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snpm_combo_pp.m
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snpm_combo_pp.m
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function snpm_combo_pp(CWD, job)
% SnPM post processing and results display
% FORMAT snpm_combo_pp(CWD)
%
% CWD - Directory containing SnPM results files
%
% If CWD is not specified then user is prompted to locate results file SnPM.mat
%
%___________________________________________________________________
%
% snpm_combo_pp.m is the post-processing function for combined
% voxel-cluster size inference. This program is a modified version
% of snpm_pp.m used in SnPM.
%
% Before using this program, first you should have run the "setup" and
% "compute" steps of SnPM. In the setup step, you should specify
% to assess cluster extent. Otherwise you will not be able to
% perform a cluster size test, and thus no combined test either.
%
% Based on SnPM_ST.mat, the supra-threshold portion of all the
% realizations, this program calculates, (a) partial test p-values
% or voxel test p-values and cluster size test p-values, then
% (b) combined functions based on p-values from (a), then (c)
% calculates p-values for combined tests, and finally (d)
% calculates the meta-combining function and perform the meta-
% combined test. This program stores information from all the
% clusters in all the permutations. So for the steps (c)-(d),
% this recorded cluster information is used without doing
% further permutations.
%
% theta = 0.5 corresponds to the equally weighted statistics, For theta > 0.5,
% the combining function becomes more sensitive to high intensity peaks;
% whereas for theta < 0.5, the combining function becomes more sensitive to
% large clusters. For theta = 0, the test becomes a cluster size test and
% for theta = 1, the test becomes a peak intensity test.
%
% Details regarding the combined and meta-combined tests
% are found in
%
% Hayasaka S, and Nichols TE. (2004)
% Combining Voxel Intensity and Cluster Extent with
% Permutation Test Framework.
%
% THIS IS A BETA VERSION. THE PROGRAM IS STILL BEING TESTED!
%
%_______________________________________________________________________
% Copyright (C) 2013 The University of Warwick
% Id: snpm_combo_pp.m SnPM13 2013/10/12
% Satoru Hayasaka, Darren Gitelman, Camille Maumet
% Based on snpm_pp.m
%-Setup
%=======================================================================
fprintf('\nSnPM: snpm_combo_pp\n'),fprintf('%c','='*ones(1,72)),fprintf('\n')
MLver=version;MLver=MLver(1);
%-Initialise variables & constants
%-----------------------------------------------------------------------
tol = 1e-4; % Tolerance for comparing real numbers
% Two reals with abs(a-b)<tol are considered equal
% ( Reals have to be compared for equality when )
% ( computing adjusted p-values )
%-SetUp figure window
%-----------------------------------------------------------------------
Finter = spm_figure('FindWin','Interactive');
Fgraph = spm_figure('FindWin','Graphics');
if isempty(Fgraph), Fgraph=spm_figure('Create','Graphics'); end
spm_clf(Finter), spm_clf(Fgraph)
set(Finter,'Name','SnPM PostProcess');
%-Get Data
%=======================================================================
% Get analysis directory
if nargin==0
tmp = spm_select(1,'SnPM.mat','Select SnPM.mat for analysis...');
CWD = spm_str_manip(tmp,'hd');
end
load(fullfile(CWD,'SnPM'))
%-Load Config file & SnPM permutation data
if exist(fullfile(CWD,'SnPMcfg.mat'),'file')
load(fullfile(CWD,'SnPMcfg'))
else
fprintf('Error!! Cannot find SnPMcfg.mat file.\n');
fprintf('Make sure to run Setup and Compute in SnPM.\n');
fprintf('Check if\n %s \nis the right directory.\n',CWD);
fprintf('\nEnding the program .........\n\n');
return;
end
%-Ask whether positive or negative effects be analysed
%-----------------------------------------------------------------------
if STAT == 'T'
bNeg = job.Tsign==-1;
else
bNeg = 0;
end
%-Form full Tmax distribution
%-----------------------------------------------------------------------
%-Tmin are in second column of MaxT, stored with *+ve* values
if bhPerms
MaxT = [ MaxT; flipud(fliplr(MaxT)) ];
PiCond = [PiCond; -flipud(PiCond)];
end
%-Take MaxT for increases or decreases according to bNeg
MaxT = MaxT(:,bNeg+1);
nPerm = size(MaxT,1);
[StMaxT, iStMaxT] = sort(MaxT);
%-Load statistic image
%-----------------------------------------------------------------------
load(fullfile(CWD,'SnPMt'))
load(fullfile(CWD,'XYZ'))
%-Negate if looking at negative contrast
%-----------------------------------------------------------------------
if bNeg
SnPMt = -SnPMt;
CONT = -CONT;
end
%-Get ORIGIN, etc
DIM = [V(1).dim(1) V(1).dim(2) V(1).dim(3)];
VOX = [V(1).mat(1,1) V(1).mat(2,2) V(1).mat(3,3)];
MAT = V(1).mat;
IMAT = inv(MAT);
ORIGIN = IMAT(1:3,4);
% Template vol structure
Vs0 = V(1);
Vs0.dt = [spm_type('float64'), spm_platform('bigend')];
% Vs0 = struct('fname', '',...
% 'dim', [DIM,spm_type('float')],...
% 'mat', MAT,...
% 'pinfo', [1 0 0]',...
% 'descrip', '');
%-Write out filtered statistic image? (Get's done later)
%-----------------------------------------------------------------------
WrtFlt = isfield(job.WriteFiltImg, 'name'); %spm_input('Write filtered statistic img?','+1','y/n',[1,0],2);
if WrtFlt
%WrtFltFn = 'SnPMt_filtered';
%WrtFltFn=spm_input('Filename ?','+1','s',WrtFltFn);
% WrtFltFn = [WrtFltFn, '.img'];
WrtFltFn = job.WriteFiltImg.name;
end
%-Get inference parameters
%=======================================================================
%-Get corrected threshold
%-----------------------------------------------------------------------
alpha = job.Thr.Clus.ClusMass.PFilt; %spm_input('Corrected p value for filtering','+1','e',0.05);
%-Compute critical threshold for level alpha test
%-----------------------------------------------------------------------
if alpha < 1
c=ceil((1-alpha)*nPerm);
C_MaxT=StMaxT(c);
else
%-Just use voxels with +ve valued SnPMt
C_MaxT=0;
end
%-Ask whether SupraThreshold cluster size test required
%-----------------------------------------------------------------------
%-If chosen alpha specifies a critical threshold less than the threshold
% ST_Ut used to collect suprathreshold data in snpm_cp, then it makes
% no sense to analyse by spatial extent since the voxels are individually
% significant.
%-bST flags whether spatial extent information was collected.)
bSpatEx = bST & exist(fullfile(CWD,'SnPM_ST.mat'))==2;
if bSpatEx && (C_MaxT <= ST_Ut) && (alpha ~= 1)
str = 'Voxelwise corrected threshold = %g, which is smaller ';
str = [str 'than minimum saved suprathreshold information (%g)'];
str = [str '\nAll results significant voxelwise.'];
warning('SnPM:VoxelwiseCorrThreshSmaller', sprintf(str,C_MaxT,ST_Ut))
end
if bSpatEx~=1
error('SnPM:SupraStatMissing', 'Suprathreshold stats not collected! Cannot do cluster-combining!')
end
%-Get primary threshold for STC analysis if requested
%-----------------------------------------------------------------------
if bSpatEx
% Save original ST_Ut
ST_Ut_0 = ST_Ut;
%-Threshold must be greater or equal to that (ST_Ut) used to collect
% suprathreshold data in snpm_cp
%-If a test level alpha has been set, then it there's no sense in having
% the threshold greater than C_MaxT, above which voxels are individually
% significant
%tmp = 0;
primaryThresh = job.Thr.Clus.ClusMass.PrimThresh;
if bVarSm
%-If using pseudo-statistics then can't use (uncorrected)
% upper tail p-values to specify primary threshold
if alpha == 1 % Not filtering on significance
if ~(primaryThresh>=ST_Ut)
error('SnPM:PseudoStatWithPValueThreshold', ...
['Using pseudo-statistics you can''t use (uncorrected)'...
'upper tail p-values to specify primary threshold']);
end
else
if ~(primaryThresh>=ST_Ut && primaryThresh<C_MaxT)
error('SnPM:PseudoStatWithPValueThreshold', ...
['Using pseudo-statistics you can''t use (uncorrected)'...
'upper tail p-values to specify primary threshold']);
end
end
else
%-Statistic image is t with df degrees of freedom
pU_ST_Ut = 1-spm_Tcdf(ST_Ut,df);
if alpha==1 % Not filtering on significance
if ~( primaryThresh>=ST_Ut || (primaryThresh>0 && primaryThresh<=pU_ST_Ut_filt))
error('SnPM:InvalidPrimaryThresh', ['Primary threshold must be >=' num2str(ST_Ut) ...
' and >0 and <=' num2str(pU_ST_Ut_filt) ]);
end
else
pU_C_MaxT = 1-spm_Tcdf(C_MaxT,df);
if ~((primaryThresh>=ST_Ut && primaryThresh<C_MaxT) || ...
(primaryThresh>pU_C_MaxT && primaryThresh<=pU_ST_Ut_filt))
error('SnPM:InvalidPrimaryThresh', ['Primary threshold must be >=' num2str(ST_Ut) ...
' and <' num2str(C_MaxT) ' or >' num2str(pU_C_MaxT) ...
' and <= ' num2str(pU_ST_Ut_filt)]);
end
clear pU_C_MaxT
end
clear pU_ST_Ut
if (primaryThresh < 1)
primaryThresh = spm_invTcdf(1-primaryThresh,df);
end
end
ST_Ut = primaryThresh;
%
% Getting combined test parameters
%
Theta = job.Thr.Clus.ClusMass.Theta;
if (Theta<0+tol || Theta>1-tol)
error('SnPM:InvalidTheta', 'Theta should be between 0 and 1');
end
mTheta = Theta/(1-Theta); %-Weight for mass combining
%-Picking which combining test
% Only mass combining for now
iW = 3;% = spm_input('Choose combining function',1,'b', ...
% 'Fisher|Tippet|Mass|All',[1:4],1);
end
%-Show permutation distributions?
%-----------------------------------------------------------------------
%ShwDst = spm_input('Display permutation distribution[s]?','+1','y/n',[1,0],1);
ShwDst = 1;
%=======================================================================
%- C O M P U T A T I O N
%=======================================================================
set(Finter,'Pointer','Watch')
%-Calculate distribution of Maximum Suprathreshold Cluster size
%-Calculate critical Suprathreshold Cluster Size
%=======================================================================
if bSpatEx
fprintf('Working on spatial extent...\n');
%-Compute suprathreshold voxels - check there are some
%---------------------------------------------------------------
fprintf('\tComputing suprathreshold voxels...');
Q = find(SnPMt > ST_Ut);
SnPMt = SnPMt(Q);
XYZ = XYZ(:,Q);
if isempty(Q)
set(Finter,'Pointer','Arrow')
figure(Fgraph)
axis off
text(0,0.97,CWD,'Fontsize',16,'FontWeight','Bold');
text(0,0.93,sprintf('No voxels above threshold %4.2f',ST_Ut));
ShowDist(MaxT,C_MaxT);
return
end
fprintf('done\n')
%-Load & condition statistics
%---------------------------------------------------------------
fprintf('\tLoading & conditioning SupraThreshold statistics...');
load(fullfile(CWD,'SnPM_ST'))
%-SnPM_ST stores columns of [x;y;z;abs(t);perm] with perm negative
% where the exceedence was t < -ST_Ut_0
%-Trim statistics according to threshold ST_Ut, if ST_Ut > ST_Ut_0
tmp = find(SnPM_ST(4,:)>ST_Ut);
SnPM_ST = SnPM_ST(:,tmp);
clear tmp;
%-Negate perm numbers if looking at negative contrast
if bNeg
SnPM_ST(5,:) = -SnPM_ST(5,:);
end
if bhPerms
%-Renumber negative perms according to -flipud PiCond
tQ = SnPM_ST(5,:)<0;
SnPM_ST(5,tQ) = nPerm +1 +SnPM_ST(5,tQ);
else
%-Not bhPerms: Lose entries for negative excursions
SnPM_ST = SnPM_ST(:,SnPM_ST(5,:)>0);
end
fprintf('done\n')
%-Calculate distribution of Maximum SupraThreshold Cluster size
%---------------------------------------------------------------
% ClInfo = [x;y;z;ClusterPeak;ClusterSize;ClusterMass;ClusterInd;Perm]
ClInfo = [];
fprintf('\tComputing dist. of max SupraThreshold cluster size: ');
MaxSTCS = zeros(nPerm,1);
SetLvl = zeros(nPerm,1);
fprintf('\nPerms left: ');
% preallocate ClInfo to improve speed. This will be far larger than it
% needs to be but it should be OK.
ClInfo = zeros(8,size(SnPM_ST,2));
ClInfoEnd = 0;
for i = nPerm:-1:1
if (rem(i,10)==0)
fprintf('\b\b\b\b%-4u',i)
drawnow
end
tQ = (SnPM_ST(5,:)==i);
if any(tQ)
%-Filter out data for this perm
subSnPM_ST = SnPM_ST(:,tQ);
%-Compute cluster labellings for this perm
%===== SnPM99 change =================
Locs_mm = SnPM_ST(1:3,tQ);
Locs_mm (4,:) = 1;
Locs_vox = IMAT * Locs_mm;
%===== SnPM99 change =================
tmp = spm_clusters(Locs_vox(1:3,:));
%-Work out maximum cluster size (honest!)
SetLvl(i) = max(tmp);
tmpCS = zeros(1,SetLvl(i));
tmpCS = diff(find([diff([0,sort(tmp)]),1]));
MaxSTCS(i) = max(tmpCS);
for j=1:SetLvl(i)
%-Recording peak, its location, and excess mass
% then creates a big vector ClInfo with
% all cluster info
tmpCLoc = (tmp==j);
subsubST = subSnPM_ST(:,tmpCLoc);
[tmpPeak, tmpPLoc] = max(subsubST(4,:));
tmpXYZ = subsubST(1:3,tmpPLoc);
tmpMass = sum((subsubST(4,:)-ST_Ut).^mTheta);
tmpVec = [tmpXYZ; tmpPeak; tmpCS(j); tmpMass; j; i];
ClInfo(:,ClInfoEnd+j) = tmpVec;
end
ClInfoEnd = ClInfoEnd+SetLvl(i);
end
end
% get rid of any unused entries in ClInfo
ClInfo = ClInfo(:,1:ClInfoEnd);
clear tmpCS tmpCLoc tmpPeak tmpPLoc tmpXYZ tmpMass tmpVec;
fprintf('\b\b\b\bdone\n');
%-Save perm 1 stats for use later - [X;Y;Z;T;perm;STCno]
STCstats = [ SnPM_ST(:,tQ); tmp];
%-Compute critical SupraThreshold Cluster size
[StMaxSTCS, iStMaxSTCS] = sort(MaxSTCS);
if alpha < 1
C_STCS = StMaxSTCS(c);
else
C_STCS = 0;
end
%-Check XYZ for points > ST_Ut in perm 1 matches
% XYZ computed above for SnPMt > ST_Ut
if ~all(all( SnPM_ST(1:3,SnPM_ST(5,:)==1) == XYZ ))
error('SnPM:InvalidSTXYZ', 'ST XYZ don''t match between STCS & thresh')
end
%-- VOXEL CLUSTER COMBINED TEST STATISTICS ----
%-- Assigning each voxel corrected voxel and cluster p-values
% then calculate the combining functions
% CorrPs = [Vox p; Cl p; Fisher p; Tippet p; ExcessMass p; Meta p]
% ComboF = [Fisher Wf; Tippet Wt; Excess mass Wm; Meta Wa]
CorrPs = ones(6,size(ClInfo,2));
ComboF = zeros(4,size(ClInfo,2));
fprintf('Calculating corr p-values for each voxel / cluster\n');
for ip = 1:(nPerm-1)
if rem(ip,50)==0, fprintf('.'), end
Qvox = find(ClInfo(4,:)>StMaxT(ip));
CorrPs(1,Qvox) = (nPerm-ip)/nPerm;
Qcl = find(ClInfo(5,:)>StMaxSTCS(ip));
CorrPs(2,Qcl) = (nPerm-ip)/nPerm;
end
fprintf('.Done!\n');
fprintf('Calculating voxel-cluster combining functions\n');
ComboF(1,:) = -2*(2*Theta*log(CorrPs(1,:)) + ...
2*(1-Theta)*log(CorrPs(2,:)));
ComboF(2,:) = 1 - min(2*Theta*log(CorrPs(1,:)), ...
2*(1-Theta)*log(CorrPs(2,:)));
ComboF(3,:) = ClInfo(6,:);
fprintf('.Done!\n');
%-- Corrected p-value for combo function
MaxWf = zeros(nPerm,1);
MaxWt = zeros(nPerm,1);
MaxWm = zeros(nPerm,1);
for i=1:nPerm
QPerm = find(ClInfo(8,:) == i);
if ~isempty(QPerm)
tmpWf = ComboF(1,QPerm);
MaxWf(i) = max(tmpWf);
tmpWt = ComboF(2,QPerm);
MaxWt(i) = max(tmpWt);
tmpWm = ComboF(3,QPerm);
MaxWm(i) = max(tmpWm);
end
end
[StMaxWf, iStMaxWf] = sort(MaxWf);
[StMaxWt, iStMaxWt] = sort(MaxWt);
[StMaxWm, iStMaxWm] = sort(MaxWm);
C_Wcomb = zeros(4,1);
if alpha < 1
C_Wcomb(1) = StMaxWf(c);
C_Wcomb(2) = StMaxWt(c);
C_Wcomb(3) = StMaxWm(c);
else
C_Wcomb(1) = 0;
C_Wcomb(2) = 0;
C_Wcomb(3) = 0;
end
fprintf('Calculating corr p-values for combining functions\n');
for ip = 1:(nPerm-1)
if rem(ip,50)==0, fprintf('.'), end
Qcomb = find(ComboF(1,:)>StMaxWf(ip));
CorrPs(3,Qcomb) = (nPerm-ip)/nPerm;
Qcomb = find(ComboF(2,:)>StMaxWt(ip));
CorrPs(4,Qcomb) = (nPerm-ip)/nPerm;
Qcomb = find(ComboF(3,:)>StMaxWm(ip));
CorrPs(5,Qcomb) = (nPerm-ip)/nPerm;
end
fprintf('.Done!\n');
%
% Doing Meta-combining
%
%-First, calculate the meta statistic
fprintf('Calculating meta-combining function\n');
ComboF(4,:) = 1 - min([log(CorrPs(3,:))' ...
log(CorrPs(4,:))' log(CorrPs(5,:))'],[],2)';
% Then calculate the max meta-combining function
MaxWa = zeros(nPerm,1);
for ip=1:nPerm
QPerm = find(ClInfo(8,:) == ip);
if ~isempty(QPerm)
tmpWa = ComboF(4,QPerm);
MaxWa(ip) = max(tmpWa);
end
end
% Corrected critical meta-statistic
[StMaxWa, iStMaxWa] = sort(MaxWa);
if alpha < 1
C_Wcomb(4) = StMaxWa(c);
else
C_Wcomb(4) = 0;
end
% P-value calculation for meta-combining
fprintf('Calculating corr p-values for meta-combining \n');
for ip = 1:(nPerm-1)
if rem(ip,50)==0, fprintf('.'), end
Qcomb = find(ComboF(4,:)>StMaxWa(ip));
CorrPs(6,Qcomb) = (nPerm-ip)/nPerm;
end
fprintf('.Done!\n');
% Combining all the MaxW distributions to be plotted later
MaxW = [MaxWf; MaxWt; MaxWm; MaxWa];
end
%-Save some time consuming results
%-----------------------------------------------------------------------
if bSpatEx, save SnPM_pp.mat STCstats MaxSTCS SetLvl, end
if bSpatEx, save SnPM_combo.mat CorrPs ComboF ClInfo MaxWf MaxWt MaxWm MaxWa, end
%-Filter data at specified corrected p-value alpha
%=======================================================================
if bSpatEx
%-Analysing spatial extent
%-NB:alpha==1 implies C_MaxT==C_STCS==0.
% Since ST_Ut>0 filtering has no effect if alpha==1, so skip it.
if alpha<1
%-Filter on significance of cluster size
%---------------------------------------------------------------
fprintf('Filtering on cor.sig. at suprathreshold cluster level...');
nSTC = max(STCstats(6,:));
STCS = diff(find([diff([0,sort(STCstats(6,:))]),1]));
Q = [];
for i = 1:nSTC
tQ = find(STCstats(6,:)==i);
ttQ= find(ClInfo(7,:)==i & ClInfo(8,:)==1);
if ( STCS(i) > C_STCS || max(STCstats(4,tQ)) > C_MaxT || ...
max(ComboF(iW,ttQ)) > C_Wcomb(iW))
Q = [Q tQ];
end
end
if ~isempty(Q)
SnPMt = SnPMt(Q);
XYZ = XYZ(:,Q);
STCstats = STCstats(:,Q);
end
fprintf('done\n')
end
else
%-Truncate at critical threshold for level alpha test
% NB if alpha==1 then C_MaxT is set to 0, and filter on +ve SnPMt
fprintf('Filtering on cor.sig. at voxel level...');
Q = find(SnPMt > C_MaxT);
if length(Q)
SnPMt = SnPMt(Q);
XYZ = XYZ(:,Q);
end
fprintf('done\n')
end
%-Return if there are no voxels
%-----------------------------------------------------------------------
if isempty(Q)
set(Finter,'Pointer','Arrow')
figure(Fgraph)
axis off
text(0,0.97,CWD,'Fontsize',16,'FontWeight','Bold');
tmp='voxels'; if bSpatEx, tmp='suprathreshold clusters'; end
text(0,0.93,sprintf(...
'No %s significant at alpha=%6.4f (corrected)',tmp,alpha));
if bSpatEx
ShowDist(MaxT,C_MaxT,MaxSTCS,C_STCS);
else
ShowDist(MaxT,C_MaxT);
end
return
end
%-Characterize local excursions in terms of maxima:
% #voxels STC_N; MaxTs STC_SnPMt; locations STC_XYZ, & region# STC_r
%-----------------------------------------------------------------------
%===== SnPM99 change =============================================
TempXYZmm = XYZ;
TempXYZmm(4,:) = 1;
TempXYZvoxel = IMAT*TempXYZmm;
TempXYZvoxel= TempXYZvoxel(1:3,:);
[STC_N, STC_SnPMt, STC_XYZ, STC_r] = spm_max(SnPMt,TempXYZvoxel);
TempXYZvoxel = STC_XYZ;
TempXYZvoxel(4,:) = 1;
TempXYZmm = MAT * TempXYZvoxel;
STC_XYZ = TempXYZmm(1:3,:);
%===== SnPM99 change =============================================
%-Compute adjusted significances for local maxima, & regions (if required)
%-----------------------------------------------------------------------
Pt = ones(size(STC_r));
for i = 1:length(STC_r)
%-Use a > b -tol rather than a >= b to avoid comparing reals
Pt(i) = sum(MaxT > STC_SnPMt(i) -tol) / nPerm;
end
%if ~bVarSm
% Pu = 1 - spm_Tcdf(STC_SnPMt,df);
% end
if bSpatEx
%-Compute single step adjusted p-values for region size: pSTSC_SS
Pn = ones(1,length(STC_r));
Ww = ones(4,length(STC_r));
Pw = ones(4,length(STC_r));
for i = 1:length(STC_r)
Pn(i) = sum(MaxSTCS>=STC_N(i)) / nPerm;
Qvox = find((ClInfo(1,:)==STC_XYZ(1,i)) & ...
(ClInfo(2,:)==STC_XYZ(2,i)) & ...
(ClInfo(3,:)==STC_XYZ(3,i)) & ...
(ClInfo(8,:)==1));
if ~isempty(Qvox)
Ww(:,i) = ComboF(:,Qvox);
Pw(:,i) = CorrPs(3:6,Qvox);
end
end
save('SnPM_combo','Ww','Pw','STC_XYZ','-append');
end
%=======================================================================
%-D I S P L A Y
%=======================================================================
figure(Fgraph)
if (ShwDst)
axis off
if (bSpatEx)
text(0,0.97,'Permutation Distribution','Fontsize',16,'FontWeight','Bold');
ShowDist(MaxT,C_MaxT,MaxSTCS,C_STCS);
else
text(0,0.97,'Permutation Distributions','Fontsize',16,'FontWeight','Bold');
ShowDist(MaxT,C_MaxT);
end
end
% Change the style of pressing ' return' to clicking on a new button.
%spm_print
%disp('Press <RETURN> to continue'); pause
if false
if spm_input('Review permutation distributions.',1,'bd',...
'Print & Continue|Continue',[1,0],1)
spm_print
end
end
spm_clf(Fgraph)
%-Maximium intenisty projection of SPM{Z}
%=======================================================================
hmip = axes('Position',[0.05 0.5 0.5 0.5]);
snpm_mip(SnPMt,XYZ,MAT,DIM); axis image
if bVarSm
title('SnPM{Pseudo-t}','FontSize',16,'Fontweight','Bold')
else
title('SnPM{t}','FontSize',16,'Fontweight','Bold')
end
%-Design matrix and contrast
%=======================================================================
hDesMtx = axes('Position',[0.65 0.6 0.2 0.2]);
imagesc((spm_DesMtx('Sca', [H,C,B,G],HCBGnames) + 1)*32)
xlabel 'Design Matrix'
set(hDesMtx,'XTick',[],'XTickLabel','')
hConAxes = axes('Position',[0.65 0.8 0.2 0.1]);
h = bar(CONT(1,:), 'FaceColor',[1 1 1]*.8, 'BarWidth', 1);
hold on
tX = get(h,'XData'); tY = get(h,'YData');
bar_width = get(h, 'BarWidth');
set(gca,'Xlim',[min(tX(:))-bar_width/2 max(tX(:))+bar_width/2])
title 'contrast'; axis off; hold off
%-Table of regional effects
%=======================================================================
%-Table headings
%-----------------------------------------------------------------------
hTable = axes('Position',[0.1 0.1 0.8 0.46],...
'YLim',[0,27],'YLimMode','manual',...
'DefaultTextInterpreter','Tex',...
'DefaultTextVerticalAlignment','Baseline',...
'Visible','off');
% 'DefaultHorizontalAlignment','right',...
y = 26;
dy=1;
text(0,y,['P values & statistics: ',spm_str_manip(CWD,'a40')],...
'FontSize',12,'FontWeight','Bold','Interpreter','none');
y = y -dy;
line([0 1],[y y],'LineWidth',3,'Color','r')
y = y -dy;
tCol = [ 0.00 0.16 ... %-Cluster
0.25 0.43 ... %-combo cluster
0.55 0.71 ... %-Voxel
0.86 0.93 1.00]; %-XYZ
PF = spm_platform('fonts'); %-Font names (for this platform)
%-Construct table header
%-----------------------------------------------------------------------
set(gca,'DefaultTextFontName',PF.helvetica,'DefaultTextFontSize',10)
Hp = [];
h = text(0.10,y, 'cluster-level','FontSize',10,'HorizontalAlignment','Center' );
h = line([tCol(1),0.20],[1,1]*(y-dy/4),'LineWidth',0.5,'Color','r');
h = text(tCol(1),y-9*dy/8, '\itp_{corrected}'); Hp = [Hp,h];
h = text(tCol(2),y-9*dy/8, '\itk ');
h = text(0.37,y, 'combo cluster-level','FontSize',10, 'HorizontalAlignment','Center'); Hp=[Hp,h];
h = line([tCol(3),0.50],[1,1]*(y-dy/4),'LineWidth',0.5,'Color','r'); Hp=[Hp,h];
h = text(tCol(3),y-9*dy/8, '\itp_{corrected}'); Hp = [Hp,h];
h = text(tCol(4),y-9*dy/8, '\itw '); Hp=[Hp,h];
text(0.67,y, 'voxel-level','FontSize',10, 'HorizontalAlignment','Center');
line([tCol(5),0.80],[1,1]*(y-dy/4),'LineWidth',0.5,'Color','r');
h = text(tCol(5),y-9*dy/8, '\itp_{FWE-corr}');
if ~bVarSm
h = text(tCol(6),y-9*dy/8, '\itt');
else
h = text(tCol(6)-0.02,y-9*dy/8, 'Pseudo-t');
end
text(tCol(7),y-dy/2,'{x,y,z} mm','FontSize',10);
y = y - 7*dy/4;
line([0 1],[y y],'LineWidth',1,'Color','r')
y = y - 5*dy/4;
% text(0.00,y,'region','FontSize',10);
%text(0.00,y,'size {k}','FontSize',10);
%if bSpatEx
% text(0.11,y,'P(K_{max}>= k)','FontSize',10);
% text(0.62,y,'w','FontSize',10);
% text(0.68,y,'P(W_{max}>= w)','FontSize',10);
%
%end
%
%text(0.35,y,'P(T_{max}>= u)','FontSize',10);
%
%text(0.84,y,'{x,y,z} mm','FontSize',10);
%y = y -0.8;
%line([0 1],[y y],'LineWidth',3,'Color',[0 0 0])
%y = y -1;
Fmtst = { '%0.4f','%0.0f', ... %-cluster
'%0.4f','%6.2f', ... %-combo cluster
'%0.4f','%6.2f', ... %-voxel
'%3.0f','%3.0f', '%3.0f'}; %-XYZ
%-Column Locations
%-----------------------------------------------------------------------
%tCol = [ 0.08 0.18 0.33 ...
% 0.44 0.66 0.76 ...
% 0.86 0.93 1.00]; %-XYZ
%-List of maxima
%-----------------------------------------------------------------------
r = 1;
bUsed = zeros(size(STC_SnPMt));
while max(STC_SnPMt.*(~bUsed)) && (y > 3)
[null, i] = max(STC_SnPMt.*(~bUsed)); % Largest t value
j = find(STC_r == STC_r(i)); % Maxima in same region
%-Print region and largest maximum
%-------------------------------------------------------------------
StrAttr = {'Fontsize',10,'ButtonDownFcn','get(gcbo, ''UserData'')',...
'HorizontalAlignment','right'};
StrAttrB = {StrAttr{:},'FontWeight','Bold'};
% text(0.00,y,sprintf('%0.0f',r),'UserData',r,StrAttrB{:})
if bSpatEx
text(tCol(1)+0.09,y,sprintf(Fmtst{1},Pn(i)),...
'UserData',Pn(i),StrAttrB{:})
text(tCol(3)+0.08,y,sprintf(Fmtst{3},Pw(iW,i)),...
'UserData',Pw(iW,i),StrAttrB{:})
text(tCol(4)+0.04,y,sprintf(Fmtst{4},Ww(iW,i)),...
'UserData',Ww(iW,i),StrAttrB{:})
else
set(Hp, 'Visible','off')
end
text(tCol(2)+0.03,y,sprintf(Fmtst{2},STC_N(i)),'UserData',STC_N(i),StrAttrB{:})
text(tCol(5)+0.08,y,sprintf(Fmtst{5},Pt(i)),...
'UserData',Pt(i),StrAttrB{:})
text(tCol(6)+0.04,y,sprintf(Fmtst{6},STC_SnPMt(i)),...
'UserData',STC_SnPMt(i),StrAttrB{:})
text(tCol(7),y,sprintf(Fmtst{7},STC_XYZ(1,i)),...
'UserData',STC_XYZ(:,i),StrAttrB{:})
text(tCol(8),y,sprintf(Fmtst{8},STC_XYZ(2,i)),...
'UserData',STC_XYZ(:,i),StrAttrB{:})
text(tCol(9),y,sprintf(Fmtst{9},STC_XYZ(3,i)),...
'UserData',STC_XYZ(:,i),StrAttrB{:})
y = y -1;
%-Print up to 3 secondary maxima (>8mm apart)
%-------------------------------------------------------------------
[null, k] = sort(-STC_SnPMt(j)); % Sort on t value
D = i;
for i = 1:length(k)
d = j(k(i));
if min( sqrt( sum((STC_XYZ(:,D) - ...
STC_XYZ(:,d)*ones(1,size(D,2))).^2) ) ) > 8
if length(D) < 3
text(tCol(5)+0.08,y,sprintf(Fmtst{5},Pt(d)),...
'UserData',Pt(d),StrAttr{:})
text(tCol(6)+0.04,y,sprintf(Fmtst{6},STC_SnPMt(d)),...
'UserData',STC_SnPMt(d),StrAttr{:})
text(tCol(7),y,sprintf(Fmtst{7},STC_XYZ(1,d)),...
'UserData',STC_XYZ(:,d),StrAttr{:})
text(tCol(8),y,sprintf(Fmtst{8},STC_XYZ(2,d)),...
'UserData',STC_XYZ(:,d),StrAttr{:})
text(tCol(9),y,sprintf(Fmtst{9},STC_XYZ(3,d)),...
'UserData',STC_XYZ(:,d),StrAttr{:})
D = [D d];
y = y -1;
end
end
end
bUsed(j) = (bUsed(j) | 1 ); %-Mark maxima as "used"
r = r + 1; % Next region
end
clear i j k D d r
%-Footnote with SnPM parameters
%=======================================================================
line([0,1],[0.5,0.5],'LineWidth',1,'Color','r')
y = 0;
if bSpatEx
tmp = sprintf('Threshold = %7.4f',ST_Ut);
if ~bVarSm
tmp=[tmp,sprintf(' (p = %6.4f)',spm_Tcdf(-ST_Ut,df))];
end
text(0,y,tmp,'FontSize',8)
text(0.7,y,sprintf('Critical STCS = %d voxels',C_STCS),'FontSize',8)
y = y -0.8;
end
text(0,y,sprintf('alpha = %6.4f, df = %d',alpha,df),'FontSize',8)
text(0.7,y,sprintf('Critical threshold = %7.4f',C_MaxT),'FontSize',8)
y = y -0.8;
text(0,y,sprintf('Volume = %d %5.2fx%5.2fx%5.2f mm voxels',...
S,VOX(1),VOX(2),VOX(3)),'FontSize',8);
y = y -0.8;
text(0,y,sprintf('Design: %s',sDesign),'FontSize',8);
y = y -0.8;
text(0,y,sprintf('Perms: %s',sPiCond),'FontSize',8);
if bVarSm
y = y -0.8;
text(0,y,sVarSm,'FontSize',8)
end
y = -1.6;
NameW = {'Fisher','Tippet','Excess mass','Meta-combining'};
if bSpatEx
text(0.7,y,sprintf('Combining function W: %s',NameW{iW}),'FontSize',8);
y = y - 0.8;
text(0.7,y,sprintf('Critical W = %7.4f',C_Wcomb(iW)),'FontSize',8);
y = y - 0.8;
text(0.7,y,sprintf('Theta = %4.2f',Theta),'FontSize',8);
end
%spm_print
%Set a button, so the user can decide whether to print the page of results to spm2.ps.
if false % interactive display inactive
if spm_input('Review results.',1,'bd','Print|Done',[1,0],1)
spm_print
end
end
set(Finter,'Pointer','Arrow')
%- Image output?
%=======================================================================
%-Write out filtered SnPMt?
if WrtFlt
Fname = WrtFltFn;
%-Dont ask about t2z conversion
%---------------------------------------------------------------
bt2z = 0;
if ~bVarSm
% bt2z = spm_input('Convert t -> z prior to writing?',...
% '+1','y/n')=='y';
tmp = sprintf('SPMt - %d df',df);
else
tmp = 'SnPMt - pseudo t';
end
%-Reconstruct filtered image from XYZ & SnPMt
%---------------------------------------------------------------
t = zeros(1,prod(DIM));
if ~bt2z
t(spm_xyz2e(XYZ,V)) = SnPMt;
else
t(spm_xyz2e(XYZ,V)) = spm_t2z(SnPMt,df);
tmp = [tmp,' (Gaussianised)'];
end
if ~bSpatEx
tmp=sprintf('%s p<%10g corrected @ voxel level',tmp,alpha);
elseif bt2z
tmp=sprintf('%s p<%10g corrected @ cluster level, u=%4.2',...
tmp,alpha,spm_t2z(ST_Ut,df));
else
tmp=sprintf('%s p<%10g corrected @ cluster level, u=%4.2',...
tmp,alpha,ST_Ut,df);
end
%-Write out to analyze file
%---------------------------------------------------------------
% Vs = Vs0;
% Vs.fname = Fname; Vs.descrip = tmp;
% Vs.dim = Vs.dim(1:3);
% Vs = sf_create_vol(Vs);
% t = reshape(t,DIM);
% for p=1:Vs.dim(3)
% Vs = spm_write_plane(Vs,t(:,:,p),p);
% end
% Vs = sf_close_vol(Vs);
% clear t
%-Write out to image file
%---------------------------------------------------------------
Vs = snpm_clone_vol(Vs0, Fname, tmp);
Vs = spm_create_vol(Vs);
t = reshape(t,DIM);
for p=1:Vs.dim(3)
Vs = spm_write_plane(Vs,t(:,:,p),p);
end
Vs = sf_close_vol(Vs);
clear t
end
%-Reset Interactive Window
%-----------------------------------------------------------------------
spm_figure('Clear','Interactive')
function ShowDist(T,cT,C,cC)
%
% Display permutation distributions on current figure, using position
% pos.
%
% We assume that the observed (aka correctly labeled data) are the first
% element in the distribution.
%
pos1 = [0.125 0.50 0.75 0.3];
pos2 = [0.125 0.08 0.75 0.3];
%
% Display intensity perm dist
%
% Bin width rule from Scott, "Multivariate Density Estimation", 1992, pg 55.
%
axes('position',pos1)
BinWd = 3.5*std(T)*length(T)^(-1/3);
nBin = floor((max(T)-min(T))/BinWd)+1;
nBin = min(max(nBin,10),50);
hist(T,nBin);
h = findobj(gca,'Type','patch');
set(h,'FaceColor',[.5 .5 .5]);
title('Permutation Distribution: Maximum Statistic','FontSize',14)
Ylim = get(gca,'ylim'); Xlim = get(gca,'xlim');
line(T(1)*[1 1],Ylim.*[1 0.95],'LineStyle',':');
text(T(1)+diff(Xlim)*0.01,Ylim(2)*0.95,'Observed','FontSize',10)
line(cT*[1 1],Ylim.*[1 0.85],'LineStyle','-');
text(cT+diff(Xlim)*0.01,Ylim(2)*0.85,'Threshold','FontSize',10);
if (nargin>2)
%
% Display cluster size perm dist
%
axes('position',pos2)
BinWd = 3.5*std(C)*length(C)^(-1/3);
nBin = floor((max(C)-min(C))/BinWd)+1;
nBin = min(max(nBin,10),50);
hist(C,nBin);