=[0 1 0 1 0 1 0 0 0 1];
temp=imread(fullfile(fileparts(which(mfilename)),sprintf('conn_vproject_icon%02d.jpg',0))); temp=double(temp); printmask=round(temp/255);
for n1=1:numel(bp),
set(data.handles(bp(n1)),'units','pixel'); pt=get(data.handles(bp(n1)),'position'); set(data.handles(bp(n1)),'units','norm');
temp=imread(fullfile(fileparts(which(mfilename)),sprintf('conn_displayroi_icon%02d.jpg',n1))); temp=double(temp); temp=temp/255; temp=max(0,min(1,(temp).^.5)); ft=min(size(temp,1)/ceil(pt(4)),size(temp,2)/ceil(pt(3))); if any(n1==[1,2]), ft=0.95*ft; elseif any(n1==[9,10]), ft=.90*ft; else ft=.70*ft; end;
maxtemp=1;%mode(round(temp(:)*100))/100;
if maxtemp<.5, temp=1-temp; maxtemp=1-maxtemp; end
temp=max(0,min(.95, .75*temp+.25*temp/maxtemp.*repmat(shiftdim(color1,-1),[size(temp,1),size(temp,2),1,size(temp,4)]) ));
if bp_isprint(n1)
if ismember(n1,[2,4,6,10]), tempprintmask=printmask(ceil(size(printmask,1)/4)+(1:ceil(size(printmask,1)/2)),ceil(size(printmask,2)/4)+(1:ceil(size(printmask,2)/2))); else tempprintmask=printmask; end
temp=.75*mean(color1)+.25*temp;
if size(temp,1)>size(temp,2), temp=temp(1:size(temp,2),:,:); end
temp(:,ceil(size(temp,2)/2+(1:size(temp,1))-size(temp,1)/2),:)=max(0,temp(:,ceil(size(temp,2)/2+(1:size(temp,1))-size(temp,1)/2),:)+(1-2*mean(color1))*repmat(.5*tempprintmask(round(linspace(1,size(tempprintmask,1),size(temp,1))),round(linspace(1,size(tempprintmask,2),size(temp,1)))),[1,1,3]));
end
tempr1=round(1:ft/10:size(temp,1)); tempr1=tempr1(1:floor(numel(tempr1)/10)*10);
tempr2=round(1:ft/10:size(temp,2)); tempr2=tempr2(1:floor(numel(tempr2)/10)*10);
temp=permute(mean(mean(reshape(temp(tempr1,tempr2,:),[10,numel(tempr1)/10,10,numel(tempr2)/10,size(temp,3)]),1),3),[2,4,5,1,3]);
str=get(data.handles(bp(n1)),'string'); set(data.handles(bp(n1)),'cdata',temp,'string','');
end
if data.thrtype==5, set(data.handles(22),'string',['connection threshold: ',data.statsname,' > ']);
elseif data.mvpathr==1, set(data.handles(22),'string','connection threshold: ');
else set(data.handles(22),'string','connection threshold: p < ');
end
set(data.handles([2,3,4,15,16,22,23]),'visible','off');
hc1=uicontextmenu('parent',hfig);
uimenu(hc1,'Label','Export table','callback',@(varargin)conn_exportlist(data.handles(8),'',get(data.handles(7),'string')),'tag','donotdelete');
%hc2=uimenu(hc1,'Label','Sort rows by','tag','donotdelete');
%uimenu(hc2,'Label','Connections','callback',@(varargin)conn_displayroi('mvpasort',1),'tag','donotdelete');
%uimenu(hc2,'Label','Seeds','callback',@(varargin)conn_displayroi('mvpasort',2),'tag','donotdelete');
%uimenu(hc2,'Label','Clusters','callback',@(varargin)conn_displayroi('mvpasort',3),'tag','donotdelete');
set(data.handles(8),'uicontextmenu',hc1);
%set(data.handles(7),'string',sprintf('%-6s %-6s %6s %6s %4s %8s %8s','Seed','Target','beta',[data.MVPAstatsname,'/',data.statsname],'dof','p-unc','p-FDR'));
%uicontrol('style','text','units','norm','position',[.53,.50,.45,.04],'string','second-level analysis results','foregroundcolor','b','backgroundcolor',color2,'fontname','monospaced','fontweight','bold','horizontalalignment','center');
%uicontrol('style','text','units','norm','position',[.03,.01,.05,.04],'string','view: ','foregroundcolor',1-color1,'backgroundcolor',color1,'fontweight','bold','horizontalalignment','right');
%uicontrol('style','text','units','norm','position',[.35,.01,.02,.04],'string','q','fontname','symbol','horizontalalignment','right','foregroundcolor',1-color1,'backgroundcolor',color1,'fontweight','bold','horizontalalignment','right');
%uicontrol('style','text','units','norm','position',[.20,.01,.02,.04],'string','z','horizontalalignment','right','foregroundcolor',1-color1,'backgroundcolor',color1,'fontweight','bold','horizontalalignment','right');
rcircle=sign([sin(linspace(0,2*pi,64)'),cos(linspace(0,2*pi,64))'])*diag([5,5]);
data.plotaxes=[];%axes('units','norm','position',[.01,.08,.58,.84],'visible','off','parent',data.hfig);
data.legendaxes=axes('parent',data.hfig);set(data.legendaxes,'units','norm','position',[.90,.82,.09,.05],'xtick',[],'ytick',[],'box','on','xcolor',.5*[1,1,1],'ycolor',.5*[1,1,1]);axis(data.legendaxes,'equal');set(data.legendaxes,'xlim',[95-20,200]);axis(data.legendaxes,'off');
cmap=jet(256);%cmap=cmap(32:224,:)*.8;
%cmap=cmap.^repmat(.1+.9*abs(linspace(1,-1,size(cmap,1)))',1,size(cmap,2));
data.legend=[patch(100+rcircle(:,1),0+rcircle(:,2),'w','edgecolor','none','facecolor',[1,.5,.5],'parent',data.legendaxes),...
patch(150+rcircle(:,1),0+rcircle(:,2),'w','edgecolor','none','facecolor',[.5,.5,1],'parent',data.legendaxes),...
text(0,0,'','horizontalalignment','left','fontsize',6+CONN_gui.font_offset,'color',.5*[1,1,1],'parent',data.legendaxes),... % ROI-to-ROI effects:
text(100+10,0,'Positive','horizontalalignment','left','fontsize',6+CONN_gui.font_offset,'color',.5*[1,1,1],'parent',data.legendaxes),...
text(150+10,0,'Negative','horizontalalignment','left','fontsize',6+CONN_gui.font_offset,'color',.5*[1,1,1],'parent',data.legendaxes),...
text(100-5,0,'Negative','horizontalalignment','left','fontsize',6+CONN_gui.font_offset,'color',.5*[1,1,1],'horizontalalignment','right','parent',data.legendaxes),...
text(150+5,0,'Positive','horizontalalignment','left','fontsize',6+CONN_gui.font_offset,'color',.5*[1,1,1],'horizontalalignment','left','parent',data.legendaxes)];
for n=1:size(cmap,1), data.legend=[data.legend patch(100+(n+[0 0 1 1])*50/size(cmap,1),[-5,5,5,-5],'w','edgecolor','none','facecolor',cmap(n,:),'tag','conn_displayroi_plotlegendcont','parent',data.legendaxes)]; end
set(data.legend,'visible','off');
set(data.legend,'buttondownfcn',@(varargin)conn_displayroi(hfig,[],'display.colorbar.limit'));
set(hfig,'userdata',data);
conn_displayroi(hfig,[],'fwec.option',[],'immediatereturn');
data=get(hfig,'userdata');
if data.displayreduced
conn_displayroi(hfig,[],'displayreduced');
set(hfig,'visible','on');
if ishandle(hmsg), delete(hmsg); end
if ishandle(hmsginit), delete(hmsginit); end
return;
else
set(hfig,'visible','on');
if ishandle(hmsg), delete(hmsg); end
if ishandle(hmsginit), delete(hmsginit); end
end
case 'advancedthr'
data=get(hfig,'userdata');
if margin>1, advanced=varargin{1}; set(data.handles(6),'value',advanced);
else advanced=get(data.handles(6),'value');
end
if advanced
conn_displayroi(hfig,[],'fwec.option',numel(data.thres_defaults)+1,'immediatereturn');
else
ok=false;for method=1:numel(data.thres_defaults), if isequal({data.thr,data.thrtype,data.mvpathr,data.mvpathrtype},data.thres_defaults{method})&&data.side==3, ok=true; break; end; end;
if ~ok,
conn_displayroi(hfig,[],'fwec.option',numel(data.thres_defaults)+1,'immediatereturn');
else
conn_displayroi(hfig,[],'fwec.option',method,'immediatereturn');
data.mvpathrtype=method;
end
end
return
case 'fwec.option'
data=get(hfig,'userdata');
if margin>1&&~isempty(varargin{1}), value=varargin{1}; set(data.handles(1),'value',value);
else value=get(data.handles(1),'value');
end
data.thres=value;
advanced=get(data.handles(6),'value');
if value<=numel(data.thres_defaults)
[data.thr,data.thrtype,data.mvpathr,data.mvpathrtype]=deal(data.thres_defaults{data.thres}{:});
data.side=3;
set(data.handles(2),'string',num2str(data.thr));
set(data.handles(3),'value',data.thrtype);
set(data.handles(15),'string',num2str(data.mvpathr));
set(data.handles(16),'value',find(data.mvpathrtype_shown==data.mvpathrtype));
if data.thrtype==5, set(data.handles(22),'string',['connection threshold: ',data.statsname,' > ']);
elseif data.mvpathr==1, set(data.handles(22),'string','connection threshold: ');
else set(data.handles(22),'string','connection threshold: p < ');
end
switch(value)
case 1, tstr3='parametric multivariate statistics (cluster-level inferences, Functional Network Connectivity, Jafri et al., 2008)';
case 2, tstr3='non-parametric statistics (cluster-level inferences, Spatial Pairwise Clustering, Zalesky et al., 2012)';
case 3, tstr3='non-parametric statistics (cluster-level inferences, Threshold Free Cluster Enhancement, Smith and Nichols 2007)';
case 4, tstr3='parametric univariate statistics (connection-level inferences, FDR corrected, Benjamini & Hochberg, 1995)';
case 5, tstr3='parametric multivariate statistics (ROI-level inferences, FDR corrected, Benjamini & Hochberg, 1995)';
case 6, tstr3='non-parametric statistics (network-level inferences, Network Based Statistics, Zalesky et al., 2010)';
end
tstr1=cellstr(get(data.handles(3),'string'));
tstr2=cellstr(get(data.handles(16),'string'));
tstr4=sprintf('%s %s %s',get(data.handles(22),'string'),get(data.handles(2),'string'),tstr1{get(data.handles(3),'value')});
tstr5=sprintf('%s %s %s',get(data.handles(23),'string'),get(data.handles(15),'string'),tstr2{get(data.handles(16),'value')});
if value==3||value==4, set(data.handles(5),'string',{tstr3,sprintf('%s',tstr4)});
else set(data.handles(5),'string',{tstr3,sprintf('%s; %s',tstr5,tstr4)});
end
if advanced,
set(data.handles(5),'string','','visible','off');
set(data.handles([2,3,4,15,16,22,23]),'visible','on');
if data.thrtype==3||data.thrtype==4, set(data.handles([15,16,23]),'visible','off'); end
else
set(data.handles(5),'visible','on');
set(data.handles([2,3,4,15,16,22,23]),'visible','off');
end
else
set(data.handles(5),'string','','visible','off');
set(data.handles([2,3,4,15,16,22,23]),'visible','on');
if data.thrtype==3||data.thrtype==4, set(data.handles([15,16,23]),'visible','off'); end
set(hfig,'userdata',data); return;
end
if margin>2&&isequal(varargin{2},'immediatereturn'), set(hfig,'userdata',data); return; end
case 'fwec.connectionlevel.value',
data=get(hfig,'userdata');
if margin>1, value=varargin{1}; set(data.handles(2),'string',num2str(value))
else value=str2num(get(data.handles(2),'string'));
end
if ~isempty(value), data.thr=max(0,value); end
data.visible='on';
case 'fwec.connectionlevel.type',
data=get(hfig,'userdata');
if margin>1, value=varargin{1}; set(data.handles(3),'value',value);
else value=max(1,min(5,get(data.handles(3),'value')));
end
docont=true;
if data.thrtype==1&&value==5&&data.side==3, data.thr=spm_invTcdf(1-data.thr/2,data.dof(1)); set(data.handles(2),'string',num2str(data.thr)); docont=false;
elseif data.thrtype==1&&value==5, data.thr=spm_invTcdf(1-data.thr,data.dof(1)); set(data.handles(2),'string',num2str(data.thr)); docont=false;
elseif data.thrtype==5&&value==1&&data.side==3, data.thr=1-spm_Tcdf(data.thr,data.dof(1)); data.thr=2*min(data.thr,1-data.thr); set(data.handles(2),'string',num2str(data.thr)); docont=false;
elseif data.thrtype==5&&value==1, data.thr=1-spm_Tcdf(data.thr,data.dof(1)); set(data.handles(2),'string',num2str(data.thr)); docont=false;
elseif data.thrtype<3&&value==5&&data.thr<1, data.thr=3; set(data.handles(2),'string',num2str(data.thr));
elseif data.thrtype==5&&value==2, data.thr=.05; set(data.handles(2),'string',num2str(data.thr));
elseif data.thrtype==5&&value==1, data.thr=.001; set(data.handles(2),'string',num2str(data.thr));
end
data.thrtype=value;
if data.thrtype==5, set(data.handles(22),'string',['connection threshold: ',data.statsname,' > ']);
elseif data.mvpathr==1, set(data.handles(22),'string','connection threshold: ');
else set(data.handles(22),'string','connection threshold: p < ');
end
if data.thrtype==3||data.thrtype==4, set(data.handles([15,16,23]),'visible','off');
else set(data.handles([15,16,23]),'visible','on');
end
data.visible='on';
if ~docont, set(hfig,'userdata',data); return; end
case 'fwec.connectionlevel.side',
data=get(hfig,'userdata');
if margin>1, value=varargin{1}; set(data.handles(4),'value',value);
else value=get(data.handles(4),'value');
end
data.side=value;
%data.plotconnoptions.LCOLOR=1+(data.side<3);
data.visible='on';
case 'fwec.clusterlevel.value',
data=get(hfig,'userdata');
if margin>1, value=varargin{1}; set(data.handles(15),'string',num2str(value));
else
str=get(data.handles(15),'string');
value=str2num(str);
end
if ~isempty(value),
data.mvpathr=max(0,value);
end
data.visible='on';
case 'fwec.clusterlevel.type',
data=get(hfig,'userdata');
if margin>1, value=varargin{1}; set(data.handles(16),'value',value);
else value=get(data.handles(16),'value');
end
data.mvpathrtype=data.mvpathrtype_shown(value);
data.visible='on';
case 'plot_design'
data=get(hfig,'userdata');
Y=repmat({''},size(data.results(1).xX.X,1),size(data.results(1).c2,2));
idx=find(data.results(1).xX.SelectedSubjects);
if isfield(data.results(1),'ynames'), for n1=1:numel(idx), for n2=1:size(data.results(1).c2,2), Y{n1,n2}=sprintf('subject %d %s',idx(n1),data.results(1).ynames{n2}); end; end
else for n1=1:numel(idx), for n2=1:size(data.results(1).c2,2), Y{n1,n2}=sprintf('subject %d condition %d',idx(n1),n2); end; end
end
conn_displaydesign(data.results(1).xX.X,Y,data.results(1).c,data.results(1).c2,data.results(1).xX.name,true);
return
case {'import_values','cluster_view'}
data=get(hfig,'userdata');
filename={fullfile(data.defaultfilepath,'results.clusters.nii')};
tfilename=conn_displayroi_selectfiles(filename,data.initfile);
if isempty(tfilename), return;
elseif iscell(tfilename), CLUSTERSFROMFILE='';
else CLUSTERSFROMFILE=tfilename;
end
hmsginit=conn_msgbox('Loading data. Please wait...','conn_displayroi',-1);
selectedsubjects=data.results(1).xX.SelectedSubjects;
if isfield(data.results(1),'ynames'), names_conditions=data.results(1).ynames;
else names_conditions={};
end
y={};name={};
y2=[];name2={};
y3=[];name3={};
txt1='';
txt2='';
if ~isempty(CLUSTERSFROMFILE)
[Cdata,Cnames,Ccoords,Csamples] = conn_mtx_read(CLUSTERSFROMFILE);
[ok,Cidx]=ismember(Cnames,data.results(1).names2);
if ~all(ok), fprintf('warning: unable to find ROI %s. Skipping\n',sprintf('%s ',Cnames{~ok})); Cdata(~ok,~ok)=0; end
for value=1:max(Cdata(:))
[isource,itarget]=find(Cdata==value);
if isempty(y3), y2(end+1)=0; else y2(end+1)=size(y3,3); end
if numel(Csamples)>=value, name2{end+1}=Csamples{value}; else name2{end+1}=sprintf('cluster %d',value); end
for ni=1:numel(isource)
ty=permute(data.results(1).data(:,Cidx(itarget(ni)),:,Cidx(isource(ni))),[1,3,2]); % (seed) [subjects x targets x conditions]
if ~isempty(selectedsubjects)&&~rem(size(ty,1),nnz(selectedsubjects)) % fill-in with NaN for missing data
tty=nan(size(ty,1)/nnz(selectedsubjects)*numel(selectedsubjects),size(ty,2));
tty(repmat(logical(selectedsubjects),size(ty,1)/nnz(selectedsubjects),1),:)=ty;
ty=tty;
if isempty(names_conditions), names_conditions=arrayfun(@(n)sprintf('condition %d',n),1:size(ty,2),'uni',0); end
y3=cat(3,y3,ty);
name3{end+1}=sprintf('%s connection between %s and %s',name2{end},Cnames{isource(ni)},Cnames{itarget(ni)});
for n=1:size(ty,2)
y{end+1}=ty(:,n);
name{end+1}=sprintf('%s at %s',name3{end},names_conditions{n});
end
end
end
end
else
for value=1:size(data.list2,1)
isource=[];
itarget=[];
if data.list2(value,2)>0, % connection
txt2=sprintf('connectivity between %s and %s',data.names2{data.list2(value,1)},data.names2{data.list2(value,2)});
isource=data.list2(value,1);
itarget=data.list2(value,2);
elseif data.list2(value,1)>0, % seed
txt1=data.list2txt{value};
if isempty(y3), y2(end+1)=0; else y2(end+1)=size(y3,3); end
name2{end+1}=regexp(txt1,'^(Cluster|Network|ROI) \d+\/\d+','match','once');
else % cluster
txt1=data.list2txt{value};
if isempty(y3), y2(end+1)=0; else y2(end+1)=size(y3,3); end
name2{end+1}=regexp(txt1,'^(Cluster|Network|ROI) \d+\/\d+','match','once');
end
if ~isempty(isource)
ty=permute(data.results(1).data(:,itarget,:,isource),[1,3,2]); % (seed) [subjects x targets x conditions]
if ~isempty(selectedsubjects)&&~rem(size(ty,1),nnz(selectedsubjects)) % fill-in with NaN for missing data
tty=nan(size(ty,1)/nnz(selectedsubjects)*numel(selectedsubjects),size(ty,2));
tty(repmat(logical(selectedsubjects),size(ty,1)/nnz(selectedsubjects),1),:)=ty;
ty=tty;
if isempty(names_conditions), names_conditions=arrayfun(@(n)sprintf('condition %d',n),1:size(ty,2),'uni',0); end
y3=cat(3,y3,ty);
name3{end+1}=sprintf('%s %s',regexp(txt1,'^(Cluster|Network|ROI) \d+\/\d+','match','once'),regexprep(txt2,'\s+',' '));
for n=1:size(ty,2)
y{end+1}=ty(:,n);
name{end+1}=sprintf('%s %s at %s',regexp(txt1,'^(Cluster|Network|ROI) \d+\/\d+','match','once'),regexprep(txt2,'\s+',' '),names_conditions{n});
% name{end+1}=regexprep(sprintf('conn between %s and %s at %s',...
% data.names2reduced{isource},...
% data.names2reduced{itarget},...
% names_conditions{n}),'\s+',' ');
end
end
end
end
end
if isempty(y3)
elseif strcmpi(option,'import_values')
if ~isfield(CONN_x,'filename')||isempty(CONN_x.filename)||~isfield(CONN_x,'Setup')||~isfield(CONN_x.Setup,'nsubjects')||isempty(CONN_x.Setup.nsubjects)||any(rem(cellfun(@numel,y),CONN_x.Setup.nsubjects)), % no conn project loaded
[tfilename,tfilepath]=uiputfile('*.mat','Save data as',fileparts(tfilename));
if ischar(tfilename), conn_savematfile(fullfile(tfilepath,tfilename),'name','y'); fprintf('data saved to file %s\n',fullfile(tfilepath,tfilename)); end
elseif get(data.handles(9),'value')||isempty(y2), % one variable per connection
conn_importl2covariate(name,y);
else % one variable per cluster
breaks=[y2,size(y3,3)];
y2=zeros([size(y3,1),size(y3,2),numel(breaks)-1]);
for n=1:numel(breaks)-1,
y2(:,:,n)=mean(y3(:,:,breaks(n)+1:breaks(n+1)),3);
end
y3={};name3={};
for n=1:size(y2,2),
y3=[y3,num2cell(permute(y2(:,n,:),[1,3,2]),1)];
name3=[name3, cellfun(@(s)sprintf('%s at %s',s,names_conditions{n}),name2,'uni',0)];
end
conn_importl2covariate(name3,y3);
end
else
if get(data.handles(9),'value')||isempty(y2), % one plot per connection
conn_rex('test',data.results(1).xX,reshape(y3(selectedsubjects,:,:),[nnz(selectedsubjects)*size(y3,2),size(y3,3)]),data.results(1).c,names_conditions,name3,[],[],true,data.results(1).c2,[],true);
else % one plot per cluster
breaks=[y2,size(y3,3)];
y2=zeros([size(y3,1),size(y3,2),numel(breaks)-1]);
for n=1:numel(breaks)-1,
y2(:,:,n)=mean(y3(:,:,breaks(n)+1:breaks(n+1)),3);
end
conn_rex('test',data.results(1).xX,reshape(y2(selectedsubjects,:,:),[nnz(selectedsubjects)*size(y2,2),size(y2,3)]),data.results(1).c,names_conditions,name2,[],[],true,data.results(1).c2,[],true);
end
end
if ishandle(hmsginit), delete(hmsginit); end
return
case 'export_data'
data=get(hfig,'userdata');
if margin>1, tfilename=varargin{1};
else
[tfilename,tpathname]=uiputfile({'*.nii','NIFTI files (*.nii)'; '*', 'All Files (*)'},'Output data to file:',data.defaultfilepath);
if ischar(tfilename)&&~isempty(tfilename),
tfilename=fullfile(tpathname,tfilename);
else return;
end
end
if isfield(data.results,'data'), Y=permute(data.results(1).data,[1,3,4,2]);
else Y=permute(cat(4,data.results.y),[1,3,4,2]);
end
Y=Y(:,:,data.displaytheserois(data.displaytheserois<=size(Y,3)),data.displaytheserois(data.displaytheserois<=size(Y,3)));
Y=permute(Y,[3,4,1,2]);
ColumnNames=data.names2(data.displaytheserois);
ColumnGroups=data.clusters(data.displaytheserois);
SampleNames=repmat({''},size(Y,3),size(Y,4));
idx=find(data.results(1).xX.SelectedSubjects);
if isfield(data.results(1),'ynames'), for n1=1:numel(idx), for n2=1:size(Y,4), SampleNames{n1,n2}=sprintf('subject %d %s',idx(n1),data.results(1).ynames{n2}); end; end
else for n1=1:numel(idx), for n2=1:size(Y,4), SampleNames{n1,n2}=sprintf('subject %d measure %d',idx(n1),n2); end; end
end
conn_mtx_write(tfilename,Y(:,:,:),ColumnNames, data.xyz2(data.displaytheserois), SampleNames);
conn_disp('fprintf','Connectivity matrix data saved in %s\n',tfilename);
return
case {'export','export_mask'}
data=get(hfig,'userdata');
if margin>1, tfilename=varargin{1};
else
[tfilename,tpathname]=uiputfile({'*.nii','NIFI files (*.nii)'; '*.txt','text files (*.txt)'; '*.csv','CSV-files (*.csv)'; '*.mat','MAT-files (*.mat)'; '*', 'All Files (*)'},'Output mask to file:',fullfile(data.defaultfilepath,'results.nii'));
if ischar(tfilename)&&~isempty(tfilename),
tfilename=fullfile(tpathname,tfilename);
else return
end
end
if ~isempty(tfilename)
[nill,nill,tfileext]=fileparts(tfilename);
z=data.CM_z;
R=z(data.displaytheserois(data.displaytheserois<=size(z,1)),data.displaytheserois);
R(isnan(R))=0;
z=data.CM_z0;
R_unthresholded=z(data.displaytheserois(data.displaytheserois<=size(z,1)),data.displaytheserois);
R_unthresholded(isnan(R))=0;
ColumnNames=data.names2(data.displaytheserois);
ColumnGroups=data.clusters(data.displaytheserois);
R_clusters_unthresholded=data.CLUSTER_labels(data.displaytheserois(data.displaytheserois<=size(z,1)),data.displaytheserois);
R_clusters=R_clusters_unthresholded;R_clusters(R==0)=0;
%R_clusters=R_clusters_unthresholded;R_clusters(~ismember(R_clusters,unique(R_clusters(R~=0))))=0;
if ~isempty(data.CLUSTER_selected)
[ok,idx1]=ismember(R_clusters,data.CLUSTER_selected);
R_clusters(~ok)=0;
R_clusters(ok)=idx1(ok);
R_clusters_names=data.CLUSTER_selected_names;
else R_clusters_names={};
end
switch(tfileext)
case '.nii'
conn_mtx_write(tfilename,R,ColumnNames, data.xyz2(data.displaytheserois));
conn_mtx_write(conn_prepend('',tfilename,'.orig.nii'),R_unthresholded,ColumnNames,data.xyz2(data.displaytheserois));
conn_mtx_write(conn_prepend('',tfilename,'.mask.nii'),double(R~=0),ColumnNames,data.xyz2(data.displaytheserois));
conn_disp('fprintf','Thresholded connectivity matrix saved in %s\n',tfilename);
if ~isempty(R_clusters_names),
for n=1:numel(R_clusters_names)
idx=find(strncmp(data.list2txt(data.list2visible),R_clusters_names{n},numel(R_clusters_names{n})),1);
if ~isempty(idx),R_clusters_names{n}=data.list2txt{data.list2visible(idx)}; end
end
conn_mtx_write(conn_prepend('',tfilename,'.clusters.orig.nii'),R_clusters_unthresholded,ColumnNames,data.xyz2(data.displaytheserois),R_clusters_names);
conn_mtx_write(conn_prepend('',tfilename,'.clusters.nii'),R_clusters,ColumnNames,data.xyz2(data.displaytheserois),R_clusters_names);
conn_disp('fprintf','Suprathreshold connectivity clusters saved in %s\n',conn_prepend('',tfilename,'.clusters.nii'));
end
case '.txt'
%fh=fopen(tfilename,'wt');
fh={};
for nt=1:numel(ColumnNames), fh{end+1}=sprintf('%s\t',ColumnNames{nt}); end; fh{end+1}=sprintf('\n');
for nt=1:numel(ColumnNames), for nt2=1:numel(ColumnNames), fh{end+1}=sprintf('%f\t',R(nt,nt2)); end; fh{end+1}=sprintf('\n'); end
%fclose(fh);
conn_fileutils('filewrite_raw',tfilename, fh);
conn_disp('fprintf','Thresholded connectivity matrix saved in %s\n',tfilename);
case '.csv'
%fh=fopen(tfilename,'wt');
fh={};
for nt=1:numel(ColumnNames), fh{end+1}=sprintf(',%s',ColumnNames{nt}); end; fh{end+1}=sprintf('\n');
for nt=1:numel(ColumnNames),
fh{end+1}=sprintf('%s',ColumnNames{nt});
for nt2=1:numel(ColumnNames), fh{end+1}=sprintf(',%f',R(nt,nt2)); end; fh{end+1}=sprintf('\n');
end
conn_fileutils('filewrite_raw',tfilename, fh);
%fclose(fh);
conn_disp('fprintf','Thresholded connectivity matrix saved in %s\n',tfilename);
case '.mat',
conn_savematfile(tfilename,'R','R_unthresholded','ColumnNames','ColumnGroups');
conn_disp('fprintf','Thresholded connectivity matrix saved in %s\n',tfilename);
end
end
return
case 'openfolder'
data=get(hfig,'userdata');
conn_fileutils('cd',data.defaultfilepath);
try
if ispc, [nill,nill]=system(sprintf('start "%s"',data.defaultfilepath));
else [nill,nill]=system(sprintf('open ''%s''',data.defaultfilepath));
end
end
return
case 'bookmark',
data=get(hfig,'userdata');
tfilename=[];
descr='';
if isfield(CONN_gui,'slice_display_skipbookmarkicons'), SKIPBI=CONN_gui.slice_display_skipbookmarkicons;
else SKIPBI=false;
end
conn_args={'displayroi','initfile',data.initfile};
opts={};%{'forcecd'};
[fullfilename,tfilename,descr]=conn_bookmark('save',...
tfilename,...
descr,...
conn_args,...
opts);
if isempty(fullfilename), return; end
if ~SKIPBI,
tht=conn_msgbox('Printing bookmark icon. Please wait...','',-1);
conn_print(gcbf,conn_prepend('',fullfilename,'.jpg'),'-nogui','-r50','-nopersistent');
if ishandle(tht), delete(tht); end
end
return;
case 'mvpaextend'
data=get(hfig,'userdata');
if margin>1, value=varargin{1}; set(data.handles(16),'value',find(mvpathrtype_shown==value,1));
else value=data.mvpathrtype_shown(get(data.handles(16),'value'));
end
if isequal(data.mvpathrtype_shown,1:numel(data.mvpathrtype_all)), data.mvpathrtype_shown=[14:16,5:7,23:25,29:30,1]; %[15,24,6,1]; %[1,3,9,21];
else data.mvpathrtype_shown=1:numel(data.mvpathrtype_all);
end
docont=false;
if ~any(value==data.mvpathrtype_shown), value=data.mvpathrtype_shown(1); docont=true; end
set(data.handles(16),'string',data.mvpathrtype_all(data.mvpathrtype_shown),'value',find(data.mvpathrtype_shown==value,1));
data.mvpathrtype=value;
if ~docont, set(hfig,'userdata',data); return; end
data.visible='on';
% case 'mvpaside',
% hfig=gcbf;
% data=get(hfig,'userdata');
% value=get(data.handles(17),'value');
% data.mvpaside=value;
% data.visible='on';
case 'mvpasort',
data=get(hfig,'userdata');
data.mvpasortresultsby=varargin{1};
data.visible='on';
case {'displayreduced','roi.select'}
data=get(hfig,'userdata');
if strcmpi(option,'roi.select'), data.displayreduced=2;
elseif margin>1, data.displayreduced=varargin{1};
end
olddisplaytheserois=data.displaytheserois;
oldclusters=data.clusters;
switch(data.displayreduced),
case 0,
data.displaytheserois=1:length(data.names2);
case 1,
data.displaytheserois=1:length(data.names);
case 2,
if margin>2&&~isempty(varargin{2}),
initial=varargin{2};
listrois=[];
for n1=1:length(initial),
idx=strmatch(initial{n1},data.names,'exact');
if isempty(idx),
idx=strmatch(initial{n1},data.names); % allows partial-name matches
end
if isempty(idx), fprintf('warning: unable to find ROI %s. Skipping\n',initial{n1}); end
listrois=[listrois,idx(:)'];
end
if numel(listrois)>1, data.displaytheserois=listrois;
else
if numel(listrois)==1, conn_disp('Please select more than one ROI'); end
return;
end
else
idxresortv=1:numel(data.names);
temp=regexp(data.names,'BA\.(\d*) \(L\)','tokens'); itemp=~cellfun(@isempty,temp); idxresortv(itemp)=-2e6+cellfun(@(x)str2double(x{1}),temp(itemp));
temp=regexp(data.names,'BA\.(\d*) \(R\)','tokens'); itemp=~cellfun(@isempty,temp); idxresortv(itemp)=-1e6+cellfun(@(x)str2double(x{1}),temp(itemp));
[nill,idxresort]=sort(idxresortv);
[nill,tidx]=ismember(data.displaytheserois,idxresort);
answ=listdlg('Promptstring','Select ROIs to include in this group-analysis:','selectionmode','multiple','liststring',data.names2(idxresort),'initialvalue',sort(tidx),'ListSize',[520 300]);
if numel(answ)>1, data.displaytheserois=sort(idxresort(answ));
else
if numel(answ)==1, conn_disp('Please select more than one ROI'); end
return;
end
end
end
new1displaytheserois=olddisplaytheserois(ismember(olddisplaytheserois,data.displaytheserois)); % existing ones
new2displaytheserois=data.displaytheserois(~ismember(data.displaytheserois,olddisplaytheserois)); % new ones
data.displaytheserois=[reshape(new1displaytheserois,1,[]),reshape(new2displaytheserois,1,[])];
data.source=data.source(data.source==0 | data.source<=length(data.displaytheserois));if isempty(data.source),data.source=1;end
%results=conn_process('results_roi',data.displaytheserois);
if margin<=1, h=conn_msgbox('Updating ROI-level results. Please wait...','conn_displayroi',-1);
else h=[];
end
for nresults=1:size(data.results(1).data,4), %numel(data.results) % note: obsolete?
domvpa=data.displaytheserois;
ndims=min(4,ceil(sqrt(size(data.results(1).data,1))/4));
ndims=max(1,min(min(numel(domvpa),size(data.results(1).data,2)), ndims ));
if ndims<numel(domvpa)
y=data.results(1).data(:,domvpa,:,nresults);
y(:,any(any(isnan(y),1),3),:)=[]; % subjects x rois x conditions
sy=[size(y),1,1];
y=reshape(permute(y,[1,3,2]),sy(1)*sy(3),sy(2)); % (subjects x conditions) x rois
[Q,D,R]=svd(y,0);
ndims=max(1,min(size(R,2),ndims));
d=D(1:size(D,1)+1:size(D,1)*min(size(D))).^2;
ndims=min([ndims,find(cumsum(d)/sum(d)>.95,1)]); % 95 percent variance
y=y*R(:,1:ndims);
MVPAy=permute(reshape(y,[sy(1),sy(3),ndims]),[1,3,2]);
%data.results(nresults).MVPApcacov=d(1:ndims)/sum(d);
else
y=data.results(1).data(:,domvpa,:,nresults);
y=y(:,~any(any(isnan(y),1),3),:);
MVPAy=y;
%data.results(nresults).MVPApcacov=[];
end
[dataresults(nresults).MVPAh,dataresults(nresults).MVPAF,dataresults(nresults).MVPAp,dataresults(nresults).MVPAdof,dataresults(nresults).MVPAstatsname]=conn_glm(data.results(1).xX.X,MVPAy(:,:),data.results(1).c,kron(data.results(1).c2,eye(size(MVPAy,2))));
if isequal(dataresults(nresults).MVPAstatsname,'T'),
%data.results(nresults).MVPAstatsname='F'; data.results(nresults).MVPAdof=[1,data.results(nresults).MVPAdof]; data.results(nresults).MVPAF=data.results(nresults).MVPAF.^2;
dataresults(nresults).MVPAp=2*min(dataresults(nresults).MVPAp,1-dataresults(nresults).MVPAp);
end
end
if ishandle(h), close(h); end
data.MVPAF=cat(1,dataresults.MVPAF);
data.MVPAp=cat(1,dataresults.MVPAp);
temp={dataresults.MVPAdof};
if any(cellfun('length',temp)>1), temp=cellfun(@(x)[ones(1,max(0,2-length(x))),x(:)'],temp,'uni',0); end
data.MVPAdof=cell2mat(temp(:));
%data.MVPAdof=cat(1,data.results.MVPAdof);
data.MVPAstatsname=dataresults(1).MVPAstatsname;
%data.MVPApcacov=cat(1,data.results.MVPApcacov);
%if size(data.MVPAdof,2)>1&&~any(diff(data.MVPAdof(:,1))), data.MVPAstatsname=[data.MVPAstatsname,'(',num2str(data.MVPAdof(1)),')']; end
if ~any(any(diff(data.MVPAdof,1,1),1),2),
if size(data.MVPAdof,2)==1, data.MVPAdofstr=repmat({['(',num2str(data.MVPAdof(1)),')']},numel(data.MVPAdof),1);
else data.MVPAdofstr=repmat({['(',num2str(data.MVPAdof(1,1)),',',num2str(data.MVPAdof(1,2)),')']},size(data.MVPAdof,1),1);
end
else
if size(data.MVPAdof,2)==1, data.MVPAdofstr=arrayfun(@(dof)['(',num2str(dof(1)),')'],data.MVPAdof,'uni',0);
else data.MVPAdofstr=cellfun(@(dof)['(',num2str(dof(1)),',',num2str(dof(2)),')'],num2cell(data.MVPAdof,2),'uni',0);
end
end
%set(data.handles(7),'string',sprintf('%-6s %-6s %6s %6s %4s %8s %8s','Seed','Target','beta',[data.MVPAstatsname,'/',data.statsname],'dof','p-unc','p-FDR'));
if ~isequal(olddisplaytheserois,data.displaytheserois)||~isequal(oldclusters,data.clusters), %if ~isempty(new2displaytheserois) % skip recomputing clusters when only removing ROIs?
data.xy2=zeros(length(data.names2),2); data.xy2(data.displaytheserois,:)=200*[cos(2*pi*(0:numel(data.displaytheserois)-1)'/numel(data.displaytheserois)),sin(2*pi*(0:numel(data.displaytheserois)-1)'/numel(data.displaytheserois))];
data.xy2_clusters=[];
data.clusters=[];
data.names_clusters={};
if isfield(data,'clusters_options')&&~isempty(data.clusters_options), data=conn_displayroi_clusters(data); end
end
if ~isequal(olddisplaytheserois,data.displaytheserois)||~isequal(oldclusters,data.clusters),
data.PERM=[];
data.tfceZ=[];
data.cMVPAF=[];
%f=conn_dir(conn_displayroi_simfilename(data.roifile,'all'),'-R','-cell');
%if ~isempty(f), conn_fileutils('spm_unlink',f{:}); end
end
if ishandle(h), close(h); end
data.proj=[];data.x=[];data.y=[];data.z=[];